Enhancing Oil Recovery Predictions by Leveraging Polymer Flooding Simulations and Machine Learning Models on a Large-Scale Synthetic Dataset

Timur Imankulov, Yerzhan Kenzhebek (), Samson Dawit Bekele and Erlan Makhmut
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Timur Imankulov: National Engineering Academy of the Republic of Kazakhstan, Almaty 050010, Kazakhstan
Yerzhan Kenzhebek: Department of Computer Science, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan
Samson Dawit Bekele: National Engineering Academy of the Republic of Kazakhstan, Almaty 050010, Kazakhstan
Erlan Makhmut: Department of Computer Science, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan

Energies, 2024, vol. 17, issue 14, 1-21

Abstract: 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 ( R 2 ) of 0.909, and the dense neural network and cascade-forward neural network achieved R 2 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.

Keywords: enhanced oil recovery; artificial neural network; polymer flooding; oil recovery factor; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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