Аннотация
Estimating the amount of CO 2 trapped is critical for assessing the feasibility of long-term geological carbon storage (GCS). This study introduces a novel approach that uses engineered dimensionless features to develop a machine learning (ML) framework. A traditional reservoir simulator was used to generate 200-year GCS simulation data. Reservoir and operational parameters were used to derive engineered dimensionless parameters, which were then used as inputs. Based on these dimensionless parameters, the ML model predicts CO 2 trapping indices. Six ML models were trained and evaluated - Random Forest (RF), CatBoost, XGBoost, Light Gradient Boosting Machine (LightGBM), Gradient Boosting Regressor (GBR), and symbolic regression (SR). CatBoost outperformed other models, with R 2 values of 0.94 for supercritical and dissolved CO 2 , and 0.92 for residually trapped and mineralized CO 2 . In addition, the SR model was used to produce closed-form equations for the trapping indices. The SR-derived formulas also demonstrated reasonable accuracy, with test-set R 2 values ranging from approximately 0.87 to 0.92. Shapley Additive Explanations (SHAP) were also used to measure feature importance, and the main predictors for each trapping mechanism were identified. For solubility trapping, brine salinity and injected pore volume were the dominant factors, together contributing around 60% of the impact on the output, whereas residual trapping was governed primarily by residual gas saturation and storage time, with a combined contribution of about two-thirds. For mineral trapping, storage duration and temperature were the dominant features. The analysis also indicated that the supercritical CO 2 fraction was controlled mainly by elapsed storage time and brine salinity, while gravitational effects played a secondary role. In summary, the proposed framework indicates a computationally efficient alternative to full-physics simulations and is suitable for rapid screening and sensitivity analysis within the modeled class of heterogeneous carbonate saline-aquifer systems.