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<p>Reliable estimation of the minimum miscibility pressure (MMP) is a critical requirement for the successful design of gas injection processes in enhanced oil recovery, as miscibility strongly controls displacement efficiency and recovery performance. Direct laboratory measurements of MMP are expensive and time-intensive, while traditional empirical correlations often fail to account for the complex interactions between fluid composition and reservoir conditions, leading to limited predictive reliability. To overcome these challenges, an interpretable data-driven framework based on a stacking ensemble learning strategy is developed for MMP prediction. The framework combines Random Forest, Gradient Boosting, Support Vector Regression, and XGBoost models and incorporates polynomial feature expansion together with recursive feature elimination to explicitly capture nonlinear and interaction effects among gas composition, oil composition, and thermodynamic variables. Model performance was assessed using an independent test dataset and evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). The optimized ensemble model demonstrates a substantial improvement over individual base learners, achieving an RMSE of 2.21, an MAE of 1.317, and an R² of 0.912. Beyond predictive accuracy, model interpretability was enhanced through SHapley Additive exPlanations and partial dependence analysis. The results reveal that the interaction between reservoir temperature and the molecular weight of heavy oil fractions exerts the strongest influence on MMP, followed by key compositional interactions involving nitrogen and carbon dioxide. These trends are physically consistent with established miscibility and phase-behavior principles. The main contribution of this work lies in the integration of an optimized stacking ensemble with interaction-focused feature engineering and explainable artificial intelligence techniques. This combination enables accurate, transparent, and physically meaningful MMP predictions, advancing beyond previous studies that emphasize either accuracy or interpretability alone. The proposed framework offers a practical and reliable tool for supporting gas injection design and decision-making in diverse reservoir systems.</p>

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Advanced ensemble modeling and explainable AI for predicting miscibility dynamics in gas-oil systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月17日

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  • Mohammadali Ahmadi

摘要

Reliable estimation of the minimum miscibility pressure (MMP) is a critical requirement for the successful design of gas injection processes in enhanced oil recovery, as miscibility strongly controls displacement efficiency and recovery performance. Direct laboratory measurements of MMP are expensive and time-intensive, while traditional empirical correlations often fail to account for the complex interactions between fluid composition and reservoir conditions, leading to limited predictive reliability. To overcome these challenges, an interpretable data-driven framework based on a stacking ensemble learning strategy is developed for MMP prediction. The framework combines Random Forest, Gradient Boosting, Support Vector Regression, and XGBoost models and incorporates polynomial feature expansion together with recursive feature elimination to explicitly capture nonlinear and interaction effects among gas composition, oil composition, and thermodynamic variables. Model performance was assessed using an independent test dataset and evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). The optimized ensemble model demonstrates a substantial improvement over individual base learners, achieving an RMSE of 2.21, an MAE of 1.317, and an R² of 0.912. Beyond predictive accuracy, model interpretability was enhanced through SHapley Additive exPlanations and partial dependence analysis. The results reveal that the interaction between reservoir temperature and the molecular weight of heavy oil fractions exerts the strongest influence on MMP, followed by key compositional interactions involving nitrogen and carbon dioxide. These trends are physically consistent with established miscibility and phase-behavior principles. The main contribution of this work lies in the integration of an optimized stacking ensemble with interaction-focused feature engineering and explainable artificial intelligence techniques. This combination enables accurate, transparent, and physically meaningful MMP predictions, advancing beyond previous studies that emphasize either accuracy or interpretability alone. The proposed framework offers a practical and reliable tool for supporting gas injection design and decision-making in diverse reservoir systems.