Robust ensemble learning frameworks for predicting minimum miscibility pressure in pure nitrogen and gas mixtures containing nitrogen–crude oil systems: Insights from explainable artificial intelligence

Abstract Miscible gas injection techniques, such as nitrogen injection, are among the attractive enhanced oil recovery (EOR) techniques for improving oil recovery factors in oil reservoirs. A key challenge in implementing these techniques is accurately determining the minimum miscibility pressure (MMP). While laboratory experiments offer reliable results, they are costly and time‐consuming, and existing empirical correlations often have moderate accuracy, which limits their practical use. In this study, robust ensemble methods, namely light gradient boosting machine (LightGBM), extra trees (ET), and categorical boosting (CatBoost), were implemented for modelling MMP in pure nitrogen and gas mixtures containing nitrogen–crude oil systems. An extensive experimental database involving 164 data points was used to elaborate on the predictive models. The findings revealed that the proposed ensemble methods achieved outstanding accuracy in training and test datasets, with ET consistently outperforming the other models. The ET model provided the most consistent MMP predictions with a total root mean square error (RMSE) of only 0.3197 MPa and a determination coefficient of 0.9976. Additionally, the ET model exhibited very small RMSE values across a broad range of operational conditions. Furthermore, the Shapley additive explanations (SHAP) method further validated the interpretability of the ET model, allowing for clear insights into the impact of input features. This study underlines the significant potential of machine learning to enhance MMP prediction in pure nitrogen and gas mixtures containing nitrogen–crude oil systems, thereby aiding in the appropriate design of this kind of EOR process and supporting better decision‐making in reservoir management.