Petroleum Science >2025,??Issue1:??- DOI: https://doi.org/10.1016/j.petsci.2024.09.015
Carbon dioxide storage and cumulative oil production predictions in unconventional reservoirs applying optimized machine-learning models Open?Access
文章信息
作者：Shadfar Davoodi, Hung Vo Thanh, David A. Wood, Mohammad Mehrad, Sergey V. Muravyov, Valeriy S. Rukavishnikov
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引用方式：Shadfar Davoodi, Hung Vo Thanh, David A. Wood, Mohammad Mehrad, Sergey V. Muravyov, Valeriy S. Rukavishnikov, Carbon dioxide storage and cumulative oil production predictions in unconventional reservoirs applying optimized machine-learning models, Petroleum Science, Volume 22, Issue 1, 2025, Pages 296-323, https://doi.org/10.1016/j.petsci.2024.09.015.
文章摘要
Abstract: To achieve carbon dioxide (CO2) storage through enhanced oil recovery, accurate forecasting of CO2 subsurface storage and cumulative oil production is essential. This study develops hybrid predictive models for the determination of CO2 storage mass and cumulative oil production in unconventional reservoirs. It does so with two multi-layer perceptron neural networks (MLPNN) and a least-squares support vector machine (LSSVM), hybridized with grey wolf optimization (GWO) and/or particle swarm optimization (PSO). Large, simulated datasets were divided into training (70%) and testing (30%) groups, with normalization applied to both groups. Mahalanobis distance identifies/eliminates outliers in the training subset only. A non-dominated sorting genetic algorithm (NSGA-II) combined with LSSVM selected seven influential features from the nine available input parameters: reservoir depth, porosity, permeability, thickness, bottom-hole pressure, area, CO2 injection rate, residual oil saturation to gas flooding, and residual oil saturation to water flooding. Predictive models were developed and tested, with performance evaluated with an overfitting index (OFI), scoring analysis, and partial dependence plots (PDP), during training and independent testing to enhance model focus and effectiveness. The LSSVM-GWO model generated the lowest root mean square error (RMSE) values (0.4052 MMT for CO2 storage and 9.7392 MMbbl for cumulative oil production) in the training group. That trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group (RMSE of 0.6224 MMT for CO2 storage and 12.5143 MMbbl for cumulative oil production). PDP analysis revealed that the input features “area” and “porosity” had the most influence on the LSSVM-GWO model's prediction performance. This paper presents a new hybrid modeling approach that achieves accurate forecasting of CO2 subsurface storage and cumulative oil production. It also establishes a new standard for such forecasting, which can lead to the development of more effective and sustainable solutions for oil recovery.
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Keywords: Hybrid machine learning; Least-squares support vector machine; Grey wolf optimization; Feature selection; Carbon dioxide storage; Enhanced oil recovery