Enhancing Oil Recovery Predictions by Leveraging Polymer Flooding Simulations and Machine Learning Models on a Large-Scale Synthetic Dataset
Imankulov T. Kenzhebek Y. Bekele S.D. Makhmut E.
July 2024Multidisciplinary Digital Publishing Institute (MDPI)
Energies
2024#17Issue 14
Polymer flooding is a prominent enhanced oil recovery process that is widely recognized for its cost-effectiveness and substantial success in increasing oil production. In this study, the Buckley–Leverett mathematical model for polymer flooding was used to generate more than 163,000 samples that reflect different reservoir conditions using seven input parameters. We introduced artificial noise into the dataset to simulate real-world conditions and mitigate overfitting. Seven classic machine learning models and two neural networks were trained on this dataset to predict the oil recovery factor based on the input parameters. Among these, polynomial regression performed best with a coefficient of determination ((Formula presented.)) of 0.909, and the dense neural network and cascade-forward neural network achieved (Formula presented.) scores of 0.908 and 0.906, respectively. Our analysis included permutation feature importance and metrics analysis, where key features across all models were identified, and the model’s performance was evaluated on a range of metrics. Compared with similar studies, this research uses a significantly larger and more realistic synthetic dataset that explores a broader spectrum of machine learning models. Thus, when applied to a real dataset, our methodology can aid in decision-making by identifying key parameters that enhance oil production and predicting the oil recovery factor given specific parameter values.
artificial neural network , enhanced oil recovery , machine learning , oil recovery factor , polymer flooding
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National Engineering Academy of the Republic of Kazakhstan, Almaty, 050010, Kazakhstan
Department of Computer Science, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Joldasbekov Institute of Mechanics and Engineering, Almaty, 050010, Kazakhstan
National Engineering Academy of the Republic of Kazakhstan
Department of Computer Science
Joldasbekov Institute of Mechanics and Engineering
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