Machine learning for recovery factor estimation of an oil reservoir: A tool for derisking at a hydrocarbon asset evaluation

Petroleum - Tập 8 - Trang 278-290 - 2022
Ivan Makhotin1, Denis Orlov1, Dmitry Koroteev1, Evgeny Burnaev1, Aram Karapetyan2, Dmitry Antonenko2
1Skolkovo Institute of Science and Technology, Moscow, Russia
2JSC Zarubezhneft, Moscow, Russia

Tài liệu tham khảo

Rui, 2017, A quantitative oil and gas reservoir evaluation system for development, J. Nat. Gas Sci. Eng., 42, 31, 10.1016/j.jngse.2017.02.026 Demirmen, 2007, Reserves estimation: the challenge for the industry, J. Petrol. Technol., 59, 80, 10.2118/103434-JPT Li, 2020, Applications of artificial intelligence in oil and gas development, Arch. Comput. Methods Eng., 1 Guthrie, 1955, The use of multiple-correlation analyses for interpreting petroleum-engineering data Arps, 1967, A statistical study of recovery efficiency, Bull. Dent., 14 Sharma, 2010, Classification of oil and gas reservoirs based on recovery factor: a data-mining approach Mahmoud, 2019, Estimation of oil recovery factor for water drive sandy reservoirs through applications of artificial intelligence, Energies, 12, 3671, 10.3390/en12193671 Han, 2018, A hybrid pso-svm-based model for determination of oil recovery factor in the low-permeability reservoir, Petroleum, 4, 43, 10.1016/j.petlm.2017.06.001 Aliyuda, 2019, Machine-learning algorithm for estimating oil-recovery factor using a combination of engineering and stratigraphic dependent parameters, Interpretation, 7, 10.1190/INT-2018-0211.1 Belyaev, 2016, Gtapprox: surrogate modeling for industrial design, Adv. Eng. Software, 102, 29, 10.1016/j.advengsoft.2016.09.001 2016 Burnaev, 2014, Efficiency of conformalized ridge regression, 605 Roy, 2012, Robustness of random forests for regression, J. Nonparametric Statistics, 24, 993, 10.1080/10485252.2012.715161 Gómez-Ríos, 2017, A study on the noise label influence in boosting algorithms: adaboost, gbm and xgboost, 268 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Meinshausen, 2006, Quantile regression forests, J. Mach. Learn. Res., 7, 983 Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Stat., 1189 Vovk, 2005 Burnaev, 2016, Conformalized kernel ridge regression, 45 Hartigan, 1979, Algorithm as 136: a k-means clustering algorithm, J. Roy. Stat. Soc. C (Appl. Stat.), 28, 100 Jain, 2010, Data clustering: 50 years beyond k-means, Pattern Recogn. Lett., 31, 651, 10.1016/j.patrec.2009.09.011 Arthur, 2007, The advantages of careful seeding, 1027 Li, 2017, Application of t-sne to human genetic data, J. Bioinf. Comput. Biol., 15, 1750017, 10.1142/S0219720017500172 Maaten, 2008, Visualizing data using t-sne, J. Mach. Learn. Res., 9, 2579 Twala, 2008, Good methods for coping with missing data in decision trees, Pattern Recogn. Lett., 29, 950, 10.1016/j.patrec.2008.01.010 Orlov, 2019, Advanced analytics of self-colmatation in terrigenous oil reservoirs, J. Petrol. Sci. Eng., 182, 106306, 10.1016/j.petrol.2019.106306 Erofeev, 2019, Prediction of porosity and permeability alteration based on machine learning algorithms, Transport Porous Media, 128, 677, 10.1007/s11242-019-01265-3 Kotsiantis, 2013, Decision trees: a recent overview, Artif. Intell. Rev., 39, 261, 10.1007/s10462-011-9272-4 Fetkovich, 1996, Useful concepts for decline curve forecasting, reserve estimation, and analysis, SPE Reservoir Eng., 11, 13, 10.2118/28628-PA Jin, 2019 Temirchev, 2020, Deep neural networks predicting oil movement in a development unit, J. Petrol. Sci. Eng., 184, 106513, 10.1016/j.petrol.2019.106513 Simonov, 2018, Application of machine learning technologies for rapid 3d modelling of inflow to the well in the development system Temirchev, 2019, Reduced order reservoir simulation with neural-network based hybrid model Naderi, 2016, Nonlinear risk optimization approach to water drive gas reservoir production optimization using doe and artificial intelligence, J. Nat. Gas Sci. Eng., 31, 575, 10.1016/j.jngse.2016.03.069 Panja, 2018, Application of artificial intelligence to forecast hydrocarbon production from shales, Petroleum, 4, 75, 10.1016/j.petlm.2017.11.003