Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.241858

Keywords:

Buckley-Leverett model, neural network, machine learning, architecture, metric, training

Abstract

The problem of oil displacement was solved using neural networks and machine learning classifiers. The Buckley-Leverett model is selected, which describes the process of oil displacement by water. It consists of the equation of continuity of oil, water phases and Darcy’s law. The challenge is to optimize the oil displacement problem. Optimization will be performed at three levels: vectorization of calculations; implementation of classical algorithms; implementation of the algorithm using neural networks. A feature of the method proposed in the work is the identification of the method with high accuracy and the smallest errors, comparing the results of machine learning classifiers and types of neural networks. The research paper is also one of the first papers in which a comparison was made with machine learning classifiers and neural and recurrent neural networks. The classification was carried out according to three classification algorithms, such as decision tree, support vector machine (SVM) and gradient boosting. As a result of the study, the Gradient Boosting classifier and the neural network showed high accuracy, respectively 99.99 % and 97.4 %. The recurrent neural network trained faster than the others. The SVM classifier has the lowest accuracy score. To achieve this goal, a dataset was created containing over 67,000 data for class 10. These data are important for the problems of oil displacement in porous media. The proposed methodology provides a simple and elegant way to instill oil knowledge into machine learning algorithms. This removes two of the most significant drawbacks of machine learning algorithms: the need for large datasets and the robustness of extrapolation. The presented principles can be generalized in countless ways in the future and should lead to a new class of algorithms for solving both forward and inverse oil problems

Supporting Agency

  • The research is funded by a grant from the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan under the project No. AP09563558

Author Biographies

Beimbet Daribayev, Al-Farabi Kazakh National University

PhD

Department of Computer Science

Aksultan Mukhanbet, Al-Farabi Kazakh National University

Master of Engineering Science

Department of Computer Science

Yedil Nurakhov, Al-Farabi Kazakh National University

Master of Computer Science

Department of Computer Science

Timur Imankulov, Al-Farabi Kazakh National University

PhD

Department of Computer Science

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Published

2021-10-29

How to Cite

Daribayev, B., Mukhanbet, A., Nurakhov, Y., & Imankulov, T. (2021). Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks. Eastern-European Journal of Enterprise Technologies, 5(4 (113), 55–63. https://doi.org/10.15587/1729-4061.2021.241858

Issue

Vol. 5 No. 4 (113) (2021): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2021 Beimbet Daribayev, Aksultan Mukhanbet, Yedil Nurakhov, Timur Imankulov

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