SOCAR Proceedings

SOCAR Proceedings

Published by "OilGasScientificResearchProject" Institute of State Oil Company of Azerbaijan Republic (SOCAR).

SOCAR Proceedings is published from 1930 and is intended for oil and gas industry specialists, post-graduate (students) and scientific workers.

Journal is indexed in Web of Science (Emerging Sources Citation Index), SCOPUS and Russian Scientific Citation Index, and abstracted in EI’s Compendex, Petroleum Abstracts (Tulsa), Inspec, Chemical Abstracts database.

B. A. Baghirov1, A. M. Salmanov3, S. O. Heydarli2, O. V. Rajabli3

1Azerbaijan National Academy of Sciences, Baku, Azerbaijan; 2SOCAR, Baku, Azerbaijan; 3«OilGasScientificResearchProject» Institute, SOCAR, Baku, Azerbaijan

Determining tectonic fault characteristics in oil and gas field structures using Juxtaposition diagrams: a case study of the Darvin Bank


The type of tectonic faults plays an important role in issues such as migration and accumulation of hydrocarbons, formation of traps, determination of the direction of prospecting and exploration works, preparation of development projects, etc. For this reason, determining the characteristics type of tectonic faults in the exploration, development and exploitation of oil-gas fields is one of the main problems. There have been a number of approaches communicated both internationally and in local Azerbaijani publications. Juxtaposition diagrams, which are drawn up by taking into account the spatial position of faults and lithological characteristics of the surrounding sediments, are widely used in the search for fault-related traps and in the separation or integration of production objects in fields. It is also possible to determine the amount of shale material entering the fault gouge with these graphs. Considering that the Darvin Bank field is complicated by tectonic faults, the juxtaposition diagrams proposed by Knipe R. J. were used to determine the connection between the tectonic blocks. According to Upper Kirmaki Suite (KSupper) horizon, the faults numbered 11 and 3a in the northwest part of the field are permeable, while faults numbered 3, 6, and 7 are classified as non-permeable. The reliability of the results has been confirmed by the results of Cluster analysis applied in previous studies, and the application of this method in other oil and gas fields of Azerbaijan has been suggested.

Keywords: fault characteristics; permeable or sealing faults; Juxtaposition diagrams; production objects; Cluster analysis; shale gouge ratio.

Date submitted: 07.10.2024     Date accepted: 04.12.2024     Date published: 11.12.2024

The type of tectonic faults plays an important role in issues such as migration and accumulation of hydrocarbons, formation of traps, determination of the direction of prospecting and exploration works, preparation of development projects, etc. For this reason, determining the characteristics type of tectonic faults in the exploration, development and exploitation of oil-gas fields is one of the main problems. There have been a number of approaches communicated both internationally and in local Azerbaijani publications. Juxtaposition diagrams, which are drawn up by taking into account the spatial position of faults and lithological characteristics of the surrounding sediments, are widely used in the search for fault-related traps and in the separation or integration of production objects in fields. It is also possible to determine the amount of shale material entering the fault gouge with these graphs. Considering that the Darvin Bank field is complicated by tectonic faults, the juxtaposition diagrams proposed by Knipe R. J. were used to determine the connection between the tectonic blocks. According to Upper Kirmaki Suite (KSupper) horizon, the faults numbered 11 and 3a in the northwest part of the field are permeable, while faults numbered 3, 6, and 7 are classified as non-permeable. The reliability of the results has been confirmed by the results of Cluster analysis applied in previous studies, and the application of this method in other oil and gas fields of Azerbaijan has been suggested.

Keywords: fault characteristics; permeable or sealing faults; Juxtaposition diagrams; production objects; Cluster analysis; shale gouge ratio.

Date submitted: 07.10.2024     Date accepted: 04.12.2024     Date published: 11.12.2024

References

  1. Baghirov, B. A. (2011). Oil-gas field geology. Baku: ADNA.
  2. Baghirov, B. A., Maharramov, F. F., Sharifov, C. C. (2021). Methodology for determining fault properties in layers. News of the Azerbaijan Academy of Engineering, 13(3), 45-54
  3. Salmanov, A. M., Maharramov, B. I., Qaragezov, E. Sh., Kerimov, N. S. (2023). Geology of oil-gas fields and development stages of Azerbaijan area of Caspian Sea. Baku: Mars Print.
  4. Allan, U. S. (1989). Model for hydrocarbon migration and entrapment within faulted structures. AAPG Bulletin, 73(7), 803-811.
  5. Knipe, R. J. (1997). Juxtaposition and seal diagrams to help analyze fault seals in hydrocarbon reservoirs. AAPG Bulletin, 81(2), 187-195.
  6. Eminov, A. Sh., Suleymanova, V. M., Heydarli, S. O., Jabizade. N. I. (2022). Determining the properties of tectonic faults based on cluster analysis (example of the Darwin field). News of Higher Technical Schools of Azerbaijan, 19(8), 58-65.
  7. Ahmadov, E. H. (2015). New method for clarifying structural-tectonic faults of oil-gas fields. Geophysics News in Azerbaijan, 1-2, 50-54.
  8. Ahmadov, E. H., Muradov, E. M., Karagozov, E. Sh., Aliyev, F. R. (2019). Study of the properties of tectonic faults. Innovations in Geophysics in Azerbaijan, 3, 20-24.
  9. Salmanov, A. M., Ahmadov, E. H. (2015). New approach to clarifying the nature of tectonic faults in oil-gas fields. Azerbaijan Geologist, 19, 8-14.
  10. Tozer, R. J., Borthwick, A. M. (2010). Variation in fluid contacts in the Azeri field, Azerbaijan: sealing faults or hydrodynamic aquifer. The Geological Society of London, 347, 103-112.
  11. Matthew, J. R., Clifton, H. E., Myers, G., et al. (1999). Reservoir characterization and modeling of the shallow water Gyuneshli field, Azerbaijan. Azerbaijan Geologist, 3, 9-21.
  12. Suleymanova, V. M., Jabizade, N. I., Zeynalova, S. A. (2022). On the separation of similar objects based on the application of cluster analysis. News of Higher Technical Schools of Azerbaijan, 14(3), 125-133.
  13. Pei, Y., Paton, D. A., Knipe, R. J., Wu, K. (2015) A review of fault sealing behavior and its evaluation in siliciclastic rocks. Earth Science Review, 150, 121-138.
  14. Phung, P. V., Vu, A. T., Nguyen, T. Q., Nguyen, T. T. (2018). Fault seal capacity study for potential cluster prospects in Song Hong Basin, Vietnam. International Journal of Applied Engineering Research, 13(5), 2458-2467.
  15. Yielding, G., Freeman, B., Needham, D. T. (1997). Quantitative fault seal prediction. AAPG Bulletin, 81(6), 897-917.
  16. Suleymanova, V. M., Heydarli, S. O., Guliyev, Z. A., Heydarli, S. Y. (2022). Determination of tectonic disturbances based on geological-mathematical research. Oil and Gas, 132(6), 26-35.
  17. Heydarli, S. O., Rajabli, O. V. (2024, April). Determining the characteristics of tectonic faults using the Knipe graph (in the example of the Darvin field). In: XVIII Annual International Conference of Students and Young Researcers «Perfect education - the key to success in oil production». Azerbaijan Society of Petroleum Geologist.
Read more Read less

DOI: 10.5510/OGP2024SI101010

E-mail: ahmed.salmanov@socar.az


A. Kh. Shakhverdiev, R. R. Ilyazov

Sergo Ordzhonikidze Russian State University for Geological Prospecting, Moscow, Russia

Combining gas logging, well logging and cuttings microbiota data to improve geosteering and flow profile prediction in a horizontal well


Drilling horizontal wells is known has a problem of ‘non-measurement’ zone of downhole logging devices. In this regard, there is a high probability of leaving the target interval when drilling horizontal wells, and this in turn is fraught with a reduction in the potential flow rate or productivity of the well. In such cases, the importance and necessity of thorough interpretation of gas logs conducted by the geological and technological research station increases. This method allows solving a whole range of tasks, namely: prompt identification of oil and gas promising reservoirs in the well section, study of their filtration-capacity properties and saturation character. At the same time, a special study of drill cuttings microbiota can serve as an additional source of information. DNA sequencing technology of drill cuttings microbiota is proposed as a technique to increase the informativeness of cuttings. Application of this method allows to identify natural biomarkers, as well as to monitor their presence and concentration in various technological processes of drilling and oil and gas production. A comparative analysis of practical application of gas logging and DNA sequencing technology of mud microbiota is presented. Verification of the used methods on the data of geophysical well survey and field geophysical survey is carried out. Identification and comparison of information during drilling of a well with the data obtained during its operation allows to determine with high reliability the source of watering and working oil-saturated intervals.

Keywords: gas logging; geophysical surveys; gas chromatography; horizontal well drilling; drilling cuttings; DNA sequencing; geosteering.

Date submitted: 07.10.2024     Date accepted: 14.12.2024     Date published: 23.12.2024

Drilling horizontal wells is known has a problem of ‘non-measurement’ zone of downhole logging devices. In this regard, there is a high probability of leaving the target interval when drilling horizontal wells, and this in turn is fraught with a reduction in the potential flow rate or productivity of the well. In such cases, the importance and necessity of thorough interpretation of gas logs conducted by the geological and technological research station increases. This method allows solving a whole range of tasks, namely: prompt identification of oil and gas promising reservoirs in the well section, study of their filtration-capacity properties and saturation character. At the same time, a special study of drill cuttings microbiota can serve as an additional source of information. DNA sequencing technology of drill cuttings microbiota is proposed as a technique to increase the informativeness of cuttings. Application of this method allows to identify natural biomarkers, as well as to monitor their presence and concentration in various technological processes of drilling and oil and gas production. A comparative analysis of practical application of gas logging and DNA sequencing technology of mud microbiota is presented. Verification of the used methods on the data of geophysical well survey and field geophysical survey is carried out. Identification and comparison of information during drilling of a well with the data obtained during its operation allows to determine with high reliability the source of watering and working oil-saturated intervals.

Keywords: gas logging; geophysical surveys; gas chromatography; horizontal well drilling; drilling cuttings; DNA sequencing; geosteering.

Date submitted: 07.10.2024     Date accepted: 14.12.2024     Date published: 23.12.2024

References

  1. Lazutkina, N. E., Martynov, V. G., Hohlova, M. S. (2009). Geophysical studies of wells. Moscow: Infra-Engineering.
  2. Lukyanov, E. E. (1977). Studies of wells in the process of drilling. Moscow: Nedra.
  3. Schetinina, N. V., Malshakov, A. V., Basyrov, M. A., et al. (2017). Innovative technologies and approaches to interpretation of logging data in horizontal wells. SPE-187903-MS. In: SPE Russian Petroleum Technology Conference, Moscow, Russia.
  4. Shchetinina, N. V., Malshakov, A. V., Basyrov, M. A., et al. (2016). New approaches and technologies of interpretation of geophysical survey data of horizontal wells. Scientific and Technical Bulletin of Rosneft, 43, 6-14.
  5. Shchetinina, N. V., Basyrov, M. A., Zyryanova, I. A., et al. (2017). Technologies of interpretation of geophysical survey data of horizontal wells: present and future. Oil Industry, 11, 26-31.
  6. Gagarin, A. V., Gazizov, R. K., Novikov, N. O., et al. (2016). Prospects for the use of information obtained during the study of horizontal wells in corporate tools of geological modelling. Scientific and Technical Bulletin of Rosneft, 43, 15-19.
  7. Ilyazov, R. R., Nikiforov, S. A., Chernikov, E. Y., Rakhimov, T. R. (2023). Gas logging for geosteering and rapid determination of interfluid contacts while horizontal drilling. Oil Industry, 4, 72-77.
  8. Levitsky, A. Z. (1992). The use of geological and technological information in drilling. Moscow: Nedra.
  9. Jurcic, H., Cogelja, Z., Maretic, S. (2012). Petrophysical parameters evaluation in unconventional reservoirs by well logging and mud logging data interactive correlation method. SPE-150961-MS. In: SPE/EAGE European Unconventional Resources Conference and Exhibition, Vienna, Austria.
  10. Wei, Y., Jianbo, W., Shuai, L., et al. (2014). Logging identification of the Longmaxi mud shale reservoir in the Jiaoshiba area, Sichuan Basin. Natural Gas Industry B, 1(2), 230—236.
  11. Shi, W., Zhang, C., Yuan, S., et al. (2015). A crossplot for mud logging interpretation of unconventional gas shale reservoirs and its application. The Open Petroleum Engineering Journal, 8, 365-271.
  12. Ighodalo, E., Davies, G., D’Souza, S. (2017). Increasing certainty in formation evaluation utilizing advanced mud logging gas analysis. SPE-188039-MS. In: SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia.
  13. Haven, H., Arbin, P., Simon, B., et al. (1999). Applications and limitations of mud logging gas data in the detection of formation fluids and overpressure: examples from South-East Asia. In: International Conference on Gas Habitats of SE Asia and Australasia.
  14. McKinney, D., Flannery, M., Elshahawi, H., et al. (2007). Advanced mud gas logging in combination with wireline formation testing and geochemical fingerprinting for an improved understanding of reservoir architecture. SPE-109861-MS. In: SPE Annual Technical Conference and Exhibition, Anaheim, California, U.S.A.
  15. Caroli, E., Lafaurie, C., Barraud, B., Ségalini, G. (2013). Quantitative mud gas reconciliation with downhole fluid analysis: towards a quantitative fluid log. SPE-166246-MS. In: SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA.
  16. Crampin, T., Gligorijevic, A., Clarke, E., et al. (2013). Application of advanced mud gas logging for improved hydrocarbon phase determination in a highly depleted reservoir. IPTC-17088-MS. In: International Petroleum Technology Conference, Beijing, China.
  17. Azadpour, M., Manaman, N., Kadkhodaie-Ilkhchi, A., Sedghipour, M. (2015). Pore pressure prediction and modeling using well-logging data in one of the gas fields in south of Iran. Journal of Petroleum Science and Engineering, 128, 15-23. 
  18. Cely, A., Skaar, I., Yang, T. (2023). Holistic evaluation of reservoir oil viscosity in breidablikk field – including mud gas logging approach. The Society of Petrophysicists and Well Log Analysts (SPWLA).
  19. Posdyshev, A. S., Shelyakin, P. V., Shaikhutdinov, N. M., et al. (2021). Using DNA-logging to determine inflow profile in horizontal wells. SPE-206515-MS. In: SPE Russian Petroleum Technology Conference, Virtual.
  20. Shakhverdiev, E. A., Ilyazov, R. R., Filatova, K. M., Denisov, A. V. (2022). Investigation of possible causes of rapid well watering of under-saturated reservoirs. Collection of Proceedings of the Scientific Conference: Young - Earth Sciences. X International Scientific Conference of Young Scientists, Moscow.
  21. Shakhverdiev, A. Kh., Ilyazov, R. R., Arefiev, S. V., Pozdyshev, A. S. (2023). On the inclusion of highly watered reserves of under-saturated reservoirs in the category of hard-to-recover reserves. Oil Industry, 4, 34-39.
  22. Shakhverdiev, A. Kh., Panakhov, G. M., Abbasov, E. M., et al. (2006). Integrative efficiency of bed stimulation at intrastratal gas generation. Oil Industry, 11, 76-78.
  23. Shakhverdiev, A. Kh., Arefiev, S. V., Davydov, A. V. (2022). Hard-to-recover reserves of under-saturated oil reservoirs. Geology and Subsoil Use, 5(9), 78-87.
  24. Kerimov, V. Yu., Mustaev, R. N., Shakhverdiev, A. Kh., Zaitsev, V. A. (2019). Geomechanical modeling of poroperm properties in reservoirs within Sakhalin Shelf. Mining Journal, 12, 20-24.
  25. Mirzajanzadeh, A. Kh., Shakhverdiev, A. Kh., Kuznetsov, O. L., Mamedzadeh, A. M. (1996). Geomagnetic fields and oil and gas fields. Oil and Gas Geology, 6, 4-10.
  26. Mandrik, I. E., Shakhverdiev, A. Kh., Suleymanov, I. V. (2005). Oil recovery estimation and prediction using artificial neural networks. Oil Industry, 10, 36-39.
  27. Shakhverdiev, A. Kh., Maksimov, M. M., Rybitskaya, L. P., Galushko, V. V. (1998). Method for determining the location of stagnant and poorly drained zones of an oil deposit. Patent RU2105136.
  28. Shakhverdiev, A. Kh., Panahov, G. M., Suleimanov, B. A., et al. (1998). Method of hydraulic fracturing. Patent RU2122111.
  29. Pomerantz, L. I., Epstein, G. I., Levshunov, P. A. (1969). Automatic gas logging stations. Moscow: Nedra.
  30. Pomerants, L. I., Yashchenko, G. G., Mavrycheva, L. N., et al. (1963). Methodical manual for quantitative interpretation of gas logging data recorded by station AGKS-65. Moscow: ONTI VNIIIIGgeofizika.
  31. Pomerants, L. I. (1971). Presented gas indications and quantitative interpretation of gas logging data. Moscow: Nedra.
  32. Molchanov, A. A., Pomerantz, L. I., Sohranov, N. N. (1977). Prospects of application of information-measuring system for oil and gas well research. Geology of Oil and Gas, 6, 10-13.
  33. Pomerantz, L. I. (1982). Gas logging. Moscow: Nedra.
  34. Yashin, Y., Vedenin, A., Yashin, A. (2016). 60 years of chromatographic instrumentation. Analitika, 2.
  35. Epstein, G. I., Levshunov, P. A. (1969). Automatic gas logging stations. Moscow: Nedra.
  36. (2012). Federal Law of 26.06.2008 № 102-FZ (Ed. of 28.07.2012). On ensuring the uniformity of measuring instruments. Collection of Legislation of the Russian Federation.
  37. Lukyanov, E. E. (2011). Interpretation of GTI data. Novosibirsk: Historical Heritage of Siberia.
  38. Pixler, B. O. (1969). Formation evolution by analysis of hydrocarbon ratios. SPE Journal of Petroleum Technology, 6, 665-670.
  39. Haworth, J., Sellens, M., Whittaker, A. (1985). Interpretation of hydrocarbon shows using light (C1-C5) hydrocarbon gases from mud-log data. The American Association of Petroleum Geologists Bulletin, 69(8), 1305-1310.
  40. Staroselsky, V. I. (1990). Ethane, propane, butane in natural gases of oil and gas bearing basins. Moscow: Nedra.
  41. Lapierre, S. G., Prine, B. H., Pickrel, H. M. (2009). Technology for determining reservoir pressure from mud log gas improves mature, tight gas asset performance: a case study of Ada field, North Louisiana. SPE-124315-MS. In: SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana.
  42. Chapelle, F. H., O’Neill, K., Bradley, P. M., et al. (2002). A hydrogen-based subsurface community dominated by methanogens. Nature, 415, 312–325.
  43. Krumholz, L., McKinley, J. P., Ulrich, G. A., Suflita, J. M. (1997). Confined subsurface microbial communities in Cretaceous rock. Nature, 386, 64–66.
  44. Lin, L. H., Wang, P. L., Rumble, D., et al. (2007). Longterm sustainability of a high-energy, low-diversity crustal biome. Science, 314, 497–482.
  45. Heider, J., Spormann, A. M., Beller, H. R., Widdel, F. (1998). Anaerobic bacterial metabolism of hydrocarbons. FEMS Microbiology Reviews, 22, 459–473.
  46. Silva, J., Ursell, L., Percak-Dennett, E. (2018). Applying subsurface DNA diagnostics and data science in the Delaware
    Basin. SPE-189846-MS. In: SPE Hydraulic Fracturing Technology Conference and Exhibition, The Woodlands, Texas, USA.
  47. Percak-Dennett, E., Liu, J., Shojaei, H., et al. (2019). High resolution dynamic drainage height estimations using subsurface DNA diagnostics. SPE-195266-MS. In: SPE Western Regional Meeting, San Jose, California, USA.
  48. Youssef, N., Elshahed, M. S., McInerney, M. J. (2009). Microbial processes in oil fields: culprits, problems, and opportunities. Advances in Applied Microbiology, 66, 141–251.
  49. Ren, H. Y., Xiong, S. Z., Gao, G. J., et al. (2015) Bacteria in the injection water differently impacts the bacterial communities of production wells in high-temperature petroleum reservoirs. Frontiers in Microbiology, 6, 505.
Read more Read less

DOI: 10.5510/OGP2024SI101034

E-mail: ah_shah@mail.ru


R. T. Akhmetov1, L. S. Kuleshova1, V. V. Mukhametshin2, M. O. Mikhailov1, Z. N. Sagitova1, O. A. Grezina1, E. R. Vasilieva1, L. M. Eremeeva1, A. N. Salimov3

1Institute of Oil and Gas, Ufa State Petroleum Technological University (branch in Oktyabrsky), Russia; 2Ufa State Petroleum Technological University, Ufa, Russia; 3Baku Higher Oil School, SOCAR, Baku, Azerbaijan

Statistical relationships between filtration and capacitance properties of reservoirs in Western Siberia based on a generalized model of capillary curves


The paper shows that the generalized mathematical model of capillary curves implicitly contains analytical connections between filtration and capacitive properties of productive layers. The method of determining the absolute permeability presented in this paper is based on the use of a generalized model. It does not require the preliminary construction of a geological model of the residual water saturation of oil and gas reservoirs. The proposed method uses correlations between current and residual water saturation at a fixed capillary pressure. The proposed statistical model makes it possible to significantly increase the reliability and accuracy of the estimation of absolute permeability according to capillarimetry data.

Keywords: permeability; residual water saturation; capillary pressure; generalized model; analytical connections.

Date submitted: 24.07.2023     Date accepted: 10.03.2024     Date published: 15.04.2024

The paper shows that the generalized mathematical model of capillary curves implicitly contains analytical connections between filtration and capacitive properties of productive layers. The method of determining the absolute permeability presented in this paper is based on the use of a generalized model. It does not require the preliminary construction of a geological model of the residual water saturation of oil and gas reservoirs. The proposed method uses correlations between current and residual water saturation at a fixed capillary pressure. The proposed statistical model makes it possible to significantly increase the reliability and accuracy of the estimation of absolute permeability according to capillarimetry data.

Keywords: permeability; residual water saturation; capillary pressure; generalized model; analytical connections.

Date submitted: 24.07.2023     Date accepted: 10.03.2024     Date published: 15.04.2024

References

  1. Vishnyakov V. V., Suleimanov B. A., Salmanov A. V., Zeynalov, E. B. (2019). Primer on enhanced oil recovery. Gulf Professional Publishing.
  2. Economides, J. M., Nolte, K. I. (2000). Reservoir stimulation. West Sussex, England: John Wiley and Sons.
  3. Suleimanov, B. A., S. C. Rzayeva, A. F. Akberova, Akhmedova, U. T. (2021). Deep diversion strategy of the displacement front during oil reservoirs watering. SOCAR Proceedings, 4, 33-42.
  4. Suleimanov, B. A., Veliyev, E. F. , Vishnyakov, V. V. (2022). Nanocolloids for petroleum engineering: fundamentals and practices. John Wiley & Sons.
  5. Alvarado, V., Reich, E.-M., Yunfeng, Yi, et al. (2006). Integration of a risk management tool and an analytical simulator for assisted decision-making in IOR. In: SPE Europec/EAGE Annual Conference and Exhibition. Society of Petroleum Engineers.
  6. Dmitrievsky, A. N., Eremin, N. A., Safarova, E. A., et al. (2022). Implementation of complex scientific and technical programs at the late stages of operation of oil and gas fields. SOCAR Proceedings, 2, 1–8.
  7. Popov, S. N., Kusaiko, A. S. (2021). Experimental study of the effect of filtration for low-mineralized water with high temperature on changes in elastic and strength properties of reservoir rocks. In: Processes in GeoMedia-Volume II.
  8. Mukhametshin, V. Sh. (2022). Oil recovery factor express evaluation during carbonate reservoirs development in natural regimes. SOCAR Proceedings, SI1, 27-37.
  9. Mohammed, I. I., Alsultan, H. A. (2022). Facies analysis and depositional environments of the Nahr Umr formation in Rumaila oil field, Southern Iraq. The Iraqi Geological Journal, 79-92.
  10. Khisamiev, T. R., Bashirov, I. R., Mukhametshin, V. Sh., et al. (2021). Results of the development system optimization and increasing the efficiency of carbonate reserves extraction of the Turney stage of the Chetyrmansky deposit. SOCAR Proceedings, SI2, 131-142.
  11. Minnikhanov, R. N., Maganov, N. U., Khisamov, R. S. (2016). On creation of research and testing facilities to promote study of nonconventional oil reserves in Tatarstan. Oil Industry, 8, 60-63.
  12. Sun, S. Q., Wan, J. C. (2002). Geological analogs usage rates high in global survey. Oil & Gas Journal, 100(46), 49-50.
  13. Shao, S. (2023). Study on the interlayer characteristics of channel sand body in block E of S oilfield. In: International Field Exploration and Development Conference. Singapore: Springer Nature Singapore.
  14. Rabaev, R. U., Chibisov, A. V., Kotenev, A. Yu., et al. (2021). Mathematical modelling of carbonate reservoir dissolution and prediction of the controlled hydrochloric acid treatment efficiency. SOCAR Proceedings, 2, 40-46.
  15. Suleimanov, B. A., Abbasov, E. M. (2010). Bottom-hole pressure build-up during oil displacement by water with allowance for non-instantaneous inflow stopping. SOCAR Proceedings, 2, 20-24.
  16. Samylovskaya, E., Makhovikov, A., Lutonin, A., et al. (2022). Digital technologies in arctic oil and gas resources extraction: global trends and Russian experience. Resources, 11(3), 29.
  17. Yakupov, R. F., Mukhametshin, V. Sh., Tyncherov, K. T. (2018). Filtration model of oil coning in a bottom waterdrive reservoir. Periodico Tche Quimica, 15(30), 725-733.
  18. Suleimanov, B. A., Ismailov, F. S., Veliyev, E. F., et al. (2013). The influence of light metal nanoparticles on the strength of polymer gels used in oil industry. SOCAR Proceedings, 2, 24-28.
  19. Milad, M., Junin, R., Sidek, A., et al. (2021). Huff-n-puff technology for enhanced oil recovery in shale/tight oil reservoirs: Progress, gaps, and perspectives. Energy & Fuels, 35(21), 17279-17333.
  20. Grishchenko, V. A., Rabaev, R. U., Asylgareev, I. N., et al. (2021). Methodological approach to optimal geological and technological characteristics determining when planning hydraulic fracturing at multilayer facilities. SOCAR Proceedings, SI2, 182-191.
  21. Economides, M., Oligney, R., Valko, P. (2002). Unified fracture design: bridging the gap between theory and practice. Alvin, Texas: Orsa Press.
  22. Abbasov, A. A., Abbasov, E. M., Ismayilov, Sh. Z., et al. (2021). Waterflooding efficiency estimation using capacitance-resistance model with non-linear productivity index. SOCAR Procеedings, 3, 45-53.
  23. Grishchenko, V. A., Pozdnyakova, T.V., Mukhamadiyev, B. M., et al. (2021). Improving the carbonate reservoirs development efficiency on the example of the Tournaisian stage deposits. SOCAR Proceedings, SI2, 238-247.
  24. Mukhametshin, V. Sh. (2022). Oil flooding in carbonate reservoirs management. SOCAR Proceedings, SI1, 38-44.
  25. Miel, H., Hameed, A. O. S., Hussein, K. F. (2022). Modeling and monitoring the development of an oil field under conditions of mass hydraulic fracturing. Trends in Sciences, 19(8), 3436-3436.
  26. Hutton, A. C. (1987). Petrographic classification of oil shales. International Journal of Coal Geology, 8(3), 203-231.
  27. Grishchenko, V. A., Asylgareev, I. N., Bakhtizin, R. N., et al. (2021). Methodological Approach to The Resource Base Efficiency Monitoring in Oil Fields Development. SOCAR Proceedings, SI2, 229-237.
  28. Grishchenko, V. A., Tsiklis, I. M., Mukhametshin, V. Sh., et al. (2021). Methodological approaches to increasing the flooding system efficiency at the later stage of reservoir development. SOCAR Proceedings, SI2, 161-171.
  29. Muslimov, R. Kh. (2008). Methods of increasing an oil fields development efficiency at a late stage. Oil Industry, 3, 30-35.
  30. Mukhametshin, V. V., & Kuleshova, L. S. (2020). On uncertainty level reduction in managing waterflooding of the deposits with hard to extract reserves. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 331(5), 140–146.
  31. Khakimzyanov, I. N., Mukhametshin, V. Sh., Bakhtizin, R. N., et al. (2021). Determination of well spacing volumetric factor for assessment of final oil recovery in reservoirs developed by horizontal wells. SOCAR Proceedings, 2, 47-53.
  32. Suleimanov B.A, Abbasov H. F., Ismayilov, R. H. (2023). Enhanced oil recovery with nanofluid injection. Petroleum Science and Technology, 41(18), 1734-1751.
  33. Mukhametshin, V. V., Kuleshova, L. S. (2022). Improving the lower cretaceous deposits development efficiency in Western Siberia employing enhanced oil recovery. SOCAR Proceedings, SI1, 9-18.
  34. Kuleshova, L. S., Mukhametshin, V. Sh. (2022). Research and justification of innovative techniques employment for hydrocarbons production in difficult conditions. SOCAR Proceedings, SI1, 71-79.
  35. Suleimanov B. A., Rzayeva S. C., Akberova, A. F., et al. (2022). Self-foamed biosystem for deep reservoir conformance control. Petroleum Science and Technology. 40(20), 2450-2467.
  36. Chang, W. J., Al-Obaidi, S. H., Patkin, A. A. (2021). The use of oil-soluble polymers to enhance oil recovery in hard to recover hydrocarbons reserves. International Research Journal of Modernization in Engineering Technology and Science, 03(1).
  37. Mukhametshin, V. Sh., Khakimzyanov, I. N., Bakhtizin, R. N., et al. (2021). Differentiation and grouping of complex-structured oil reservoirs in carbonate reservoirs in development management problems solving. SOCAR Proceedings, SI1, 88-97.
  38. Suleimanov, B. A., Latifov, Ya. A., İbragimov, Kh. M., et al. (2017). Field testing results of enhanced oil recovery technologies using thermoactive polymer compositions. SOCAR Proceedings, 3, 17-31.
  39. Yakupov, R. F., Khakimzyanov, I. N., Mukhametshin, V. V., et al. (2021). Hydrodynamic model application to create a reverse oil cone in water-oil zones. SOCAR Proceedings, 2, 54-61.
  40. Mukhametshin, V. Sh., Khakimzyanov, I. N. (2021). Features of grouping low-producing oil deposits in carbonate reservoirs for the rational use of resources within the Ural-Volga region. Journal of Mining Institute, 252, 896-907.
  41. Akhmetov, R. T., Malyarenko, A. M., Kuleshova, L. S., et al. (2021). Quantitative assessment of hydraulic tortuosity of oil and gas reservoirs in Western Siberia based on capillarimetric studies. SOCAR Proceedings, 2, 77-84.
  42. Shodmonovna, N. E., Toxirovich, S. R., Ismailov, Sh. R. (2021). Visualization of the results of computing experiments for monitoring and analysis of filtration processes in a non-general layer of oil fields. In: 2021 International Conference on Information Science and Communications Technologies (ICISCT), IEEE.
  43. Zeigman, Yu. V., Mukhametshin, V. Sh., Khafizov, A. R., et al. (2016). Prospects of application of multi-functional well killing fluids in carbonate reservoirs. SOCAR Procеedings, 3, 33–39.
  44. Wang, X. (2021, May). Application of chemical flooding in increasing yield of tertiary oil recovery. In: IOP Conference Series: Earth and Environmental Science, IOP Publishing.
  45. Mardashov, D. V., Rogachev, M. K., Zeigman, Yu. V., et al. (2021). Well killing technology before workover operation in complicated conditions. Energies, 14(3), 654.
  46. Qing, W. (2021). Global practice of AI and big data in oil and gas industry. In: Machine Learning and Data Science in the Oil and Gas Industry, Gulf Professional Publishing.
  47. Mukhametshin, V. V., Akhmetov, R. T., Kuleshova, L. S., et al. (2021). Analytical links between porosity and permeability correlations of productive strata of western siberia based on a generalized mathematical model of capillary curves. Bulletin of the Tomsk Polytechnic University, Geo Assets Engineering, 332(8), 135-141.
  48. Brooks, R. H., Corey, A. T. (1964). Hydraulic properties of porous media. Hydrology Papers, Colorado State University, 3, 1–37.
  49. Adams, S. J., Van den Oord, R. J. (1993). Capillary pressure and saturation-height functions. Report EP 93-0001, SIPM BV.
  50. Introduction to Wireline Log Analysis (Baker Atlas) (2002). Baker Hughes, Inc.
  51. Bakker, G. G., Lippincott, R. G. (2004). Overview of petrophysics. Shell Open University, 3.
  52. Schumberger (1991). Log interpretation principles/Applications 1989. Schlumberger Educational Services.
  53. Tiab, D., Donaldson, E. (1999). Petrophysics: theory and practice of measuring reservoir rock and fluid transport properties. Houston, Texas: Gulf Professional Publishing.
  54. Amyx, J., Bass, D., & Whiting, R. (1960). Petroleum reservoir engineering. New York: McGraw-Hill Book Co.
Read more Read less

DOI: 10.5510/OGP2024SI100953

E-mail: vv@of.ugntu.ru


E. F. Veliyev1, 2, A. D. Shovgenov3

1«OilGasScientificResearchProject» Institute, SOCAR, Baku, Azerbaijan; 2Composite Materials Scientific Research Center, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan; 3Halliburton International GmbH, Moscow, Russia

Enhancing sand control in loose reservoirs: nanofluid application


This paper introduces a novel epoxy-based nanofluid for sand control in loose reservoirs prone to sand production issues. The nanofluid combines epoxy resin with carbon nitride nanoparticles and a gas generating agent to enhance both compressive strength and permeability. Experimental studies demonstrate significant improvements in compressive strength and permeability, indicating the potential effectiveness of the nanofluid in consolidating sand in oil and gas reservoirs. Sandpack flooding experiments under reservoir conditions reveal promising results, suggesting the nanofluid's suitability for various permeability characteristics. These findings propose the epoxy/g-C₃N₄ nanofluid as a promising solution for sand consolidation, warranting further research and field testing for practical application in reservoir engineering.

Keywords: sand control; epoxy nanofluid; chemical sand consolidation; graphitic carbon nitride; compressive strength enhancement; permeability control.

Date submitted: 06.01.2024     Date accepted: 05.03.2024     Date published: 10.04.2024

This paper introduces a novel epoxy-based nanofluid for sand control in loose reservoirs prone to sand production issues. The nanofluid combines epoxy resin with carbon nitride nanoparticles and a gas generating agent to enhance both compressive strength and permeability. Experimental studies demonstrate significant improvements in compressive strength and permeability, indicating the potential effectiveness of the nanofluid in consolidating sand in oil and gas reservoirs. Sandpack flooding experiments under reservoir conditions reveal promising results, suggesting the nanofluid's suitability for various permeability characteristics. These findings propose the epoxy/g-C₃N₄ nanofluid as a promising solution for sand consolidation, warranting further research and field testing for practical application in reservoir engineering.

Keywords: sand control; epoxy nanofluid; chemical sand consolidation; graphitic carbon nitride; compressive strength enhancement; permeability control.

Date submitted: 06.01.2024     Date accepted: 05.03.2024     Date published: 10.04.2024

References

  1. Cozzi, L., Gould, T., Bouckart, S., et al. (2020). World energy outlook 2020. International Energy Agency: Paris, France.
  2. Perrons, R. K. (2014). How innovation and R&D happen in the upstream oil & gas industry: Insights from a global survey. Journal of Petroleum Science and Engineering, 124, 301-312.
  3. Kleinschmidt, A. The Future of oil and gas: why we will still need oil and gas in the future. (2016). http://www. siemens.com/innovation/en/ home/pictures-of-the-future/energy-and-efficiency/the-future-of-oil-and-gas-trends.html.
  4. Veliyev, E. F., Aliyev, A. A. (2021). Innovative technologies as a priority factor of the oil and gas industry development. ANAS Transactions. Earth Sciences, 2, 81-93.
  5. Veliyev, E. F., Aliyev, A. A., Poladova, G. S. (2023). Development of novel thermoactive polymer compositions for deep fluid diversion purposes. SPE-217642-MS. In: SPE Annual Caspian Technical Conference, Baku, Azerbaijan.
  6. Suleimanov, B. A., Veliyev, E. F., Vishnyakov, V. (2022). Nanocolloids for petroleum engineering: Fundamentals and practices. John Wiley & Sons.
  7. Suleimanov, B. A., Veliyev, E. F., Aliyev, A. A. (2021). Impact of nanoparticle structure on the effectiveness of pickering emulsions for eor applications. ANAS Transactions. Earth Sciences, 1, 82-92.
  8. Suleimanov, B. A., Ismailov, F. S., Dyshin, O. A., et al. (2016). Screening evaluation of EOR methods based on fuzzy logic and Bayesian inference mechanisms. SPE-182044-MS. In: SPE Russian Petroleum Technology Conference and Exhibition, Moscow, Russia.
  9. Suleimanov, B. A., Ismailov, F. S., Dyshin, O. A., et al. (2012). Analysis of oil deposit exploration state on the base of multifractal approach. SOCAR Proceedings, 2, 20-28.
  10. Suleimanov, B. A., Ismailov, F. S., Dyshin, O. A., et al. (2011). Analysis of oil deposit exploration state on the base of multifractal approach. Oil Industry, 2, 92-96.
  11. Suleimanov, B. A., Ismailov, F. S., Dyshin, O. A., et al. (2011). Analysis of oil deposit exploration state on the base of multifractal approach. Oil Industry, 12, 111-115.
  12. Suleimanov, B. A., Latifov, Y. A., Veliyev, E. F. (2019). Softened water application for enhanced oil recovery. SOCAR Proceedings, 1, 19-29.
  13. Veliyev, E. F., Aliyev, A. A. (2021). Propagation of nano sized CDG deep into porous media. SPE-207024-MS. In: SPE Annual Caspian Technical Conference.
  14. Suleimanov, B.A, Abbasov, H. F. (2016). Effect of copper nanoparticle aggregation on the thermal conductivity of nanofluids. Russian Journal of Physical Chemistry A, 90, 420–428.
  15. Veliyev, E. F., Aliyev, A. A. (2022). The application of nanoparticles to stabilise colloidal disperse systems. ANAS Transactions. Earth Sciences, 1, 37-50.
  16. Suleimanov, B. A., Latifov, Y. A., Veliyev, E. F., et al. (2017). Low salinity and low hardness alkali water as displacement agent for secondary and tertiary flooding in sandstones. SPE-188998-MS. In: SPE Annual Caspian Technical Conference, Baku, Azerbaijan.
  17. Veliyev, E. F., Askerov, V. M., Aliyev, A. A. (2023). Enhanced oil recovery method for highly viscous oil reservoirs based on in-situ modification of physico-chemical properties. SPE-217635-MS. In: SPE Annual Caspian Technical Conference, Baku, Azerbaijan.
  18. Veliyev, E. F., Askerov, V. M., Aliyev, A. A. (2022). Enhanced oil recovery method for highly viscous oil reservoirs based on in-situ modification of physico-chemical properties. SOCAR Proceedings, SI2, 144-152.
  19. Mokheimer, E. M., Hamdy, M., Abubakar, Z., et al. (2019). A comprehensive review of thermal enhanced oil recovery: techniques evaluation. Journal of Energy Resources Technology, 141(3), 030801.
  20. Veliyev, E. F., Shovgenov, A. D. (2023). Novel water shut off method based on temporary plugging agent and gel composition shut-off. SOCAR Proceedings, SI1, 96-101.
  21. Veliyev, E. F., Aliyev, A. A. (2023). Laboratory evaluation of novel nano composite gel for water shut-off. SOCAR Proceedings, SI1, 78-86.
  22. Veliyev, E. F., Aliyev, A. A. (2023). Evaluation of novel organically crosslinked polymer gel for water shut-off purposes. SOCAR Proceedings, 2, 40-45.
  23. Veliyev, E. F., Shovgenov, A. D., Mehdiyev, B. R. (2023). Assessing silica-based gel system for high-temperature water shut-off applications. SOCAR Proceedings, SI2, 26-32.
  24. Suleimanov, B. A., Ismailov, F. S., Mursalova, M.A. (2010). Fluid with nanoparticles for bed and wellbottom zone stimulation. Oil Industry, 4, 96-99.
  25. Suleimanov, B. A., Ismailov, F. S., Veliyev, E. F. (2014). On the metal nanoparticles effect on the strength of polymer gels based on carboxymethyl cellulose, applying at oil recovery. Oil Industry, 1, 86-88.
  26. Ismailov, F. S., Abdulgasanov, F. A., Isayev, R. J. (2014). Gas treatment efficiency upgrading at off-shore gas condensate field. SOCAR Proceedings, 2, 57.
  27. Morita, N., Boyd, P. A. (1991). Typical sand production problems: case studies and strategies for sand control. SPE-22739-MS. In: SPE Annual Technical Conference and Exhibition, Dallas, Texas.
  28. Khamehchi, E., Ameri, O., Alizadeh, A. (2015). Choosing an optimum sand control method. Egyptian Journal of Petroleum, 24(2), 193-202.
  29. Abass, H. H., Nasr-El-Din, H. A., BaTaweel, M. H. (2002). Sand control: sand characterization, failure mechanisms, and completion methods. SPE-77686-MS. In: SPE Annual Technical Conference and Exhibition, San Antonio, Texas.
  30. Matanovic, D., Cikes, M., Moslavac, B. (2012). Sand control in well construction and operation. Springer Science & Business Media.
  31. Alakbari, F. S., Mohyaldinn, M. E., Muhsan, et al. (2020). Chemical sand consolidation: from polymers to nanoparticles. Polymers, 12(5), 1069.
  32. Talaghat, M. R., Esmaeilzadeh, F., Mowla, D. (2009). Sand production control by chemical consolidation. Journal of Petroleum Science and Engineering, 67(1-2), 34-40.
  33. Hassan, N. A., Yeap, W. J., Singh, R., et al. (2020). Performance review of chemical sand consolidation and agglomeration for maximum potential as downhole sand control: an operator’s experience. SPE-202419-MS. In: SPE Asia Pacific Oil and Gas Conference and Exhibition.
  34. Shaughnessy, C. M., Salathiel, W. M., Penberthy Jr., W. L. (1978). A new, low-viscosity, epoxy sand-consolidation process. Journal of Petroleum Technology, 30(12), 1805-1812.
  35. Mishra, S., Ojha, K. (2015). Chemical sand consolidation: an overview. Journal of Petroleum Engineering & Technology, 5, 21-34.
  36. Tabbakhzadeh, M. N., Esmaeilzadeh, F., Mowla, D., et al. (2024). Effects of reservoir fluids on sand packs consolidated by furan and epoxy resins: static and dynamic states. Journal of Rock Mechanics and Geotechnical Engineering.
  37. Talaghat, M. R., Bahmani, A. R. (2017). Sand production control in sandstone reservoirs using a modified ureaformaldehyde Re. Iranian Journal of Oil and Gas Science and Technology, 6(2), 33-45.
  38. Dehghani, A., Rahmanifard, H., Mowla, D. (2013). Experimental investigation of sand consolidation techniques: resin injection and in-situ combustion. International Journal of Oil, Gas and Coal Technology, 6(6), 689-704.
  39. Rama, M. S., Songire, S., Meher, P., et al. (2016). Effective resin consolidation treatment methods and compositions for clay-laden formations. SPE-178998-MS. In: SPE International Conference and Exhibition on Formation Damage Control.
  40. Riyanto, L., Saleh, M., Goh, K., et al. (2016). Novel aqueous-based consolidation restores sand control and well productivity: case history from East Malaysia. In: SPE International Conference and Exhibition on Formation Damage Control, Lafayette, Louisiana, USA.
  41. Shang, X., Bai, Y., Wang, Z., et al. (2019). A novel chemical-consolidation sand control composition: Foam amino resin system. e-Polymers, 19(1), 1-8.
  42. Vaidya, N., Prabhu, R., Santamaria, J. C., et al. (2022). A sustainable fluid system for sand consolidation. SPE-208808-MS. In: SPE International Conference and Exhibition on Formation Damage Control, Lafayette, Louisiana, USA.
  43. Mishra, S., Ojha, K. (2016). Nanoparticle induced chemical system for consolidating loosely bound sand formations in oil fields. Journal of Petroleum Science and Engineering, 147, 15-23.
  44. Kalgaonkar, R., Alnoaimi, K., Wagle, V. (2019). Novel nanoparticle based applications in oilfield: from sand consolidation to water shutoff. SPE-198539-MS. In: SPE Gas & Oil Technology Showcase and Conference, Dubai, UAE.
  45. Kalgaonkar, R. A., Chang, F. (2020). Novel system for mitigating sand production using surface modified nanoparticle. OTC-30296-MS. In: Offshore Technology Conference Asia, Kuala Lumpur, Malaysia.
  46. Zhu, J., Xiao, P., Li, H., et al. (2014). Graphitic carbon nitride: synthesis, properties, and applications in catalysis. ACS Applied Materials & Interfaces, 6(19), 16449-16465. 
  47. Wang, T., Song, B., Wang, L. (2020). A new filler for epoxy resin: study on the properties of graphite carbon nitride (g-C₃N₄) reinforced epoxy resin composites. Polymers, 12(1), 76.
  48. Song, B., Liu, Z., Wang, L., et al. (2020). Significantly strengthening epoxy by incorporating carbon nanotubes/graphitic carbon nitride hybrid nanofillers. Macromolecular Materials and Engineering, 305(8), 2000231.
  49. Iqbal, M. I., Mahapatra, S. R., Alrajawy, D., et al. (2020). Sand consolidation by chemical treatment using indigenously developed chemicals. Psychology and Education, 57(8), 3748-3751.
  50. Suleimanov, B. A. (1997). Slip effect during filtration of gassed liquid. Colloid Journal, 59(6), 749-753.
  51. Suleimanov, B. A., Rzayeva S. C., Akhmedova, U. T. (2021). Self-gasified biosystems for enhanced oil recovery. International Journal of Modern Physics B, 35(27), 2150274.
  52. Suleimanov, B. A., Rzayeva, S. C., Akberova, A. F., et al. (2022). Self-foamed biosystem for deep reservoir conformance control. Petroleum Science and Technology, 40(20), 2450-2467.
  53. Suleimanov, B. A. (2011). Mechanism of slip effect in gassed liquid flow. Colloid Journal, 73(6), 846–855.
  54. Latifov, Y. A. (2021). Non-stationary effect of thermoactive polymer composition for deep leveling of filtration profile. Scientific Petroleum, 1, 25-30.
  55. Veliyev, E. F. (2021). Softened water application to improve micellar flooding performance. Scinetific Petroleum, 2, 52-56.
Read more Read less

DOI: 10.5510/OGP2024SI100954

E-mail: elchinf.veliyev@socar.az


Kh. M. Ibrahimov, A. A. Hajiyev, A. F. Akberova

«OilGasScientificResearchProject» Institute, SOCAR, Baku, Azerbaijan

Methodology for determining the critical rate and pressure depression of the water shut-off composition


Against the backdrop of increasing water content in wells, the development of more effective methods and technologies for water shutoff is becoming increasingly important. Despite the many methods to combat formation water isolation, this problem is one of the main ones in the exploitation of oil and gas fields. One way to solve the problem is to treat the bottomhole zone with various reagents and create a waterproofing screen in it. An indicator of the effectiveness of a water-insulating screen is the stability time of the screen. The stability time of the water-insulating screen, in addition to other factors associated with the physical characteristics of the insulating composition and the porous medium, depends on its location in the near-well space. Placing the sealant away from the well, where flow rates are lower, is expensive. At the same time, close placement of the insulating composition to the well due to high flow rates increases the likelihood of destruction of the insulating screen. Therefore, to increase the efficiency of insulation work in wells with high water content, the issue of determining the critical flow velocity that destroys the water insulation is of great practical interest. The article demonstrates a methodology for conducting experiments to determine the critical velocity and depression for a specific formation and a water-insulating gel-forming composition. The idea of the presented methodology is to conduct experimental studies on a linear model of a reservoir with different reservoir properties.

Keywords: water shut-off; well stimulation; gel; production; water control; porous media; permeability.

Date submitted: 25.12.2023     Date accepted: 25.04.2024     Date published: 09.05.2024

Against the backdrop of increasing water content in wells, the development of more effective methods and technologies for water shutoff is becoming increasingly important. Despite the many methods to combat formation water isolation, this problem is one of the main ones in the exploitation of oil and gas fields. One way to solve the problem is to treat the bottomhole zone with various reagents and create a waterproofing screen in it. An indicator of the effectiveness of a water-insulating screen is the stability time of the screen. The stability time of the water-insulating screen, in addition to other factors associated with the physical characteristics of the insulating composition and the porous medium, depends on its location in the near-well space. Placing the sealant away from the well, where flow rates are lower, is expensive. At the same time, close placement of the insulating composition to the well due to high flow rates increases the likelihood of destruction of the insulating screen. Therefore, to increase the efficiency of insulation work in wells with high water content, the issue of determining the critical flow velocity that destroys the water insulation is of great practical interest. The article demonstrates a methodology for conducting experiments to determine the critical velocity and depression for a specific formation and a water-insulating gel-forming composition. The idea of the presented methodology is to conduct experimental studies on a linear model of a reservoir with different reservoir properties.

Keywords: water shut-off; well stimulation; gel; production; water control; porous media; permeability.

Date submitted: 25.12.2023     Date accepted: 25.04.2024     Date published: 09.05.2024

References

  1. Al-Ibadi, A., Civan, C., (2013). Evaluation of near-wellbore formation treatment by gel particles using dimensional analysis. SPE-164507-MS. In: SPE Production and Operations Symposium, Oklahoma City, Oklahoma, USA.
  2. Suleimanov, B. A., Veliyev, E. F., Aliyev A. A. (2021). Impact of nanoparticle structure on the effectiveness of Pickering emulsions for EOR applications. ANAS Transactions. Earth Sciences, 1, 82-92.
  3. Suleimanov, B. A., Rzayeva, S. C., Akhmedova, U. T. (2021). Self-gasified biosystems for enhanced oil recovery. International Journal of Modern Physics B, 35(27), 2150274.
  4. Suleimanov, B. A., Dyshin, O. A., Veliyev, E. F. (2016). Compressive strength of polymer nanogels used for enhanced oil recovery (EOR). SPE-181960-MS. In: SPE Russian Petroleum Technology Conference and Exhibition, Moscow, Russia.
  5. Vishnyakov, V. V., Suleimanov, Salmanov B. A., et al. (2019). Primer on enhanced oil recovery. Gulf Professional Publishing.
  6. Taha, A., Amani, M., (2019). Overview of water shutoff operations in oil and gas wells; chemical and mechanical solutions. ChemEngineering, 3(2), 51.
  7. Suleimanov, B. A. (2022). Theory and practice of enhanced oil recovery. Moscow-Izhevsk: ICS.
  8. Suleimanov, B. A., Azizov, Kh. B., Abbasov, E. M. (1998). Specific features of the gas-liquid mixture filtration. Acta Mechanica, 130(1), 121-133.
  9. Akberova, A. F. (2019). Intensification of the process of destruction of stable oil-water emulsions using new effective composite demulsifiers. Petroleum engineering, 17 (2), 68-73.
  10. Abdullayev, V. J., Veliyev, R. G., Ryabov, et al. (2023). Application of gel systems for water shut-off on Uzbekistan oil fields. SOCAR Proceedings, 1, 68-73.
  11. Abdullayev, V. J., Huseynov, M. A., Ismyilov, et al. (2014). 3D geological modeling of Guneshli reservoir for increating final development stage efficiency. SOCAR Proceedings, 2, 75–82.
  12. Abdullayev, V. J., Ibrahimov, Kh. M., Kazimov, et al. (2016). Experimental studies of oil displacement by gas and water-gas mixtures. SOCAR Proceedings, 1, 51-57.
  13. Al-Ibadi, A., Civan, C., (2013). Experimental investigation and correlation of treatment in weak and highpermeability formations by use of gel particles. SPE-164119-MS. In: SPE International Symposium on Oilfield Chemistry, The Woodlands, Texas, USA.
  14. Ibrahimov, Kh. M., Hajiyev, A. A., Akbarova, A. F. (2023). Directed influence technology for selective permeability improvement of productive layers. In: Proceedings of the international Scientific-Practical Conference «Heydar Aliyev and Azerbaijan Oil Strategy: Advances in Oil And Gas Geology and Geotechnologies».
  15. Ibragimov, Kh. M., Kazımov, F. K., Akberova, A. F. (2022). Development and laboratory test of the gelling composition for the selective isolation of formation waters. Scientific Petroleum, 2, 40-46.
  16. Suleimanov, B. A., Veliyev, E. F., Vishnyakov, V. V. (2022). Nanocolloids for petroleum engineering: Fundamentals and practices. John Wiley & Sons Ltd.
  17. Suleimanov, B. A. (2012). The mechanism of slip in the flow of gassed non-Newtonian liquids. Colloid Journal, 74, 6, 726–730.
  18. Suleimanov, B. A. (1997). Slip effect during filtration of gassed liquid. Colloid Journal, 59(6), 749-753.
  19. Suleimanov, B. A., Rzayeva, S. C., Akberova, A. F., et al. (2022). Self-foamed biosystem for deep reservoir conformance control. Petroleum Science and Technology, 40(20), 2450-2467.
  20. Suleimanov, B. A., Jamalbayov, M. A., Ibrahimov, Kh. M. (2023). Algorithm for determining the optimal coordinates of the water-shutoff composition in the bottomhole zone. ANAS Transactions. Earth Sciences, SI, 27‐30.
  21. Suleimanov, B. A., Ismailov, F. S., Veliyev, E. F. (2014). On the metal nanoparticles effect on the strength of polymer gels based on carboxymethyl cellulose, applying at oil recovery. Oil Industry, 1, 86-88.
Read more Read less

DOI: 10.5510/OGP2024SI100955

E-mail: Khidir.Ibrahimov@socar.az


A. I. Abdullayev1, M. J. Hamashayeva2, F. V. Shamilov1, E. G. Gadjiyev1, V. N. Valiyev1

1SOCAR, Baku, Azerbaijan; 2Azerbaijan State Oil and İndustry University, Baku, Azerbaijan

Application of aluminum nanoparticles in reagents for oil viscosity reduction


The problem of extracting hard-to-recover reserves, which belong to the category of heavy and highly bound oil, is one of the most pressing oil industries. Many mining companies actively use physical and chemical methods to solve this problem. The use of nanotechnology in physical and chemical methods for increasing oil production is a relatively new area. Nanoparticles easily enter the regime, their size allows them to spread more easily in porous media without reducing permeability. The main goal of the research was to reduce oil viscosity and achieve increased production. In order to increase oil recovery, the proposed method for reducing oil viscosity in reservoir conditions based on treating the bottomhole zone of production wells with a solution of caustic soda and nanoparticles affects the dimension of 40-60 nm (concentrations of 1, 0.1 and 0.01 %). The nanoparticles were obtained using an electrical conductor method and studied by transmission electron microscopy. Determination of surface tension using a DSA30 device (Kruss, Germany) using the pendant drop (PD) method. The samples for testing were oil samples, visible wells No. 222761 Binagadi-North, No. 220051 Fatmai area and No. 221937 Kirmaki area, trading company "Binagadi Oil". Based on experimental data, it was found that the proposed composition reduces the viscosity of oil by several times in all three experimental samples, nanoparticles have a positive effect on reducing viscosity, the greatest effect was noted with 0.01% nanoparticles, this composition can be used to increase the production of high-viscosity oils.

Keywords: nanotechnology; metal nanoparticles; enhanced oil recovery; reduce oil viscosity.

Date submitted: 24.02.2024     Date accepted: 04.06.2024     Date published: 30.06.2024 

The problem of extracting hard-to-recover reserves, which belong to the category of heavy and highly bound oil, is one of the most pressing oil industries. Many mining companies actively use physical and chemical methods to solve this problem. The use of nanotechnology in physical and chemical methods for increasing oil production is a relatively new area. Nanoparticles easily enter the regime, their size allows them to spread more easily in porous media without reducing permeability. The main goal of the research was to reduce oil viscosity and achieve increased production. In order to increase oil recovery, the proposed method for reducing oil viscosity in reservoir conditions based on treating the bottomhole zone of production wells with a solution of caustic soda and nanoparticles affects the dimension of 40-60 nm (concentrations of 1, 0.1 and 0.01 %). The nanoparticles were obtained using an electrical conductor method and studied by transmission electron microscopy. Determination of surface tension using a DSA30 device (Kruss, Germany) using the pendant drop (PD) method. The samples for testing were oil samples, visible wells No. 222761 Binagadi-North, No. 220051 Fatmai area and No. 221937 Kirmaki area, trading company "Binagadi Oil". Based on experimental data, it was found that the proposed composition reduces the viscosity of oil by several times in all three experimental samples, nanoparticles have a positive effect on reducing viscosity, the greatest effect was noted with 0.01% nanoparticles, this composition can be used to increase the production of high-viscosity oils.

Keywords: nanotechnology; metal nanoparticles; enhanced oil recovery; reduce oil viscosity.

Date submitted: 24.02.2024     Date accepted: 04.06.2024     Date published: 30.06.2024 

References

  1. Poletayeva, O. Yu., Kolchina, G. Yu., Leontyev, A. Yu., et al. (2021). Study of composition of high-viscous heavy oils by method of nuclear magnetic resonant spectroscopy. Journal ChemChemTech, 64(1), 237-244.
  2. Al-Tashi, Q., Patah Akhir, E. A., Abdulkadir, S. J., Mirjalili, S. (2021). Classification of reservoir recovery factor for oil and gas reservoirs: A multi-objective feature selection approach. Journal of Marine Science and Engineering, 9, 888.
  3. Panakhov, G. M., Suleimanov, B. A. (1995). Specific features of the flow of suspensions and oil disperse systems. Colloid Journal, 57(3), 359-363.
  4. Kelbaliev, G. I., Tagiev, D. B., Rasulov, S. R, Manafov M. R. (2023). Structuralization effective viscosity of non-Newtonian oils. Journal of Engineering Physics and Thermophysics, 96(1), 55-63.
  5. Suleimanov, B. A. (2011). Sand plug washing with gassy fluids. SOCAR Proceedings, 1, 30-36.
  6. Suleimanov, B. A., Abbasov, H. F. (2016). Effect of copper nanoparticle aggregation on the thermal conductivity of nanofluids. Russian Journal of Physical Chemistry A, 90(2), . P. 420–428.
  7. Latifov, Y. A. (2021). Non-stationary effect of thermoactive polymer composition for deep leveling of filtration profile. Scientific Petroleum, 1, 25-30.
  8. Suleimanov, B. A., Ismailov, F. S., Veliyev, E. F., Dyshin, O. A. (2013). The influence of light metal nanoparticles on the strength of polymer gels used in oil industry. SOCAR Proceedings, 2, 24-28.
  9. Shamilov, V. M., Babayev, E. R., Kalbaliyeva, E., Shamilov, F. S. (2017). Polymer nanocomposites for enhanced oil recovery. Materials Today: Proceedings, 4, 70-74.
  10. Suleimanov, B. A., Veliyev, E. F., Aliyev, A. A. (2021). Impact of nanoparticle structure on the effectiveness of pickering emulsions for EOR applications. ANAS Transactions, Earth Sciences, 1, 82-92.
  11. Patela, H., Shaha, S., Ahmeda, R., Ucanb, S. (2018). Effects of nanoparticles and temperature on heavy oil viscosity. Journal of Petroleum Science and Engineering, 167, 819-828.
  12. Bui, T.-A., Pham, V.-H., Nguyen, M.-R., Bui, N.-R. (2020). Effect of Al2O3 nanoparticle on rheological properties of oil. International Journal of Mechanical and Production Engineering Research and Development (IJMPERD), 10(3), 2911–2918.
  13. Askarova, A., Turakhanov, A., Markovic, S., et al. (2020). Thermal enhanced oil recovery in deep heavy oil carbonates: Experimental and numerical study on a hot water injection performance. Journal of Petroleum Science and Engineering, 194, 107456.
  14. Ghaedi, H., Ayoub, M., Sufian, S., et al. (2018). Density and refractive index measurements of transitiontemperature mixture (deep eutectic analogues) based on potassium carbonate with dual hydrogen bond donors for COcapture. Journal of Chemical Thermodynamics, 2018, 118, p. 147–158
  15. Starshov, M. I., Ajduganov, V. M. (2000). Method of treatment of bottom-hole formation zone of wells producing heavy oils and native bitumen’s. RU Patent 2144982.
  16. Lyadov, А. S., Petrukhina, N. N. (2018). Extraction and refining of heavy crude oils: problems and prospects. Russian Journal of Applied Chemistry, 91(12), 1912–1921.
  17. Poletaeva, O. Yu., Kolchina, G. Yu., Leontev, A. Yu., et al. (2019). Geometric and electronic structure of heavy highly viscous oil components. Journal ChemChemTech, 62(9), 137-142.
  18. Shamilov, V. M., Abdullayev, A. I., Veliyev, R. V. (2016). Extraction of residual oil using a nanosystem. Azerbaijan Oil Industry, 6, 58-61.
  19. Alomair, O., Alajmi, A. (2016). Experimental study for enhancing heavy oil recovery by nanofluid followed by steam flooding NFSF. SPE-184117-MS. In: SPE Heavy Oil Conference and Exhibition, Kuwait City, Kuwait. Society of Petroleum Engineers.
  20. Hendraningrat, L., Li, S., Torsater, O. (2013). Effect of some parameters influencing enhanced oil recovery process using silica nanoparticles: an experimental investigation. SPE-165955-MS. In: SPE Reservoir Characterization and Simulation Conference and Exhibition, Abu Dhabi, UAE. Society of Petroleum Engineers.
  21. McElfresh, P. M., Holcomb, D. L., Ector, D. (2012). Application of nanofluid technology to improve recovery in oil and gas wells. SPE-154827-MS. In: SPE International Oilfield Nanotechnology Conference and Exhibition, Noordwijk, Netherlands. Society of Petroleum Engineers.
  22. Caldelas, F. M. (2010). Experimental parameter analysis of nanoparticle retention in porous media. PhD Thesis. Austin: The University of Texas.
  23. Murphy, M. J. (2012). Experimental analysis of electrostatic and hydrodynamic forces affecting nanoparticle retention in porous media. PhD thesis. Austin: University of Texas.
  24. Hashemi, R., Nassar, N. N., Almao, P. P. (2014). Nanoparticle technology for heavy oil in-situ upgrading and recovery enhancement: opportunities and challenges. Applied Energy, 133, 374-387.
  25. Suleimanov, B. A., Abbasov, Kh. F., Ismailov, R. H. (2023). Thermophysical properties and mechanism of stabilization of nano- and microfluids with particles of a metal-string complex [Ni5(μ5-pppmda)4СL2]. SOCAR Proceedings, 2, 32–41.
  26. Suleimanov, B. A, Abbasov, H. F., Ismayilov, R. H. (2023). Enhanced oil recovery with nanofluid injection. Petroleum Science and Technology, 41(18), 1734-1751.
Read more Read less

DOI: 10.5510/OGP2024SI100956

E-mail: vasif.valiyev@socar.az


L. S. Kuleshova

Institute of Oil and Gas, Ufa State Petroleum Technological University, (branch in the City of Oktyabrsky), Oktyabrsky, Russia

Modeling of the oil recovery process for managing the development of deposits using artificial intelligence


Based on a comprehensive study of the process of oil recovery from deposits in carbonate reservoirs of the Tournaisian tier of the Birskaya saddle, a forecast of the final coefficient of oil recovery for deposits that have been in development for a long time was carried out. The choice of the research object is due to the presence of a significant proportion of residual oil reserves, which can potentially be extracted as part of the implementation of an effective and sound asset management strategy. Previously, more than 60 objects were divided into four groups using one of the well-known methods of reducing the dimensionality of the problem ‒ the method of principal components, after which typical dependencies were established that make it possible to successfully group objects. Using statistical forecasting methods, models of the process of developing oil reserves were obtained differentially for various groups of selected objects, including a number of different parameters reflecting, among other things, geological heterogeneity. Based on the results of the interpretation of the obtained dependencies, the areas of their use in the plane of application of artificial intelligence systems in the development of deposits in the natural regime are established. Within the framework of determining the relationships between various parameters and evaluating the reliability of the obtained models, the need for object identification in solving a wide range of tasks to improve the efficiency of operation of both objects involved in the analysis and similar ones in terms of geological and commercial characteristics has been established.

Keywords: oil recovery coefficient; oil field development; computer modeling; carbonate reservoirs of the Tournaisian tier of the Birskaya saddle; identification of objects.

Date submitted: 20.03.2024     Date accepted: 07.06.2024     Date published: 05.07.2024

Based on a comprehensive study of the process of oil recovery from deposits in carbonate reservoirs of the Tournaisian tier of the Birskaya saddle, a forecast of the final coefficient of oil recovery for deposits that have been in development for a long time was carried out. The choice of the research object is due to the presence of a significant proportion of residual oil reserves, which can potentially be extracted as part of the implementation of an effective and sound asset management strategy. Previously, more than 60 objects were divided into four groups using one of the well-known methods of reducing the dimensionality of the problem ‒ the method of principal components, after which typical dependencies were established that make it possible to successfully group objects. Using statistical forecasting methods, models of the process of developing oil reserves were obtained differentially for various groups of selected objects, including a number of different parameters reflecting, among other things, geological heterogeneity. Based on the results of the interpretation of the obtained dependencies, the areas of their use in the plane of application of artificial intelligence systems in the development of deposits in the natural regime are established. Within the framework of determining the relationships between various parameters and evaluating the reliability of the obtained models, the need for object identification in solving a wide range of tasks to improve the efficiency of operation of both objects involved in the analysis and similar ones in terms of geological and commercial characteristics has been established.

Keywords: oil recovery coefficient; oil field development; computer modeling; carbonate reservoirs of the Tournaisian tier of the Birskaya saddle; identification of objects.

Date submitted: 20.03.2024     Date accepted: 07.06.2024     Date published: 05.07.2024

References

  1. Muslimov, R. Kh. (2013). Enhancing the role of non-conventional hydrocarbon deposits for long-term sustainable economic development (on the example of the Republic of Tatarstan). Georesursy, 4(54), 45-54.
  2. Arzhilovsky, A. V., Afonin, D. G., Ruchkin, A. A., et al. (2022). Express assessment of the increase in the oil recovery as a result of water-alternating-gas technology application. Oil Industry, 9, 63-67.
  3. Mukhametshin, V. V., Kuleshova, L. S. (2023). An algorithm for justifying the selection of facilities for the introduction of innovative oil production technologies in the conditions of the Lower Cretaceous deposits of Western Siberia. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 334(10), 179–186.
  4. Mukhametshin, V. Sh., Khakimzyanov, I. N. (2021). Features of grouping low-yield oil deposits in carbonate reservoirs for the rational use of resources within the Ural-Volga region. Journal of Mining Institute, 252, 896-907.
  5. Rzayeva, S. D., Akhmedova, U. T. (2021). Enhancement of oil recovery of the reservoir by foaming compositions. Bulatovskie Readings, 1, 222-224.
  6. Brilliant, L. S., Zavialov, A. S., Danko, M. U., et al. (2022). Integration of machine learning methods and geological and hydrodynamic modeling in field development design. Oil Industry, 10, 48-53.
  7. Grishchenko, V. A., Mukhametshin, V. Sh., Rabaev, R. U. (2022). Geological structure features of carbonate formations and their impact on the efficiency of developing hydrocarbon deposits. Energies, 15, 23.
  8. Yakupov, R. F., Mukhametshin, V. Sh., Tyncherov, K. T. (2018). Filtration model of oil coning in a bottom waterdrive reservoir. Periodico Tche Quimica, 15(30), 725-733.
  9. Nasybullina, S. V., Sattarov, Rav. Z., Ibatullin, R. R., et al. (2022). Analytical tools for Tatneft PJSC carbonate reservoirs performance analysis. Oil Industry, 7, 24-27.
  10. Miroshnichenko, A. V., Sergeichev, A. V., Korotovskikh, V. A., et al. (2022). Innovative technologies for the lowpermeability reservoirs development in Rosneft Oil Company. Oil Industry, 10, 105–109.
  11. Akhmetov, R. T., Kuleshova, L. S., Veliev, E. F., et al. (2022). Substantiation of the analytical model of hydraulic tortuosity of the pore channels of reservoirs in Western Siberia according to capillary studies. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 333(7), 86–95.
  12. Mukhametshin, V. Sh., Zeigman, Yu. V., Andreev, A. V. (2017). Rapid assessment of deposit production capacity for determination of nanotechnologies application efficiency and necessity to stimulate their development. Nanotechnologies in Construction, 9(3), 20–34.
  13. Ibatullin, R. R., Gaffarov, Sh. K., Khisametdinov, M. R., Minikhairov, L. I. (2022). Review of world polymer flooding EOR projects. Oil Industry, 7, 32–37.
  14. Mukhametshin, V. Sh., Kuleshova, L. S., Safiullina, A. R. (2021). Grouping and isolation of oil deposits in carbonate reservoirs by productivity at the stage of geological exploration. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 332(12), 43–51.
  15. Mukhametshin, V. V., Andreev, V. E. (2018). Increasing the efficiency of assessing the performance of techniques aimed at expanding the use of resource potential of oilfields with hard-to-recover reserves. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 329(8), 30–36.
  16. Mousavi, S. M., Bakhtiarimanesh, P., Enzmann, F., et al. (2024). Machine-learned surrogate models for efficient oil well placement under operational reservoir constraints. SPE Journal, 29(01), 518-537.
  17. Khuzin, R. R., Andreev, V. E., Mukhametshin, V. V., et al. (2021). The effect of hydraulic compression of the reservoir on the filtration and capacitance properties of reservoir formations. Journal of Mining Institute, 251(3), 688-697.
  18. Mukhametshin, V. V., Kuleshova, L. S. (2020). On reducing the level of uncertainty in the management of flooding of deposits with hard-to-recover reserves. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 331(5), 140–146.
  19. He, D., Qu, Y., Sheng, G., et al. (2024). Oil production rate forecasting by SA-LSTM model in tight reservoirs. Lithosphere, 2024(1).
  20. Kumar, I., Tripathi, B. K., Singh, A. (2023). Attention-based LSTM network-assisted time series forecasting models for petroleum production. Engineering Applications of Artificial Intelligence, 123, 106440.
  21. Mukhametshin, V. V. (2018). Substantiation of trends in increasing the degree of production of oil reserves in the Lower Cretaceous deposits of Western Siberia based on the identification of objects. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 329(5), 117–124.
  22. Yakupov, R. F., Mukhametshin, V. Sh., Malov, A. G., et al. (2023). Сharacteristic properties of the terrigenous reservoir development with underlying water by horizontal wells. SOCAR Proceedings, 2, 61–70.
  23. Evseenkov, A. S., Guz, V. S., Shpetny, D. N., Yudin, E. V. (2023). Short–term forecasting of well flow rate based on an ensemble approach. Oil Industry, 2, 78-83.
  24. Mukhametshin, V. V. (2018). Rationale for trends in increasing oil reserves depletion in Western Siberia cretaceous deposits based on targets identification. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 329(5), 117–124.
  25. Trofimchuk, A. S., Mukhametshin, V. Sh., Khabibullin, G. I., et al. (2023). Low-permeability reservoirs flooding using horizontal wells. SOCAR Proceedings, SI2, 125–133.
  26. Gilyazetdinov, R. A., Kuleshova, L. S., Mukhametshin, V. Sh., et al. (2023). An integrated approach to predicting the results of the identification of deposits in conditions of different tectonic confinement of objects. SOCAR Proceedings, 4, 31–41.
  27. Khayredinov, N. Sh., Popov, A. M., Mukhametshin, V. Sh. (1992). Increasing the flooding efficiency of poor-producing oil deposits in carbonate collectors. Oil Industry, 9, 18–20.
  28. Salakhutdinov, A. I., Ambartsumyan, R. A., Gabdullina, N. R. (2022). Additional development of reserves at a late stage of development of a complex carbonate reservoir on the example of a field in the Timan-Pechora oil and gas province. Oil Industry, 9, 85–89.
  29. Pan, S., Yang, B., Wang, S., et al. (2023). Oil well production prediction based on CNN-LSTM model with self-attention mechanism. Energy, 284, 128701.
  30. Rogachev, M. K., Mukhametshin, V. V. (2018). Control and regulation of the hydrochloric acid treatment of the bottomhole zone based on field-geological data. Journal of Mining Institute, 231, 275-280.
  31. Mukhametshin, V. V. (2008). On solving the problems of flooding of high-viscosity oil deposits in carbonate reservoirs. Journal of Mining Institute, 174, 50–52.
  32. Rastegaev, A. V., Chernykh, I. A., Ponomareva, I. N., Martyushev, D. A. (2019) Assessment of hydraulic fracturing results based on a comprehensive analysis of microseismic monitoring data and geological and field information. Oil Industry, 8, 122–125.
  33. Li, Z., Lei, Z., Shen, W., et al. (2023). A comprehensive review of the oil flow mechanism and numerical simulations in shale oil reservoirs. Energies, 16(8), 3516.
  34. Kozhin, V. N., Demin, S. V., Bakirov, I. I. (2023). The study of new methods for the development of carbonate deposits with contact water–oil zones. Oil Industry, 3, 32-35.
  35. Mukhametshin, V. V. (2017). Elimination of uncertainties in solving problems of impact on the bottom-hole zone of wells. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 328(7), 40–50.
  36. Sergeev, V. V., Belenkova, N. G., Zeigman, Yu. V., Mukhametshin, V. Sh. (2017). Physical properties of emulsion systems containing SiO2 nanoparticles. Nanotechnologies in Construction, 9(6), 37–64.
  37. Zenchenko, E. V., Zenchenko, P. E., Nachev, V. A., et al. (2023). Acoustic and deformation investigation of hydraulic fracturing crack opening in a poroelastic model material. Oil Industry, 11, 100-103.
  38. Salakhutdinov, A. I., Boyko, S. I., Sonnykh, A. A., et al. (2023). Decision support algorithm for drilling new sections of the carbonate reservoir. Oil Industry, 11, 108-112.
  39. Stepanov, S. V., Arzhilovsky, A. V. (2023). On improving the quality of mathematical modeling in solving problems of oil field development support. Oil Industry, 4, 56-60.
  40. Shpurov, I. V., Bratkova, V. G., Vasilieva, V. S., et al. (2021). Evaluating the optimal distance between wells for the Achimov formation. Oil Industry, 11, 80–84.
  41. Daramola, G. O., Jacks, B. S., Ajala, O. A., Akinoso, A. E. (2024). AI applications in reservoir management: optimizing production and recovery in oil and gas fields. Computer Science & IT Research Journal, 5(4), 972-984.
  42. Yudin, E. V., Poroshin, I. O., Gruzdev, I. E., Markov, N.S. (2023). New approaches to rapid assessment of well productivity in heterogeneous formations. Oil Industry, 10, 61-67.
  43. Mukhametshin, V. V., Rabaev, R. U., Kuleshova, L. S., et al. (2023). Ways to increase the resource base of the Volga-Ural oil and gas province. SOCAR Proceedings, 4, 42–49.
  44. Mukhametshin, V. Sh. (1989). Dependence of oil recovery on the density of the well grid in the development of lowyield carbonate deposits. Oil Industry, 12, 26-29.
Read more Read less

DOI: 10.5510/OGP2024SI100957

E-mail: markl212@mail.ru


V. V. Mukhametshin1, R. A. Gilyazetdinov2, L. S. Kuleshova2, S. H. Novruzova3, M. A. Mammadova3, V. M. Askerov4

1Ufa State Petroleum Technological University, Ufa, Russia; 2Institute of Oil and Gas, Ufa State Petroleum Technological University, (branch in the City of Oktyabrsky), Russia; 3Azerbaijan State Oil and Industry University, Baku, Azerbaijan; 4«OilGasScientificResearchProject» Institute, SOCAR, Baku, Azerbaijan 

On the depth of identification of objects in the study of the influence of the density of the grid of wells on the degree of production of oil reserves


The article studies the effect of the well grid density on the final oil recovery coefficient under conditions of various groups of objects in carbonate reservoirs of the Volga-Ural oil and gas province. The issue being solved within the framework of this work is one of the most relevant and important when making various management decisions in conditions of uncertainty, significant variability and heterogeneity of geological and field data. More than 500 deposits of carbonate reservoirs were selected for the study, the development of which is carried out on the territory of the Ural-Volga region. To implement an integrated approach, the construction of models was carried out in several interrelated stages. Within the framework of basic modeling, field statistical methods were used to identify dependencies describing at a high quantitative and qualitative level the relationship between the oil recovery coefficient and the density of the well grid before flooding. To obtain the most representative results, a separate approach based on the preliminary division of objects into groups was also used. At the second stage, in addition, a deep differentiation of the initial objects was implemented to determine hidden patterns within already formed groups and a similar construction of models according to which interpretation was performed. When combining the results obtained, it was reliably established that models for assessing the degree of reserve production from the density of the well grid should be built not only in general by tectonic and stratigraphic elements, but also differentially by groups identified as a result of in-depth identification of development sites. It is proposed to use the obtained dependencies to solve the problems of optimizing the density of the well grid for both long-term oil deposits under development and newly commissioned and at the stage of drafting the first design documents.

Keywords: well grid density; oil recovery coefficient; modeling; grouping of deposits of the Volga-Ural oil and gas province; deep identification of objects; carbonate reservoirs.

Date submitted: 15.03.2024     Date accepted: 20.04.2024     Date published: 05.07.2024

The article studies the effect of the well grid density on the final oil recovery coefficient under conditions of various groups of objects in carbonate reservoirs of the Volga-Ural oil and gas province. The issue being solved within the framework of this work is one of the most relevant and important when making various management decisions in conditions of uncertainty, significant variability and heterogeneity of geological and field data. More than 500 deposits of carbonate reservoirs were selected for the study, the development of which is carried out on the territory of the Ural-Volga region. To implement an integrated approach, the construction of models was carried out in several interrelated stages. Within the framework of basic modeling, field statistical methods were used to identify dependencies describing at a high quantitative and qualitative level the relationship between the oil recovery coefficient and the density of the well grid before flooding. To obtain the most representative results, a separate approach based on the preliminary division of objects into groups was also used. At the second stage, in addition, a deep differentiation of the initial objects was implemented to determine hidden patterns within already formed groups and a similar construction of models according to which interpretation was performed. When combining the results obtained, it was reliably established that models for assessing the degree of reserve production from the density of the well grid should be built not only in general by tectonic and stratigraphic elements, but also differentially by groups identified as a result of in-depth identification of development sites. It is proposed to use the obtained dependencies to solve the problems of optimizing the density of the well grid for both long-term oil deposits under development and newly commissioned and at the stage of drafting the first design documents.

Keywords: well grid density; oil recovery coefficient; modeling; grouping of deposits of the Volga-Ural oil and gas province; deep identification of objects; carbonate reservoirs.

Date submitted: 15.03.2024     Date accepted: 20.04.2024     Date published: 05.07.2024

References

  1. Kuznetsova, A. A., Kulmamirov, A. L. (2023). Assessment of the influence of various factors on the efficiency of the development of the Romashkinskoye field areas. Oil Industry, 9, 22-27.
  2. Shakhverdiev, A. Kh., Arefyev, S. V., Davydov, A. V. (2022). Problems of transformation of hydrocarbon reserves into an unprofitable technogenic hard-to-recover reserves category. Oil Industry, 4, 38-43.
  3. Borodin, A., Vygodchikova, I., Panaedova, G., Mityushina, I. (2023). Rating of stability of russian companies in oil and gas and electric power industries based on interval volatility. Energies, 16(14), 5387.
  4. Tavoosi Iraj, P., Rajabi, M., Ranjbar-Karami, R. (2023). Integrated petrophysical and heterogeneity assessment of the karstified fahliyan formation in the abadan plain, Iran. Natural Resources Research, 32(3), 1067-1092.
  5. Khayredinov, N. S., Popov, A. M., Mukhametshin, V. Sh. (1992). Improving the efficiency of flooding of lowyielding oil deposits in carbonate reservoirs. Oil Industry, 9, 18-20.
  6. Chen, X., Zhang, Q., Li, Y., et al. (2024). Investigation on enhanced oil recovery and CO2 storage efficiency of temperature-resistant CO2 foam flooding. Fuel, 364, 130870.
  7. Hama, S. M., Manshad, A. K., Ali, J. A. (2024). Experimental investigation of new derived anionic natural surfactant from peanut oil: Application for enhanced oil recovery. Journal of Molecular Liquids, 395, 123876.
  8. Sergeev, V. V., Sharapov, R. R., Kudymov, A. Yu., et al. (2020). Experimental research of the colloidal systems with nanoparticles influence on filtration characteristics of hydraulic fractures. Nanotechnologies in Construction, 12(2), 100–107.
  9. Mukhametshin, V. Sh. (1989). Dependence of oil recovery on the density of the well grid in the development of low-yield carbonate deposits. Oil Industry, 12, 26-29.
  10. Odili, P. O., Daudu, C. D., Adefemi, A., et al. (2024). Integrating advanced technologies in corrosion and inspection management for oil and gas operations. Engineering Science & Technology Journal, 5(2), 597-611.
  11. Shipaeva, M. S., Nurgaliev, D. K., Sudakov, V. A., et al. (2022). Determination of well interaction based on a set of methods for retrospective analysis of well operation and geochemical studies. Oil Industry, 1, 64–69.
  12. Khuzin, R. R., Andreev, V. E., Mukhametshin, V. V., et al. (2021). The effect of hydraulic compression of the reservoir on the filtration and capacitance properties of reservoir formations. Journal of Mining Institute, 251, 688-697.
  13. Yakupov, R. F., Mukhametshin, V. Sh., Tyncherov, K. T. (2018). Filtration model of oil coning in a bottom waterdrive reservoir. Periodico Tche Quimica, 15(30), 725-733.
  14. Shpurov, I. V., Bratkova, V. G., Vasilieva, V. S., et al. (2021). Evaluating the optimal distance between wells for the Achimov formation. Oil Industry, 11, 80–84.
  15. Kanevskaya, R. D., Kuznetsov, P. V., Pimenov, A. A., et al. (2023). Computer technology for controlling oil production from an oil and gas deposit with underlying water in a fractured formation. Oil Industry, 10, 56-60.
  16. Mukhametshin, V. V., Akhmetov, R. T., Kuleshova, L. S., Grezina, O. A. (2021). Analytical relationships between filtration and capacitance parameters of productive reservoirs in Western Siberia based on a generalized mathematical model of capillary curves. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 332(8), 135-141.
  17. Kairgeldina, L. K., Sarsenbekuly, B. (2024). Alternative methods of thermal oil recovery: a review. Kazakhstan Journal for Oil & Gas Industry, 6(1), 50-63.
  18. Kuleshova, L. S. (2023). Using indirect estimates to improve development efficiency of the deposits with flooding applicat ion. SOCAR Proceedings, 3, 112–119.
  19. Cao, G., Lin, M., Zhang, L., et al. (2024). Numerical simulation of the dynamic migration mechanism and prediction of saturation of tight sandstone oil. Science China Earth Sciences, 67(1), 179-195.
  20. Al Nabhani, H., Al Riyami, O., Al Sulaimani, H., et al. (2024, April). Use of polymer flooding to enhance oil recovery from the largest oil-bearing clastic reservoir in the south of the Sultanate of Oman. SPE-218489-MS. In: SPE Conference at Oman Petroleum & Energy Show, Muscat, Oman. Society of Petroleum Engineers.
  21. Mukhametshin, V. V., Rabaev, R. U., Kuleshova, L. S., et al. (2023). Ways to increase the resource base of the Volga-Ural oil and gas province. SOCAR Proceedings, 4, 42–49.
  22. Mukhametshin, V. Sh., Kuleshova, L. S., Safiullina, A. R. (2021). Grouping and isolation of oil deposits in carbonate reservoirs by productivity at the stage of geological exploration. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 332(12), 43–51.
  23. Brilliant, L. S., Zavialov, A. S., Danko, M. U., et al. (2022). Integration of machine learning methods and geological and hydrodynamic modeling in field development design. Oil Industry, 10, 48-53.
  24. Grishchenko, V. A., Mukhametshin, V. Sh., Rabaev, R. U. (2022). Geological structure features of carbonate formations and their impact on the efficiency of developing hydrocarbon deposits. Energies, 15, 23.
  25. Salakhutdinov, A. I., Ambartsumyan, R. A., Gabdullina, N. R. (2022). Additional development of reserves at a late stage of development of a complex carbonate reservoir on the example of a field in the Timan-Pechora oil and gas province. Oil Industry, 9, 85–89.
  26. Mannapov, M. I., Nasybullin, A. V., Yemelyanov, V. V., et al. (2023). Development of the methodology for placing design injection wells in the EPSILON software package. Oil Industry, 9, 17-21.
  27. Faizov, I. A., Garifullin, A. Sh., Mukhametshin, V. Sh., et al. (2023). An industrial experiment on the Novo-Khazinskaya square of the Arlanskoye field – 60 years later. SOCAR Proceedings, 1, 107–116.
  28. Salakhutdinov, A. I., Boyko, S. I., Sonnykh, A. A., et al. (2023). Decision support algorithm for drilling new sections of the carbonate reservoir. Oil Industry, 11, 108-112.
  29. Mukhametshin, V. V. (2018). Substantiation of trends in increasing the degree of production of oil reserves in the Lower Cretaceous deposits of Western Siberia based on the identification of objects. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 329(5), 117–124.
  30. Yongsheng, M. A., Xunyu, C. A. I., Lu, Y. U. N., et al. (2022). Practice and theoretical and technical progress in exploration and development of Shunbei ultra-deep carbonate oil and gas field, Tarim Basin, NW China. Petroleum Exploration and Development, 49(1), 1-20.
  31. Nesterov, I. I., Smirnov, P. V., Konstantinov, A. O., Gursky, H. J. (2021). Types, features, and resource potential of Palaeocene–Eocene siliceous rock deposits of the West Siberian Province: a review. International Geology Review, 63(4), 504-525.
  32. Mikhailov, N. N., Varlamov, D. P., Klinkov, K. A. (2004). Modeling the effect of well placement systems on residual oil saturation of flooded formations. Drilling and Oil, 1, 13-15.
  33. Lysenko, V. D. (2008). On the density of the grid of horizontal and vertical wells. Oilfield Business, 8, 20-22.
  34. Sergeev, V. V., Belenkova, N. G., Zeigman, Yu. V., Mukhametshin, V. Sh. (2017). Physical properties of emulsion systems containing SiO2 nanoparticles. Nanotechnologies in Construction, 9(6), 37–64.
  35. Khafizov, S., Syngaevsky, P., Dolson, J. C. (2022). The West Siberian Super Basin: The largest and most prolific hydrocarbon basin in the world. AAPG Bulletin, 106(3), 517-572.
  36. Mukhametshin, V. V. (2018). Evaluation of the effectiveness of the use of nanotechnology after completion of well construction aimed at accelerating the commissioning of oil fields. Nanotechnologies in Construction, 10(1),113–131.
  37. Mukhametshin, V. Sh., Khakimzyanov, I. N. (2021). Features of grouping low-yield oil deposits in carbonate reservoirs for the rational use of resources within the Ural-Volga region. Journal of Mining Institute, 252, 896-907.
  38. Khasanov, M. M., Vakhitov, R. R., Lakman., I. A., Timiryanova, V. M. (2023). Spatial modeling of the interaction of producing wells. Oil Industry, 10, 51-55.
  39. Mukhametshin, V. Sh. (2022). Oil flooding in carbonate reservoirs management. SOCAR Proceedings, SI1, 38-44.
  40. Chelkachev, V. N. (1974). Influence of well grid density and their placement on oil recovery. Oil Industry, 6, 26-29.
  41. Zakirov, S. N. (2002). Analysis of the problem «well grid density – oil recovery». Moscow: Grail.
Read more Read less

DOI: 10.5510/OGP2024SI100958

E-mail: vv@of.ugntu.ru


A. V. Syundyukov1, N. S. Markov1, V. A. Grischchenko2, V. Sh. Mukhametshin2, R. I. Suleimanov2, A. Yu. Polyakov2, A. A. Bagirov3, E. M. Suleymanov3, E. F. Veliyev4

1LLC «NEDRA», Saint Petersburg, Russia; 2Institute of Oil and Gas, Ufa State Petroleum Technological University (branch in the city of Oktyabrsky), Russia; 3Azerbaijan State Oil and Industry University, Baku, Azerbaijan; 4OilGasScientificResearchProject Institute, SOCAR, Baku, Azerbaijan

Cyclic regulation of self-induced hydraulic fracturing cracks to improve the flooding efficiency of low-permeability reservoirs


The article is devoted to the topical issue of optimizing flooding in the development of deposits using a reservoir pressure maintenance system. In conditions of low permeability and weak reservoir connectivity, when water is injected into the injection well, when the injection pressure exceeds the pressure of rock rupture, auto-fracturing cracks occur. In order to ensure the maximum coverage factor of the reservoir by flooding during the development of the field, it is necessary to initiate and maintain the optimal fracture geometry of the auto-fracturing. The aim of the work is to analyze the modes of cyclic regulation of injection into the reservoir to maintain the optimal size of man-made cracks during the development of the field. The article presents the results of hydrodynamic modeling of flooding options with varying injection and shutdown times of injection wells, taking into account the geomechanical patterns of the spread of man-made cracks in the formation. Based on the results of the calculations performed, based on the forecast characteristics of displacement and the rate of oil extraction, the optimal modes of cyclic reservoir flooding were determined. The work is of great commercial importance, which is confirmed by the positive effect in the form of additional oil production within the framework of pilot tests of the proposed scheme.

Keywords: self-induced hydraulic fracture; low-permeability reservoir; waterflood; water injection system; horizontal well; bottom hole pressure; reservoir pressure; fracture half-length; periodic injection of water.

Date submitted: 18.03.2024     Date accepted: 25.04.2024     Date published: 05.07.2024

The article is devoted to the topical issue of optimizing flooding in the development of deposits using a reservoir pressure maintenance system. In conditions of low permeability and weak reservoir connectivity, when water is injected into the injection well, when the injection pressure exceeds the pressure of rock rupture, auto-fracturing cracks occur. In order to ensure the maximum coverage factor of the reservoir by flooding during the development of the field, it is necessary to initiate and maintain the optimal fracture geometry of the auto-fracturing. The aim of the work is to analyze the modes of cyclic regulation of injection into the reservoir to maintain the optimal size of man-made cracks during the development of the field. The article presents the results of hydrodynamic modeling of flooding options with varying injection and shutdown times of injection wells, taking into account the geomechanical patterns of the spread of man-made cracks in the formation. Based on the results of the calculations performed, based on the forecast characteristics of displacement and the rate of oil extraction, the optimal modes of cyclic reservoir flooding were determined. The work is of great commercial importance, which is confirmed by the positive effect in the form of additional oil production within the framework of pilot tests of the proposed scheme.

Keywords: self-induced hydraulic fracture; low-permeability reservoir; waterflood; water injection system; horizontal well; bottom hole pressure; reservoir pressure; fracture half-length; periodic injection of water.

Date submitted: 18.03.2024     Date accepted: 25.04.2024     Date published: 05.07.2024

References

  1. Baykov, V. A., Davletbaev, A. Ya., Asmandiyarov, R. N., et al. (2011). Special well tests to fractured water injection wells. Electronic scientific journal «Oil and gas Business», 1, 65-75.
  2. Economides, J. M., Nolte, K. I. (2000). Reservoir stimulation. West Sussex, England: John Wiley and Sons.
  3. Zdolnik, S. E., Nekipelov, Yu. V., Gaponov, M. A., Folomeev, A. E. (2016). Introduction of innovative hydrofracturing technologies on carbonate reservoirs of Bashneft PJSOC. Oil Industry, 7, 92-95.
  4. Syundyukov, A. V., Khabibullin, G. I., Trofimchuk, A. S., et al. (2021) Flood control method in fields with hard-torecover reserves. SPE-206408-RU. In: SPE Russian Petroleum Technology Conference, Virtual. Society of Petroleum Engineers.
  5. Mardashov, D. V., Rogachev, M. K., Zeigman, Yu. V., Mukhametshin, V. V. (2021). Well killing technology before workover operation in complicated conditions. Energies, 14(3), 654
  6. Iktisanov, V. A., Smotrikov, N. A., Baigushev, A. V. (2022). Filtration features in carbonate deposits, determined by data from studies of injection wells. Oil Industry, 2, 74–78.
  7. Khayredinov, N. S., Popov, A. M., Mukhametshin, V. Sh. (1992). Improving the efficiency of flooding of lowyielding oil deposits in carbonate reservoirs. Oil Industry, 9, 18-20.
  8. Mukhametshin, V. V. (2017). Elimination of uncertainties in solving problems of impact on the bottom-hole zone of wells. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 328(7), 40–50.
  9. Syundyukov, A. V., Khabibullin, G. I., Trofimchuk, A. S., Sagitov, D. K. (2022). Method for maintaining the optimal geometry of induced fracture by regulating the injection mode on low-permeability reservoirs. Oil Industry, 9, 96-99.
  10. Yudin, E. V., Podoprigora, D. G. (2022). Mode of operation and productivity of wells during flooding. SPbSETI Publishing House.
  11. Lubnin, A. A., Yudin, E. V., Shchutsky, G. A. (2013). Engineering approach to solving flooding problems. Scientific and Technical Bulletin of Rosneft Oil Company, 52(5), 14-18.
  12. Khuzin, R. R., Andreev, V. E., Mukhametshin, V. V., et al. (2021). The effect of hydraulic compression of the reservoir on the filtration and capacitance properties of reservoir formations. Journal of Mining Institute, 251(3), 688-697.
  13. Ardislamova, D. R., Kadyrova, K. R., Sypchenko, S. I., et al. (2019). Using clustering methods in hydraulic fracturing modeling. Oil Industry, 10, 112–117.
  14. Sergeev, V. V., Sharapov, R. R., Kudymov, A. Yu., et al. (2020). Experimental research of the colloidal systems with nanoparticles influence on filtration characteristics of hydraulic fractures. Nanotechnologies in Construction, 12(2), 100–107.
  15. Mukhametshin, V. V., Kuleshova, L. S. (2020). On reducing the level of uncertainty in the management of flooding of deposits with hard-to-recover reserves. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 331(5), 140–146.
  16. Arefiev, S. V., Sokolov, I. S., Pavlov, M. S., et al. (2022). Implementation of horizontal wells with multistage hydraulic fracturing for low-permeability oil reservoir development. Oil Industry, 9, 90-95.
  17. Mukhametshin, V. Sh., Khakimzyanov, I. N. (2021). Features of grouping low-yield oil deposits in carbonate reservoirs for the rational use of resources within the Ural-Volga region. Journal of Mining Institute, 252, 896-907.
  18. Babaev, M. L., Savchenko, I. V., Shkitin, А. А., et al. (2017). Technologies for involvement in the development of the complex reservoir AV11-2 Ryabchik of Samotlorskoye oil field. Oil Industry, 9, 24–29.
  19. Mukhametshin, V. V. (2021). Improving the efficiency of managing the development of the West Siberian oil and gas province fields on the basis of differentiation and grouping. Russian Geology and Geophysics, 62(12), 1672–1685.
  20. Brilliant, L. S., Zavialov, A. S., Danko, M. U., et al. (2022). Integration of machine learning methods and geological and hydrodynamic modeling in field development design. Oil Industry, 10, 48-53.
  21. Gilmiev, D. R., Kovalenko, A. P., Hrebtova, E. A., et al. (2022). Methodology for assessing the zones of localization of reserves of a multi-layer development object by the analytical method. Oil Industry, 11, 32–36.
  22. Mukhametshin, V. V. (2018). Evaluation of the effectiveness of the use of nanotechnology after completion of well construction aimed at accelerating the commissioning of oil fields. Nanotechnologies in Construction, 10(1),113–131.
  23. Wang, F., Xu, H., Liu, Y., et al. (2023). Experimental study on the enhanced oil recovery mechanism of an ordinary heavy oil field by polymer flooding. ACS omega, 8(15), 14089-14096.
  24. Mukhametshin, V. Sh. (1989). Dependence of oil recovery on the density of the well grid in the development of low-yield carbonate deposits. Oil Industry, 12, 26-29.
  25. Hassan, A. M., Al-Shalabi, E. W., Alameri, W., et al. (2023). Manifestations of surfactant-polymer flooding for successful field applications in carbonates under harsh conditions: A comprehensive review. Journal of Petroleum Science and Engineering, 220, 111243.
  26. Fattakhov, I. G., Kuleshova, L. S., Bakhtizin, R. N., et al. (2021). Complexing the hydraulic fracturing simulation results when hybrid acid-propant treatment performing and with the simultaneous hydraulic fracture initiation in separated intervals. SOCAR Proceedings, SI2, 103-111.
  27. Li, H. (2023). Layered injection technology for chemical flooding of Class-III oil reservoirs in the Daqing Oil Fields complex, Songliao Basin, Northeast China. Energy Geoscience, 4(1), 51-58.
  28. Akhmetov, R. T., Kuleshova, L. S., Veliev, E. F., et al. (2022). Substantiation of the analytical model of hydraulic tortuosity of the pore channels of reservoirs in Western Siberia according to capillary studies. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 333(7), 86–95.
  29. Baykov, V. A., Zhdanov, R. M., Mullagaliev, T. I., Usmanov, T. S. (2011). Selecting the optimal system design for the fields with low-permeability reservoirs. Electronic scientific journal «Oil and Gas Business», 1, 84-98.
  30. Yudin, E. V., Kolyuk, O. A., Zamakhov, S. V. (2021). Interpretation of reservoir pressure for low-permeability reservoirs. Oil Industry, 3, 128(5), 66-70.
  31. Grishchenko, V. A., Rabaev, R. U., Asylgareev, I. N., et al. (2021). Methodological approach to optimal geological and technological characteristics determining when planning hydraulic fracturing at multilayer facilities. SOCAR Proceedings, SI2, 182-191.
  32. Mukhametshin, V. Sh., Kuleshova, L. S., Safiullina, A. R. (2021). Grouping and isolation of oil deposits in carbonate reservoirs by productivity at the stage of geological exploration. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 332(12), 43–51.
  33. Sergeev, V. V., Belenkova, N. G., Zeigman, Yu. V., Mukhametshin, V. Sh. (2017). Physical properties of emulsion systems containing SiO2 nanoparticles. Nanotechnologies in Construction, 9(6), 37–64.
  34. Mukhametshin, V. V., Kuleshova, L. S. (2023). An algorithm for justifying the selection of facilities for the introduction of innovative oil production technologies in the conditions of the Lower Cretaceous deposits of Western Siberia. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 334(10),179–186.
  35. Bikmetova, A. R., Asalkhuzina, G. F., Davletbaev, A. Ya., et al. (2022). Estimating parameters in the horizontal wells with multistage fracturing using reservoir modeling and tracer analysis. Oil Industry, 11, 118-121.
  36. Grishchenko, V. A., Mukhametshin, V. Sh., Rabaev, R. U. (2022). Geological structure features of carbonate formations and their impact on the efficiency of developing hydrocarbon deposits. Energies, 15, 23.
  37. Gilyazetdinov, R. A., Kuleshova, L. S., Mukhametshin, V. Sh., et al. (2023). An integrated approach to predicting the results of the identification of deposits in conditions of different tectonic confinement of objects. SOCAR Proceedings, 4, 31–41.
  38. Asalkhuzina, G. F., Davletbaev, A. Ya., Khabibullin, I. L. (2016). Modeling of the reservoir pressure difference between injection and production wells in low permeable reservoirs. Vestnik Bashkirskogo Universiteta, 21(3), 537-544.
  39. Davletbaev, A. Ya., Asalkhuzina, G. F., Ivashchenko, D. S., et al. (2015). Methods of research for the development of spontaneous growth of induced fractures during flooding in low permeability reservoirs. SPE-176562-MS. In: SPE Russian Petroleum Technology Conference, Moscow, Russia. Society of Petroleum Engineers.
  40. Davletbaev, A. Ya., Baykov, V. A., Bikbulatova, G. R., et al. (2014). Field studies of spontaneous growth of induced fractures in injection wells. SPE-171232-MS. In: SPE Russian Oil and Gas Exploration & Production Technical Conference and Exhibition, Moscow, Russia. Society of Petroleum Engineers.
  41. Trofimchuk, A. S., Mukhametshin, V. Sh., Khabibullin, G. I., et al. (2023). Low-permeability reservoirs flooding using horizontal wells. SOCAR Proceedings, SI2, 125–133.
Read more Read less

DOI: 10.5510/OGP2024SI100959

E-mail: vv@of.ugntu.ru


V. P. Meshalkin1,2, R. N. Bakhtizin3, L. E. Lenchenkova3, R. N. Yakubov3, N. S. Lenchenkov3, R.R. Asadullin3

1D. I. Mendeleev Russian University of Chemical Technology, Moscow, Russia; 2A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, Moscow, Russia; 3Ufa State Petroleum Technological University, Ufa, Russia

Methodology for optimizing high-viscosity oil production using a nature-inspired algorithm


To maintain reservoir pressure above the saturation pressure of oil with gas and effectively displace oil from the reservoir, flooding remains an established operating practice in the fields of the Russian Federation. In recent years, theoretical and practical research into this method of operation has been actively developing. Thus, the low efficiency of oil displacement by flooding has been established in conditions of low oil mobility compared to water, which is especially evident in heterogeneous reservoirs. The highest reduction in the efficiency of the process of replacing oil with water is observed during the development of highly viscous oil fields. For these difficult conditions, polymer flooding technology is especially promising, because the polymer, thickening the water, increases its viscosity, increasing the displacing ability of water. However, for the practical application of polymer flooding, it is necessary to optimize the process by justifying technological parameters (polymer concentration, injection rate) to achieve maximum efficiency. For this purpose, the paper presents a methodological approach based on the use of a nature-inspired optimization algorithm ‒ a flock of migratory birds, used to solve a number of practical nonlinear optimization problems.

Keywords: flooding; polymer compositions; mathematical modeling; optimization objective function; nature-inspired algorithms; migrating bird algorithm; net present value.

Date submitted: 18.07.2023     Date accepted: 25.04.2024     Date published: 22.07.2024

To maintain reservoir pressure above the saturation pressure of oil with gas and effectively displace oil from the reservoir, flooding remains an established operating practice in the fields of the Russian Federation. In recent years, theoretical and practical research into this method of operation has been actively developing. Thus, the low efficiency of oil displacement by flooding has been established in conditions of low oil mobility compared to water, which is especially evident in heterogeneous reservoirs. The highest reduction in the efficiency of the process of replacing oil with water is observed during the development of highly viscous oil fields. For these difficult conditions, polymer flooding technology is especially promising, because the polymer, thickening the water, increases its viscosity, increasing the displacing ability of water. However, for the practical application of polymer flooding, it is necessary to optimize the process by justifying technological parameters (polymer concentration, injection rate) to achieve maximum efficiency. For this purpose, the paper presents a methodological approach based on the use of a nature-inspired optimization algorithm ‒ a flock of migratory birds, used to solve a number of practical nonlinear optimization problems.

Keywords: flooding; polymer compositions; mathematical modeling; optimization objective function; nature-inspired algorithms; migrating bird algorithm; net present value.

Date submitted: 18.07.2023     Date accepted: 25.04.2024     Date published: 22.07.2024

References

  1. Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35(3), 268-308.
  2. Yang, X. S. (2008). Nature-Inspired Metaheuristic Algorithms. UK: Luniver Press.
  3. Yang, X. S. (2010). Engineering Optimization: An Introduction with Metaheuristic Applications. USA: John Wiley and Sons.
  4. Yang, X.S. (2020). Nature inspired optimization algorithms. USA: Academic Press/Elsevier
  5. He, J., & Yu, X. (2001). Conditions for the convergence of evolutionary algorithms. Journal of Systems Architecture, 47, 601-612.
  6. Popa, A. S., Sivakumar, K., Cassidy, S. (2012, March). Associative data modeling and ant colony optimization approach for waterflood analysis. SPE-154302-MS. In: SPE Western Regional Meeting. Society of Petroleun Engineers.
  7. Sun, Q., Ertekin, T. (2020). Screening and optimization of polymer flooding projects using artificial-neural-network (ANN) based proxies. Journal of Petroleum Science and Engineering, 185, 106617.
  8. Meshalkin, V. P., Dovì, V. G., Bobkov, V. I., et al. (2021). State of the art and research development prospects of energy and resource-efficient environmentally safe chemical process systems engineering. Mendeleev Communications, 31(5), 593-604.
  9. Meshalkin, V. P., Yakubov, R. N., Lenchenkova, L. E., Chelnokov, V. V. (2021). The composite chemical-technological process of porous high watercut oil reservoirs water shutoff computer simulation. Doklady Chemistry, 501, 37-42.
  10. Safarov, F. E., Veznin, S. A., Sergeeva, N. A., et al. (2021). Development of integrated technology influencing on the high-temperature Jurassic reservoirs with a heterogeneity permeability. SOCAR Proceedings, 2, 62-76.
  11. Suleimanov, B. A., Veliyev, E. F., Vishnyakov, V. V. (2022). Nanocolloids for petroleum engineering: Fundamentals and practices. John Wiley & Sons.
  12. Vishnyakov, V. V., Suleimanov, B. A., Salmanov, A. V., Zeynalov, E. B. (2019). Primer on enhanced oil recovery. Gulf Professional Publishing.
  13. Pan, G., Zhang, L., Huang, J., et al. (2020, November). Twelve years field applications of offshore heavy oil polymer flooding from continuous injection to alternate injection of polymer-water. OTC-30277-MS. In: Offshore Technology Conference Asia. Society of Petroleun Engineers.
  14. Cannella, W. J., Huh, C., Seright, R. S. (1988, October). Prediction of xanthan rheology in porous media. SPE-18089-MS. In: Proceedings of the SPE 63rd Annual Fall Conference, Houston. Society of Petroleum Engineers.
  15. Sorbie, K. S. (1991). Polymer-improved oil recovery. Dordrecht: Springer.
  16. Suleimanov, B. A., Veliyev, E. F., Naghiyeva, N. V. (2020). Preformed particle gels for enhanced oil recovery. International Journal of Modern Physics B, 34(28), 2050260.
  17. Suleimanov, B. A., Veliyev, E. F. (2017). Novel polymeric nanogel as diversion agent for enhanced oil recovery. Petroleum Science and Technology, 35(4), 319-326.
  18. Suleimanov, B. A., Veliyev, E. F., Dyshin, O. A. (2015). Effect of nanoparticles on the compressive strength of polymer gels used for enhanced oil recovery (EOR). Petroleum Science and Technology, 33(10), 1133-1140.
  19. Qayibova, А. Q., Аbbasov, M. M. (2022). Study of innovative water-insulating composition based on ureaformaldehyde resin. Scientific Petroleum, 2, 35-39.
  20. Latifov, Y. A. (2021). Non-stationary effect of thermoactive polymer composition for deep leveling of filtration profile. Scientific Petroleum, 1, 25-30.
  21. Duman, E., Uysal, M., Alkaya, A. F. (2011). Migrating birds optimization: A new meta-heuristic approach and its application to the quadratic assignment problem. Applications of Evolutionary Computation, 6624, 254-263.
Read more Read less

DOI: 10.5510/OGP2024SI100980

E-mail: rnyakubov@mail.ru


R. A. Gilyazetdinov1, L. S. Kuleshova1, V. V. Mukhametshin2

1Institute of Oil and Gas, Ufa State Petroleum Technological University (branch in the city of Oktyabrsky), Russia; 2Ufa State Petroleum Technical University, Ufa, Russia

An experimental approach to estimating the current oil recovery coefficient using a complex of stochastic and numerical computer modeling methods


This paper presents an experimental study devoted to determining the prospects for the integrated application of stochastic and numerical computer modeling methods to solve one of the fundamental problems of field development – assessing current oil recovery based on various parameters that reflect the properties of productive formations and their saturating fluids. The object of the study was the long-term deposits of the Volga–Ural oil and gas province. As part of the initial stage, a discriminant analysis was carried out according to the tectonic-stratigraphic criterion, as a result of which three clusters of homogeneous objects were formed based on seventeen parameters. When interpreting the distribution of deposits in the axes of canonical discriminant functions, the unstable parameter of the total thickness of the reservoir was replaced by one of the variable indicators selected using the digital implementation of the uniform search method and its interpretation using known approaches. In order to reduce the influence factor of the uncertainty zone that arises when considering objects in the axes of canonical discriminant functions, the method of constructing structured grids was used. The reliability and scope of the obtained dependence were established using the Broyden-Fletcher-Goldfarb-Shanno optimization algorithm. The conclusion is made about the possibility of using the developed scientific and methodological approach to forecasting oil recovery at different values of the well grid density.

Keywords: oil recovery coefficient; modeling of nonlinear systems; deposits of the Volga-Ural oil and gas province; tectonic and stratigraphic confinement of objects; stochastic and numerical methods of computer analysis and data processing; development of oil fields, geological and field parameters.

Date submitted: 19.07.2024     Date accepted: 02.09.2024     Date published: 16.09.2024

This paper presents an experimental study devoted to determining the prospects for the integrated application of stochastic and numerical computer modeling methods to solve one of the fundamental problems of field development – assessing current oil recovery based on various parameters that reflect the properties of productive formations and their saturating fluids. The object of the study was the long-term deposits of the Volga–Ural oil and gas province. As part of the initial stage, a discriminant analysis was carried out according to the tectonic-stratigraphic criterion, as a result of which three clusters of homogeneous objects were formed based on seventeen parameters. When interpreting the distribution of deposits in the axes of canonical discriminant functions, the unstable parameter of the total thickness of the reservoir was replaced by one of the variable indicators selected using the digital implementation of the uniform search method and its interpretation using known approaches. In order to reduce the influence factor of the uncertainty zone that arises when considering objects in the axes of canonical discriminant functions, the method of constructing structured grids was used. The reliability and scope of the obtained dependence were established using the Broyden-Fletcher-Goldfarb-Shanno optimization algorithm. The conclusion is made about the possibility of using the developed scientific and methodological approach to forecasting oil recovery at different values of the well grid density.

Keywords: oil recovery coefficient; modeling of nonlinear systems; deposits of the Volga-Ural oil and gas province; tectonic and stratigraphic confinement of objects; stochastic and numerical methods of computer analysis and data processing; development of oil fields, geological and field parameters.

Date submitted: 19.07.2024     Date accepted: 02.09.2024     Date published: 16.09.2024

References

  1. Farfan, J., Cirac, G., Avansi, G. D., et al. (2024). End-to-end dimensionality reduction and regression from 3D geological uncertainties to estimate oil reservoir simulations. Applied Soft Computing, 162, 111799.
  2. Fursov, A. Ya., Galimova, A. F., Karipova, T. A. (2024). Some methodological techniques for forecasting reserve growth in the exploration of multi-layer deposits. Oil Industry, 2, 23-26.
  3. Beloshapka, I. E., Ganiev, D. I. (2018). The use of filtration studies to study technologies for the development of unconventional reservoirs and hard-to-recover oil reserves. Bulletin of the Peoples' Friendship University of Russia. Series Engineering Research, 19(3), 343-357.
  4. Shi, D., Cheng, S., Bai, W., et al. (2024). Numerical simulation of fracture propagation induced by water injection in tight oil reservoirs. Processes, 12(8), 1767.
  5. Lobanov, D. S., Abbasova, G. G., Galkin, S. V. (2022). Analysis of the current effectiveness of geological and technical measures for the operational control of recoverable reserves based on multidimensional statistical models. Geology, Geophysics and Development of Oil and Gas Fields, 10(370), 38-43.
  6. Lashari, N., Hussain, T., Shams, K., et al. (2024). Synergistic effect of graphene oxide and partially hydrolyzed polyacrylamide for enhanced oil recovery: Merging coreflood experimental and CFD modeling approaches. Journal of Molecular Liquids, 394, 123733.
  7. Manzhai, V. N., Ulyanyuk, M. P., Rozhdestvensky, E. A. (2021). A promising technology for increasing oil recovery in fields with different reservoir permeability. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 332(9), 92-99.
  8. Sergeev, V. V., Belenkova, N. G., Zeigman, Yu. V., Mukhametshin, V. Sh. (2017). Physical properties of emulsion systems containing SiO2 nanoparticles. Nanotechnology in Construction, 9(6), 37-64.
  9. Fan, Z., Liu, X., Wang, Z., et al. (2024). A novel ensemble machine learning model for oil production prediction with two-stage data preprocessing. Processes, 12(3), 587.
  10. Khuzin, R. R., Andreev, V. E., Mukhametshin, V. V., et al. (2021). The effect of hydraulic compression of the reservoir on the filtration and capacitance properties of reservoir formations. Journal of Mining Institute, 251, 688-697.
  11. Nasybullin, A. V., Burlutsky, E. A., Khayarova, D. R., et al. (2024). Investigation of nonlinear effects of filtration of polymer solutions through porous media. Oil Industry, 3, 67-69.
  12. Suleymanova, M. V., Mironenko, A. A., Safin, A. Z. (2023). Relaxation of residual oil reserves at the final stage of development. Oil and Gas Exposition, 1, 72-75.
  13. Ding, X., Li, H., Ou, S. A. (2024). Review of intelligent research dynamics in oil and gas exploration and development. Academic Journal of Science and Technology, 10(1), 100-104.
  14. Imran, M. M. H., Jamaludin, S., Ayob, A. F. M. (2024). A critical review of machine learning algorithms in maritime, offshore, and oil & gas corrosion research: A comprehensive analysis of ANN and RF models. Ocean Engineering, 295, 116796.
  15. Zha, W., Liu, Y., Wan, Y., Ruilan, L. (2022). Forecasting monthly gas field production based on the CNN-LSTM model. Energy, 260, 124889.
  16. Fatikhov, S. Z., Yakupov, R. F. (2023). Monitoring of the energy status of oil deposits using continuous well monitoring systems. Actual Problems of Oil and Gas, 2(41), 259-271.
  17. Chiglintseva, A. S., Sorokin, I. A., Yakupov, R. F., et al. (2023). The results of the approbation of multiphase flow models for pressure recalculation in the PC «RN-VEGA». Oil Industry, 5, 106-110.
  18. Khodabakhshi, M. J., Bijani, M. (2024). Predicting scale deposition in oil reservoirs using machine learning optimization algorithms. Results in Engineering, 22, 102263.
  19. Sergeev, V. V., Sharapov, R. R., Kudymov, A. Yu., et al. (2020). An experimental study of the effect of colloidal systems with nanoparticles on the filtration characteristics of hydraulic fracturing cracks. Nanotechnology in Construction, 12(2), 100-107.
  20. Daramola, G. O., Jacks, B. S., Ajala, O. A., Akinoso, A. E. (2024). AI applications in reservoir management: optimizing production and recovery in oil and gas fields. Computer Science & IT Research Journal, 5(4), 972-984.
  21. Mukhametshin, V. Sh., Zeigman, Yu. V., Andreev, A. V. (2017). Express assessment of the potential of the extractive capabilities of deposits to determine the effectiveness of the use of nanotechnology and the need to stimulate their entry into development. Nanotechnology in Construction, 9(3), 20-34.
  22. Tyncherov, K. T., Mukhametshin, V. Sh., Khuzina, L. B. (2017). Method to control and correct telemtry well information in the basis of residue number system. Journal of Fundamental and Applied Sciences, 9(2S), 1370–1374.
  23. Mukhametshin, V. V., Kuleshova, L. S. (2020). On reducing the level of uncertainty in the management of flooding of deposits with hard-to-recover reserves. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 331(5), 140-146.
  24. Rogachev, M. K., Mukhametshin, V. V., Kuleshova, L. S. (2019). Improving the efficiency of using the resource base of liquid hydrocarbons in the Jurassic sediments of Western Siberia. Journal of Mining Institute, 240, 711-715. 
  25. Suleimanov, B. A., Lyatifov, Y. A., Ibrahimov, Kh. M., Guseynova, N. I. (2017). Field testing results of enhanced oil recovery technologies using thermoactive polymer compositions. SOCAR Proceedings, 3, 17-31.
  26. Zahedi-Seresht, M., Sadeghi Bigham, B., Khosravi, S., Nikpour, H. (2024). Oil production optimization using Q-learning approach. Processes, 12(1), 110.
  27. Durova, M. A., Zein, A. N., Tsaplin, D. O., et al. (2024). Using machine learning methods to analyze optimal oil drilling sites. In: 6th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE).
  28. Sircar, A., Yadav, K., Rayavarapu, K., et al. (2021). Application of machine learning and artificial intelligence in oil and gas industry, Petroleum Research, 6(4), 379-391.
  29. Suleimanov, B. A., Ismailov, F. S., Dyshin, O. A., Veliyev, E. F. (2016). Screening evaluation of EOR methods based on fuzzy logic and Bayesian inference mechanisms. SPE-182044-MS. In: SPE Russian Petroleum Technology Conference and Exhibition, Moscow, Russia.
  30. Mukhametshin, V. Sh., Khakimzyanov, I. N. (2021). Features of grouping low-yielding oil deposits in carbonate reservoirs for the rational use of resources within the Ural-Volga region. Journal of Mining Institute, 252, 896-907.
  31. Vishnyakov, V. V., Suleimanov, B. A., Salmanov, A. V., Zeynalov, E. B. (2019). Primer on enhanced oil recovery. Gulf Professional Publishing.
  32. Saveliev, O. Yu., Borodkin, A. A., Nagunov, M. V. (2015). An improved approach to conducting block-factor analysis of development. Oil Industry, 10, 74-77.
  33. Yin, Y., Sun, Z. (2024). A multiscale approach for assessing shale oil availability: digital core, molecular simulation, and machine learning analysis. SPE Journal, 29(06), 1-10.
  34. Khayredinov, N. S., Popov, A. M., Mukhametshin, V. S. (1992). Improving the efficiency of flooding of lowyielding oil deposits in carbonate reservoirs. Oil Industry, 9, 18-20.
  35. Fan, D., Sun, H., Yao, J., et al. (2021). Well production forecasting based on ARIMA-LSTM model considering manual operations. Energy, 220, 119708.
  36. Mukhametshin, V. Sh. (2020). Rationale for the production of hard-to-recover deposits in carbonate reservoirs. IOP Conference Series: Earth and Environmental Science, 579, 012012.
  37. Wang, R., Wang, Z., Osumanu, A., et al. (2019). Grid density overlapping hierarchical algorithm for clustering of carbonate reservoir rock types: A case from Mishrif Formation of West Qurna-1 oilfield, Iraq. Journal of Petroleum Science and Engineering, 182, 106209.
  38. Mukhametshin, V. V. (2017). Elimination of uncertainties in solving problems of impact on the bottom-hole zone of wells. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 328(7), 40-50.
  39. Grishchenko, V. A., Mukhametshin, V. Sh., Rabaev, R. U. (2022). Geological structure features of carbonate formations and their impact on the efficiency of developing hydrocarbon deposits. Energies, 15(23), 9002.
  40. Plitkina, Yu. A. (2021). Improving the efficiency of the reservoir pressure maintenance system in low-permeability heterogeneous reservoirs with hard-to-recover reserves. News of higher educational institutions. Oil and Gas, 3(147), 63-78.
  41. Nazmutdinov, R. S. (2017). Development of oil fields with a marginal shear gradient. Bulatov Readings, 2,175-181.
  42. Mukhametshin, V. S. (1989). Dependence of oil recovery on the density of the well grid during the development of low-yield carbonate deposits. Oil Industry, 12, 26-29.
  43. Zhong, H., He, Y., Yang, E., et al. (2022). Modeling of microflow during viscoelastic polymer flooding in heterogenous reservoirs of Daqing Oilfield. Journal of Petroleum Science and Engineering, 210, 110091.
  44. Courbet, B., Benoit, C., Couaillier, V., Haider, F. (2011). Space discretization methods. Aerospace Lab Journal, 2, 1-14.
  45. Liseikin, V. D. (1996). An overview of methods for constructing structural adaptive grids. Journal of Computational Mathematics and Mathematical Physics, 36, 40.
  46. Zhao, W. (2021). A Broyden–Fletcher–Goldfarb–Shanno algorithm for reliability-based design optimization. Applied Mathematical Modelling, 92, 447-465.
  47. Guo, J., Wan, Z. (2021). Two modified single-parameter scaling Broyden–Fletcher–Goldfarb–Shanno algorithms for solving nonlinear system of symmetric equations. Symmetry, 13(6), 970.
  48. Sazonov, B. F., Ponomarev, A. G., Berezhnaya, G. N. (2010). The density of the well grid at a late stage of the development of an oil deposit. Oil Industry, 6, 60-64.
  49. Origbo, O. A., Mbachu, I. I. (2024). Forecasting dead oil viscosity using machine learning processes for Niger Delta region. International Journal of Current Science Research and Review, 7(1), 820-830.
  50. Khakimzyanov, I. N., Mukhametshin, V. Sh., Bakhtizin, R. N., et al. (2021). Substantiation of the need to take into account interference between wells when discharging the grid of wells on the Pashy horizon of the Bavlinskoye field. SOCAR Proceedings, SI1, 77-87.
  51. Mukhametshin, V. V., Gilyazetdinov, R. A., Kuleshova, L. S., et al. (2024). On the depth of identification of objects in the study of the influence of the density of the grid of wells on the degree of production of oil reserves. SOCAR Proceedings, SI1, 26-31.
Read more Read less

DOI: 10.5510/OGP2024SI100984

E-mail: gilyazetdinov_2023@mail.ru


S. Z. Fatikhov1, V. Sh. Mukhametshin2, R. F. Yakupov2,3, M. R. Yakupov4, M. M. Veliev2

1Bashneft – Petrotest LLC, Ufa, Russia; 2Institute of Oil and Gas, Ufa State Petroleum Technological University (branch in the city of Oktyabrsky), Russia; 3Bashneft – Dobycha LLC, Ufa, Russia; 4Kazan Federal University, Kazan, Russia

Control of the productivity and the reservoir energy state by processing and analysis of permanent downhole gauges data using machine learning


The issues of high-quality hydrodynamic studies of wells are relevant at any stage of the development of an oil field. The reliability and reliability of the results depend on the significant technological indicators of the development of deposits, deposits and the parameters of the operation of individual wells. This paper presents a new approach to the analysis of hydrodynamic studies of wells by the method of steady-state sampling, based on cluster analysis. The steady-state sampling method is a significant and most effective tool in analyzing measurements of continuous monitoring systems, which allows you to obtain more reliable data for further use in solving various development tasks. The new clustering-based method improves the quality of analysis and reduces the negative impact of the human factor on the success of data operations. This is achieved by automatically dividing the data into groups or clusters, which allows you to more accurately determine the characteristics of steady-state modes. The article provides a detailed description of the new method, its advantages and possibilities of application in the practice of analyzing hydrodynamic studies of wells based on data from continuous monitoring systems. The obtained results of testing the presented approach to the analysis of large amounts of information in a single research plane allow us to say about the high level of its relevance and the possibility of further improvement of algorithms, which will reduce the level of uncertainty in the implementation of digital diagnostics of well operation.

Keywords: well testing; permanent downhole gauges (PDG); telemetry systems; inflow performance relationship (IPR); reservoir pressure; clustering; machine learning methods; multilayer systems.

Date submitted: 18.07.2024     Date accepted: 03.09.2024     Date published: 16.09.2024

The issues of high-quality hydrodynamic studies of wells are relevant at any stage of the development of an oil field. The reliability and reliability of the results depend on the significant technological indicators of the development of deposits, deposits and the parameters of the operation of individual wells. This paper presents a new approach to the analysis of hydrodynamic studies of wells by the method of steady-state sampling, based on cluster analysis. The steady-state sampling method is a significant and most effective tool in analyzing measurements of continuous monitoring systems, which allows you to obtain more reliable data for further use in solving various development tasks. The new clustering-based method improves the quality of analysis and reduces the negative impact of the human factor on the success of data operations. This is achieved by automatically dividing the data into groups or clusters, which allows you to more accurately determine the characteristics of steady-state modes. The article provides a detailed description of the new method, its advantages and possibilities of application in the practice of analyzing hydrodynamic studies of wells based on data from continuous monitoring systems. The obtained results of testing the presented approach to the analysis of large amounts of information in a single research plane allow us to say about the high level of its relevance and the possibility of further improvement of algorithms, which will reduce the level of uncertainty in the implementation of digital diagnostics of well operation.

Keywords: well testing; permanent downhole gauges (PDG); telemetry systems; inflow performance relationship (IPR); reservoir pressure; clustering; machine learning methods; multilayer systems.

Date submitted: 18.07.2024     Date accepted: 03.09.2024     Date published: 16.09.2024

References

  1. Levchenko, I. S., Kagan, K. G., Levchenko, V. S., et al. (2023). Experience in evaluating the productive characteristics of an operational facility by hydrodynamic and geochemical methods. Geology, Geophysics and Development of Oil and Gas Fields, 10(382), 57-65.
  2. Ladeishchikova, T. S., Volkov, V. A., Sobyanin, N. N., Mitroshin, A. V. (2021). Indirect methods of estimating the current reservoir pressure in the well for use in the construction of integrated field models. Oilfield Business, 7(631), 39-45.
  3. Zakirov, A. A., Zinoviev, A. M., Shiryaev, E. S. (2023). Borehole studies to determine operational characteristics. Ashirov Readings, 1(15), 104-106.
  4. Mukhametshin, V. V. (2020). Oil production facilities management improving using the analogy method. SOCAR Proceedings, 4, 42-50.
  5. Mbaya, J. H., Hannafi, J. (2021). Mathematical modeling of fluid flow and total heat transfer process in wellbore. Science World Journal, 16(2), 133-137.
  6. Cherepanov, S. S., Ponomareva, I. N., Yerofeev, A. A., Galkin, S. V. (2014). Determination of rock fracturing parameters based on a comprehensive analysis of core study data, hydrodynamic and geophysical studies of wells. Oil Industry, 2, 94-96.
  7. Nazarova, N. G. (2018). The method of calculating the productivity coefficient of wells with heterogeneous permeability of reservoirs. Oil. Gas. Innovations, 4(209), 51-55.
  8. Chiglintseva, A. S., Sorokin, I. A., Yakupov, R. F., et al. (2023). The results of the approbation of multiphase flow models for pressure recalculation in the PC «RN-VEGA». Oil Industry, 5, 106-110.
  9. Mardashov, D. V., Rogachev, M. K., Zeigman, Yu. V., Mukhametshin, V. V. (2021). Well Killing Technology before Workover Operation in Complicated Conditions. Energies, 14(3), 654.
  10. Akhmetov, R. T., Kuleshova, L. S., Veliyev, E. F., et al. (2022). Substantiation of an analytical model of reservoir pore channels hydraulic tortuosity in Western Siberia based on capillary research data. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 333(7), 86–95.
  11. Shcherbakov, A. A., Khizhnyak, G. P., Galkin, V. I. (2019). Forecasting the productivity coefficient of wells with a side trunk (using the example of the Unvinsky field). Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 330(5). 93-99.
  12. Asalkhuzina, G. F., Davletbaev, A. Ya., Khabibullin, I. L., Akhmetova, R. R. (2020). On the issue of choosing the duration of modes during hydrodynamic studies of wells in steady-state injection modes in low-permeability reservoirs. Bulletin of the Tyumen State University. Physical and Mathematical Modeling. Oil, Gas, and Energy, 6, 1(21), 135-149.
  13. Chuikin, E. P., Savenok, O. V., Harutyunyan, A. S., Petrushin, E. O. (2015). Analysis of the effectiveness of hydrodynamic studies of wells at the Priobskoye field. Oil. Gas. Innovations, 11, 74-78.
  14. Mukhametshin, V. V., Andreev, V. E. (2018). Increasing the efficiency of assessing the performance of techniques aimed at expanding the use of resource potential of oilfields with hard-to-recover reserves. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 329(8), 30–36.
  15. Mukhametshin, V. Sh., Zeigman, Yu. V., Andreev, A. V. (2017). Rapid assessment of deposit production capacity for determination of nanotechnologies application efficiency and necessity to stimulate their development. Nanotechnologies in Construction, 9(3), 20–34.
  16. Mukhametshin, V. V., Kuleshova, L. S. (2019). Substantiation of flooding systems for low-yield oil deposits in conditions of limited information. SOCAR Proceedings, 2, 16-22.
  17. Indrupsky, I. M., Ibragimov, I. I., Tsagan-Manjiev, T. N., et al. (2023). Laboratory, numerical and field assessment of the effectiveness of cyclic mechanical action on the carbonate reservoir of the Tournaisian tier. Journal of Mining Institute, 262, 581–593.
  18. Padhy, G. S., Al-Rashidi, T., Gezeeri, T. M., et al. (2019). Role of geomechanics and integrated reservoir characterization in production enhancement from a heterogeneous carbonate reservoir: a success story from Kuwait. SPE–194920–MS. In: SPE Middle East Oil and Gas Show and Conference.
  19. Mukhametshin, V. V., Kuleshova, L. S. (2020). On uncertainty level reduction in managing waterflooding of the deposits with hard to extract reserves. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 331(5), 140–146. 
  20. Mukhametshin, V. Sh. (1989). Dependence of crude-oil recovery on the well spacing density during development of low-producing carbonate deposits. Oil Industry, 12, 26–29.
  21. Mukhametshin, V. V., Kuleshova, L. S. (2023). An algorithm for justifying the selection of facilities for the introduction of innovative oil production technologies in the conditions of the Lower Cretaceous deposits of Western Siberia. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 334(10), 179-186.
  22. Martyushev, D. A. (2021). An experimental study of the effect of downhole pressure of producing wells on the production of reserves from complex carbonate reservoirs. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 332(5),110-119.
  23. Agbasi, O. E., Sen, S., Inyang, N. J., Etuk, S. E. (2021). Assessment of pore pressure, wellbore failure and reservoir stability in the Gabo field, Niger Delta, Nigeria-Implications for drilling and reservoir management. Journal of African Earth Sciences, 173, 104038.
  24. Agishev, E. R., Dubinsky, G. S., Mukhametshin, V. V., et al. (2023). Prediction of hydraulic fracturing fracture parameters based on the study of reservoir rock geomechanics. Journal of Luminescence, 257, 107-116. 
  25. Popov, S. N., Chernyshov, S. E., Gladkikh, E. A. (2022). The effect of deformations of the terrigenous reservoir in the process of reducing downhole and reservoir pressure on changes in the permeability and productivity of the well. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 333(9), 148-157.
  26. Poplygin, V. V., Mordvinov, V. A. (2009). Estimation of changes in the productivity coefficients of producing wells at bottom-hole pressure below saturation pressure. Bulletin of the Perm State Technical University. Geology, geoinformation systems, mining and oil business, 8(4), 53-58.
  27. Khuzin, R. R., Andreev, V. E., Mukhametshin, V. V., et al. (2021). Influence of hydraulic compression on porosity and permeability properties of reservoirs. Journal of Mining Institute, 251(3), 688-697.
  28. Martyushev, D. A. (2014). The assessment of fracturing of carbonate reservoirs is probably statistical. Oil Industry, 4, 51–53.
  29. Mukhametshin, V. V. (2017). Elimination of uncertainties in solving problems of impact on the bottom-hole zone of wells. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 328(7), 40-50.
  30. Irfan, S. A., Shafie, A., Yahya, N., Zainuddin, N. (2019). Mathematical modeling and simulation of nanoparticleassisted enhanced oil recovery - a review. Energies, 12(8), 1575.
  31. Imqam, A., Bai, B., Wei, M., et al. (2016). Use of hydrochloric acid to remove filter–cake damage from preformed particle gel during conformance – control treatments. SPE Production & Operations, 31(3), 247-257.
  32. Bakhitov, R. R. (2019). Application of machine learning algorithms in the tasks of forecasting the productivity coefficient of wells of carbonate deposits. Oil Industry, 9, 82-85.
  33. Rogachev, M. K., Mukhametshin, V. V. (2018). Control and regulation of the hydrochloric acid treatment of the bottomhole zone based on field-geological data. Journal of Mining Institute, 231, 275-280.
  34. Suleimanov, B. A., Latifov, Ya. A., Ibragimov, Kh. M., Guseinova, N. I. (2017). Field testing results of enhanced oil recovery technologies using thermoactive polymer compositions. SOCAR Proceedings, 3, 17-31.
  35. Martyushev, D. A., Ponomareva, I. N. (2017). Investigation of the features of the production of fractured–pore reservoir reserves using data from hydrodynamic studies of wells. Oil Industry, 10, 102-104.
  36. Pedersen, S., Durdevic, P., Yang, Z. (2015). Review of slug detection, modeling and control techniques for offshore oil & gas production processes. IFAC-PapersOnline, 48(6), 89-96.
  37. Dieva, N. N., Volpin, S. G., Korneeva, D. A., Steinberg, Yu. M. (2014). Increasing the information content of studies of wells operating at bottom-hole pressure below saturation pressure by the method of steady-state sampling. Drilling and Oil, 1, 41-43.
  38. Mullagaliev, T. I., Kochanov, D. N., Trifonov, M. D., et al. (2023). Development of a virtual flow meter for Zarubezhneft JSC. Oil Industry, 2, 55-59.
  39. Scikit-Learn User's Guide https://scikit-learn.org/stable/user_guide.html
  40. Zhao, W., Tang, H., Lu, F., et al. (2023). Mathematical model for oil recovery prediction of polymer microsphere conformance control based on the stream tube method. Materials, 16(4), 1476.
  41. Fatikhov, S. Z., Fedorov, V. N., Gimaev, A. F., Malov, A. G. (2018). Comprehensive analysis of measurements of bottom-hole pressure and productivity of strata of multilayer objects in wells equipped with continuous monitoring systems. Oil Industry, 2, 76-80.
  42. Wang, Q., Jia, X., Chen, Z. (2016). Mathematical modeling of the solvent chamber evolution in a vapor extraction heavy oil recovery process. Fuel, 186, 339-349.
  43. Ahmadi, M. A., Ebadi, M., Shokrollahi, A., Majidi, S. M. J. (2013). Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Applied Soft Computing, 13(2), 1085-1098.
Read more Read less

DOI: 10.5510/OGP2024SI100985

E-mail: vsh@of.ugntu.ru


J. A. Ismailova1, Xiaolong Yin2, D. N. Delikesheva1, G. Zh. Moldabayeva1, A. A. Ismailov3, S. Zh. Abileva1

1Satbayev University, Almaty, Kazakhstan; 2Eastern Institute of Technology, Ningbo, China / Colorado School of Mines, Golden, Colorado, USA; 3Kazakh-British Technical University, Almaty, Kazakhstan
 

Accounting for melting properties in the multi-solid paraffin  prediction model for analyzing well production at fields in the Republic of Kazakhstan


The deposition of paraffin on pipeline walls is a serious flow issue. leading to a reduction in oil production due to the decreased flow cross-sectional area. and in severe cases. to complete blockage. This problem also results in increased energy consumption and causes failure of surface equipment due to paraffin plugs. Traditional methods predict paraffin crystallization temperature by considering the impact of temperature on the solubility parameters of individual components in both liquid and solid phases as well as their molar volumes. The goal of this study is to improve the prediction of paraffin deposition in Kazakhstan crude oil by incorporating an evaluation of melting properties into the multi-solid (MS) body prediction model. The process of calculating and adjusting melting properties to enhance the accuracy of the MS model is described in detail. This approach helps to overcome the limitations faced by most existing models when adapting to different types of crude oil. In this study, the focus is on optimizing the multi-solid (MS) body prediction model for paraffin deposition by integrating the melting properties of crude oil components. The improvement of the MS model is crucial because paraffin deposition is a significant issue in Kazakhstan's oil fields, where the wide range of reservoir conditions can cause standard prediction models to fall short. By enhancing the accuracy of melting point calculations, the updated model can better predict the onset of paraffin crystallization and its deposition tendencies under varying pressure and temperature conditions.

Keywords: melting properties; multi solid solution; petroleum; solid solution; paraffin deposition.

Date submitted: 11.09.2024     Date accepted: 25.09.2024     Date published: 11.10.2024

The deposition of paraffin on pipeline walls is a serious flow issue. leading to a reduction in oil production due to the decreased flow cross-sectional area. and in severe cases. to complete blockage. This problem also results in increased energy consumption and causes failure of surface equipment due to paraffin plugs. Traditional methods predict paraffin crystallization temperature by considering the impact of temperature on the solubility parameters of individual components in both liquid and solid phases as well as their molar volumes. The goal of this study is to improve the prediction of paraffin deposition in Kazakhstan crude oil by incorporating an evaluation of melting properties into the multi-solid (MS) body prediction model. The process of calculating and adjusting melting properties to enhance the accuracy of the MS model is described in detail. This approach helps to overcome the limitations faced by most existing models when adapting to different types of crude oil. In this study, the focus is on optimizing the multi-solid (MS) body prediction model for paraffin deposition by integrating the melting properties of crude oil components. The improvement of the MS model is crucial because paraffin deposition is a significant issue in Kazakhstan's oil fields, where the wide range of reservoir conditions can cause standard prediction models to fall short. By enhancing the accuracy of melting point calculations, the updated model can better predict the onset of paraffin crystallization and its deposition tendencies under varying pressure and temperature conditions.

Keywords: melting properties; multi solid solution; petroleum; solid solution; paraffin deposition.

Date submitted: 11.09.2024     Date accepted: 25.09.2024     Date published: 11.10.2024

References

  1. Hansen, J. H., Fredenslund, A., Pedersen, K. S., Rønningsen, H. P. (1988). A thermodynamic model for predicting wax formation in crude oils. AIChE Journal, 34(12), 1937-1942.
  2. Won, K. W. (1986). Thermodynamics for solid solution–liquid–vapor equilibria: wax phase formation from heavy hydrocarbon mixtures. Fluid Phase Equilibria, 30, 265-279.
  3. Won, K. W. (1989). Thermodynamic calculation of cloud point temperatures and wax phase compositions of refined hydrocarbon mixtures. Fluid Phase Equilibria, 53, 377-396.
  4. Baishemirov, Z., Tang, J. G., Imomnazarov, K., Mamatqulov, M. (2016). Solving the problem of two viscous incompressible fluid media in the case of constant phase saturations. Open Engineering, 6, 742-745.
  5. Ivanchina, E. D., Ivashkina, E. N., Nazarova, G. Y., Seitenova, G. Z. (2018). Influence of feedstock group composition on the octane number and composition of the gasoline fraction of catalytically cracked vacuum distillate. Petroleum Chemistry, 58, 225-236.
  6. Karches, T. (2012). Detection of dead‐zones with analysis of flow pattern in open channel flows. Pollack Periodica, 7(2), 139-146.
  7. Karches, T., Buzas, K. (2013). Investigation of residence time distribution and local mean age of fluid to determine dead‐zones in flow field. International Journal of Computational Methods and Experimental Measurements, 1(2), 132-141.
  8. Bieliatynskyi, A., Krayushkina, E., Skrypchenko, A. (2016). Modern technologies and materials for cement concrete pavement's repair. Procedia Engineering, 134, 344-347.
  9. Panfilov, M. B., Baishemirov, Z. D., Berdyshev, A. S. (2020). Macroscopic model of two‐phase compressible in double porosity media. Fluid Dynamics, 55, 936-951.
  10. Prokopov, V. G., Fialko, N. M., Sherenkovskaya, G. P., et al. (1993). Effect of coating porosity on the process of heat‐ transfer with gas‐thermal deposition. Powder Metallurgy and Metal Ceramics, 32, 118-121.
  11. Ostanin, V. (2022). Effects of repulsion and attraction between rotating cylinders in fluids. Scientific Herald of Uzhhorod University. Series Physics, 51, 39-47.
  12. Prokopov, V. G., Shvets, Y. I., Fialko, N. M., et al. (1989). Mathematical‐modeling of the convective heat‐transfer processes during formation of the gas‐thermal coating layer. Doklady Akademii Nauk Ukrainskoy RSR, Seriya A - Fiziko-Matematicheskiye Tekhticheskiye Nauki, 6, 71-76.
  13. Nichita, D. V., Goual, L., Abbas, F. (2001). Wax precipitation in gas condensate mixtures. SPE Production and Facilities, 16 (04), 250–259.
  14. Tugashova, L., Bazhenov, R., Abdyldaeva, U., et al. (2022). Simulation modeling approach used in the crude oil refining process. Journal of Physics: Conference Series, 2373, 062003
  15. Pan, H., Firoozabadi, A., Fotland, P. (1997). Pressure and composition effect on wax precipitation: experimental data and model results. SPE Production and Facilities, 12(04), 250-258.
  16. Pedersen, K. S., Skovborg, P., Roenningsen, H. P. (1991). Wax precipitation from North Sea crude oils. 4. Thermodynamic modeling. Energy & Fuels, 5(6), 924-932.
  17. Coutinho, J. A. P. (1998). Predictive UNIQUAC: a new model for the description of multiphase solid–liquidequilibria in complexhydrocarbonmixtures. Industrial and Engineering Chemestry Research, 37(12), 4870-4875.
  18. Coutinho, J. A. P., Edmonds, B., Moorwood, T., et al. (2006). Reliable wax predictions for flow assurance. Energy & Fuels, 20(3), 1081-1088.
  19. Riazi, M. R. (2005). Characterization and properties of petroleum fractions. 1st edition. Baltimore, MD: ASTM International.
  20. Chen, W. H., Zhang, X. D., Zhao, Z. C., Yin, C. Y. (2008). Thermodynamic phase equilibria of wax precipitation in crude oils. Fluid Phase Equilibria, 255(1), 31-36.
  21. Yang, J., Wang, W., Shi, B., et al. (2016). Prediction of wax precipitation with new modified regular solution model. Fluid Phase Equilibria, 423, 128-137.
  22. Lira‐Galeana, C., Firoozabadi, A., Prausnitz, J. M. (1996). Thermodynamics of wax precipitation in petroleum mixtures. AIChE Journal, 42(1), 239-248.
  23. Escobar‐Remolina, J. C. M. (2006). Prediction of characteristics of wax precipitation in synthetic mixtures and fluids of petroleum: a new model. Fluid Phase Equilibria, 240(2), 197-203.
  24. Dalirsefat, R., Feyzi, F. (2007). A thermodynamic model for wax deposition phenomena. Fuel, 86(10-11), 1402-1408.
  25. Solaimany, N. A. R., Dabir, B., Islam, M. R. (2007). A multi‐solid phase thermodynamic model for predicting wax precipitation in petroleum mixtures. Energy Sources, 27(1-2), 173-184.
  26. Ghanaei, E., Esmaeilzadeh, F., Fathi, J. (2008). New multi‐solid thermodynamic model for the prediction of wax formation. International Journal of Chemical and Biological Engineering, 1(1), 44-49.
  27. Ghotbi, C., Mashhadi Meighani, H., Jafari Behbahani, T. (2016). Thermodynamic modeling of wax precipitation in crude oil based on PC‐saft model. In: 12th International Conference on Heat Transfer. Fluid Mechanics and Thermodynamics, Malaga, Spain.
  28. Mansourpoor, M., Azin, R., Osfouri, S., Izadpanah, A. A. (2018). Study of wax disappearance temperature using multi‐solid thermodynamic model. Journal of Petroleum Exploration and Production Technology, 9, 437-448.
  29. Eyitayo, S. I., Lawal, K. A., Guobadia, K. O. et al. (2020). A comparative evaluation of selected correlations for estimating wax‐appearance temperature of crude oils. SPE-203618-MS. In: SPE Nigeria Annual International Conference and Exhibition. Society of Petroleum Engineers.
  30. Asbaghi, E. V., Assareh, M. (2021). Application of a sequential multi‐solid–liquid equilibrium approach using PC‐SAFT for accurate estimation of wax formation. Fuel, 284, 119010.
  31. Van der Geest, C., Melchuna, A., Bizarre, L., et al. (2021). Critical review on wax deposition in single‐phase flow. Fuel, 293, 120358.
  32. Flory, P. J. (1942). Thermodynamics of high polymer solutions. Journal of Chemical Physics, 10, 51-61.
  33. Yao, B., Zhao, D., Zhang, Z., Huang, C. (2021). Safety study on wax deposition in crude oil pipeline. Processes, 9(9), 1572.
  34. Asbaghi, E. V., Nazari, F., Assareh, M., Nezhad, M. M. (2022). Toward an efficient wax precipitation model: applicationof multi‐solid framework and PC‐SAFT with focus on heavy end characterization for different crude types. Fuel, 310(B), 122205.
  35. Huanquan, P., Abbas, F., Fotland, P. (1997). Pressure and composition effect on wax precipitation: experimental data and model results. SPE Production and Facilities, 12(04), 250-258.
  36. Xu, Y., Hou, X., Shi, Y., et al. (2021). Correlation between the microstructure and corrosion behaviour of copper/316 L stainless‐steel dissimilar‐metal welded joints. Corrosion Science Journal, 191, 109729.
  37. Hosseinipoura, A., Japper-Jaafar, A., Yusup, S. (2016). The effect of CO2 on wax appearance temperature of crude oils. Procedia Engineering, 148, 1022-1029.
  38. Wilson, G. M. (1964). Vapor-liquid equilibrium. XI. A new expression for the excess free energy of mixing. Journal of American Chemical Society, 86(2), 127-130.
  39. Abrams, D. S., Prausnitz, J. M. (1975). Statistical thermodynamics of liquid mixtures: A new expression for the excess Gibbs energy of partly or completely miscible systems. AIChE Journal, 21(1), 116-128.
  40. Baibekova, A., Ismailova, J., Delikesheva, D., et al. (2024). Improvement of the delumping method in order to obtain detailed characteristics of the fluid. Eastern-European Journal of Enterprise Technologies, 4(6(130)), 29-37.
  41. Sarsenova, A., Ismailova, J., Ismailov, A., et al. (2024). Development of predictive model for determination of paraffin deposition in kazakh crude oil. Eastern-European Journal of Enterprise Technologies, 3(6(129)), 21-35.
  42. Ismailova, J., Baibekova, A., Abdukarimov, A., et al. (2024). Pressure-volume-temperature analysis of caspian oil to improve the analytical delumping procedure. Eastern-European Journal of Enterprise Technologies, 1(6(127)), 22-29.
  43. Buktukov, N. S., Gumennikov, Ye. S., Moldabayeva, G. Zh., et al. (2024). New solutions for mechanized small diameter shaft sinking for residual oil production. SOCAR Proceedings, 1, 81-86.
  44. Moldabayeva, G. Z., Efendiyev, G. M., Kozlovskiy, A. L., et al. (2023). Modeling and adoption of technological solutions in order to enhance the effectiveness of measures to limit water inflows into oil wells under conditions of uncertainty. ChemEngineering, 7(5), 89.
  45. Ruwoldt, J., Humborstad Sørland, G., Simon, S., et al. (2019). Inhibitor-wax interactions and PPD effect on wax crystallization: New approaches for GC/MS and NMR. Journal of Petroleum Science and Engineering, 177, 53–68.
  46. Mashhadi Meighani, H., Ghotbi, C., Jafari Behbahani, T., Sharifi, K. (2020). A new investigation of wax precipitation in iranian crude oils: Experimental method based on FTIR spectroscopy and theoretical predictions using PC-SAFT model. Journal of Molecular Liquids, 249, 970–979.
  47. Subramanie, P. A. P., Padhi, A., Ridzuan, N., Adam, F. (2021). Experimental study on the effect of wax inhibitor and nanoparticles on rheology of Malaysian crude oil. Journal of King Saud University - Engineering Sciences, 32, 479–490.
  48. Belati, A., Cajaiba, J. (2022). Measurement of wax appearance temperature using RGB image analysis and FBRM. Fuel, 220, 264–269.
  49. Wang, S., Li, Y., Zheng, Y., et al. (2023). Paraffin deposition prevention and removal techniques in Arctic pipelines. Journal of Petroleum Technology, 75(6), 145–159.
  50. Zhao, Y., Liu, Z., Wang, G., et al. (2024). Impact of temperature on paraffin deposition in offshore pipelines. Energy and Fuels, 38(3), 1345–1354.
  51. Vieira, L. C., Buchuid, M. B., Lucas, E. F. (2023). Wax deposition kinetics and inhibition methods in crude oils under high-pressure conditions. Journal of Applied Polymer Science, 141(2), 120–129.
Read more Read less

DOI: 10.5510/OGP2024SI100986

E-mail: g.moldabayeva@satbayev.university


Kh. M. Ibrahimov, A. A. Hajıyev, A. F. Akbarova

"OilGasScientificResearchProject" Institute, SOCAR, Baku, Azerbaijan

"LiquiPerfPro" reagent for increasing the permeability of the well bottom hole zone


The purpose of the technology's development is to reduce the skin effect and restore the permeability of wells, which have experienced a significant reduction in permeability for various reasons, including contamination of the filter zone by oil-based drilling fluids during drilling. The developed alkaline-based new chemical composition, "LiquiPerfPro", reduces the interfacial surface tension on the rock surfaces contaminated by oil-based drilling fluid in the near-wellbore zone, altering the wetting angle in favor of oil and creating a hydrophilic surface. Additionally, it removes the oil screen formed on the weighting components present in the drilling fluid and reduces adhesion forces. After reacting with the oil-based component, the weighting components undergo segregation, increasing their ability to migrate and enabling them to be easily displaced from the pores. Furthermore, after the weighting particles are cleaned from the oil screen, their surfaces become open to other solvents, allowing the application of other traditional methods for deeper cleaning of contamination. Moreover, the "LiquiPerfPro" reagent, due to its physico-chemical properties, has the ability to penetrate a significant distance from the wellbore into the drainage area. The application of the "LiquiPerfPro" reagent is also favorable for lithologically weakly cemented rocks, as it does not create the risk of formation collapse, unlike traditional acid treatment methods, and does not damage the matrix. The reagent can be applied to both production and injection wells.

Keywords: Alkali; acid; permeability; reservoir model; wellbore zone; new chemical composition; surfactant; organic solvent; oil-based drilling mud.

Date submitted: 21.08.2024     Date accepted: 20.10.2024     Date published: 05.11.2024

The purpose of the technology's development is to reduce the skin effect and restore the permeability of wells, which have experienced a significant reduction in permeability for various reasons, including contamination of the filter zone by oil-based drilling fluids during drilling. The developed alkaline-based new chemical composition, "LiquiPerfPro", reduces the interfacial surface tension on the rock surfaces contaminated by oil-based drilling fluid in the near-wellbore zone, altering the wetting angle in favor of oil and creating a hydrophilic surface. Additionally, it removes the oil screen formed on the weighting components present in the drilling fluid and reduces adhesion forces. After reacting with the oil-based component, the weighting components undergo segregation, increasing their ability to migrate and enabling them to be easily displaced from the pores. Furthermore, after the weighting particles are cleaned from the oil screen, their surfaces become open to other solvents, allowing the application of other traditional methods for deeper cleaning of contamination. Moreover, the "LiquiPerfPro" reagent, due to its physico-chemical properties, has the ability to penetrate a significant distance from the wellbore into the drainage area. The application of the "LiquiPerfPro" reagent is also favorable for lithologically weakly cemented rocks, as it does not create the risk of formation collapse, unlike traditional acid treatment methods, and does not damage the matrix. The reagent can be applied to both production and injection wells.

Keywords: Alkali; acid; permeability; reservoir model; wellbore zone; new chemical composition; surfactant; organic solvent; oil-based drilling mud.

Date submitted: 21.08.2024     Date accepted: 20.10.2024     Date published: 05.11.2024

References

  1. Rahimi, A., Jami, M., Divandari, H., Safari, M. (2022). Chapter 4 - Alkaline flooding / in book: Chemical methods. Enhanced oil recovery series. Gulf Professional Publishing.
  2. Ismailov, F. S., Samedov, A. M., Agazade, A. D., et al. (2018). Composition for removal of asphalten-smoloparaphın deposıts. Eurasian Patent EA201650128.
  3. Guo, J., Liu, Q., Li, M., et al. (2006). The effect of alkali on crude oil/water interfacial properties and the stability of crude oil emulsions. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 273, 213–218.
  4. Touhami, Y., Hornof, V., Neale, G. H. (1998). Dynamic interfacial tension behavior of acidified oil/surfactant-enhanced alkaline system 1. Experimental studies. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 132, 61–74.
  5. Suleimanov, B. A., Ibrahimov, Kh. M., Aghazade, O. D., Shafiyev, T. Kh. (2019). Composition for the acid treatment of the bottom zone of the formation. Patent of Azerbaijan Republic İ 2019 0092.
  6. Hosseini, E. (2019). The effect of alkaline-surfactant on the wettability, relative permeability and oil recovery of carbonate reservoir rock: experimental investigation. Journal of Petroleum Exploration and Production Technology, 9(16), 2877-2891.
  7. Von, R. W., Hill, K. (1998). Alkyl polyglycosides-properties and applications of a new class of surfactants. Angewandte Chemie International Edition, 37(10), 1328–1345.
  8. Ibrahimov, Kh. M., Shafiyev, T. Kh. (2018). On the effectiveness of treatment of the bottom zone with a new composition of acid. Azerbaijan Oil İndustry, 5, 24-27.
  9. Nasr-El-Din, H. A., Al-Otaibi, M. B., Al-Qahtani, A. A. (2007). An effective fluid formulation to remove drilling-fluid mud cake in horizontal and multilateral wells. SPE Drilling & Completion, 22, 26-32.
  10. Wei, P., Li, J., Xie, Y., et al. (2020). Alkyl polyglucosides for potential application in oil recovery process: adsorption behavior in sandstones under high temperature and salinity. Journal of Petroleum Science and Engineering, 189, 107057.
  11. Gurbanov, A. G., Hajikarimova, L. Q., Akbarova, A. F. (2022). A new inhibitor against asphaltene-resin-paraffin and salt deposits. Scientific Petroleum, 2, 40-46.
  12. Speight, J. G. (2016). Introduction to enhanced recovery methods for heavy oil and tar sands. Laramie, Wyoming, USA: CD&W Inc.
  13. Suleimanov, B. A., Rzayeva, S. C., Akhmedova, U. T. (2021). Self-gasified biosystems for enhanced oil recovery. International Journal of Modern Physics B, 35(27), 2150274.
  14. Kazempour, M. (2012). Effect of alkalinity on oil recovery during polymer floods in sandstone. SPE Reservoir Evaluation & Engineering, 15(02), 195-209.
  15. Suleimanov, B. A., Rzayeva, S. C., Akberova, A. F., Akhmedova, U. T. (2022). Self-foamed biosystem for deep reservoir conformance control. Petroleum Science and Technology, 40(20), 2450-2467.
  16. Liu, Q., Dong, M., Yue, X., et al. (2006). Synergy of alkali and surfactant in emulsification of heavy oil in brine. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 273, 219–228.
  17. Madani, M., Zargar, G., Takassi, M. Al. (2009). Fundamental investigation of an environmentally-friendly surfactant agent for chemical enhanced oil recovery. Fuel, 238, 186-197.
  18. Suleimanov, B. A., Guseynova, N. I., Rzayeva, S. C., Tuleshova, G. D. (2018). Results of acidizing injection wells on the Zhetybai field (Kazakhstan). Petroleum Science and Technology, 36(3), 193-199.
  19. Ojukwu, C., Onyekonwu, M. O., Ogolo, N. (2013). Alkaline surfactant polymer (local) enhanced oil recovery: An experimental approach. In: Nigeria Annual International Conference and Exhibition, Lagos, Nigeria.
  20. Suleimanov, B. A., Rzayeva, S. C., Akberova, A. F., Akhmedova, U. T. (2021). Deep diversion strategy of the displacement front during oil reservoirs watering. SOCAR Proceedings, 4, 33-42.
  21. Ramsey, M. S. (2019). Practical wellbore hydraulics and hole cleaning: unlock faster, more efficient, and trouble-free drilling operations. Gulf Professional Publishing.
  22. Suleimanov, B. A., Rzayeva, S. C., Keldibayeva, S. S. (2020). A new microbial enhanced oil recovery (MEOR) method for oil formations containing highly mineralized water. Petroleum Science and Technology, 38(23), 999-1006.
  23. Rudin, J., Wasan, D. T. (1992). Mechanisms for lowering of interfacial tension in alkali/acidic oil systems 2. Theoretical studies. Colloids and Surfaces, 68(1-2), 81–94.
  24. Ibrahimov, Kh. M., Qurbanov, A. G., Kazımov, F. K., et al. (2022). Development and laboratory test of the gelling composition for the selective isolation of formation waters. Scientific Petroleum, 2, 40-46.
  25. Ibrahimov, Kh.M., Huseynova, N.I., Hajiyev, A.A. (2021). Development of new controlling methods for the impact on the productive formation for "Neft Dashlary" oilfield. Scientific Petroleum, № 1, 37-42.
  26. Standnes, D. C., Austad, T. (2000). Wettability alteration in chalk: 2. Mechanism for wettability alteration from oil-wet to water-wet using surfactants. Journal of Petroleum Science and Engineering, 28(3), 123–43.
  27. Ibragimov, H. M., Guseynov, Sh. Sh. (2018). Field test of a thermochemical reagent for cleaning well bottomhole zone. In: Readings of A. I. Bulatov. Materials of II International Scientific and Practical Conference. Vol. 2. Development oil and gas fields, Part 1, 173-175.
  28. Suleimanov, B. А., Ibraghimov, Kh. М., Hajiyev, А. А. (2024). Method for cleaning the bottom hole zone of the formation. Eurasian Patent EA046507.
  29. Vishnyakov, V., Suleimanov, B., Salmanov, A., Zeynalov, E. (2020). Primer on enhanced oil recovery. Gulf Professional Publishing.
  30. Rudin, J, Wasan, D.T. (1992). Mechanisms for lowering of interfacial tension in alkali/acidic oil systems 1. Experimental studies. Colloids and Surface, 68, 67–79.
  31. Suleimanov, B. A., Suleymanov, A. A., Samedov, A. M., et al. (2023). Salt prevention method. Eurasian Patent EA042065.
  32. Suleimanov, B. A., Abbasov, H. F., Ismayilov, R. H. (2022). Enhanced oil recovery with nanofluid injection. Petroleum Science and Technology, 41(18), 2094959.
  33. Van, S. L., Chon, B. (2013). Well-pattern investigation and selection by surfactant-polymer flooding performance in heterogeneous reservoir consisting of interbedded low-permeability layer. Korean Journal of Chemical Engineering, 8, 1-10.
  34. Ibrahimov, Kh. M., Hajiyev, A. A., Huseynova, N. I., et al. (2024). Consideration of the geological and technical condition of the reservoir and wellbore bottom zone in the selection of the cement composition applied to the production wellbore flow zone. Scientific Petroleum, 1, 36-43.
  35. Suleimanov, B. A., Veliyev, E. F., Vishnyakov, V. V. (2022). Nanocolloids for petroleum engineering: Fundamentals and practices. John Wiley & Sons.
Read more Read less

DOI: 10.5510/OGP2024SI101002

E-mail: Khidir.Ibrahimov@socar.az


V. J. Abdullayev, M. A. Huseynov, R. R. Mammadov

"OilGasScientificResearchProject" Institute, SOCAR, Baku, Azerbaijan

Research methods of the potential of long-term operated reservoirs based on the hydrodynamic model


Studying the potential of long-term operated fields and evaluating their efficiency requires the application of modern approaches. In this regard, by using three-dimensional geological and hydrodynamic models of the Pirallahi Island field, various complex impact mechanisms were modeled, as well as the solution of the mentioned issues based on the established model was considered on the basis of simulation to maintain the development rate of the field and increase the oil production rate. When there is no accurate information about the initial condition of the field, as well as when the data is partially insufficient and inaccurate in some intervals of the long-term production performance, methods of restoring information in different ways for building a hydrodynamic model were investigated, and ways to minimize the negative impact of this type of uncertainty on the model were also studied. For this purpose, a new method called intermediate initialization was developed and applied to the construction of the reservoir model. On the basis of the established model, a more efficient method of field exploitation, water injection into formations, optimal placement of new production and injection wells was selected, modeling was carried out for various options and scenarios, a plan of necessary measures was drawn up to maximize the life of the field, development parameters were predicted and the results were compared.

Keywords: field; formation; well; development; model; information recovery; intermediate initialization method; prediction.

Date submitted: 21.07.2024     Date accepted: 10.10.2024     Date published: 05.11.2024

Studying the potential of long-term operated fields and evaluating their efficiency requires the application of modern approaches. In this regard, by using three-dimensional geological and hydrodynamic models of the Pirallahi Island field, various complex impact mechanisms were modeled, as well as the solution of the mentioned issues based on the established model was considered on the basis of simulation to maintain the development rate of the field and increase the oil production rate. When there is no accurate information about the initial condition of the field, as well as when the data is partially insufficient and inaccurate in some intervals of the long-term production performance, methods of restoring information in different ways for building a hydrodynamic model were investigated, and ways to minimize the negative impact of this type of uncertainty on the model were also studied. For this purpose, a new method called intermediate initialization was developed and applied to the construction of the reservoir model. On the basis of the established model, a more efficient method of field exploitation, water injection into formations, optimal placement of new production and injection wells was selected, modeling was carried out for various options and scenarios, a plan of necessary measures was drawn up to maximize the life of the field, development parameters were predicted and the results were compared.

Keywords: field; formation; well; development; model; information recovery; intermediate initialization method; prediction.

Date submitted: 21.07.2024     Date accepted: 10.10.2024     Date published: 05.11.2024

References

  1. Vishnyakov, V. V., Suleimanov, B. A., Salmanov, A. V., Zeynalov, E. B. (2019). Primer on enhanced oil recovery. Gulf Professional Publishing.
  2. Suleimanov, B. A., Feyzullayev, Kh. A. (2024). Simulation study of water shut-off treatment for heterogeneous layered oil reservoirs. Dispersion Science and technology. Published online: 16 Apr 2024.
  3. Abdullayev, V. J., Huseynov, M. A., Ismyilov, M. M., Nabiyev, K. M. (2014). 3D geological modeling of «Guneshli» reservoir for increasing final development stage efficiency. SOCAR Proceedings, 2, 75-82.
  4. Mammadov, T., Javadzade, R., Abdullayev, V. J. (2017). Impact of thin oil-rim to the development of gazcondensate fields in the South Caspian Basin. SPE-189010-MS. In: SPE Annual Caspian Technical Conference and Exhibition held in Baku, Azerbaijan.
  5. Abdullayev, V. J. (2012). Investigation of water injection stimulation based on development process simulation at "Guneshli" field. SOCAR Proceedings, 1, 16-24.
  6. Abdullayev, V. J., Veliyev, R. G., Ryabov, S. S., Krupin, G. G., Rahimov, U. Z. (2023). Application of gel systems for water shut-off on Uzbekistan oil fields. SOCAR Proceedings, 1, 68-73.
  7. Suleimanov, B. A., Ismailov, F. S., Dyshin, O. A. (2014). Statistical modeling of life cycle of oil reservoir development. Journal of the Japan Petroleum Institute, 57(1), 47-57.
  8. Suleimanov, B. A., Ismailov, F. S., Dyshin, O. A., Keldibayeva, S. S. (2015). Statistical modeling of oil reservoir life cycle. SPE-177337-MS. In: SPE Annual Caspian Technical Conference & Exhibition, Baku, Azerbaijan.
  9. Dormenev, V. (2022). Waterflood optimization on the West Siberia field with hydrodynamic modeling. SPE-212388-STU. In: SPE Annual Technical Conference and Exhibition, Houston, Texas, USA.
  10. Brilliant, L. S., Zavialov, A. S., Danko, M. U., et al. (2022). Integration of machine learning methods and geological and hydrodynamic modeling in field development design. Oil Industry, 3, 48–53.
  11. Ilyushin, P. Yu., Syuzev, A. V., Vyatkin, K. A., et al. (2022). Efficiency of replacing technical water with produced water on the results of laboratory studies and hydrodynamic modeling of the waterflooding process at a field in the Perm region. Oil Industry, 2, 92–96.
  12. Suleimanov, B. A., Dyshin, O. A. (2013). Application of discrete wavelet transform to the solution of boundary value problems for quasi-linear parabolic equations. Applied Mathematics and Computation, 219, 7036-7047.
  13. Suleimanov, B. A., Abbasov, E. M., Dyshin, O. A. (2008). Wavelet method for solving the unsteady porous-medium flow problem with discontinuous coefficients. Computational Mathematics and Mathematical Physics, 48(12), 2194-2210.
  14. Suleimanov, B. A., Feyzullayev, Kh. A., Abbasov, E. M. (2019). Numerical simulation of water shut-off performance for heterogeneous composite oil reservoirs. Applied and Computational Mathematics, 18(3), 261-271.
  15. Varavva, A., Yamaletdinov, A., Apasov, R., et al. (2022). Auto-adaptation of a pilot well with the use of special options for hydrodynamic modeling and the results of stationary and non-stationary modeling of gas-liquid flow inside the well during PLT on COI. SPE-212104-MS. In: SPE Annual Caspian Technical Conference, Nur-Sultan, Kazakhstan.
  16. Chen, Z., Zhang, Q., Zhang, J., Chen, T. (2024). Wave-current coupling based on SWAN wave model and a threedimensional nodal discontinuous Galerkin hydrodynamic model. ISOPE-I-24-412. In: 34th International Ocean and Polar Engineering Conference, Rhodes, Greece.
  17. Suleimanov, B. A., Guseynova, N. I. (2019). Analyzing the state of oil field development based on the Fisher and Shannon information measures. Automation and Remote Control, 80, 882-896.
  18. Volkov, M., Fesina, Y., Kudryavaya, N., et al. (2024). Horizontal well flow profile assessment: advanced thermalhydrodynamic modeling with fracture flow analysis. URTEC-4054832-MS. In: SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA.
  19. Suleimanov, B. A., Abbasov, E. M., Dyshin, O. A. (2008). Application of wavelet transforms to the solution of boundary value problems for linear parabolic equations. Computational Mathematics and Mathematical Physics, 48(2), 251-268.
  20. Suleimanov, B. A., Feyzullayev, Kh. A., Abbasov, E. M. (2019). Numerical simulation of water shut-off performance for heterogeneous composite oil reservoirs. Applied and Computational Mathematics, 18(3), 261-271.
  21. Suleimanov, B. A., Feyzullayev, Kh. A. (2019). Numerical simulation of water shut-off for heterogeneous composite oil reservoirs. SPE-198388-MS. In: SPE Annual Caspian Technical Conference, Baku, Azerbaijan.
  22. Gurianov, A., Katashov, A., Ovchinnikov, K., et al. (2019). Estimation of the largest russian oil field development efficiency using the combination of hydrodynamic modeling and horizontal well production logging methods using markers. SPE-196846-MS. In: SPE Russian Petroleum Technology Conference, Moscow, Russia.
Read more Read less

DOI: 10.5510/OGP2024SI101003

E-mail: Mehdi.Huseynov@socar.az


Sh. Z. Ismailov, Y. Y. Shmoncheva, G. V. Jabbarova

Azerbaijan State Oil and Industry University, Baku, Azerbaijan

Application of machine learning algorithms for optimizing the trajectory of inclined wells


Machine learning, a subset of artificial intelligence, allows computers to learn and improve from data without explicit programming. Machine learning algorithms are categorized into supervised, unsupervised, and reinforcement learning, each serving different applications such as classification, clustering, and decision-making. In the oil and gas industry, machine learning is applied to drilling processes, reservoir characterization, and exploration. It improves efficiency in predicting reservoir properties, optimizing drilling parameters, and detecting anomalies. The methodology for analyzing well trajectory includes evaluating survey data with calculation methods like tangential, balanced tangential, average angle, radius of curvature, and minimum curvature. These methods help optimize wellbore paths. This study outlines control criteria essential for optimizing borehole trajectory management in oil and gas well drilling. The deviation correction criterion aims to maintain the borehole path within a designated trajectory, minimizing deviation from the planned design profile. Optimal control conditions are defined through mathematical criteria involving radius vectors and design trajectory alignment. The control framework incorporates efficiency measures, calculating the trajectory's costeffectiveness and operational constraints. Machine learning algorithms facilitate these control strategies, focusing particularly on zenith angle correction for trajectory stabilization. These methods provide adaptable options for deflector angle and correction length, ensuring alignment with target intervals. The approach enhances trajectory accuracy, minimizes costs, and complies with technical, geological, and technological constraints inherent to drilling. Software incorporating machine learning and these methods was developed and tested, demonstrating improvements in analyzing survey data and optimizing well trajectory, contributing to more efficient drilling operations and reduced costs.

Keywords: machine learning; mathematical models; control criteria; well trajectory optimization; survey data analysis; calculation methods.

Date submitted: 10.08.2024     Date accepted: 15.10.2024     Date published: 05.11.2024

Machine learning, a subset of artificial intelligence, allows computers to learn and improve from data without explicit programming. Machine learning algorithms are categorized into supervised, unsupervised, and reinforcement learning, each serving different applications such as classification, clustering, and decision-making. In the oil and gas industry, machine learning is applied to drilling processes, reservoir characterization, and exploration. It improves efficiency in predicting reservoir properties, optimizing drilling parameters, and detecting anomalies. The methodology for analyzing well trajectory includes evaluating survey data with calculation methods like tangential, balanced tangential, average angle, radius of curvature, and minimum curvature. These methods help optimize wellbore paths. This study outlines control criteria essential for optimizing borehole trajectory management in oil and gas well drilling. The deviation correction criterion aims to maintain the borehole path within a designated trajectory, minimizing deviation from the planned design profile. Optimal control conditions are defined through mathematical criteria involving radius vectors and design trajectory alignment. The control framework incorporates efficiency measures, calculating the trajectory's costeffectiveness and operational constraints. Machine learning algorithms facilitate these control strategies, focusing particularly on zenith angle correction for trajectory stabilization. These methods provide adaptable options for deflector angle and correction length, ensuring alignment with target intervals. The approach enhances trajectory accuracy, minimizes costs, and complies with technical, geological, and technological constraints inherent to drilling. Software incorporating machine learning and these methods was developed and tested, demonstrating improvements in analyzing survey data and optimizing well trajectory, contributing to more efficient drilling operations and reduced costs.

Keywords: machine learning; mathematical models; control criteria; well trajectory optimization; survey data analysis; calculation methods.

Date submitted: 10.08.2024     Date accepted: 15.10.2024     Date published: 05.11.2024

References

  1. Carpenter, C. (2021). Reservoir simulation, modeling of a complex offshore field uses AI and machine learning. SPE JPT, 73(07), 44–45.
  2. Suleimanov, B. A., Huseynova, N. I. (2024). Artificial intelligence (AI) evaluation of current reservoir pressure distribution based on oil production data. ANAS Transactions. Earth Sciences, 1, 158-170.
  3. Suleimanov, B. A., Ismailov, F. S., Huseynova, N. I., Veliev, E. F. (2018) The application of fuzzy logic and multifractal analysis for reservoir management. In: COIA 2018 - 6th international conference on control and optimization with industrial applications. Vol. 1, 34-36.
  4. Suleimanov, B. A. , Ismailov, F.S., Dyshin, O. A., Veliyev, E. F. (2016). Selection methodology for screening evaluation of EOR methods. Petroleum Science and Technology, 34(10), 961-970.
  5. Norlund, P., Jiang, F. (2022). Improving machine learning approaches to seismic fault imaging through training augmentation. IPTC-21940-EA. In: International Petroleum Technology Conference, Riyadh, Saudi Arabia.
  6. Samarkin, Y., Glatz, G., Waheed, U., et al. (2023). FMI logs deblurring and inpainting using deep learning. In: 84th EAGE Annual Conference & Exhibition. European Association of Geoscientists & Engineers.
  7. Olneva, T., Semin, D. (2019). Machine learning approach: sweet spots mapping based on anisotropic seismic data. SPE-196121-MS. In: SPE Annual Technical Conference and Exhibition, Calgary, Alberta, Canada.
  8. Jiang, F., Norlund, P., Dietz, D. (2021). Assisted fault identification and surface extraction by machine learning — A case study from Oman. In: First International Meeting for Applied Geoscience & Energy Expanded Abstracts.
  9. Adegoke, T. M., Adeleye, J. O., Morakinyo, A. M. (2024). Prediction of equivalent circulation density of drilling fluids using machine learning. SPE-221595-MS. In: SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria.
  10. Elgaddafi, R. M., Al Saba, M. T., Ahmed, R., et al. (2024). Application of machine learning method for modeling settling behavior of a spherical particle in fibrous drilling fluids. SPE-218631-MS. In: SPE Conference at Oman Petroleum & Energy Show, Muscat, Oman.
  11. Shadravan, A., Tarrahi, M., Amani, M. (2017). Intelligent tool to design drilling, spacer, cement slurry, and fracturing fluids by use of machine-learning algorithms. SPE Drilling & Completion, 32, 131–140.
  12. Suleimanov, B. A., Veliyev, E. F., Shovgenov, A. D. (2022). Well cementing: fundamentals and practices. Moscow-Izhevsk: ICS.
  13. Suleimanov, B. A., Veliyev, E. F., Aliyev, A. A. (2023). Oil and gas well cementing for engineers. John Wiley & Sons.
  14. Varadarajan, P. A., Roguin, G., Abolins, N., Ringer, M. (2021). A digital twin for real-time drilling hydraulics simulation using a hybrid approach of physics and machine learning. OTC-31278-MS. In: Offshore Technology Conference, Virtual and Houston, Texas.
  15. Carpenter, C. (2023). Machine-learning model developed for rate-of-penetration optimization. SPE JPT, 75, 59–61.
  16. Ma, Z., Hsu, M., Hu, H., et al. (2024). Hybrid strategies for interpretability of rate of penetration prediction: automated machine learning and SHAP interpretation. ARMA-2024-0315. In: 58th U.S. Rock Mechanics/Geomechanics Symposium, Golden, Colorado, USA.
  17. Urdaneta, C., Jeong, C., Wu, X., Chen, J. (2024). Deep learning method for improving rate of penetration prediction in drilling. SPE Journal, 29, 3440–3448.
  18. Bai, K., Fan, H., Zhang, H., et al. (2022). Real time torque and drag analysis by combining of physical model and machine learning method. URTEC-3723045-MS. In: SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA.
  19. Khalifa, H., Tomomewo, O. S., Doghmane, B., et al. (2024). Machine learning-based drill bit wear prediction for enhanced drilling performance. ARMA-2024-0916. In: 58th U.S. Rock Mechanics/Geomechanics Symposium, Golden, Colorado, USA.
  20. Zhan, G. D., Dossary, M. J., Luu, T. P., et al. (2023). On field implementation of real-time bit-wear estimation with bit agnostic deep learning artificial intelligence model along with physics-hybrid features. SPE-214603-MS. In: SPE/IADC Middle East Drilling Technology Conference and Exhibition, Abu Dhabi, UAE.
  21. Mal, A., Ødegård, S. I., Helgeland, S., et al. (2022). Prediction of stuck pipe incidents using models powered by deep learning and machine learning. SPE-208778-MS. In: IADC/SPE International Drilling Conference and Exhibition, Galveston, Texas, USA.
  22. Gomes, D., Jaritz, T., Robinson, T. S., Revheim, O. E. (2024). Enhancing stuck pipe risk detection in exploration wells using machine learning based tools: A Gulf of Mexico case study. SPE-217963-MS. In: IADC/SPE International Drilling Conference and Exhibition, Galveston, Texas, USA.
  23. Carpenter, C. (2022). Machine learning aids early detection of stuck pipe in extended-reach wells. SPE JPT, 74, 82–84.
  24. Wang, J., Jing, H., Ozbayoglu, E., et al. (2024). Enhancing well kick classification in drilling operations using a novel PCA-based machine learning approach. ARMA-2024-0626. In: 58th U.S. Rock Mechanics/Geomechanics Symposium, Golden, Colorado, USA.
  25. Obi, C. E., Falola, Y., Manikonda, K., et al. (2023). A machine learning approach for gas kick identification. SPE Drilling & Completion, 38, 663–681.
  26. Wang, H., Zhang, R., Deng, Z., et al. (2024). A machine-learning-based workflow for drilling risk prediction of wellbore instability and trajectory optimization in ultra-deep formation. URTEC-4034008-MS. In: SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA.
  27. Ray, P. L., Tipton, J. R., Cheng, S., Francom, D. (2024). Wellbore stability uncertainty quantification using a bayesian machine learning framework. SPE-220771-MS. In: SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA.
  28. Liu, P., Li, J., Chen, B., et al. (2024). Digital wellbore stability prediction with machine learning. IPTC-23359-MS. In: International Petroleum Technology Conference, Dhahran, Saudi Arabia.
  29. AlBahrani, H., Morita, N. (2022). Risk-controlled wellbore stability criterion based on a machine-learning-assisted finite-element model. SPE Drilling & Completion, 37(01), 38–66.
  30. Abdulmutalibov, T. E., Shmoncheva, Y. Y., Jabbarova, G. V. (2023). Advancements in applications of machine learning for formation damage predictions. SPE-217610-MS. In: SPE Caspian Technical Conference and Exhibition, Baku, Azerbaijan.
  31. Thabet, S., Zidan, H. M., Elhadidy, A., et al. (2024). Prediction of total skin factor in perforated wells using models powered by deep learning and machine learning. SPE-219187-MS. In: GOTECH, Dubai, UAE.
Read more Read less

DOI: 10.5510/OGP2024SI101004

E-mail: yelena.shmoncheva@asoiu.edu.az


M. A. Jamalbayov1, N. A. Valiyev2, Kh. M. Ibrahimov1, M. M. Babayev1, S. H. Novruzova3

1“OilGasScientificResearchProject” Institute, SOCAR, Baku, Azerbaijan; 2SOCAR, Baku, Azerbaijan; 3Azerbaijan State Oil and Industry University, Baku, Azerbaijan

Energy and efficiency optimization in sucker-rod pumping using discrete-imitation modeling concept: application to well operations in the Bibi-Eibat field of Azerbaijan


This study is a continuation of the authors' previous works on dynamic system modeling, where a new “discrete-imitation concept” was developed. A methodology for its application to the computational modeling of “pump-well-reservoir” systems was proposed and computational studies were conducted for a hypothetical well using a simulator developed based on this concept. The relationship between pumping rate, pump fillage, and stroke speed has been thoroughly analyzed. The conditions for the transition to intermittent mode have been established. This work aims to utilize a simulator based on the author's concept to optimize a real sucker-rod pump well at very low reservoir pressure and productivity. For this purpose, Well No. 1220 at the Bibi-Heybat field (Azerbaijan) was selected. The study investigates the effects of operational parameters on overall well efficiency. The results show that increasing stroke speed to 5.0 1/min slightly improves production by 0.14%, while reducing it to 3.0 1/min increases pump fillage but reduces output. Depth adjustments show that deeper pump installations improve production, with a 8.33% increase observed at a depth of 133.3 m. Notably, intermittent operation with 28% active daily time achieves production levels comparable to continuous pumping while reducing energy consumption and costs. It has been established that increasing the stroke speed from 4 to 5 1/min and lowering the pump installation depth by 6 meters increases the production rate by 10.4%. The results ultimately confirmed the validity of the new concept. These findings also underscore the practical value of the proposed simulator in energy and efficiency optimization of sucker-rod pumping systems.

Keywords: discrete imitation; reservoir modeling; pumping optimization; sucker-rod pump; stroke speed; intermittent pumping.

Date submitted: 16.08.2024     Date accepted: 14.11.2024     Date published: 21.11.2024

This study is a continuation of the authors' previous works on dynamic system modeling, where a new “discrete-imitation concept” was developed. A methodology for its application to the computational modeling of “pump-well-reservoir” systems was proposed and computational studies were conducted for a hypothetical well using a simulator developed based on this concept. The relationship between pumping rate, pump fillage, and stroke speed has been thoroughly analyzed. The conditions for the transition to intermittent mode have been established. This work aims to utilize a simulator based on the author's concept to optimize a real sucker-rod pump well at very low reservoir pressure and productivity. For this purpose, Well No. 1220 at the Bibi-Heybat field (Azerbaijan) was selected. The study investigates the effects of operational parameters on overall well efficiency. The results show that increasing stroke speed to 5.0 1/min slightly improves production by 0.14%, while reducing it to 3.0 1/min increases pump fillage but reduces output. Depth adjustments show that deeper pump installations improve production, with a 8.33% increase observed at a depth of 133.3 m. Notably, intermittent operation with 28% active daily time achieves production levels comparable to continuous pumping while reducing energy consumption and costs. It has been established that increasing the stroke speed from 4 to 5 1/min and lowering the pump installation depth by 6 meters increases the production rate by 10.4%. The results ultimately confirmed the validity of the new concept. These findings also underscore the practical value of the proposed simulator in energy and efficiency optimization of sucker-rod pumping systems.

Keywords: discrete imitation; reservoir modeling; pumping optimization; sucker-rod pump; stroke speed; intermittent pumping.

Date submitted: 16.08.2024     Date accepted: 14.11.2024     Date published: 21.11.2024

References

  1. Babayev, M., Penkov, G., Asadov, S. (2024). Enhancing SAGD efficiency: A study on steam quality and injection rate optimization. In: Improved Oil and Gas Recovery, 8.
  2. Clemens, L. (2018). Sucker rod pumping system optimisation based on a novel finite elements analysis. SPE-192475-MS. In: SPE Middle East Artificial Lift Conference and Exhibition, Manama, Bahrain.
  3. Zhou, R. F., Bai, L., Dong, K. X., Dai, Y. X. (2011). Suspended load prediction on sucker rod suspension load based on artificial neural network. Advanced Materials Research, 217–218, 1040–1043.
  4. Miska, S. Z., Khodabandeh, A., Rajtar, J. M. (1994). Computer-aided design and optimization of sucker rod pumping systems. SPE-26966-MS. In: SPE Latin America/Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina.
  5. Solodkiy, V. P., Kazantsev, A. V., Hudorozhkov, A. V. (2020). Influence of the electric drive control system on the sucker-rod pump energy efficiency. Oil Industry, 2, 50-53.
  6. Guo, B., Lyons, W. C., Ghalambor, A. (2007). Petroleum production engineering. a computer-assisted approach. Gulf Professional Publishing, Elsevier Inc.
  7. Caicedo, S., Araujo, A. (2008). Evaluating artificial lifted wells with resistive downhole heating through an integrated numerical model. In: Rio Oil and Gas 2008 Expo and Conference, Rio de Janeiro, RJ (Brazil).
  8. Zhiqian, H. (1995). The progress and prospect of equipment manufacturing in China for oil and gas exploration and production. SPE-29974-MS. In: The International Meeting on Petroleum Engineering, Beijing, China.
  9. Saputelli, L. A. (1998). Low pressure gas collection system: solving environmental problems. PETSOC-98-18. In: The Annual Technical Meeting, Calgary, Alberta.
  10. Radwan, A., Naderi, K., Ramanathan, R. (2022). Tailored metal oxide nanoparticles-based fluids for production enhancement via engineered uplift pressure mechanism: multi-basin case studies. URTEC-3722922-MS. In: The SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA.
  11. Garcia-James, C. J. (2016). Experience with progressive cavity pumps in the soldado field, offshore Trinidad. SPE-180802-MS. In: The SPE Trinidad and Tobago Section Energy Resources Conference, Port of Spain, Trinidad and Tobago.
  12. Balboshin, V. A., Garifov, K. M., Kadyrov, A. Kh., et al. (2024). Sucker rod pump valves for horizontal wells which can be deployed in dual completion units. Oil Industry, 7, 61-64.
  13. Grigoriev, S. L., Demidov, O. V. (2010). The hydraulic drive of sucker rod well pump represents a new technology for oil production. Territory Neftegaz, 10, 59.
  14. Molchanov, A. G., Pevnev, V. G., Tarasov, K. V. (2013). The rod well pump is driven hydraulically and features inertial counterbalancing. Territory Neftegaz, 4, 52-55.
  15. Molchanov, A. G., Pevnev, V. G., Tarasov, K. V. (2013). The use of a hydraulic drive of sucker rod pump with an inertial balance. Oil Industry, 5, 105-107.
  16. Suleimanov, B. A., Dyshin, O. A. (2013). Application of discrete wavelet transform to the solution of boundary value problems for quasi-linear parabolic equations. Applied Mathematics and Computation, 219, 7036-7047.
  17. Abbasov, E. M., Dyshin, O. A., Suleimanov, B. A. (2008). Application of wavelet transforms to the solution of boundary value problems for linear parabolic equations. Computational Mathematics and Mathematical Physics, 48(2), 251-268.
  18. Suleimanov, B. A. (1997). Slip effect during filtration of gassed liquid. Colloid Journal, 59(6), 749-753.
  19. Abbasov, E. M., Dyshin, O. A., Suleimanov, B. A. (2008). Wavelet method for solving the unsteady porous-medium flow problem with discontinuous coefficients. Computational Mathematics and Mathematical Physics, 48(12), 2194-2210.
  20. Suleimanov, B. A., Veliyev, E. F., Vishnyakov, V. V. (2022). Nanocolloids for petroleum engineering: Fundamentals and practices. John Wiley & Sons.
  21. Suleimanov, B. A. (2011). Mechanism of slip effect in gassed liquid flow. Colloid Journal, 73(6), 846–855.
  22. Abbasov, E. M., Dyshin, O. A., Suleimanov, B. A. (2009). Wavelet method for solving second-order quasilinear parabolic equations with a conservative principal part. Computational Mathematics and Mathematical Physics, 49(9), 1554-1566.
  23. Suleimanov, B. A., Azizov, Kh. F., Abbasov, E. M. (1996). Slippage effect during gassed oil displacement. Energy Sources, 18(7), 773–779.
  24. Suleimanov, B. A., Abbasov, E. M., Sisenbayeva, M. R. (2017). Mechanism of gas saturated oil viscosity anomaly near to phase transition point. Physics of Fluids, 29, 012106
  25. Jamalbayov, M. A., Ibrahimov, Kh. M. (2023). New waterflooding efficiency evaluation method (on the example of 9th horizon of the Guneshli field). Scientific Petroleum, 1, 43-47.
  26. Hajikarimova, L. G., Azimova, E. Sh. (2023). An innovative approach to protect sucker rod pumps in sandy wells. Scientific Petroleum, 1, 51-54.
  27. Aliev, F. A., Jamalbayov, M. A., Valiyev, N. A., et al. (2023). Computer model of pump–well–reservoir system based on the new concept of imitational modeling of dynamic systems. International Applied Mechanics, 59, 352–362.
  28. Jamalbayov, M. A., Valiyev, N. A. (2024). The discrete-imitation modeling concept of the “sucker-rod pumpwell-reservoir” system and the optimization of the pumping process. Petroleum Research, Available online 8 April 2024.
  29. Jamalbayov, M. A., Valiyev, N. A. (2024). The discrete-imitational modeling of the pump-well-reservior system with intermittent sucker-rod pumping. SPE-221528-MS. In: SPE Middle East Artificial Lift Conference and Exhibition, Manama, Bahrain.
  30. Takacs, G. (1997). Profitability of sucker-rod pump operations is improved through proper installation design. SPE-38994-MS. In: The Latin American and Caribbean Petroleum Engineering Conference, Rio de Janeiro, Brazil.
  31. Basos, G. Yu., Valovskiy, K. V., Zairov, B. F., et al. (2010). The results of dedicated sucker rod pumping units application in Nurlatneft NGDU. Oil Industry, 9, 96-99.
Read more Read less

DOI: 10.5510/OGP2024SI101005

E-mail: mehemmed.camalbeyov@socar.az


R. F. Yakupov, V. Sh. Mukhametshin, R. A. Gilyazetdinov, Sh. G. Mingulov, R. R. Stepanova, L. Z. Samigullina

Institute of Oil and Gas, Ufa State Petroleum Technological University (branch in the city of Oktyabrsky), Russia

Hydraulic fracturing in deposits of terrigenous reservoirs of Ural-Volga deposits


The work is devoted to improving the processes of developing layers of terrigenous objects of one of the deposits of the Ural-Volga region and improving the technical and economic performance of enterprises of the fuel and energy complex. At the moment, most of the fields in the oil-bearing territory under consideration are characterized by high rates of reserve production, which are accompanied by a significant increase in waterlogging and complications affecting the completeness of resource extraction. However, with the general trend towards the development of reserves in the fields, deposits and areas of the zone with low production are often distinguished, characterized by low permeability and high heterogeneity, including in highly productive development facilities. The authors conducted a review of hydraulic fracturing methods in world and Russian practice. The classification of hydraulic fracturing methods and the features of its implementation are described. In the conditions of the field under consideration, the domestic experience of hydraulic fracturing and modern technologies in the design of hydraulic fracturing are analyzed. The influence of geological, technological factors and features of the development of the research object on the efficiency of hydraulic fracturing has been studied. The regularities of the influence of geological and technological parameters on the efficiency of hydraulic fracturing have been revealed, which can be successfully taken into account when designing operations in various geological and physical conditions of development. Based on the results of combining the results of the study, a number of conclusions were made that reduce the risks of making low-effective management decisions, which, in conditions of the need for operational development of residual oil reserves, is an integral part of the strategy of subsurface users.

Keywords: hydraulic fracturing; terrigenous reservoir; colmatation; bottom-hole formation zone; reperforation; waterlogging; fluid flow rate; displacement characteristics.

Date submitted: 01.10.2024     Date accepted: 02.12.2024     Date published: 10.12.2024

The work is devoted to improving the processes of developing layers of terrigenous objects of one of the deposits of the Ural-Volga region and improving the technical and economic performance of enterprises of the fuel and energy complex. At the moment, most of the fields in the oil-bearing territory under consideration are characterized by high rates of reserve production, which are accompanied by a significant increase in waterlogging and complications affecting the completeness of resource extraction. However, with the general trend towards the development of reserves in the fields, deposits and areas of the zone with low production are often distinguished, characterized by low permeability and high heterogeneity, including in highly productive development facilities. The authors conducted a review of hydraulic fracturing methods in world and Russian practice. The classification of hydraulic fracturing methods and the features of its implementation are described. In the conditions of the field under consideration, the domestic experience of hydraulic fracturing and modern technologies in the design of hydraulic fracturing are analyzed. The influence of geological, technological factors and features of the development of the research object on the efficiency of hydraulic fracturing has been studied. The regularities of the influence of geological and technological parameters on the efficiency of hydraulic fracturing have been revealed, which can be successfully taken into account when designing operations in various geological and physical conditions of development. Based on the results of combining the results of the study, a number of conclusions were made that reduce the risks of making low-effective management decisions, which, in conditions of the need for operational development of residual oil reserves, is an integral part of the strategy of subsurface users.

Keywords: hydraulic fracturing; terrigenous reservoir; colmatation; bottom-hole formation zone; reperforation; waterlogging; fluid flow rate; displacement characteristics.

Date submitted: 01.10.2024     Date accepted: 02.12.2024     Date published: 10.12.2024

References

  1. Gilyazetdinov, R. A., Mukhametshin, V. Sh., Gizzatullina, A. A., et al. (2024). Development and adaptation of hybrid algorithms for assessing the degree of well interaction. SOCAR Proceedings, 1, 70-75.
  2. Yamkin, M. A., Safiullina, E. U., Yamkin, A.V. (2024). Analysis of the results of modeling the flow of liquid to a hydraulic fracturing crack. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 335(4), 14-21.
  3. Fu, H., Huang, L., Hou, B., et al. (2024). Experimental and numerical investigation on interaction mechanism between hydraulic fracture and natural fracture. Rock Mechanics and Rock Engineering, 57, 10571-10582.
  4. Khuzin, R. R., Andreev, V. E., Mukhametshin, V. V., et al. (2021). Influence of hydraulic compression on porosity and permeability properties of reservoirs. Journal of Mining Institute, 251(3), 688-697.
  5. Dzhabrailov, A. V., Yusifov, T. Yu., Latypov, I. D., et al. (2010). Recovery of the well from inactivity by means of hydraulic fracturing. Oil Industry, 8, 58-59.
  6. Huang, L., Liao, X., Fan, M., et al. (2024). Experimental and numerical simulation technique for hydraulic fracturing of shale formations. Advances in Geo-Energy Research, 13(2), 83-88.
  7. Yin, P. F., Yang, S. Q., Ranjith, P. G. (2024). Anisotropic mechanical behaviors of shale rock and their relation to hydraulic fracturing in a shale reservoir: a review. Energies, 17(7), 1761.
  8. Grishchenko, V. A., Rabaev, R. U., Asylgareev, I. N., et al. (2021). Methodological approach to optimal geological and technological characteristics determining when planning hydraulic fracturing at multilayer facilities. SOCAR Proceedings, SI2, 182-191.
  9. Sudad, H. A. O., Chang, W. J. (2024). Techniques to boost oil production in the development of multi-reservoir fields. Journal of Waste Management & Recycling Technology, 2(3), 1-7.
  10. Zhong, C., Chen, R., Liu, B., et al. (2024). Trends in polyacrylamide utilization and treatment for hydraulic fracturing. npj Materials Sustainability, 2(1), 15.
  11. Shirinkin, D., Kochnev, A., Krivoshchekov, S., et al. (2024). High permeability streak identification and modelling approach for carbonate reef reservoir. Energies, 17(1), 236.
  12. Khairullin, M. H., Shamsiev, M. N., Khisamov, R. S., Salimyanov, I. T. (2009). Evaluation of the effectiveness of hydraulic fracturing based on hydrodynamic studies of vertical wells. Oil Industry, 7, 54-56.
  13. Mandrik, I. E., Guzeev, V. V., Gromov, M. A. (2009). Neuroinformational approaches to predicting the effectiveness of hydraulic fracturing. Oil Industry, 6, 44-48.
  14. Martyushev, D. A., Yang, Y., Kazemzadeh, Y., et al. (2024). Understanding the mechanism of hydraulic fracturing in naturally fractured carbonate reservoirs: Microseismic monitoring and well testing. Arabian Journal for Science and Engineering, 49(6), 8573-8586.
  15. Salimov, V. G., Salimov, O.V. (2011). Analysis of the growth of the height of the hydraulic fracturing crack in the terrigenous reservoirs of the Devonian of the south-east of Tatarstan. Oil Industry, 1, 81-83. 
  16. Tomsky, K. O., Ivanova, M. S. (2024). Optimization of the location of a multihole well in a thin oil rim, complicated by the presence of an extensive gas cap. Journal of Mining Institute, 265, 140-146.
  17. Akhtyamov, A. A., Makeev, G. A., Baidyukov, K. N., et al. (2018). Corporate hydraulic fracturing simulator "RN-GRID": from software implementation to industrial implementation. Oil Industry, 5, 94-97.
  18. Settari, A. (1980). Simulation of hydraulic fracturing processes. SPE Journal, 20(6), 487-500.
  19. Burkhanov, R. N., Lutfullin, A. A., Raupov, I. R., et al. (2024). Localization and involvement in the development of residual recoverable reserves of a multi-layer oil field. Journal of Mining Institute, 268, 599-612.
  20. Osman, M. E., Abou-Kassem, J. H. (1996). Effect of boundary conditions on pressure behavior of finiteconductivity fractures in bounded stratified reservoirs. Journal of Petroleum Science and Engineering, 15, 291-307.
  21. Grishchenko, V. A., Mukhametshin, V. Sh., Rabaev, R. U. (2022). Geological structure features of carbonate formations and their impact on the efficiency of developing hydrocarbon deposits. Energies, 15(23), 9002.
  22. Stabinskas, A. P., Sultanov, Sh. Kh., Mukhametshin, V. Sh., et al. (2021). Evolution of Hydraulic Fracturing Fluid: from Guar Systems to Synthetic Gelling Polymers. SOCAR Proceedings, SI2, 172-181.
  23. Mukhametshin, V. Sh., Kuleshova, L. S., Safiullina, A. R. (2021). Grouping and determining oil reservoirs in carbonate reservoirs by their productivity at the stage of geological exploration. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 332(12), 43–51.
  24. Zeigman, Yu. V., Mukhametshin, V. Sh., Sergeev, V. V., Kinzyabaev, F. S. (2017). Experimental study of viscosity properties of emulsion system with SiO2 nanoparticles. Nanotechnologies in Construction, 9(2), 16–38.
  25. Qin, Q., Zhou, K., Wei, B., et al. (2024). Experimental and simulation study on deep reservoir fracturing technology: a review and future perspectives. Geoenergy Science and Engineering, 242, 213209.
  26. Yalaev, T. R., Kanevskaya, R. D., Buyanov, A. V., Byzova, S. I. (2024). On the applicability of the geomechanical approach for predicting fracture intensity in carbonate objects. Oil Industry, 9, 19-24.
  27. Kuleshova, L. S., Mukhametshin, V. V., Gilyazetdinov, R. A. (2024). The role and significance of the tectonicstratigraphic factor in the formation of the structural features of hydrocarbon deposits of the Volga-Ural oil and gas province. SOCAR Proceedings, 1, 10-17.
  28. Mukhametshin, V. V., Gilyazetdinov, R. A., Kuleshova, L. S., et al. (2024). On the depth of identification of objects in the study of the influence of the density of the grid of wells on the degree of production of oil reserves. SOCAR Proceedings, SI1, 26-31.
  29. Asalkhuzina, G. F., Davletbaev, A. Ya., Fedorov, A. I., et al. (2018). Diagnosis of fracture reorientation during repeated hydraulic fracturing using mining/pressure data analysis and modeling in the geomechanical module of the RN-KIN software package. Oil Industry, 11, 114-118.
  30. Akhmetov, R. T., Mukhametshin, V. V., Kuleshova, L. S., Grezina, O. A. (2021). Production facilities grouping based on the parameters of the capillary pressure curves generalized model on the example of Western Siberia oil fields. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 332(11), 89–97.
  31. Osiptsov, A. A. (2017). Fluid mechanics of hydraulic fracturing: a review. Journal of Petroleum Science and Engineering, 156, 513-535.
  32. Chirkunov, A. P., Tuktarov, T. A., Khanipov, M. N., et al. (2024). The methodology of a comprehensive rating assessment of the effectiveness of oil field flooding systems. Oil Industry, 7, 44-46.
  33. Al-Shammari, A., Sinha, S., Sheikh, B., et al. (2024, January). A successful acid fracturing treatment in asphaltene problematic reservoir, Burgan oilfield Kuwait. SPE-217779-MS. In: SPE Hydraulic Fracturing Technology Conference and Exhibition, The Woodlands, Texas, USA.
  34. Mulyukov, D. R., Fedorov, A. I. (2024). Analysis of the development direction of hydraulic fracturing cracks in the system of development of hard-to-recover reserves based on the management of the stressed state of the reservoir. Oil Industry, 1, 54-59.
  35. Zenchenko, E. V., Trimonova, M. A., Turuntaev, S. B. (2019). Laboratory modeling of hydraulic fracturing and related processes. Oil Industry, 10, 68-71.
  36. Zhidkova, N. A., Zakharova, A. A. (2009). Mathematical support of the PI-FRAC software module for predicting crack parameters in injection and absorption wells at injection pressures above hydraulic fracturing pressure. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 314,1, 66-71.
  37. Kashnikov, Yu. A., Yakimov, S. Yu. (2019). Geomechanical and hydrodynamic assessment of the impact of downhole pressure on well performance. Oil Industry, 11, 111-115.
  38. Martyushev, D. A., Ponomareva, I. N., Filippov, E. V. (2022). Studying the direction of hydraulic fracture in carbonate reservoirs: Using machine learning to determine reservoir pressure. Petroleum Research, 8(2), 226-333.
  39. Nwanwe, O. I., Izuwa, N. C., Ohia, N. P., et al. (2024). Determining optimal controls placed on injection/production wells during waterflooding in heterogeneous oil reservoirs using artificial neural network models and multiobjective genetic algorithm. Computational Geosciences. Published: 06 July 2024.
  40. Daramola, G. O., Jacks, B. S., Ajala, O. A., Akinoso, A. E. (2024). AI applications in reservoir management: optimizing production and recovery in oil and gas fields. Computer Science & IT Research Journal, 5(4), 972-984.
  41. Jamalbayov, M. A., Yildirim, B., Abdullazada, A. (2024). The early determination method of reservoir drive of oil deposits based on Jamalbayli indexes. SPE Journal, 29(09), 4771-4780.
  42. Amer, M. M., Al-Obaidi, D. A. (2024). Permeability prediction and facies distribution for Yamama reservoir in Faihaa oil field: role of machine learning and cluster analysis approach. The Iraqi Geological Journal, 57(1C), 29-46.
  43. Vishnyakov, V. V., Suleimanov, B. A., Salmanov, A. V., Zeynalov, E. B. (2019). Primer on enhanced oil recovery. Gulf Professional Publishing.
  44. Panakhov, G. M., Suleimanov, B. A. (1995). Specific features of the flow of suspensions and oil disperse systems. Colloid Journal, 57(3), 359-363.
  45. Suleimanov, B. A., Veliyev, E. F., Vishnyakov, V. V. (2022). Nanocolloids for petroleum engineering: Fundamentals and practices. John Wiley & Sons.
  46. Suleimanov, B. A., Ismailov, F. S., Dyshin, O. A., Veliyev, E. F. (2016). Screening evaluation of EOR methods based on fuzzy logic and Bayesian inference mechanisms. SPE-182044-MS. In: SPE Russian Petroleum Technology Conference and Exhibition, Moscow, Russia.
  47. Suleimanov, B. A., Ismailov, F. S., Dyshin, O. A. (2014). Statistical modeling of life cycle of oil reservoir development. Journal of the Japan Petroleum Institute, 57(1), 47-57.
Read more Read less

DOI: 10.5510/OGP2024SI101008

E-mail: vsh@of.ugntu.ru


M. G. Abdullayev

Azerbaijan State Oil and Industry University, Baku, Azerbaijan

New composition for demulsification in downhole conditions


Based on laboratory experiments on destruction of oil emulsions of various fields a new effective composition of demulsifier has been developed. The article investigates the demulsifying and inhibiting properties of the new composition for breaking oil emulsions. The known technologies were studied, and numerous experiments were carried out to investigate the new composition. The requirement of low density of components was fulfilled, which is an important property of the obtained composition, high demulsification at low consumption of the composition was achieved, and high inhibitory properties of this composition regarding well equipment were provided. The composition based on homogeneous mixture of polyethylene polyamine with rosin and methanol has low cost, is characterised by simplicity and ease of preparation. The proposed composition differs from many known demulsifiers by simplicity of preparation in field conditions, high efficiency. The developed composition provides sufficiently high results even at a small consumption compared to known demulsifiers, while its price is much cheaper. The proposed composition can also be used as a corrosion inhibitor. Economic efficiency of the proposed composition is achieved by reducing the concentration from 150 mg/l according to the prototype to 100 mg/l. In addition, the composition is easily prepared in field conditions, including offshore oil fields, and is also characterised by high inhibiting properties. When 0.1% of rosin is added to methanol, the inhibiting ability of methanol increases 5 times, i.e. it allows the composition to be used as a corrosion inhibitor in an aggressive environment with the presence of hydrogen sulphide.

Keywords: oil-water emulsion; demulsification; well; composition; rosin; methanol.

Date submitted: 30.09.2024     Date accepted: 29.11.2024     Date published: 04.12.2024

Based on laboratory experiments on destruction of oil emulsions of various fields a new effective composition of demulsifier has been developed. The article investigates the demulsifying and inhibiting properties of the new composition for breaking oil emulsions. The known technologies were studied, and numerous experiments were carried out to investigate the new composition. The requirement of low density of components was fulfilled, which is an important property of the obtained composition, high demulsification at low consumption of the composition was achieved, and high inhibitory properties of this composition regarding well equipment were provided. The composition based on homogeneous mixture of polyethylene polyamine with rosin and methanol has low cost, is characterised by simplicity and ease of preparation. The proposed composition differs from many known demulsifiers by simplicity of preparation in field conditions, high efficiency. The developed composition provides sufficiently high results even at a small consumption compared to known demulsifiers, while its price is much cheaper. The proposed composition can also be used as a corrosion inhibitor. Economic efficiency of the proposed composition is achieved by reducing the concentration from 150 mg/l according to the prototype to 100 mg/l. In addition, the composition is easily prepared in field conditions, including offshore oil fields, and is also characterised by high inhibiting properties. When 0.1% of rosin is added to methanol, the inhibiting ability of methanol increases 5 times, i.e. it allows the composition to be used as a corrosion inhibitor in an aggressive environment with the presence of hydrogen sulphide.

Keywords: oil-water emulsion; demulsification; well; composition; rosin; methanol.

Date submitted: 30.09.2024     Date accepted: 29.11.2024     Date published: 04.12.2024

References

  1. Suleimanov, B. A. (2006). Specific features of heterogenous systems flow. Moscow-Izhevsk, ICS.
  2. Tapdigov, Sh. Z., Ahmad, F. F., Hamidov, N. N., Bayramov, E.E. (2022). Increase in the efficiency of water shut-off with the application of polyethylenpolyamine added cement. Chemical Problems, 20, 59-67.
  3. Suleimanov, B. A., Veliyev, E. F., Aliyev, A. A. (2021). Impact of nanoparticle structure on the effectiveness of Pickering emulsions for EOR applications. ANAS Transactions. Earth Sciences, 1, 82-92.
  4. Suleimanov, B. A. (1995). Use of multiple - function microemulsion for bottom hole zone treatment. Oil Industry, 12, 65 - 67.
  5. Abdullaev, M. G., Yusifov, R. A., Agayev, N. A., et al. (2007). Composition for downhole oil demulsification. Patent of Republic of Azerbaijan a 20050232.
  6. Franca, L. M., Kotisso, Y. (2024). Flowback emulsions issues for complex crude oil fluid-phases separation testing and manage by demulsification or water clarification. SPE-219038-MS. In: SPE Water Lifecycle Management Conference and Exhibition, Abu Dhabi, UAE.
  7. Noïk, C., Chen, J., Dalmazzone, C. (2006). Electrostatic demulsification on crude oil: a state-of-the-art review. SPE-103808-MS. In: International Oil & Gas Conference and Exhibition in China, Beijing, China.
  8. Suleimanov, B. А., Panakhov, G. М., Abbasov, E. М. (1996). Effect of emulsion in field conditions on operation of oil wells. Azerbaijan Oil Industry, 5, 26- 29.
  9. Mead, S. L., Navarrete, R. C. (2003). Water wetting of solids during the field demulsification process. PETSOC-2003-069-EA. In: Canadian International Petroleum Conference, Calgary, Alberta.
  10. Dhandhi, Y., Bhardwaj, V., Saw, R. K., Naiya, T. K. (2024). Demulsification of water-in-crude oil field emulsion using green demulsifier based on sesamum indicum: synthesis, characterization, performance, and mechanism. SPE Journal, 29(08), 4166-4178.
  11. Chang, H., Zhang, Y., Abhijit Dandekar, A., et al. (2022). Emulsification characteristics and electrolyte-optimized demulsification of produced liquid from polymer flooding on Alaska North Slope. SPE Production & Operations, 37(02), 263-279.
  12. Abasov, M. T., Abbasov, M. I., Abdullaev, M. K., et al. (1991). Composition for removing paraffin and asphaltresin deposits. Patent SU1629493.
  13. Silin, М. А., Magadova, L. A., Khuzina, G. С., et al. (2015). Composition for breaking down water-oil emulsions and protecting oil field equipment from corrosion. Patent RU2549534.
  14. Abdelfatah, E., Chen, Y., Berton, P., et al. (2020). Tuning ionic liquids for simultaneous dilution and demulsification of water-in-bitumen emulsions at ambient temperature. SPE Journal, 25(02), 759–770.
  15. Panakhov, G. M., Suleimanov, B. A. (1995). Specific features of the flow of suspensions and oil disperse systems. Colloid Journal, 57(3), 359-363.
  16. Castro, L. U. (2001). Demulsification treatment and removal of in-situ emulsion in heavy-oil reservoirs. SPE-68852-MS. In: SPE Western Regional Meeting, Bakersfield, California.
  17. Ismayilov, F. S., Dashdiyev, R. A., Suleimanov, B. A., et al. (2014). Demulsifier. Patent EA020481.
  18. Nazarenko, O. B., Shubin, B. G. (2001). Hydrocarbon liquid dehydration method. Patent RU2174857.
  19. Dutta, B. K., Ahmed, H. H. (2003). Production improvement by downhole demulsification - a simple and costeffective approach. SPE-81568-MS. In: Middle East Oil Show, Bahrain.
  20. Hou, Q., Zheng, X., Guo, D., et al. (2019). PDEA-based amphiphilic polymer enables pH-responsive emulsions for a rapid demulsification. SPE-193640-MS. In: SPE International Conference on Oilfield Chemistry, Galveston, Texas, USA.
  21. Ekoue-Kovi, K., Jakubowski, W. (2024). The design of green molecules for demulsification. IPTC-23886-MS. In: International Petroleum Technology Conference, Dhahran, Saudi Arabia.
  22. Gumerov, O. A., Gumerov, C. O., Izosimov, V. A. (2014). Experience in the use of downhole demulsification to improve operational efficiency of the ESP Arlan field. Oil and Gas Business, 6, 292-315.
  23. Adewunmi, A. A., Kamal, M. Sh., Gbadamosi, A., Patil, Sh. (2023). Demulsification of heavy crude oil emulsion driven by natural materials. SPE-213624-MS. In: Middle East Oil, Gas and Geosciences Show, Manama, Bahrain.
  24. Kumar, S., Rajput, V. S., Mahto, V. (2021). Experimental studies on demulsification of heavy crude oil-in-water emulsions by chemicals, heating, and centrifuging. SPE Production & Operations, 36(02), 375-386.
  25. Gong, C., Towner, J. W. (2001). Study of dynamic interfacial tension for demulsification of crude oil emulsions. SPE-65012-MS. In: SPE International Symposium on Oilfield Chemistry, Houston, Texas.
  26. Wiggett, A. J., Ricza, T. (2013). Enhancement of heavy oil demulsification. SPE-164335-MS. In: SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain.
  27. Nguyen, D., Sadeghi, N. (2012). Stable emulsion and demulsification in chemical EOR flooding: challenges and best practices. SPE-154044-MS. In: SPE EOR Conference at Oil and Gas West Asia, Muscat, Oman.
  28. Phukan, M., Koczo, K., Falk, B., Palumbo, A. (2010). New silicon copolymers for efficient demulsification. SPE-128553-MS. In: SPE Oil and Gas India Conference and Exhibition, Mumbai, India.
  29. Ismayilov, F. S., Suleimanov, B. A., Samedov, A. M., et al. (2012). Demulsifier. Patent RU2443754.
  30. Dhandhi, Y., Naiya, T. K. (2023). Synthesis of green polyethylene glycol-lauric acid demulsifier from a natural source and its application in demulsification of field emulsion: experimental and modeling approach. SPE Production & Operations, 38(04), 709–723.
  31. Jiang, H., Wang, D., Sun, N., et al. (2023). Study on synergistic demulsification mechanism of microwaves and ionic liquids on heavy oil emulsions. SPE Production & Operations, 38(01), 113-124.
  32. Abdelfatah, E., Chen, Y., Berton, P., et al. (2019). Tuning ionic liquids for simultaneous dilution and demulsification of water-in-bitumen emulsions at ambient temperature. SPE-193615-MS. In: SPE International Conference on Oilfield Chemistry, Galveston, Texas, USA.
  33. Tapdiqov, Sh. Z., Malikov, E. Y., Ahmed, F. F., et al. (2024). The physical-mechanical behavior and chemical bonding nature of poly-N-vinylpyrrolidone modified cement concrete. Heliyon, 10(4), e26039.
  34. Humbatova, S. F., Tapdiqov, Sh. Z., Guliyeva, J. E., et al. (2024). Investigation of mass gradient of concrete filled with polyacrylamide/Fe3O4 magnetite nanoparticles in Caspian sea and formation water medium. Chemical Problems, 22(2), 95-102.
Read more Read less

DOI: 10.5510/OGP2024SI101007

E-mail: malik.abdullayev.1952@gmail.com


R. R. Azizov, V. K. Berdin, M. I. Bayazitov, V. N. Blagochinnov, R. F. Kadirov, M. V. Demchenko*, S. V. Ilyin, V. Y. Pivovarov

Ufa State Petroleum Technological University, Russia, Ufa

Numerical study of a small-dimensional volume roller pump


One of the solutions to reduce capital and operating costs for oil production can be the development of energy-intensive equipment with increased reliability characteristics. The work is devoted to the study of the design of a volumetric multistage roller rotary pump. Experimentally observing the behavior of fluid inside a pump during operation is expensive, but predictive tools based on computational fluid dynamics, have emerged as a viable alternative approach to pump design and optimization. The article presents the results of a numerical study of a roller pump in the Ansys Fluent software package using a dynamically tunable mesh by the overset mesh method. A method for studying the working volume of chambers separated by roller partitions is proposed. Analysis of the pump operation showed the characteristic pulsation of the values of the main parameters. For a pump with an operating speed of 3000 rpm, a chamber diameter of 40 mm and a productivity at a given speed of 15 m3/day, an increase in amplitude is observed at the initial moment of time and after about 6 seconds from the beginning of the simulation a quasi-stationary mode appears. The flow rate at the outlet is 1 mm/s, at the inlet 0.5 mm/s, the torque values on the pump shaft are in the range from -0.02 N·mm to 0.14 N·mm, the shaft power is from ‒ 2 N·mm/s to 12 N·mm/s, shaft rotation speed from 70 to 130 rps. All pump parameters correlate with the fluid flow rate at the pump inlet.

Keywords: fuel and energy complex; oil and gas industry; well equipment; volumetric rotor pump; computational fluid dynamics; numerical simulation; moving mesh.

Date submitted: 24.07.2023     Date accepted: 10.03.2024     Date published: 29.07.2024

One of the solutions to reduce capital and operating costs for oil production can be the development of energy-intensive equipment with increased reliability characteristics. The work is devoted to the study of the design of a volumetric multistage roller rotary pump. Experimentally observing the behavior of fluid inside a pump during operation is expensive, but predictive tools based on computational fluid dynamics, have emerged as a viable alternative approach to pump design and optimization. The article presents the results of a numerical study of a roller pump in the Ansys Fluent software package using a dynamically tunable mesh by the overset mesh method. A method for studying the working volume of chambers separated by roller partitions is proposed. Analysis of the pump operation showed the characteristic pulsation of the values of the main parameters. For a pump with an operating speed of 3000 rpm, a chamber diameter of 40 mm and a productivity at a given speed of 15 m3/day, an increase in amplitude is observed at the initial moment of time and after about 6 seconds from the beginning of the simulation a quasi-stationary mode appears. The flow rate at the outlet is 1 mm/s, at the inlet 0.5 mm/s, the torque values on the pump shaft are in the range from -0.02 N·mm to 0.14 N·mm, the shaft power is from ‒ 2 N·mm/s to 12 N·mm/s, shaft rotation speed from 70 to 130 rps. All pump parameters correlate with the fluid flow rate at the pump inlet.

Keywords: fuel and energy complex; oil and gas industry; well equipment; volumetric rotor pump; computational fluid dynamics; numerical simulation; moving mesh.

Date submitted: 24.07.2023     Date accepted: 10.03.2024     Date published: 29.07.2024

References

  1. Blagochinnov, V. N., Isakov, A. V., Grekov, S. N. (2017). Multistage rotary pump. RU Patent 173857.
  2. Bayazitov, M. I., Berdin, V. K., Azizov, V. K., et al. (2024). Display roller pump. RU Patent 224933.
  3. Rancic, S. (2014). Reduction of pressure pulsations on automotive transmission oil vane pump. PhD Thesis. McMaster University, Hamilton.
  4. Takemori, C., Paladino, E., Lessa, L. (2005). Numerical simulation of oil flow in a power steering pump. SAE Technical Paper 2005-01-4061. https://doi.org/10.4271/2005-01-4061.
  5. Rana, D., Kumar, N. (2014). Experimental and computational fluid dynamic analysis of external gear pump. International Journal of Engineering Development and Research, 2(2), 2474-2478.
  6. Loganathan, S., Govindarajan, S., Suresh Kumar, J., et al. (2011). “Design and development of vane type variable flow oil pump for automotive application. SAE Technical Paper 2011-28-0102. https://doi.org/10.4271/2011-28-0102
  7. Altare, G., Rundo, M. (2016). Computational fluid dynamics analysis of gerotor lubricating pumps at high speed: geometric features influencing the filling capability. ASME Journal of Fluids Engineering, 138(11), 111101.
  8. Frosina, E., Senatore, A., Buono, D., Olivetti, M. (2014). A tridimensional CFD analysis of the oil pump of an high performance engine. SAE Technical Paper 2014-01-1712. https://doi.org/10.4271/2014-01-1712.
  9. Rundo, M., Altare, G. (2018). Lumper parameter and threedimensional computational fluid dynamics simulation of variable displacement vane pump for engine lubrication. ASME Journal of Fluids Engineering, 140(6), 061101.
  10. Gherardini, F., Zardin, B., Leali, F. (2016). A parametric CAD-based method for modelling and simulation of positive displacement machines. Journal of Mechanical Science and Technology, 30, 3253–3263.
  11. Pellegri, M., Vacca, A., Frosina, E., et al. (2017). Numerical analysis and experimental validation of gerotor pumps: A comparison between a lumped parameter and a computational fluid dynamics-based approach. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 231, 4413–4430.
  12. Zhao, X., Vacca, A., Dhar, S. (2018). Numerical modeling of a helical external gear pump with continuous-contact gear profile: a comparison between a lumped-parameter and a 3D CFD approach of simulation. In: Proceedings of the Bath/ASME Symposium on Fluid Power and Motion Control, Bath, UK.
Read more Read less

DOI: 10.5510/OGP2024SI100981

E-mail: m.v.demchenko@yandex.ru


R. R. Azizov, V. K. Berdin, M. I. Bayazitov, V. N. Blagochinnov, R. F. Kadirov, M. V. Demchenko*, S. V. Ilyin, V. Y. Pivovarov

Ufa State Petroleum Technological University, Russia, Ufa

Using ansys fluent udf for numerical study of the movement of the rollers of a downhole positive displacement pump


In order to improve the design and increase the technical characteristics of pump and compressor equipment, computational modeling of fluid dynamics is used. Numerical research techniques of positive displacement machines are quite diverse and are widely used for vane and gerotor pumps. The roller rotary pump considered in this work is used for oil production in the sidetracks of oil wells. The difference in the design is the use of rollers as partitions separating the working chambers of the pump. The authors of the article propose a method for studying the movement of rollers in the Ansys (Fluent) software package, and related issues of creating userdefined functions (UDF) to describe reciprocating motion using the macro DEFINE_CG_MOTION (name, dt, vel, omega, time, dtime). It is shown that the result of calculating the movement speed in ansys fluent differs from the graph of the given function. A sine law is substantiated to describe the speed of movement of the roller’s center of mass. The method for determining the coefficients in a sinusoidal dependence is described and it is shown how these coefficients affect the movement of the roller. The results obtained in the study can be used when performing verification of analytical calculation methods with finite element modeling data using UDF Ansys, and will also help young researchers understand the writing of UDF DEFINE_CG_MOTION.

Keywords: fuel and energy complex; oil and gas industry; well equipment; volumetric pump; Computational Fluid Dynamics (CFD); numerical simulation; moving mesh; User Define Function (UDF).

Date submitted: 20.07.2023     Date accepted: 10.03.2024     Date published: 29.07.2024

In order to improve the design and increase the technical characteristics of pump and compressor equipment, computational modeling of fluid dynamics is used. Numerical research techniques of positive displacement machines are quite diverse and are widely used for vane and gerotor pumps. The roller rotary pump considered in this work is used for oil production in the sidetracks of oil wells. The difference in the design is the use of rollers as partitions separating the working chambers of the pump. The authors of the article propose a method for studying the movement of rollers in the Ansys (Fluent) software package, and related issues of creating userdefined functions (UDF) to describe reciprocating motion using the macro DEFINE_CG_MOTION (name, dt, vel, omega, time, dtime). It is shown that the result of calculating the movement speed in ansys fluent differs from the graph of the given function. A sine law is substantiated to describe the speed of movement of the roller’s center of mass. The method for determining the coefficients in a sinusoidal dependence is described and it is shown how these coefficients affect the movement of the roller. The results obtained in the study can be used when performing verification of analytical calculation methods with finite element modeling data using UDF Ansys, and will also help young researchers understand the writing of UDF DEFINE_CG_MOTION.

Keywords: fuel and energy complex; oil and gas industry; well equipment; volumetric pump; Computational Fluid Dynamics (CFD); numerical simulation; moving mesh; User Define Function (UDF).

Date submitted: 20.07.2023     Date accepted: 10.03.2024     Date published: 29.07.2024

References

  1. Pyatov, I. S., Donchenko, A. M., Glebov, S. F., Totanov, A. S. (2017). Submersible gerotor pumps - myths and practice. Drilling and Oil, 6, 59-61.
  2. Panachev, M. V., Orlov, A. Yu., Bondar, A. F., et al. (2022). Multistage vane pump. RU Patent 2775342.
  3. Bychkov, N. A., Mosin, A. V., Polezhaev, R. M. (2012). Multistage pumping device. Utility Model RU Patent 119043.
  4. Pyatov, I. S., Kolesov, S. E., Ivanovsky, V. N. (2022). Multistage trochoidal pump and pump stage. RU Patent 2775052.
  5. Ryl, S. A., Kurochkin, A. V. (2020). Rotary piston hydraulic machine with free pistons. RU Patent 2739893.
  6. Ryl, S. A., Kurochkin, A. V. (2021). Downhole pumping unit with a submersible multi-stage rotary-piston type pump based on the Ryl hydraulic machine. RU Patent 2744877.
  7. Pescherenko, M. P., Pescherenko, S. N., Fadeikin, A. S. (2017). Displacement roller pump. RU Patent 2627488.
  8. Blagochinnov, V. N., Isakov, A. V., Grekov, S. N. (2017). Multistage rotary pump. RU Patent 173857.
  9. El-Hennawi, A.H., Mahmoud, N. A., Hussin, A. E., Hamed, A. M. (2019). Numerical and experimental study of a small-scale sliding vane pump. Journal of Al Azhar University Engineering Sector, 14(52), 891-902.
  10. Frosina, E., Senatore, A., Buono, D., Olivetti, M. (2014). A tridimensional CFD analysis of the oil pump of an high performance engine. In: SAE 2014 World Congress and Exhibition, Detroit, MI, United States, 8 – 10 April.
  11. Frosina, E., Senatore, A., Buono, D., et al. (2015). Vane pump power split transmission: three-dimensional computational fluid dynamics modeling. In: ASME/BATH 2015 Symposium on Fluid Power and Motion Control, FPMC. American Society of Mechanical Engineers.
  12. Zhang, Q., Xu, X. (2014). Numerical simulation on cavitation in a vane pump with moving mesh. In: ICCM2014 - International Conference on Computational Methods, 28-30th July, Cambridge, England.
  13. El-Hennawi, A. H., Eltahan, M., Magooda, M., Moharm, K. (2020). Numerical and experimental validation of an unbalanced oil vane pump using RANS approach /in: Farouk, M., Hassanein, M. (eds). Recent advances in engineering mathematics and physics. Springer, Cham.
Read more Read less

DOI: 10.5510/OGP2024SI100982

E-mail: m.v.demchenko@yandex.ru


F. Q. Hasanov1, S. B. Bayramov2, E. E. Abdullayev1, J. R. Damirova2

1“OilGasScientificResearchProject” Institute, SOCAR, Baku, Azerbaijan; 2Azerbaijan State Oil and Industry University, Baku, Azerbaijan

Socker-rod pumping unit with hydraulic transmission mechanism in the rod string


In existing sucker-rod pumping units the lower end of the rod string which hanged from the wireline hanger is directly connected with the plunger of the well pump. So, the plunger of the well pump is moving up and down according to the movement of the hanger after the deformation is ended and then the fluid is raised to the surface from the formation. The wireline hanger is affected by the weights of the rod string, the lifted fluid column and the dynamic loads when it is in an upward movement. To reduce the loads on the wireline hanger and increase the operating efficiency of the pumping unit, a hydraulic transmission mechanism operating on the principle of the hydraulic press was used in the intermediate part of the rod string. The rod string hanged from the wireline hanger is connected to the rod-shaped piston of the hydraulic transmission mechanism, and the groove-shaped piston of the mechanism is connected to the rod string of the well pump plunger. Due to the installed hydraulic transmission mechanism in the rod string, the plunger of the pump moves downwards in the upward movement of the rod string, and vice versa in the downward movement of the rod string, the plunger of the pump rises and lifts the liquid. As a result, the maximum and minimum loads on the hanger are reduced by more than 2 times.

Keywords: rod pump unit; pump compressor pipe; well pump; rod string; wireline hanger; deformation; stroke length; inertial forces; frictional forces; hydraulic press; hydraulic transmission mechanism; dynamometer card.

Date submitted: 19.04.2024     Date accepted: 14.11.2024     Date published: 21.11.2024

In existing sucker-rod pumping units the lower end of the rod string which hanged from the wireline hanger is directly connected with the plunger of the well pump. So, the plunger of the well pump is moving up and down according to the movement of the hanger after the deformation is ended and then the fluid is raised to the surface from the formation. The wireline hanger is affected by the weights of the rod string, the lifted fluid column and the dynamic loads when it is in an upward movement. To reduce the loads on the wireline hanger and increase the operating efficiency of the pumping unit, a hydraulic transmission mechanism operating on the principle of the hydraulic press was used in the intermediate part of the rod string. The rod string hanged from the wireline hanger is connected to the rod-shaped piston of the hydraulic transmission mechanism, and the groove-shaped piston of the mechanism is connected to the rod string of the well pump plunger. Due to the installed hydraulic transmission mechanism in the rod string, the plunger of the pump moves downwards in the upward movement of the rod string, and vice versa in the downward movement of the rod string, the plunger of the pump rises and lifts the liquid. As a result, the maximum and minimum loads on the hanger are reduced by more than 2 times.

Keywords: rod pump unit; pump compressor pipe; well pump; rod string; wireline hanger; deformation; stroke length; inertial forces; frictional forces; hydraulic press; hydraulic transmission mechanism; dynamometer card.

Date submitted: 19.04.2024     Date accepted: 14.11.2024     Date published: 21.11.2024

References

  1. Basos, G. Yu., Valovskiy, K. V., Zairov, B. F., et al. (2010). The results of dedicated sucker rod pumping units application in Nurlatneft NGDU. Oil Industry, 9, 96-99.
  2. Solodkiy, E. M., Kazantsev, V. P., Hudorozhkov, A. V., Churin, A. V. (2020). Influence of the electric drive control system on the sucker-rod pump energy efficiency. Oil Industry, 2, 50-53.
  3. Balboshin, V. A., Garifov, K. M., Kadyrov, A. Kh., et al. (2024). Sucker rod pump valves for horizontal wells which can be deployed in dual completion units. Oil Industry, 7, 61-64.
  4. Grigoriev, S. L., Demidov, O. V. (2010). The hydraulic drive of sucker rod well pump represents a new technology for oil production. Territory Neftegaz, 10, 59.
  5. Pogrebnaya, I. A., Mikhaylova, S. V. (2020). Use of hydraulic drive of sucker-rod pump 120-6-24 in oil and gas industry as an alternative to import substitution of small-sized pumps. IOP Conference Series: Materials Science and Engineering, 1111, 012046.
  6. Zhou, R. F., Bai, L., Dong, K. X., Dai, Y. X. (2011). Suspended load prediction on sucker rod suspension load based on artificial neural network. Advanced Materials Research, 217–218, 1040–1043.
  7. Composite catalog of oil field equipment and services, 2002-2003. Gulf Energy.
  8. (2011). Pumping unit. General catalog Lufkin. Oil Field Products Group.
  9. Molchanov, A. G., Pevnev, V. G., Tarasov, K. V. (2013). The rod well pump is driven hydraulically and features inertial counterbalancing. Territory Neftegaz, 4, 52-55.
  10. Molchanov, A. G., Pevnev, V. G., Tarasov, K. V. (2013). The use of a hydraulic drive of sucker rod pump with an inertial balance. Oil Industry, 5, 105-107.
  11. Guo, B., Lyons, W. C., Ghalambor, A. (2007). Petroleum production engineering. A computer-assisted approach. Gulf Professional Publishing, Elsevier Inc.
  12. Caicedo, S., Araujo, A. (2008). Evaluating artificial lifted wells with resistive downhole heating through an integrated numerical model. In: Rio Oil and Gas 2008 Expo and Conference, Rio de Janeiro, RJ (Brazil).
  13. Zhiqian, H. (1995). The progress and prospect of equipment manufacturing in China for oil and gas exploration and production. SPE-29974-MS. In: The International Meeting on Petroleum Engineering, Beijing, China.
  14. Saputelli, L. A. (1998). Low pressure gas collection system: solving environmental problems. PETSOC-98-18. In: The Annual Technical Meeting, Calgary, Alberta.
  15. Radwan, A., Naderi, K., Ramanathan, R. (2022). Tailored metal oxide nanoparticles-based fluids for production enhancement via engineered uplift pressure mechanism: multi-basin case studies. URTEC-3722922-MS. In: The SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA.
  16. Garcia-James, C. J. (2016). Experience with progressive cavity pumps in the soldado field, offshore Trinidad. SPE-180802-MS. In: The SPE Trinidad and Tobago Section Energy Resources Conference, Port of Spain, Trinidad and Tobago.
  17. Suleimanov, B. A., Dyshin, O. A. (2013). Application of discrete wavelet transform to the solution of boundary value problems for quasi-linear parabolic equations. Applied Mathematics and Computation, 219, 7036-7047.
  18. Abbasov, E. M., Dyshin, O. A., Suleimanov, B. A. (2008). Application of wavelet transforms to the solution of boundary value problems for linear parabolic equations. Computational Mathematics and Mathematical Physics, 48(2), 251-268.
  19. Suleimanov, B. A. (1997). Slip effect during filtration of gassed liquid. Colloid Journal, 59(6), 749-753.
  20. Suleimanov, B. A., Veliyev, E. F., Vishnyakov, V. V. (2022). Nanocolloids for petroleum engineering: Fundamentals and practices. John Wiley & Sons.
  21. Suleimanov, B. A. (2011). Mechanism of slip effect in gassed liquid flow. Colloid Journal, 73(6), 846–855.
  22. Abbasov, E. M., Dyshin, O. A., Suleimanov, B. A. (2009). Wavelet method for solving second-order quasilinear parabolic equations with a conservative principal part. Computational Mathematics and Mathematical Physics, 49(9), 1554-1566.
  23. Suleimanov, B. A., Azizov, Kh. F., Abbasov, E. M. (1996). Slippage effect during gassed oil displacement. Energy Sources, 18(7), 773–779.
  24. Suleimanov, B. A., Abbasov, E. M., Sisenbayeva, M. R. (2017). Mechanism of gas saturated oil viscosity anomaly near to phase transition point. Physics of Fluids, 29, 012106.
Read more Read less

DOI: 10.5510/OGP2024SI101006

E-mail: FazilQ.Hasanov@socar.az


Sh. G. Sharipov, P. G. Romanenkov, E. S. Shepshelevich, A. D. Abramov, I. H. Iksanov, M. P. Romanenkova, O. N. Nagaev, D. O. Valov

Ufa State Petroleum Technological University, Ufa, Russia

Improvement of the information system of remote monitoring of the technical condition of gas turbine engines


The implementation in the information system for remote monitoring (ISRM) of the technical condition of gas turbine engines (GTE) of a new method (hereinafter referred to as the Method) for early detection of deterioration in the technical condition of the rotor supports of a power turbine (PT) GTE is considered. The method is based on an expert rule, which analyzes the parameters of the engine oil system. The distinctive advantages of the Method are the possibility of use when atmospheric conditions and engine operating modes change, as well as its versatility. Automatic monitoring of the technical condition of the GTE in real-time mode and automated analysis of changes in the historical values of its operating parameters depending on time, performed in the ISRM, make it possible to timely identify the deterioration of the technical condition of engine components and carry out their maintenance or repair. The structure and graphical user interface of the ISRM are considered. The algorithm that implements the Method is described in detail, the script in which this algorithm is programmed, and the graphical user interface created to display and analyze the results of applying the Method are described. The procedure for testing the ISRM functionality created for the application of the Method using the historical values of the GTE parameters is outlined. The implementation of the Method in ISRM showed its openness and versatility for the implementation of new methods for diagnosing equipment using algorithms for analytical processing of the values of its operating parameters.

Keywords: remote monitoring of the technical condition; gas turbine engine; real-time mode; new method for assessing technical condition; implementation of the method; power turbine rotor support; early detection of faults; reduction of operating costs.

Date submitted: 18.03.2024     Date accepted: 03.12.2024     Date published: 11.12.2024

The implementation in the information system for remote monitoring (ISRM) of the technical condition of gas turbine engines (GTE) of a new method (hereinafter referred to as the Method) for early detection of deterioration in the technical condition of the rotor supports of a power turbine (PT) GTE is considered. The method is based on an expert rule, which analyzes the parameters of the engine oil system. The distinctive advantages of the Method are the possibility of use when atmospheric conditions and engine operating modes change, as well as its versatility. Automatic monitoring of the technical condition of the GTE in real-time mode and automated analysis of changes in the historical values of its operating parameters depending on time, performed in the ISRM, make it possible to timely identify the deterioration of the technical condition of engine components and carry out their maintenance or repair. The structure and graphical user interface of the ISRM are considered. The algorithm that implements the Method is described in detail, the script in which this algorithm is programmed, and the graphical user interface created to display and analyze the results of applying the Method are described. The procedure for testing the ISRM functionality created for the application of the Method using the historical values of the GTE parameters is outlined. The implementation of the Method in ISRM showed its openness and versatility for the implementation of new methods for diagnosing equipment using algorithms for analytical processing of the values of its operating parameters.

Keywords: remote monitoring of the technical condition; gas turbine engine; real-time mode; new method for assessing technical condition; implementation of the method; power turbine rotor support; early detection of faults; reduction of operating costs.

Date submitted: 18.03.2024     Date accepted: 03.12.2024     Date published: 11.12.2024

References

  1. Achouch, M., Dimitrova, M., Ziane, K., et al. (2022). On predictive maintenance in Industry 4.0: overview, models, and challenges. Applied Sciences, 12, 8081.
  2. Cakir, M., Guvenc, M. A., Mistikoglu, S. (2021). The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system. Computers & Industrial Engineering, 151, 106948.
  3. Semivelichenko, E. A., Sharipov, Sh. G., Romanenkov, P. G., et al. (2020). Gas turbine installation remote monitoring system. RU Patent 2726317.
  4. Romanenkov, P. G., Shepshelevich, E. S., Iksanov, I. H., et al. (2024). Method for operating gas turbine unit. RU Patent 2816352.
  5. Romero, D., Vernadat, F. (2016). Enterprise information systems state of the art: Past, present and future trends. Computers in Industry, 79, 3-13.
  6. Jasko, S., Skrop, A., Holczinger, T., et al. (2020). Development of manufacturing execution systems in accordance with Industry 4.0 requirements: A review of standard- and ontology-based methodologies and tools. Computers in Industry, 123, 103300.
  7. CODRA Ordinal Software. SCADA and MES: the pyramids secret. https://www.ordinal.fr/en/topics/scada-andmes-the-pyramids-secret
  8. Sunyaev, A., Dehling, T., Strahringer, S. et al. (2023). The future of enterprise information systems. Business & Information Systems Engineering, 65(6), 731-751.
  9. Kumar, S., Suhaib, M., Asjad, M. (2020). Industry 4.0: complex, disruptive, but inevitable. Management and Production Engineering Review, 11(1), 43-51.
  10. Chohan, B. S., Xu, X., Lu, Y. (2022). MES Dynamic interoperability for SMEs in the Factory of the Future perspective. Procedia CIRP, 107, 1329-1335.
  11. Tien, K.-W., Prabhu, V. (2024). Phase-type distribution models for performance evaluation of condition-based maintenance. Production & Manufacturing Research, 12(1), 2380723.
  12. Arts, J., Boute, R. N., Loeys, S., Staden, H. E. (2024). Fifty years of maintenance optimization: Reflections and perspectives. European Journal of Operational Research, Available online 5 July, In Press.
  13. Karki, B. R., Porras, J. (2021). Digitalization for sustainable maintenance services: A systematic literature review. Digital Business, 1(2), 100011.
  14. Gorski, E. G., Loures, E. F., Santos, E. A., et al. (2022). Towards a smart workflow in CMMS/EAM systems: An approach based on ML and MCDM. Journal of Industrial Information Integration, 26, 100278.
  15. Errandonea, I., Beltran, S., Arrizabalaga, S. (2020). Digital Twin for maintenance: A literature review. Computers in Industry, 123, 103316.
  16. Dinter, R., Tekinerdogan, B., Catal, C. (2022). Predictive maintenance using digital twins: A systematic literature review. Information and Software Technology, 151, 107008.
  17. Paschou, T., Rapaccini, M., Adrodegari, F., Saccani, N. (2020). Digital servitization in manufacturing: A systematic literature review and research agenda. Industrial Marketing Management, 89, 278-292.
  18. Lopes, I. S., Figueiredo, M. C., Sa, V. (2020). Criticality evaluation to support maintenance management of manufacturing systems. International Journal of Industrial Engineering and Management, 11(1), 3-18.
  19. 6.4.3.1. Single Exponential Smoothing. (2012). NIST/SEMATECH e-Handbook of Statistical Methods. https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc431.htm
  20. Nahmias, S., Olsen, T. L. (2015). Production and operations analysis. (7th ed.). Long Grove, Illinois: Waveland Press, Inc.
Read more Read less

DOI: 10.5510/OGP2024SI101009

E-mail: olegnagaev@yandex.ru


L. F. Aslanov1,2, U. L. Aslanli1,2, F. L. Aslanov2

1«Oilgasscientificreserchproject» Institute, SOCAR, Baku, Azerbaijan; 2Azerbaijan University of Architecture and Construction, Baku, Azerbaijan

 Construction of foundations for reservoirs in the Caspian sea


The development of oil and gas fields located in the shelf zones of seas and oceans is associated with the creation of a special infrastructure. This required the development and construction of special hydraulic structures, facilities and the development of technological processes that meet the conditions for organizing work on drilling, production and transportation of oil and gas from offshore fields. Ensuring the normal operation of offshore wells is associated with the development of the field, which requires the laying of underwater product pipelines, the construction of tank farms, installations for oil preparation and loading it into tankers, gas compressor and pumping stations, special installations for water purification and pumping it into the reservoir, etc. The intermediate section is made in the form of separate vertical piles, connected at the top by beams with reinforced concrete flooring laid on them. Large section bored piles are widely used in the various kinds of buildings. In the present article the manufacturing technique and principles for determining the bearing capacity of large section bored piles with a “hard core” were considered. Technological parameters of large section bored piles with a “hard core” are given in manufacturing in urban area hear existing buildings, and structures and also in the construction of offshore structures. The data on determination of bearing capacity of single bored piles with a “hard core” are discussed. It was established that these piles bear much larger horizontal and vertical loads, and are the most cost effective than precast and bored piles.

Keywords: Bored piles, “hard core”, soil resistance, casing pipe inventory, hanging pile grillage.

Date submitted: 01.10.2024     Date accepted: 10.12.2024     Date published: 18.12.2024

The development of oil and gas fields located in the shelf zones of seas and oceans is associated with the creation of a special infrastructure. This required the development and construction of special hydraulic structures, facilities and the development of technological processes that meet the conditions for organizing work on drilling, production and transportation of oil and gas from offshore fields. Ensuring the normal operation of offshore wells is associated with the development of the field, which requires the laying of underwater product pipelines, the construction of tank farms, installations for oil preparation and loading it into tankers, gas compressor and pumping stations, special installations for water purification and pumping it into the reservoir, etc. The intermediate section is made in the form of separate vertical piles, connected at the top by beams with reinforced concrete flooring laid on them. Large section bored piles are widely used in the various kinds of buildings. In the present article the manufacturing technique and principles for determining the bearing capacity of large section bored piles with a “hard core” were considered. Technological parameters of large section bored piles with a “hard core” are given in manufacturing in urban area hear existing buildings, and structures and also in the construction of offshore structures. The data on determination of bearing capacity of single bored piles with a “hard core” are discussed. It was established that these piles bear much larger horizontal and vertical loads, and are the most cost effective than precast and bored piles.

Keywords: Bored piles, “hard core”, soil resistance, casing pipe inventory, hanging pile grillage.

Date submitted: 01.10.2024     Date accepted: 10.12.2024     Date published: 18.12.2024

References

  1. Abbasov, E. M., Dyshin, O. A., Suleimanov, B. A. (2008). Wavelet method for solving the unsteady porous-medium flow problem with discontinuous coefficients. Computational Mathematics and Mathematical Physics, 48(12), 2194-2210.
  2. Abbasov, E. M., Dyshin, O. A., Suleimanov, B. A. (2008). Application of wavelet transforms to the solution of boundary value problems for linear parabolic equations. Computational Mathematics and Mathematical Physics, 48(2), 251-268.
  3. Abbasov, E. M., Dyshin, O. A., Suleimanov, B. A. (2009). Wavelet method for solving second-order quasilinear parabolic equations with a conservative principal part. Computational Mathematics and Mathematical Physics, 49(9), 1554-1566.
  4. Abo-Youssef, A., Morsy, M. S., ElAshaal, A., et al. (2021). Numerical modelling of passive loaded pile group in multilayered soil. Innovative Infrastructure Solutions, 6, 101.
  5. Alekseev, A. G., Bezvolev, S. G. (2023). Determination of the bearing capacity of a helical pile using screwing torque considering shaft geometry. Soil Mechanics and Foundation Engineering, 60, 189–197.
  6. Alzabeebee, S., Ismael, B. H., Keawsawasvong, S., et al. (2024). An evolutionary polynomial computing of pile capacity using the results of high-strain dynamic test. Transportation Infrastructure Geotechnology, 11, 3160–3177.
  7. Angurana, D. I., Yadav, J. S., Khatri, V. N. K. (2024). Estimation of uplift capacity of helical pile resting in cohesionless soil. Transportation Infrastructure Geotechnology, 11, 833–864.
  8. Armaghani, D. J., Harandizadeh, H., Momeni, E.m et al. (2022). An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity. Artificial Intelligence Review, 55, 2313–2350.
  9. Aslanov, L. F., Aslanli, U. L. (2024). Study of the stress-strain state of the pontoon element of the support block. SOCAR Proceedings, 2, 115-121.
  10. Aslanov, L. F., Aslanov, F. L. (2024). Choosing an effective design solution for fixing offshore hydro-technical structures to shelf ground. In: Çiner, A., et al. Recent research on geotechnical engineering, remote sensing, geophysics and earthquake seismology. MedGU 2021. Advances in Science, Technology & Innovation. Springer, Cham.
  11. Aslanov, L. F., Aslanli, U. L. (2024). Study of marine hydraulic structures under seismic effects. In: Ksibi, M., et al. Recent advances in environmental science from the Euro-Mediterranean and Surrounding regions (4th Edition). EMCEI 2022. Advances in Science, Technology & Innovation. Springer, Cham.
  12. Aslanov, L. F. (2015). Wave interaction of offshore structure and shelf soil through large section piles with a ‘hard core’ on the half-space model. Oil Industry, 2, 78-81.
  13. Aslanov, L. F. (2015). Interaction between large cross-sections bored piles with ‘hard core’ under dynamic loads and shelf soils. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 5, 21-25.
  14. Aslanov, L. F. (2016). Reflected wafes from bored or CFA piles of large section in the offshore soils. Oil Industry, 7, 112-116.
  15. Bokov, I. A., Fedorovskii, V. G. (2021) Taking into account the soil depth inhomogeneity in calculation of the piles settlement. Soil Mechanics and Foundation Engineering, 58, 267–272.
  16. Buragadda, V., Orekanti, E. R. (2024). Predicting the allowable settlement of reinforced soil foundations: a laboratory study. Geotechnical and Geological Engineering, 42, 2271–2291.
  17. Campione, G. (2024). Influence of shallow foundations on the response of steel wind towers. International Journal of Civil Engineering, 22, 1309–1319.
  18. Chaabani, H., Mesmoudi, S., Boutahar, L., et al. (2022). Buckling of porous FG sandwich plates subjected to various non-uniform compressions and resting on Winkler–Pasternak elastic foundation using a finite element model based on the high-order shear deformation theory. Acta Mechanica, 233, 5359–5376.
  19. Chen, S., Chen, X. (2024). Estimation of pile bearing capacity using hybrid models based on modified radial base function. Multiscale and Multidisciplinary Modeling, Experiments and Design, 7, 5347-5363.
  20. Chen, S. S., Kao, C. J., Shi, J. Y. (2023). Dynamic analysis of cylindrical foundations under torsional loading via generic discrete-element models simulating soil stratum. Scientific Reports, 13, 19163. 
  21. Chimdesa, F. F., Chimdesa, F. F., Jilo, N. Z., et al. (2023). Numerical analysis of pile group, piled raft, and footing using finite element software PLAXIS 2D and GEO5. Scientific Reports, 13, 15875.
  22. Dubrakova, K., Bulgakov, A., Bock, T. (2023). Determination of homogeneous foundation’s settlement based on the integral estimation method. In: Vatin, N., Pakhomova, E. G., Kukaras, D. (eds). Modern problems in construction. Lecture notes in civil engineering, Vol 287. Springer, Cham.
  23. Elgridly, E. A., Fayed, A. L., Ali, A. A. A. F. (2022). Efficiency of pile groups in sand soil under lateral static loads. Innovative Infrastructure Solutions, 7, 26.
  24. Fattah, M. Y., al-Omari, R. R., Kallawi, A. M. (2024). Load sharing between shaft and tip of pile group in saturated and unsaturated soil. Transportation Infrastructure Geotechnology, 11, 2117–2147.
  25. Feng, S. J., Xi, W., Zhang, X. L., et al. (2024). Experimental and numerical investigations on the mechanical response of full-scale PHC pile foundations for solar power generation. Acta Geotechnica, 19, 5293–5314.
  26. Gang, L. (2024). Improving the estimation of the pile bearing capacity via hybridization technique based on adaptive network based fuzzy inference. Journal of Ambient Intelligence and Humanized Computing, 15, 4043-4060.
  27. Gotman, N. Z., Evdokimov, A. G. (2023). Calculation of bridge-support pile foundations taking karst deformations into account in the base. Soil Mechanics and Foundation Engineering, 60, 401–409.
  28. Hadi, A. I., Sunaryo, Farid, M., et al. (2024). Analysis of earthquake-prone areas based on the seismic wave velocity, young's modulus, shear modulus, and Poisson’s ratio for disaster risk reduction in Bengkulu city, Indonesia. Natural Hazards, 120, 14683-14702.
  29. Hajiyev, M., Damirov, M. (2023). Stress-strain state and bearing capacity of compressed reinforced concrete elements of anular section. Architectural Studies, 9(22), 35-46.
  30. Hajiyev, M. A., Guliyev, F. M., Ovsii, D. (2023). Calculation of the normal force and bending moment from compression stresses in concrete. Lecture Notes in Civil Engineering, 299, 167-174.
  31. Hassona, F., Hakeem, B. M. (2024). Numerical investigation of the carrying capacity of single polyurethane foam pile in clay and sand soils. Journal of Umm Al-Qura University for Engineering and Architecture, 15, 78–92.
  32. He, P., Deshpande, V., Newson, T. (2023). Undrained capacity of shallow octagonal foundations under combined VHM loading. Geotechnical and Geological Engineering, 41, 1275–1286.
  33. He, L., Chen, X., Wang, Z., et al. (2022). A case study on the bearing characteristics of a bottom uplift pile in a layered foundation. Scientific Reports, 12, 22457.
  34. Golafzani, H. S., Eslami, A., Chenari, J. R., et al. (2022). Optimized selection of axial pile bearing capacity predictive methods based on multi-criteria decision-making (MCDM) models and database approach. Soft Computing, 26, 5865–5881.
  35. Hoang, V. N. V., Thanh, P. T. (2024). Influence of non-uniform elastic foundations on free vibration behavior of nanocomposite plates interacting with a fluid environment based on a novel shear deformation theory. Acta Mechanica, 235, 4607–4637.
  36. Huded, P. M., Dash, S. R. (2024). Pile foundation in alternate layered liquefiable and non-liquefiable soil deposits subjected to earthquake loading. Earthquake Engineering and Engineering Vibration, 23, 359–376.
  37. Jassim, A., Ganjian, N., Eslami, A. (2022). Design and fabrication of frustum confining vessel apparatus for model pile testing in saturated soils. Innovative Infrastructure Solutions, 7, 280.
  38. Jin, J., Liu, Y., Liu, F. X., et al. (2022). Effects of combined loadings on the bearing performance of a typical pile foundation. Mechanics of Solids, 57, 352–369.
  39. Kalinin, A., Prolygin, A., Aleksandrova, N. (2022). Substantiation of the method for calculating soil deformation modulus. in: mottaeva, a. (eds) technological advancements in construction. In: Mottaeva, A. (eds) Technological advancements in construction. Lecture notes in civil engineering, Vol. 180. Springer, Cham.
  40. Kong, Gq., Liu, Zp., Wang, Lh., et al. (2023). Experimental studies on the behavior of a single shaped pile under oblique pullout loads. Acta Geotechnica, 18, 4733–4746.
  41. Kumar, M., Kumar, D. R., Khatti, J., et al. (2024). Prediction of bearing capacity of pile foundation using deep learning approaches. Frontiers of Structural and Civil Engineering, 18, 870–886.
  42. Lai, V. Q., Shiau, J., Keawsawasvong, S., et al. (2022). Bearing capacity of ring foundations on anisotropic and heterogenous clays: FEA, NGI-ADP, and MARS. Geotechnical and Geological Engineering, 40, 3913–3928.
  43. Magade, S. B., Ingle, R. K. (2021). Analysis methods for pile foundation: a critical review of the literature and recommended suggestions. Innovative Infrastructure Solutions, 6, 14.
  44. Mahmood, A., Alshameri, B., Khalid, M. H., et al. (2022). Comparative study of various interpretative methods of the pile load test. Innovative Infrastructure Solutions, 7, 102.
  45. Majumder, M., Chakraborty, D. (2021). Three-dimensional numerical analysis of under-reamed pile in clay under lateral loading. Innovative Infrastructure Solutions, 6, 55.
  46. Maralapalle, V. C., Hegde, R. (2024). Experimental and empirical study on piles socketed ınto the rock. Soil Mechanics and Foundation Engineering, 61, 56–61.
  47. Mellal, F., Bennai, R., Avcar, M., et al. (2023). On the vibration and buckling behaviors of porous FG beams resting on variable elastic foundation utilizing higher-order shear deformation theory. Acta Mechanica, 234, 3955–3977.
  48. Mirsepahi, M., Nayeri, A., Lajevardi, S. H., et al. (2021). Effect of multi-faced twin tunneling in different depths on a single pile. Innovative Infrastructure Solutions, 6, 42.
  49. Murali, A. K., Tran, K. M., Haque, A., et al. (2022). Experimental and numerical investigation of the load-bearing mechanisms of piles socketed in soft rocks. Rock Mechanics and Rock Engineering, 55, 5555–5576.
  50. Nguyen, T. H., Nguyen, K. V. T., Ho, V. C., et al. (2024). Efficient hybrid machine learning model for calculating load-bearing capacity of driven piles. Asian Journal of Civil Engineering, 25, 883–893.
  51. Pantelidis, L. (2021). The equivalent modulus of elasticity of soil mediums for designing shallow foundations. Geotechnical and Geological Engineering, 39, 3863–3873.
  52. Pham, T. A., Nguyen, D. H., Duong, Ha. T. (2022). Development of deep learning neural network for estimating pile bearing capacity. In: Ha-Minh, C., Tang, A. M., Bui, T.Q., et al. (eds). CIGOS 2021. Emerging technologies and applications for green infrastructure. Lecture notes in civil engineering, Vol 203. Springer, Singapore.
  53. Roohi, M., Faeli, M., Irani, M., et al. (2021). Calculation of land subsidence and changes in soil moisture and salinity using remote sensing techniques. Environmental Earth Sciences, 80, 423.
  54. Sharafutdinov, R. F., Razvodovskii, D. E., Zakatov, D. S. (2024). Single bored pile settlement prediction taking into account the elastic-plastic behavior of the soil. Soil Mechanics and Foundation Engineering, 61, 213–222.
  55. Shen, Y. (2024). Optimized systems of multi-layer perceptron predictive model for estimating pile-bearing capacity. Journal of Engineering and Applied Science, 71, 52.
  56. Shubham, K., Metya, S., Sinha, A. K. (2024). Surrogate model-based prediction of settlement in foundation over cavity for reliability analysis. Transportation Infrastructure Geotechnology, 11, 1294–1320.
  57. Sidorov, V. V., Le, D. A. (2024). Investigations of soil models used to study soil base liquefaction. Soil Mechanics and Foundation Engineering, 61, 138–144.
  58. Singh, M., Viladkar, M. N., Shekhawat, P. S., et al. (2022). Bearing capacity of strip footings on jointed rock mass. Arabian Journal of Geosciences, 15, 1579.
  59. Sofiyev, A. H., Turan, F., Kadıoglu, F., et al. (2022). Influences of two-parameter elastic foundations on nonlinear free vibration of anisotropic shallow shell structures with variable parameters. Meccanica, 57, 401–414.
  60. Suleimanov, B. A., Dyshin, O. A. (2013). Application of discrete wavelet transform to the solution of boundary value problems for quasi-linear parabolic equations. Applied Mathematics and Computation, 219, 7036-7047.
  61. Suleimanov, B. A, Ismailov, F. S., Dyshin, O. A. (2014). Statistical modeling of life cycle of oil reservoir development. Journal of the Japan Petroleum Institute, 57(1), 47-57.
  62. Suleimanov, B. A., Veliyev, E. F., Aliyev, A. A. (2023). Oil and gas well cementing for engineers. John Wiley & Sons.
  63. Swarnkar, D. C., Singh, A. K., Shubham, K. (2024). Application of ANN for prediction of settlement of ring foundation. Signal, Image and Video Processing, 18, 7537–7554.
  64. Verumandy, K., Arulrajah, A., Mirzababaei, M., et al. (2024). Static load testing of instrumented screw piles in soft soil deposits. International Journal of Geosynthetics and Ground Engineering, 10, 10.
  65. Wibowo, L. S. B., Pradono, M. H., Fauzi, H. A., et al. (2024). Seismic behavior for reinforced concrete building due to foundation settlement on different soil types. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 48, 861–870.
  66. Wu, W., Wang, G., Wang, L., et al. (2024). Analysis of surface vibration response induced by PHC piles driven into non-uniform saturated soil layer. Mechanics of Solids, 59, 813-830.
  67. Wu, W., Wang, G., Wang, L., et al. (2024). Dynamic response analysis of non-uniform unsaturated soil layer roadbed under uniform moving load. Mechanics of Solids, 59, 280–296.
  68. Xiao, L., Du, K. (2024). Evaluation of driven piles’ load capacity by optimization-based prediction algorithms. International Journal on Interactive Design and Manufacturing (IJIDeM), Published 09 June.
  69. Yaychi, B. M., Esmaeili-Falak, M. (2024). Estimating axial bearing capacity of driven piles using tuned random forest frameworks. Geotechnical and Geological Engineering, 42, 7813-7834.
  70. Yuan, W. H., Wang, H. C., Li, Y. J., et al. (2024). Large deformation assessment of the bearing capacity factor for rigid footing: effect of soil heterogeneity. Computational Particle Mechanics, 11, 2923-2941.
  71. Zhang, A., Dieudonné, A. C. (2023). Effects of carbonate distribution inhomogeneity on the improvement level of bio-cemented sands: A DEM study. In: Barla, M., Di Donna, A., Sterpi, D., Insana, A. (eds). Challenges and Innovations in Geomechanics. IACMAG 2022. Lecture notes in civil engineering, Vol 288. Springer, Cham.
  72. Zhao, X., Shen, Y., Melentijevic, S., et al. (2024). The load-bearing mechanism of rock-socketed piles considering rock fragmentation. Rock Mechanics and Rock Engineering, 57, 10071-10097.
  73. Zhu, J., Zhu, D. (2022). Calculation of building settlement induced by tunneling based on an equivalent beam model. Soil Mechanics and Foundation Engineering, 59, 411–416.
  74. Aslanov, L. F., Aslanli, U. L. (2024). Determination of load-bearing capacity of piles used in stationary offshore platforms. SOCAR Proceedings, 1, 116-123.
  75. Aslanov, L. F. (2022). Optimization of the calculation of the piles of fixed offshore platforms. In: El-Askary, H., Erguler, Z. A., Karakus, M., Chaminé, H. I. (eds). Research developments in geotechnics, geo-informatics and remote sensing. CAJG 2019. Advances in science, technology & innovation. Springer, Cham.
  76. Aslanov, L. F., Aslanov, F. L. (2024). Some tasks of increasing and identifying the reserves of the bearing capacity of anchor fastenings of offshore fixed platforms. In: Bezzeghoud, M., et al. Recent research on geotechnical engineering, remote sensing, geophysics and earthquake seismology. MedGU 2022. Advances in science, technology & innovation. Springer, Cham.
Read more Read less

DOI: 10.5510/OGP2024SI101031

E-mail: latif.aslanov@bk.ru


S. R. Sakhibgareev1, A. D. Badikova1, I. G. Ibragimov1, M. A. Tsadkin2, E. F. Gumerova2

1Ufa State Petroleum Technological University, Ufa, Russia; 2Ufa University of Science and Technology, Ufa, Russia

Catalytic cracking of heavy petroleum feedstocks on zeolite-containing catalysts promoted with metal chloride additives


The article presents the research results of the thermocatalytic transformation of fuel oil of West-Siberian oil in a batch reactor at atmospheric pressure, the active component of the catalyst is a metal chloride complex on a carrier, which is used as a deeply decationized Y zeolite in the H-form (HYmmm ‒ decationization degree 94%) with a hierarchical structure: micro-, meso-, macroporous. The process of applying the active additive to the surface of the carrier was carried out through a high-temperature melt of a metal chloride complex based on metal chlorides VIII MPT in order to increase the acidity of the cracking catalyst. To determine the structure and composition of the catalysts, X-ray diffraction analysis, scanning electron microscopy (SEM), and differential thermal analysis (DTA) were used. The basic diagram of the laboratory installation is described and the experimental results are compared when varying the process temperature and the volumetric feed rate of raw materials. It has been shown that the use of a zeolite-containing HYmmm catalyst with a 5% addition of an active metal chloride complex leads to an increase in the yield of light cracking products (boiling point ‒ 350 °C) at a relatively low temperature of 450 °C and a volumetric feed rate of 3 h-1 with 30.7% wt. up to 47.3 wt.% at a volumetric feed rate of 2 h-1. It was also found that the highest yield of lower (C2-C4) olefins is 20.7% wt. observed at a temperature of 550 °C and a volumetric feed rate of 1 h-1. Based on the results of experimental data, the process of thermocatalytic transformation of fuel oil was optimized using “desirability” profiling in the STATISTICA software by modeling the characteristics of the process and equations describing these mathematical models were obtained.

Keywords: catalytic cracking; fuel oil of West-Siberian oil; metal chloride complex; gasoline fraction; light hydrocarbons; X-ray diffraction analysis; scanning electron microscopy; differential thermal analysis.

Date submitted: 18.07.2023     Date accepted: 25.04.2024     Date published: 22.07.2024

The article presents the research results of the thermocatalytic transformation of fuel oil of West-Siberian oil in a batch reactor at atmospheric pressure, the active component of the catalyst is a metal chloride complex on a carrier, which is used as a deeply decationized Y zeolite in the H-form (HYmmm ‒ decationization degree 94%) with a hierarchical structure: micro-, meso-, macroporous. The process of applying the active additive to the surface of the carrier was carried out through a high-temperature melt of a metal chloride complex based on metal chlorides VIII MPT in order to increase the acidity of the cracking catalyst. To determine the structure and composition of the catalysts, X-ray diffraction analysis, scanning electron microscopy (SEM), and differential thermal analysis (DTA) were used. The basic diagram of the laboratory installation is described and the experimental results are compared when varying the process temperature and the volumetric feed rate of raw materials. It has been shown that the use of a zeolite-containing HYmmm catalyst with a 5% addition of an active metal chloride complex leads to an increase in the yield of light cracking products (boiling point ‒ 350 °C) at a relatively low temperature of 450 °C and a volumetric feed rate of 3 h-1 with 30.7% wt. up to 47.3 wt.% at a volumetric feed rate of 2 h-1. It was also found that the highest yield of lower (C2-C4) olefins is 20.7% wt. observed at a temperature of 550 °C and a volumetric feed rate of 1 h-1. Based on the results of experimental data, the process of thermocatalytic transformation of fuel oil was optimized using “desirability” profiling in the STATISTICA software by modeling the characteristics of the process and equations describing these mathematical models were obtained.

Keywords: catalytic cracking; fuel oil of West-Siberian oil; metal chloride complex; gasoline fraction; light hydrocarbons; X-ray diffraction analysis; scanning electron microscopy; differential thermal analysis.

Date submitted: 18.07.2023     Date accepted: 25.04.2024     Date published: 22.07.2024

References

  1. Khadzhiev, S. N., Kadiev, Kh. M., Kadiev, M. Kh. (2014). Synthesis and properties of nanoscale systems ‒ effective catalysts for the hydroconversion of heavy oil feedstock. Petrochemistry, 54(5), 327-351.
  2. Ivanova, A. S., Korneeva, E. V., Bukhtiyarova, G. A., Nuzhdin, A. L. (2011). Hydrocracking of vacuum gas oil in the presence of supported Ni-W catalysts. Kinetics and Catalysis, 3, 457-469.
  3. Meng, X., Xu, C., Gao, J., Zhang, Q. (2004). Effect of catalyst to oil weight ratio on gaseous product distribution during heavy oil catalytic pyrolysis. Chemical Engineering and Processing: Process Intensification, 43(8), 65-70.
  4. Ershov, D. S., Khafizov, A. R., Mustafin, I. A., et al (2017). Current state and development trends of the catalytic cracking process. Fundamental Research, 12, 282-285.
  5. Kondrasheva, N. K., Vasil'ev, V. V., Boitsova, A. A. (2017). Study of feasibility of producing high-quality petroleum coke from Heavy Yarega. Oil Chemistry and Technology of Fuels and Oils, 6, 663-669.
  6. Lappas, A. A., Iatridis, D. K., Papapetrou, M. C., et al (2015). Feedstock and catalyst effects in fluid catalytic cracking ‒ Comparative yields in bench scale and pilot plant reactors. Chemical Engineering Journal, 278, 140-149.
  7. Nefedov, B. K., Radchenko, E. D., Aliev, R. R. (1992). Catalysts of deep oil refining processes. Moscow: Chemistry.
  8. Morozov, M. A., Akimov, A. S., Fedushchak, T. A., et al. (2018). Cracking of heavy hydrocarbon feedstock in the presence of cobalt. Catalysis in Industry, 2, 33-38.
  9. Naranov, E., Gerzeliev, I., Dementev, K., Kolesnichenko, N. V. (2019). The role of zeolite catalysis in modern petroleum refining: Contribution from domestic technologies. Petroleum Chemistry, 59(3), 247-261.
  10. Sakhibgareev, S. R., Tsadkin, M. A., Badikova, A. D., Gumerova, E. F. (2022). Catalysts for destruction of hydrocarbon raw materials based on barium chloride. Известия вузов. Химия и химическая технология, 65(9), 64-73.
  11. Sakhibgareev, S. R., Tsadkin, M. A., Badikova, A. D., et al. (2020). High-temperature catalytic destruction of fuel oil on a modified catalyst based on metal chlorides. Oil Refining and Petrochemistry. Scientific and Technological Achievements and Best Practices, 10, 12-14.
  12. Mirsky, Ya. V., Dorogochinsky, A. Z., Zlotchenko, V. N., Meged, N. F. (1967) Synthetic zeolites and their use in oil refining and petrochemistry. Zeolite catalysts and adsorbents. Grozny: TsNIITEneftekhim.
  13. Ali, M. A., Tatsumi, T., Masuda, T. (2002). Development of heavy oil hydrocracking catalysts using amorphous silicaalumina and zeolites as catalyst supports. Applied Catalysis A: General, 233, 77-90.
  14. Sahu, R., Song, B. J, Im, J. S., et al. (2018). A review of recent advances in catalytic hydrocracking of heavy residues. Journal of Industrial and Engineering Chemistry, 27, 12-24.
  15. Fumoto, E., Sugimoto, Y., Sato, S., Takanohashi, T. (2015). Catalytic cracking of heavy oil with iron oxide-based catalysts using hydrogen and oxygen species from steam. Journal of the Japan Petroleum Institute, 58, 329-335.
  16. Nguyen-Huy, C., Shin, E. (2016). Amelioration of catalytic activity in steam catalytic cracking of vacuum residue with ZrO2-impregnated macro–mesoporous red mud. Fuel, 179, 17-24.
  17. Minsker, K. S., Masagutov, R. M., Kukovitsky, M. M., et al. (1986). Method for obtaining C₃-C₄ – hydrocarbons. SU Patent 1342911.
  18. Enikolopov, N. S., Ivanova, S. P., Berlin, A. A., et al. (1987). Method for obtaining C₄ – hydrocarbons. SU Patent 1216195.
  19. Al-Khattaf, S. (2002). The influence of Y-zeolite unit cell size on the performance of FCC catalysts during gas oil catalytic cracking. Applied Catalysis A: General, 231, 293–306.
Read more Read less

DOI: 10.5510/OGP2024SI100983

E-mail: samat.sax2014@yandex.ru


I. M. Saydakhmedov

Tashkent Institute of Chemical Technology, Tashkent, Uzbekistan

 Mixed diesel fuel with additive sesame oil esters for diesel engines


Currently, the issue of expanding the raw material base to increase the production of diesel fuels and improve their environmental performance is a relevant and important area. One of the types of raw materials for the production of alternative types of biofuels for diesel engines are vegetable oils. The use of bioadditives can reduce the emission of harmful substances compared to the use of petroleum diesel fuels. In this article, the features of using diesel fuels with the addition of mixtures of methyl esters of sesame oil in diesel engines are studied for the first time. Mixtures of fatty acid esters of sesame oil were obtained by direct transesterification of sesame oil with methyl alcohol at a temperature of 80-90 °C in the presence of potassium hydroxide. The results of the study established for the first time that the addition of sesame oil and methyl esters synthesized on its basis as additional components of diesel fuel in the amount of 3 and 5% by weight, respectively, improves the cetane number of diesel fuel by up to 7 points, as well as reduces emissions of harmful substances with exhaust gases by 25% rel. Some weight gain of the fuel is observed ‒ the values of density, kinematic viscosity, concentration of actual resins and iodine number increase. It is shown that the production of diesel fuel with the addition of mixtures of methyl esters of sesame oil allows to significantly expand resources and obtain diesel fuels with improved environmental characteristics.

Keywords: diesel fuel; sesame oil; sesame oil esters; environmental characteristics.

Date submitted: 10.10.2023     Date accepted: 10.12.2024     Date published: 19.12.2024

Currently, the issue of expanding the raw material base to increase the production of diesel fuels and improve their environmental performance is a relevant and important area. One of the types of raw materials for the production of alternative types of biofuels for diesel engines are vegetable oils. The use of bioadditives can reduce the emission of harmful substances compared to the use of petroleum diesel fuels. In this article, the features of using diesel fuels with the addition of mixtures of methyl esters of sesame oil in diesel engines are studied for the first time. Mixtures of fatty acid esters of sesame oil were obtained by direct transesterification of sesame oil with methyl alcohol at a temperature of 80-90 °C in the presence of potassium hydroxide. The results of the study established for the first time that the addition of sesame oil and methyl esters synthesized on its basis as additional components of diesel fuel in the amount of 3 and 5% by weight, respectively, improves the cetane number of diesel fuel by up to 7 points, as well as reduces emissions of harmful substances with exhaust gases by 25% rel. Some weight gain of the fuel is observed ‒ the values of density, kinematic viscosity, concentration of actual resins and iodine number increase. It is shown that the production of diesel fuel with the addition of mixtures of methyl esters of sesame oil allows to significantly expand resources and obtain diesel fuels with improved environmental characteristics.

Keywords: diesel fuel; sesame oil; sesame oil esters; environmental characteristics.

Date submitted: 10.10.2023     Date accepted: 10.12.2024     Date published: 19.12.2024

References

  1. Markov, V. А., Devyanin, S. N., Semyonov, V. G., et al. (2011). Use of vegetable oils and fuels based on them in diesel engines. Moscow: NIC Ingener Ltd, Oniko-M Ltd.
  2. Canakci, M., Van Gerpen, J. (2001). Biodiesel production from oil and fats with high free fatty acids. Transactions of the American Society of Agricultural Engineers, 44(5), 1429-1436.
  3. Devyanin, S. N. (2005). Improving the environmental performance of transport diesel engines using biofuel blends. Health and Safety, 12, 27-33.
  4. Orsik, L. S., Sorokin, N. T., Fedorenko, V. F., et al. (2008). Bioenergetics: global experience and projected growth. Moscow: FGNU Rosinformagrotekh publ.
  5. Kul'chitskii, A. R. (2000). The toxicity of automobile and tractor engines. Vladimir: Vladimir State University publ.
  6. Markov, V. A., Devianin, S. N., Zykov, S. A., Gaidar, S. M. (). Biofuel for internal combustion engines. Moscow: NITs Inzhener publ.
  7. Vasil'ev, I. P. (2009). Impact of vegetable-based fuels on the environmental and economic performance of diesel. Vladimir: Dalia publ.
  8. Patrakhal'tsev, N. N. (2008). Improving the economic and environmental performance of internal combustion engines through the use of alternative fuels. Moscow: RUDN publ.
  9. Babu, A. K., Devaradjane, G. (2003). Vegetable oils and their derivatives as fuels for CI engines: an overview. SAE Technical Paper Series, 2003-01-0767, 1–18.
  10. Yoshimoto, Y., Onodera, M., Tamaki, H. (2001). Performance and emission characteristics of diesel engines fueled by vegetable oils. SAE Technical Paper Series, 2001-01-1807/4227, 1–8.
  11. Spessert, B. M., Arendt, I., Schlelcher, A. (2004). Influence of RME and vegetable oils on exhaust gas and noise emissions of small industrial diesel engines. SAE Technical Paper Series, 2004-32-0070, 1–15.
  12. Lapuerta, M., Armas, O., Ballesteros, R. (2002). Diesel particulate emissions from biofuels derived from spanish vegetable oils. SAE Technical Paper Series, 2002-01-1657, 1–7.
  13. Hamasaki, K., Tajima, H., Takasaki, K., et al. (2001). Utilization of waste vegetable oil methyl ester for diesel fuel. SAE Technical Paper Series, 2001-01-2021, 1–6.
  14. Fedorenko, V. F., Sorokin, N. T., Buklagin, D. S., et al. (2010). Innovative development of alternative energy. Part 1. Moscow: Rosinformagrotekh publ.
  15. Markov, V. A., Devianin, S. N., Spiridonova, L. V. (2015). Experimental research of diesel engine running on oil diesel fuel and linseed oil mixtures. Alternative Fuel Transport, 3, 55–64.
  16. Markov, V. A., Sa Boven, Neverov, V. A., Zykov, S. A. (2017). Mustard oil as an ecological additive to petroleum diesel fuel. Autogas Filling Complex + Alternative Fuel, 16(1), 10–21.
  17. Markov, V. A., Loboda, S. S., In Min. (2017). The use of blends of petroleum diesel fuel and camelina oil as a motor fuel. Alternative Fuel Transport, 5, 29–40.
  18. Abbasov, M. E. (2014). Optimization method. Sankt-Petersburg: VVM publ.
  19. Ogunkunle, O., Ahmed, N. A. (2019). A review of global current scenario of biodiesel adoption and combustion in vehicular diesel engines. Energy Reports, 5, 1560-1579.
  20. Bae, C., Kim, J. (2017). Alternative fuels for internal combustion engines. Proceedings of the Combustion Institute, 36(3), 3389-3413.
Read more Read less

DOI: 10.5510/OGP2024SI101032

E-mail: saydahmedovee@gmail.com


Sh. Z. Tapdiqov1,2, D. B. Taghiyev2

1«Oilgasscientificreserchproject» Institute, SOCAR, Baku, Azerbaijan; 2Institute of Catalysis and Inorganic Chemistry named after Acad. M. Nagiyev, Ministry of Science and Education of the Republic of Azerbaijan, Baku, Azerbaijan

Immobilization and in vitro kinetic controlled release study of doxorubicin from temperature-sensitive mPEG-Ala-Asp polypeptide hydrogel


The loading of the doxorubicin drug into a new polyethylene glycol-polypeptide based temperature-sensitive hydrogel and the investigation of its properties have been carried out. It was determined that during the immobilization of the drug, equilibrium is reached, and saturation occurs within 3 hours in a solution with an initial antibiotic concentration of approximately 4.8 mg/mL. Currently, 84-87 % of the drug is adsorbed, meaning that 100 mg of gel drug capacity was 405.8 mg/gr. The samples were kept in different pH solutions (pH = 2, 7.4, and 8.6) at 37 °C, and the release of doxorubicin was kinetically studied over intervals ranging from 0-300 minutes. It was determined that changes in the ionic strength of the solution lead to a change in the polarity degree of the chemical bond between the hydrogel and doxorubicin. The results of doxorubicin release from the gel were tested against various kinetic models, including zero-order, first-order, Higuchi square root law, Korsmeyer-Peppas, and Hixson-Crowell square root models. When applying the kinetic results of doxorubicin release from the gel to the Korsmeyer-Peppas model, in acidic and neutral mediums, the value of n > 1, which is attributed to the breaking of protein segments in the terminal groups of the matrix. This makes the creation of thin-coated drug formulations from the matrix more advantageous. In an alkaline medium, however, n < 1, indicating that the release follows a non-Fickian mechanism, characterized by both diffusion and erosion. To ensure release in an alkaline medium, it is more appropriate to prepare cylindrical, spherical, or film-type drug forms from the PEG-Ala-Asp gel. Also, since the gel formation time can be adjusted, such systems can be recommended as analogues of injectable thermosetting insulation composites for the oil and gas industry.

Keywords: temperature sensitive; polyethyenglycol; polypeptide gel; release kinetic; doxorubicine.

Date submitted: 10.10.2024     Date accepted: 11.12.2024     Date published: 18.12.2024

The loading of the doxorubicin drug into a new polyethylene glycol-polypeptide based temperature-sensitive hydrogel and the investigation of its properties have been carried out. It was determined that during the immobilization of the drug, equilibrium is reached, and saturation occurs within 3 hours in a solution with an initial antibiotic concentration of approximately 4.8 mg/mL. Currently, 84-87 % of the drug is adsorbed, meaning that 100 mg of gel drug capacity was 405.8 mg/gr. The samples were kept in different pH solutions (pH = 2, 7.4, and 8.6) at 37 °C, and the release of doxorubicin was kinetically studied over intervals ranging from 0-300 minutes. It was determined that changes in the ionic strength of the solution lead to a change in the polarity degree of the chemical bond between the hydrogel and doxorubicin. The results of doxorubicin release from the gel were tested against various kinetic models, including zero-order, first-order, Higuchi square root law, Korsmeyer-Peppas, and Hixson-Crowell square root models. When applying the kinetic results of doxorubicin release from the gel to the Korsmeyer-Peppas model, in acidic and neutral mediums, the value of n > 1, which is attributed to the breaking of protein segments in the terminal groups of the matrix. This makes the creation of thin-coated drug formulations from the matrix more advantageous. In an alkaline medium, however, n < 1, indicating that the release follows a non-Fickian mechanism, characterized by both diffusion and erosion. To ensure release in an alkaline medium, it is more appropriate to prepare cylindrical, spherical, or film-type drug forms from the PEG-Ala-Asp gel. Also, since the gel formation time can be adjusted, such systems can be recommended as analogues of injectable thermosetting insulation composites for the oil and gas industry.

Keywords: temperature sensitive; polyethyenglycol; polypeptide gel; release kinetic; doxorubicine.

Date submitted: 10.10.2024     Date accepted: 11.12.2024     Date published: 18.12.2024

References

  1. Bromberg, L. (1998). Novel family of thermogelling materials via C–C bonding between poly (acrylic acid) and poly(ethyleneoxide)-b-poly (propylene oxide)-b-poly(ethylene oxide). Journal of Physical Chemistry, 102, 1956–1963.
  2. Suleimanov, B. А., Gurbanov, А. Q., Tapdiqov, Sh. Z. (2022). Isolation of water inflow into the well with a thermosetting gel-forming. SOCAR Proceedings, 4, 21-26.
  3. Bajpai, A. K., Shukla, S. K., Bhanu, S., et al. (2008). Responsive polymers in controlled drug delivery. Progress Polymer Science, 33, 1088–1118.
  4. Cong, H., Li, L., Zheng, S. (2013). Poly(N-isopropylacrylamide)-block-poly (acrylic acid) hydrogels: synthesis and rapid thermoresponsive properties. Polymer, 54, 1370–1380.
  5. Oh, H. J., Joo, M. K., Sohn, Y. S., et al. (2008). Block sequence affects thermosensitivity and nano-assembly: PEG-L-PADL-PA and PEG-DL-PA-L-PA block copolymers. Macromolecules, 41, 8204–8209.
  6. Suleimanov, B. A., Abdullaev, V. Dz., Tapdiqov, Sh. Z., Mamedov, S. M. (2024). Gel-forming composition for isolation of water influx into well. Eurasian Patent EA046438.
  7. Roberts, J. J., Elder, R. M., Neumann, A. J., et al. (2014). Interaction of hyaluronan binding peptides with glycosaminoglycans in poly (ethylene glycol) hydrogels. Biomacromolecules, 15, 1132–1141.
  8. Cai, L., Dinh, C. B., Heilshorn, S. C. (2014). One-pot synthesis of elastin-like polypeptide hydrogels with grafted VEGFmimetic peptides. Biomaterial Science, 2, 757–765.
  9. Jeyong, Y., Joo, M. K., Bahk, K. H., et al. (2009). Enzymatically degradable temperature-sensitive polypeptide as a new in-situ gelling biomaterial. Biomacromolecules, 10, 2476-2481.
  10. Ji-Yu, L., Po-Liang, L., Yuan-Kai, L., et al. (2016). A poloxamer-polypeptide thermosensitive hydrogel as a cell scaffold and sustained release depot. Polymer Chemistry, 7, 2976-2985.
  11. Narayan, B., Hassna, R. R., Jonathan, G., et al. (2005). PEG-grafted chitosan as an injectable thermosensitive hydrogel for sustained protein release. Journal of Controlled Release, 103, 609–624
  12. Constantinou, A. P., Georgiou, T.K. (2016). Thermoresponsive gels based on ABC triblock copolymers: effect of the length of the PEG side group. Polymer Chemistry, 7, 2045-2056.
  13. Huynh, C. T., Lee, D. S. (2015). Controlled release. In: Kobayashi, S., Müllen, K. (eds). Encyclopedia of polymeric nanomaterials. Berlin, Heidelberg: Springer.
  14. Arcamone, F. (1981). Doxorubicin: anticancer, antibiotics. In: Medicinal chemistry. A series of monographs, Vol. 17, de Stevens, G. (ed.). New York: Academic Press.
  15. Beijnen, J. H., van der Houwen, O. A. G. J., Underberg, W. J. M. (1986). Aspects of the degradation kinetics of doxorubicin in aqueous solution. Internationul Journal of Pharmaceutics, 32, 123-131.
  16. Nakanishi, T., Fukushima, S., Okamoto, K., et al. (2001). Development of the polymer micelle carrier system for doxorubicin. Journal of Controlled Release, 74, 295–302.
  17. Miyauchi, M., Yamada, N., Okano, T., et al. (1990). Polymer micelles as novel drug carrier: Adriamycin-conjugated poly (ethylene glycol)- poly (aspartic acid) block copolymer. Journal of Controlled Released, 11, 269–278.
  18. Yokoyama, M., Okano, T., Sakurai, Y., et al. (1991). Toxicity and antitumor activity against solid tumors of micelle-forming polymeric anticancer drug and its extremely long circulation in blood. Cancer Research, 51, 3229–3236.
  19. Yokoyama, M., Okano, T., Sakurai, Y., et al. (1994). Improved synthesis of adriamycin-conjugated poly (ethylene oxide)-poly (aspartic acid) block copolymer and formation of unim- odal micellar structure with controlled amount of physically entrapped adriamycin. Journal of Controlled Released, 32, 269–277.
  20. Chen, P., Qiu, M., Deng, Ch., et al. (2015). pH-responsive chimaeric pepsomes based on asymmetric poly(ethylene glycol)-b-poly(L-glutamic acid) triblock copolymer for efficient loading and active ıntracellular delivery of doxorubicin hydrochloride. American Chemical Society, 16, 1322-1330.
  21. Tapdigov, Sh. Z. (2021). Encapsulation and in vitro controlled release of doxycycline in temperature-sensitive hydrogel composed of polyethyleneglycol–polypeptide (L-alanine–co–L-aspartate). Journal of Biomimetics, Biomaterials and Biomedical Engineering, 49, 119-129.
  22. Du, J. Z., Du, X. J., Mao, C. Q., Wang, J. (2011). Tailor-made dual pH-sensitive polymer-doxorubicin nanoparticles for efficient anticancer drug delivery. Journal of the American Chemical Society, 133, 17560-17563.
  23. Tapdigov, Sh. Z. (2020). Designing poly-N-vinylpyrrolidone based hydrogel and applied Higuchi, Korsmeyer-Peppas, Hixson-Crowell kinetic models for controlled release of doxorubicine. Chemical Problems, 17(2), 207-213.
  24. Tapdigov, Sh. Z. (2021). Electrostatic and hydrogen bond immobilization trypsine onto pH-sensitive N- vinylpyrrolidone and 4-vinylpyridine co-grafted chitosan hydrogel. Macromolecules Research, 29(2), 120-128.
  25. Tapdigov, Sh. Z. (2021). The bonding nature of the chemical interaction between trypsin and chitosan-based carriers in immobilization process depend on entrapped method: A Review. International Journal of Biological Macromolecules, 183, 1676–1696.
Read more Read less

DOI: 10.5510/OGP2024SI101033

E-mail: shamo.z.tapdiqov@socar.az