Kazakhstan journal for oil & gas industryKazakhstan journal for oil & gas industry2707-42262957-806XKMG Engineering10802110.54859/kjogi108021Review ArticleApplication of proxy models for oil reservoirs performance predictionZhetruovZh. T.zh.zhetruov@niikmg.kzhttps://orcid.org/0000-0002-0562-8265ShayakhmetK. N.k.shayakhmet@niikmg.kzKarsybayevKuat K.k.karsybayev@niikmg.kzBulakbayAzamat M.a.bulakbay@niikmg.kzKulzhanovaSara B.s.kulzhanova@niikmg.kzKMG Engineering LLP200720224247561805202212072022Copyright © 2022, Zhetruov Z.T., Shayakhmet K.N., Karsybayev K.K., Bulakbay A.M., Kulzhanova S.B.2022<p><em>The evolution of oil and gas reservoirs development parameters prediction has received new opportunities due to the development of digital technologies and computing power. The idea and first experiments in the use of artificial neural networks for various kinds of applied problems as classification of workover actions, automatic interpretation of geophysical well logging and core analyses results can be considered as an important milestone for the oil industry. The application of machine learning for reservoir development parameters prediction is currently a pressing and unresolved issue. Disputes arising in attempts to industrialize this technology are associated with so-called “black box” – a situation when the constructed model cannot explain physical laws and it is almost impossible to track intermediate results in the process of calculating non-linear dependencies. Given the problems described above, the current best practice is to combine machine learning models and physically meaningful analytical models as described in this paper.</em></p>machine learningartificial neural networksprediction of development parametersмашиналық оқытужасанды нейрондық желілеригеру параметрлерін болжаумашинное обучениеискусственные нейронные сетипрогноз параметров разработки1.Bruce, W.A. An Electrical Device for Analyzing Oil-reservoir Behavior. – Pet. Technol., 1943, 151, р. 112–124. DOI: 10.2118/943112-G.2.Wahl W.; Mullins L.; Barham R.; Bartlett W. Matching the Performance of Saudi Arabian Oil Fields with an Electrical Model. – J. Pet. Technol. 1962, 14, р.1275–1282. DOI: 10.2118/414-PA3.Albertoni A.; Lake L.W. Inferring interwell connectivity only from well-rate fluctuations in waterfloods. – SPE Reserv. Eval. Eng., 2003, 6, р. 6–16. DOI: 10.2118/83381-PA.4.Yousef A.A.; Gentil P.H.; Jensen J.L.; Lake L.W. A Capacitance Model to Infer Interwell Connectivity from Production and Injection Rate Fluctuations. – SPE Reserv. Eval. Eng., 2006, 9, р. 630–646. DOI: 10.2118/95322-PA.5.Sayarpour M., Zuluaga E., Kabir C.S., Lake L.W. The use of capacitance-resistance models for rapid estimation of waterflood performance and optimization. – J. Pet. Sci. Eng., 2009, 69, р. 227–238. DOI: 10.1016/j.petrol.2009.09.006.6.Kaviani D.; Jensen J.L.; Lake L.W. Estimation of interwell connectivity in the case of unmeasured fluctuating bottomhole pressures. – J. Pet. Sci. Eng., 2012, р. 90–91, 79–95. DOI:10.1016/j.petrol.2012.04.008.7.Soroush, M.; Kaviani, D.; Jensen, J.L. Interwell connectivity evaluation in cases of changing skin and frequent production interruptions. – J. Pet. Sci. Eng., 2014, 122, р. 616–630. DOI:10.1016/j.petrol.2014.09.001.8.Zhao H.; Kang, Z.; Zhang X.; Sun H.; Cao L.; Albert C. R. INSIM: A Data-Driven Model for History Matching and Prediction for Waterflooding Monitoring and Management with a Field Application – SPE Reserv. Simul. Symp., February 2015. Doi: https://doi.org/SPE-173213-MS9.Voronoi, G.F. Nouvelles applications des paramètres continus à la théorie de formes quadratiques. – Journal für die reine und angewandte Mathematik, 1908, 134. p. 198—287. DOI: https://doi.org/10.2118/205488-PA