Rapid screening and optimization of CO2 enhanced oil recovery operations in unconventional reservoirs: A case study

Abstract

CO₂ injection not only effectively enhances oil recovery (EOR) but also facilitates CO₂ utilization and storage. Rapid screening and optimization of CO₂-EOR operations is urgently needed for unconventional reservoirs. However, it remains challenging due to a limited understanding of fluid flow in multiscale porous media and the problem complexity invoked by numerous factors. This work developed a new interpretable machine learning (ML) framework to specifically address this issue. Three different methods, namely random forest (RF), support vector regression (SVR), and artificial neural network (ANN), were used to establish proxy models using the data from a specific unconventional reservoir, and the RF model demonstrated a preferable performance. To enhance the interpretability of the established models, the multiway feature importance analysis and Shapley Additive Explanations (SHAP) were proposed to quantify the contribution of individual features to the model output. Based on the results of model interpretability, the genetic algorithm (GA) was coupled with RF (RF-GA model) to optimize the CO₂-EOR process. The proposed framework was validated by comparing the GA-RF predictions with simulation results under different reservoir conditions, which yielded a minimum relative error of 0.34% and an average relative error of 5.3%. The developed interpretable ML method was capable of rapidly screening suitable CO₂-EOR strategies based on reservoir conditions and provided a practical example for field applications.