Using Machine Learning Algorithms to Solve Polymer Flooding Problem

Abstract

Summary The application of different methods of machine learning in the oil and gas industry is becoming relevant. The data–driven approach makes it possible to build excellent oil prediction models to increase oil recovery. This article discusses machine learning algorithms for solving the problem of polymer injection into an oil reservoir. The problem of supervised learning, which is one of the classes of machine learning problems, is considered. Our problem belongs to the class of regression problems in terms of machine learning methods. The considered generated data from the mathematical model were used for the training and test set. To build a machine learning model, such parameters of the oil production problem as porosity, viscosity of the oil phase, polymer injection ratio, absolute rock permeability and oil recovery factor were used. The value of the oil recovery factor was chosen as the output parameter of the regression model. Over 350 thousand generated data were applicated to implement multiple regression methods. Different regression algorithms were developed and tested, and it was also found that for our synthetic data, the considered models train quite well and predict the value of the oil recovery factor. An artificial neural network with multiple hidden layers with optimally selected hyperparameters was built. The hyperparameters of the artificial neural network were optimally selected. To prevent overfitting, the early stopping function was used, where training stops at the number of epochs without improvement. The tensorflow deep learning library was used to implement regression algorithms for predicting the oil recovery factor. In this way, it is supposed that the data-based different regression algorithms reviewed in the article can be valuable for predicting the oil recovery factor using engineering data from diffrerent oil fields during processing stages.

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