Effect of Reservoir Heterogeneity on Well Placement Prediction in CO2-EOR Projects Using Machine Learning Surrogate Models: Benchmarking of Boosting-Based Algorithms

Effect of Reservoir Heterogeneity on Well Placement Prediction in CO2-EOR Projects Using Machine Learning Surrogate Models: Benchmarking of Boosting-Based Algorithms

T. Esfandi, S. Sadeghnejad, A. Jafari

Carbon capture and storage (CCS) during enhanced oil recovery (EOR) in underground reservoirs offer both environmental and economic benefits. This study explores the efficiency of five machine learning boosting algorithms (AdaBoost, CatBoost, Gradient Boosting, LightGBM, and XGBoost) to achieve accurate well placement during CO2-EOR. The research considers various reservoir scenarios with different geological heterogeneity levels and various parameters (well locations, well distance in inverted five-spot patterns, pattern angle, and flow rates) are explored using a compositional reservoir simulator. The results demonstrate that LightGBM outperformed the other algorithms with the lowest error and fastest run time.

Geoenergy Science and Engineering, 233, 212564.
Corresponding Author: Saeid Sadeghnejad

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