Release:2021. Vol. 7. № 2 (26)
About the authors:Vitaly P. Kosyakov, Cand. Sci. (Phys.-Math.), Senior Researcher, Tyumen Branch of the Khristianovich Institute of Theoretical and Applied Mechanics of the Siberian Branch of the Russian Academy of Sciences; Associate Professor, Department of Oil and Gas Flow Metering, University of Tyumen; eLibrary AuthorID, Web of Science ResearcherID; email@example.com; ORCID: 0000-0002-2297-408X
This article presents the methodology involving the combined use of machine learning elements and a physically meaningful filtration model. The authors propose using a network of radial basis functions for solving the problem of restoring hydraulic conductivity in the interwell space for an oil field. The advantage of the proposed approach in comparison with classical interpolation methods as applied to the problems of reconstructing the filtration-capacitive properties of the interwell space is shown. The paper considers an algorithm for the interaction of machine learning methods, a filtration model, a mechanism for separating input data, a form of a general objective function, which includes physical and expert constraints. The research was carried out on the example of a symmetrical element of an oil field. The proposed procedure for finding a solution includes solving a direct and an adjoint problem.
Basniev K. S., Dmitriev N. M., Kanevskaya R. D., Maksimov V. M. 2006. Underground Hydromechanics. Moscow-Izhevsk: Institute of Computer Research. 488 pp. [In Russian]
Zakirov I. S. 2006. Development of the Theory and Practice of Oil Field Development. Moscow, Izhevsk: Institute of Computer Technologies. 356 pp. [In Russian]
Rozhenko A. I., 2018. Comparison of Radial Basis Functions. Sibirskiy zhurnal vychislitelnoy matematiki, vol. 21, no. 3, pp. 273-292. DOI: 10.15372/SJNM20180304 [In Russian]
Broomhead D. H., Lowe D. 1988. “Multivariable functional interpolation and adaptive networks”. Complex Systems: Journal, vol. 2, pp. 321-355.
Ertekin T., Sun Q. 2019. “Artificial intelligence applications in reservoir engineering: a status check”. Energies, vol. 12, art. 2897.
Glorot X., Bordes A., Bengio Y. 2011. “Deep sparse rectifier neural networks”. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, vol. 15, pp. 315-323.
Kosyakov V. P. 2020. “Structural and parametric identification of an aquifer model for an oil reservoir”. Lobachevskii Journal of Mathematics, vol. 41, pp. 1242-1247. DOI: 10.1134/S1995080220070239
Misbahuddin M. 2020. “Estimating Petrophysical properties of shale rock using conventional neural networks CNN”. Society of Petroleum Engineers. DOI: 10.2118/204272-STU
Musakaev E. N., Rodionov S. P., Legostaev D. Yu., Kosyakov V. P. 2009. “Parameter identification for sector filtration model of an oil reservoir with complex structure”. AIP Conference Proceedings, vol. 2125, art. 030113. DOI: 10.1063/1.5117495
Otchere D. A., Arbi Ganat T. O., Gholami R., Ridha S. 2021. “Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models”. Journal of Petroleum Science and Engineering, vol. 200, art. 108182. DOI: 10.1016/j.petrol.2020.108182