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

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 opt...

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Autores principales:	Beimbet Daribayev, Aksultan Mukhanbet, Yedil Nurakhov, Timur Imankulov
Formato:	article
Lenguaje:	EN RU UK
Publicado:	PC Technology Center 2021
Materias:	buckley-leverett model neural network machine learning architecture metric training Technology (General) T1-995 Industry HD2321-4730.9
Acceso en línea:	https://doaj.org/article/50830689be59400886bfe52725be72c1
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spelling	oai:doaj.org-article:50830689be59400886bfe52725be72c12021-11-04T14:06:45ZImplementation of the solution to the oil displacement problem using machine learning classifiers and neural networks1729-37741729-406110.15587/1729-4061.2021.241858https://doaj.org/article/50830689be59400886bfe52725be72c12021-10-01T00:00:00Zhttp://journals.uran.ua/eejet/article/view/241858https://doaj.org/toc/1729-3774https://doaj.org/toc/1729-4061The 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 problemsBeimbet DaribayevAksultan MukhanbetYedil NurakhovTimur ImankulovPC Technology Centerarticlebuckley-leverett modelneural networkmachine learningarchitecturemetrictrainingTechnology (General)T1-995IndustryHD2321-4730.9ENRUUKEastern-European Journal of Enterprise Technologies, Vol 5, Iss 4 (113), Pp 55-63 (2021)
institution	DOAJ
collection	DOAJ
language	EN RU UK
topic	buckley-leverett model neural network machine learning architecture metric training Technology (General) T1-995 Industry HD2321-4730.9
spellingShingle	buckley-leverett model neural network machine learning architecture metric training Technology (General) T1-995 Industry HD2321-4730.9 Beimbet Daribayev Aksultan Mukhanbet Yedil Nurakhov Timur Imankulov Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks
description	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
format	article
author	Beimbet Daribayev Aksultan Mukhanbet Yedil Nurakhov Timur Imankulov
author_facet	Beimbet Daribayev Aksultan Mukhanbet Yedil Nurakhov Timur Imankulov
author_sort	Beimbet Daribayev
title	Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks
title_short	Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks
title_full	Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks
title_fullStr	Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks
title_full_unstemmed	Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks
title_sort	implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks
publisher	PC Technology Center
publishDate	2021
url	https://doaj.org/article/50830689be59400886bfe52725be72c1
work_keys_str_mv	AT beimbetdaribayev implementationofthesolutiontotheoildisplacementproblemusingmachinelearningclassifiersandneuralnetworks AT aksultanmukhanbet implementationofthesolutiontotheoildisplacementproblemusingmachinelearningclassifiersandneuralnetworks AT yedilnurakhov implementationofthesolutiontotheoildisplacementproblemusingmachinelearningclassifiersandneuralnetworks AT timurimankulov implementationofthesolutiontotheoildisplacementproblemusingmachinelearningclassifiersandneuralnetworks
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Implementation of the solution to the oil displacement problem using machine learning classifiers and neural networks

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