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.

Keywords

CO₂ enhanced oil recovery / Interpretable machine learning / Feature interaction analysis / Unconventional oil reservoir

CRediT authorship contribution statement

Shuqin Wen: Writing-review & editing, Writing-original draft, Visualization, Validation, Software, Methodology, Conceptualization. Bing Wei: Writing-review & editing, Supervision, Project administration, Funding acquisition, Conceptualization. Junyu You: Writing-review & editing, Writing-original draft, Resources, Methodology, Funding acquisition, Conceptualization. Yujiao He: Writing-review & editing, Visualization, Software. Qihang Ye: Visualization. Jun Lu: Writing-review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors gratefully acknowledge the financial support of National Key Research and Development Program of China (2023YFE0120700), National Natural Science Foundation of China (52274041 and 52304023), Distinguished Young Sichuan Science Scholars(2023NSFSC1954), and Natural Science Foundation of Chongqing (CSTB2022NSCQ-MSX0403), Chongqing Municipal Support Program for Overseas Students Returning for Entrepreneurship and Innovation (2205012980950154).

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