Application of machine learning algorithms for enhanced modeling of a carbonate reef reservoir

Abstract


The development and modeling of carbonate reservoirs with complex structure is an actual task. In conditions of high heterogeneity of reservoir properties due to the peculiarities of formation, frequent change of sedimentation cycles and the presence of diagenetic transformations, there is a high degree of uncertainty in the modeling process and, as a consequence, in the forecast of development indicators. Underestimation of the influence of diagenetic processes on changes in filtration-capacitance properties can have a critical impact on the organization and management of the waterflood system. When studying the geological structure of the Alpha field, intervals of highly permeable reservoirs (up to 18 Darcies) were identified. This study proposes an approach to identify such intervals in order to refine the dynamic model of the field based on machine learning methods. The paper compares the following algorithms: gradient bousting, random forest and support vector method. Based on the results of the study, the optimal algorithms were identified that allow predicting high permeability intervals with a high degree of accuracy. To improve model adaptation to the field development history, it is suggested to use a model trained on core and geophysical well survey data. To take into account the risks associated with highly permeable intervals, when drilling new wells, it is recommended to use a model trained only on geophysical well tests. In this paper, sensitivity analysis was performed when specifying properties for highly permeable intervals - absolute permeability and relative phase permeabilities. The permeability cube of the dynamic model was updated, the model was adapted and calculations on waterflood system optimization were performed. Based on the predictive analysis on the model with highly permeable intervals, a number of measures were proposed to optimize the development system to reduce the risk of water breakthrough in highly permeable intervals. According to the results of the forecast calculations, these measures will provide additional 750 thousand m3 of oil.

Full Text

2

About the authors

S. N Krivoschekov

Perm National Research Polytechnic University

A. A Kochnev

Perm National Research Polytechnic University

I. S Putilov

Perm National Research Polytechnic University

D. O Shirinkin

Perm National Research Polytechnic University

A. N Botalov

Perm National Research Polytechnic University

E. S Ozhgibesov

Perm National Research Polytechnic University

P. O Chalova

Perm National Research Polytechnic University

References

  1. Diagenetic process and their effect on reservoir quality in Miocene carbonate reservoir Offshore, Sarawak, Malaysia / H.T. Janjuhah, A.M.A. Salim, D. Ghosh, A. Wahid // In Proceedings of the International Conference on Integrated Petroleum Engineering and Geosciences. – Singapore, 2016. doi: 10.1007/978-981-10-3650-7_48
  2. Farzaneh, S.A. Using diagenetic processes in facies modeling of a carbonate reservoir / S.A. Farzaneh, A.A. Dehghan, A. Lakzaie // Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. – 2013. – No. 35 (6). – P. 516–528. doi: 10.1080/15567036.2010.504946
  3. Study of the influence of nonlinear dynamic loads on elastic modulus of carbonate reservoir rocks / E. Riabokon, M. Turbakov, E. Kozhevnikov, V. Poplygin, M. Guzev // Energies. – 2021. – Vol. 14, no. 24. doi: 10.3390/en14248559
  4. Progress of research on dolomitization and dolomite reservoir / Q. Huang, W. Liu, Y. Zhang, S. Shi, K. Wang // Advances in Earth Science. – 2015. – No. 30 (5). – P. 539. doi: 10.11867/j.issn.1001-8166.2015.05.0539
  5. A review of development methods and EOR technologies for carbonate reservoirs / Z.X. Xu., S.Y. Li, B.F. Li, D.Q. Chen, Z.Y. Liu, Z.M. Li // Petroleum Science. – 2020. – No. 17. – P. 990–1013. doi: 10.1007/s12182-020-00467-5.
  6. Щербаков, А.А. Оценка эффективности мероприятий по интенсификации добычи нефти (на примере месторождений Соликамской депрессии) / А.А. Щербаков, Г.П. Хижняк, В.И. Галкин // Геология, геофизика и разработка нефтяных и газовых месторождений. – 2019. – № 2. – С. 70–73. doi: 10.30713/2413-5011-2019-2-70-73
  7. Ерофеев, А.А. Оценка условий применения методов обработки кривых восстановления давления скважин в карбонатных коллекторах / А.А. Ерофеев, И.Н. Пономарева, М.С. Турбаков // Инженер-нефтяник. – 2011. – № 3. – С. 12–15.
  8. Production optimization for water flooding in fractured-vuggy carbonate reservoir–From laboratory physical model to reservoir operation / B. Yang, J. He, D. Lyu, H. Tang, J. Zhang, X. Li, J. Zhao // Journal of Petroleum Science and Engineering. – 2020. – No. 184. – P. 106520. doi: 10.1016/j.petrol.2019.106520
  9. Mechanistic Study and Optimization of High Water Cut Phase Management Strategies in Fracture-Vuggy Carbonate Reservoirs with Bottom Water / M. Zhao, X. Xin, G. Yu, R. Hu, Y. Gong // Processes. – 2023. – No. 11. – P. 3135. doi: 10.3390/pr11113135
  10. Waterflooding in giant carbonate reservoir; successes and challenges / S. Mahmood, P. Salazar, X. Zhao, M. Pointing, A. Sayed // In Proceedings of the Abu Dhabi International Petroleum Exhibition and Conference. – Abu Dhabi, UAE, 2017. doi: 10.2118/188532-MS
  11. Integrated Waterflooding Effect Evaluation Methodology for Carbonate Fractured-Vuggy Reservoirs Based on the Unascertained Measure–Mahalanobis Distance Theory / Z. Su, S. Gao, Z. Li, T. Li, N. Kang // Processes. – 2024. – No. 12. – P. 274. doi: 10.3390/pr12020274
  12. Jiao, F. Practice and knowledge of volumetric development of deep fractured-vuggy carbonate reservoirs in Tarim basin NW China / F. Jiao // Petroleum Exploration and Development, – 2019. – No. 46 (3). – P. 576–582. doi: 10.1016/S1876-3804(19)60037-6
  13. Dehghani, K. Modeling of waterflood in a vuggy carbonate reservoir / K. Dehghani, K.A. Edwards, P.M. Harris // In Proceedings of the SPE Annual Technical Conference and Exhibition. – San Antonio, Texas, 1997. doi: 10.2118/38567-MS
  14. Cho, Y. Stochastic discrete fracture network modeling in shale reservoirs via integration of seismic attributes and petrophysical data / Y. Cho // Interpretation. – 2021. – No. 9 (4). – P. SG47–SG58. doi: 10.1190/INT-2020-0210.1
  15. Impact and lessons of using high permeability streaks in history matching a giant offshore Middle East carbonate reservoir / K.M. Brantferger, G.S. Kompanik, H.M. Al-Jenaibi, W.S. Dodge, H. Patel // In Proceedings of the Abu Dhabi International Petroleum Exhibition and Conference. – Abu Dhabi, United Arab Emirates, 2012. doi: 10.2118/161426-MS
  16. High Permeability Streaks Characterisations in Middle East Carbonate / E. Poli, C. Maza, A. Virgone, F. Gisquet, C. Fraisse, P. Cecile // In Proceedings of the International Petroleum Technology Conference. – Doha, Qatar, 2009. doi: 10.2523/IPTC-13385-ABSTRACT
  17. Using Machine Learning to Capture High-Permeability Streaks in Reservoir Models / S. Manish Kumar, P. Humberto, E.J. Obeida, B.J. Houcine, K.K. Chakib, M. Hussein // In Proceedings of the ADIPEC. – Abu Dhabi, UAE, 2022. doi: 10.2118/211661-MS
  18. A systematic review of data science and machine learning applications to the oil and gas industry / Z. Tariq, M.S. Aljawad, A. Hasan, M. Murtaza, E. Mohammed, A. El Husseiny, S. Alarifi, M. Mahmoud, A. Abdulraheem // Journal of Petroleum Exploration and Production Technology. – 2021. – No. 11. – P. 4339–4374. doi: 10.1007/s13202-021-01302-2
  19. Choubey, S. Artificial intelligence techniques and their application in oil and gas industry / S. Choubey, G.P. Karmakar // Artificial Intelligence Review. – 2021. – No. 54 (5). – P. 3665–3683. doi: 10.1007/s10462-020-09935-1
  20. An Anomaly Detection Model for Oil and Gas Pipelines Using Machine Learning / S.S. Aljameel, D.M. Alomari, S. Alismail, F. Khawaher, A.A. Alkhudhair, F. Aljubran, R.M. Alzannan // Computation. – 2022. – No. 10. – P. 138. doi: 10.3390/computation10080138
  21. Application of machine learning and artificial intelligence in oil and gas industry / A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, H. Oza // Petroleum Research. – 2021. – No. 6 (4). – P. 379–391. doi: 10.1016/j.ptlrs.2021.05.009
  22. Fluid and lithofacies prediction based on integration of well-log data and seismic inversion: A machine-learning approach / L. Zhao, C.Zou, Y. Chen, W. Shen, Y. Wang, H. Chen, J. Geng // Geophysics. – 2021. – No. 86 (4). – P. M151–M165. doi: 10.1190/geo2020-0521.1
  23. Machine learning based fluids and lithofacies prediction based on the integration of well logging data and seismic inversion / C. Zou, L. Zhao, Y. Chen, Y. Wang // In Proceedings of the SEG 2020 Workshop: 2nd SEG Borehole Geophysics. – 2020. DOI: 2020.10.1190/bhgp2020-32.1
  24. Talebkeikhah, M. A comparison of machine learning approaches for prediction of permeability using well log data in the hydrocarbon reservoirs / M. Talebkeikhah, Z. Sadeghtabaghi, M. Shabani // Journal of Human, Earth, and Future. – 2021. – No. 2. – P. 82–99. doi: 10.28991/HEF-2021-02-02-01
  25. Wood, D.A. Predicting porosity, permeability and water saturation applying an optimized nearest-neighbour, machine-learning and data-mining network of well-log data / D.A. Wood // Journal of Petroleum Science and Engineering. – 2020. – No. 184. – P. 106587. doi: 10.1016/j.petrol.2019.106587
  26. Miah, M.I. Log data-driven model and feature ranking for water saturation prediction using machine learning approach / M.I. Miah, S. Zendehboudi, S. Ahmed // Journal of Petroleum Science and Engineering. – 2020. – No. 194. – P. 107291. doi: 10.1016/j.petrol.2020.107291
  27. Bestagini, P. A machine learning approach to facies classification using well logs / P. Bestagini, V. Lipari, S. Tubaro // In Proceedings of the SEG International Exposition and Annual Meeting. – Houston, Texas, 2017. doi: 10.1190/segam2017-17729805.1
  28. Salehi, S.M. Automatic identification of formation iithology from well log data: a machine learning approach / S.M. Salehi, B. Honarvar // Journal of Petroleum Science Research. – 2014. – No. 3. – P. 73–82. doi: 10.14355/jpsr.2014.0302.04
  29. Well-Logging based lithology prediction using Machine Learning / Y. Meshalkin, A. Shakirov, D. Orlov, D. Koroteev // Data Science in Oil & Gas. – 2020. – No. 2020 (1). – P. 1–5. doi: 10.3997/2214-4609.202054010
  30. A machine-learning-based approach to assistive well-log correlation / S. Brazell, A.C. Bayeh, M. Ashby, D. Burton // Petrophysics. – 2019. – No. 60 (4). – P. 469–479. doi: 10.30632/PJV60N4-2019a1
  31. Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type / M. Hussain, S. Liu, U. Ashraf, M. Ali, W. Hussain, N. Ali, A. Anees // Energies. – 2022. – No. 15. – P. 4501. doi: 10.3390/en15124501
  32. Permeability Prediction Using Rock-Typing, Flow Zone Indicator and Machine Learning Techniques in a Brownfield Offshore Malaysia / B. Kantaatmadja, F. Kasim, W.N.S. Zainudin, E. Elsebakhi, E. Jr, A. Ali // In Proceedings of the International Petroleum Technology Conference, Virtual. – 2021. doi: 10.2523/IPTC-21436-MS
  33. Al Khalifah, H. Permeability prediction and diagenesis in tight carbonates using machine learning techniques / H. Al Khalifah, P.W.J. Glover, P. Lorinczi // Marine and Petroleum Geology. – 2020. – No. 112. – P. 104096. doi: 10.1016/j.marpetgeo.2019.104096
  34. Alameedy, U.S. Evaluating machine learning techniques for carbonate formation permeability prediction using well log data / U.S. Alameedy, A.T. Almomen, N. Abd // The Iraqi Geological Journal. – 2023. – No. 56. – P. 175–187. doi: 10.46717/igj.56.1D.14ms-2023-4-23
  35. High Permeability Streak Identification and Modelling Approach for Carbonate Reef Reservoir / D. Shirinkin, A. Kochnev, S. Krivoshchekov, I. Putilov, A. Botalov, N. Kozyrev, E. Ozhgibesov // Energies. – 2024. – No. 17. – P. 236. doi: 10.3390/en17010236
  36. Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions / D. Otchere, T. Ganat, J. Ojero, B.N. Tackie-Otoo, M. Taki // Journal of Petroleum Science and Engineering. – 2022. – No. 208. – P. 109244. doi: 10.1016/j.petrol.2021.109244
  37. Abbas, M.A. Improving permeability prediction in carbonate reservoirs through gradient boosting hyperparameter tuning / M.A. Abbas, W.J. Al-Mudhafar, D.A. Wood // Earth Science Informatics. – 2023. – No. 16 (4). – P. 3417–3432. doi: 10.1007/s12145-023-01099-0
  38. Rahimi, M. Reservoir facies classification based on random forest and geostatistics methods in an offshore oilfield / M. Rahimi, M.A. Riahi // Journal of Applied Geophysics. – 2022. – No. 201. – P. 104640. doi: 10.1016/j.jappgeo.2022.104640
  39. Amagada, P.U. An Inferable Machine Learning Approach for Reservoir Lithology Characterization Using Drilling Data / P.U. Amagada // In Proceedings of the SPE Annual Technical Conference and Exhibition. – San Antonio, Texas, USA, 2023. doi: 10.2118/217485-STU
  40. A systematic machine learning method for reservoir identification and production prediction / W. Liu, Z. Chen, Y. Hu, L. Xu // Petroleum Science. – 2023. – No. 20 (1). – P. 295–308. doi: 10.1016/j.petsci.2022.09.002
  41. Li, W. Machine learning and data analytics for geoscience applications—Introduction / W. Li, W. Hu, A. Abubakar // Geophysics. – 2020. – No. 85 (4). – P. WAi–WAii. doi: 10.1190/geo2020-0518-spseintro.1
  42. A mini-review of machine learning in big data analytics: Applications, challenges, and prospects / I.K. Nti, J.A. Quarcoo, J. Aning, G.K. Fosu // Big Data Mining and Analytics. – 2022. – No. 5 (2). – P. 81–97. doi: 10.26599/BDMA.2021.9020028
  43. Maganathan T., Senthilkumar S., Balakrishnan V. Machine learning and data analytics for environmental science: a Review, Prospects and Challenges / T. Maganathan, S. Senthilkumar, V. Balakrishnan // IOP Conference Series: Materials Science and Engineering. – 2020. – No. 955 (1). – P. 012107. doi: 10.1088/1757-899X/955/1/012107
  44. Enhanced Reservoir Description: Using Core and Log Data to identify Hydraulic (Flow) Units and Predict Permeability in Uncored Intervals/Wells / J.O. Amaefule, M. Altunbay, D. Tiab, D. Kersey, D. Keelan // In Proceedings of the SPE Annual Technical Conference and Exhibition. – Houston, Texas, 1993. doi: 10.2118/26436-MS
  45. Garrouch, A.A. Exploring the link between the flow zone indicator and key open-hole log measurements: An application of dimensional analysis / A.A. Garrouch, A.A. Al-Sultan // Petroleum geoscience. – 2019. – No. 25. – P. 219–234. doi: 10.1144/petgeo2018-035

Statistics

Views

Abstract - 20

PDF (Russian) - 15

PDF (English) - 4

Refbacks

  • There are currently no refbacks.

Copyright (c) 2025 Krivoschekov S.N., Kochnev A.A., Putilov I.S., Shirinkin D.O., Botalov A.N., Ozhgibesov E.S., Chalova P.O.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies