Improved XGBoost model for predicting minimum miscibility pressure in CO2 flooding

CO2-enhanced oil recovery (EOR) is a key technology to improve oil recovery rates and support carbon capture, utilization, and storage (CCUS). Injecting CO2 into reservoirs reduces crude oil viscosity, enhancing its mobility. A critical factor in CO2-EOR is determining the Minimum Miscibility Pressure (MMP). This study aims to construct an MMP prediction model for pure and impure CO2-EOR based on an improved eXtreme Gradient Boosting (XGBoost) algorithm, introducing the critical temperature of the injection gas (Tcm) as a new research variable to explore its impact on MMP. The data used in this study comprises 218 experimental datasets, totaling 2,398 samples, covering both pure and impure CO2-EOR scenarios. Before model construction, important features related to MMP were identified by combining reservoir physical theory with Pearson correlation analysis, and dimensionality reduction was performed using principal component analysis to eliminate redundant information. To optimize the hyperparameters of XGBoost, this study introduced the Particle Swarm Optimization (PSO) algorithm to ensure optimal model parameter configuration. Additionally, Shapley Additive Explanations (SHAP) analysis was employed to evaluate the model's interpretability, resulting in a CO2-MMP prediction model with good explanatory capability. The results indicate that the proposed method achieved an RMSE of 0.2347 and an R2 of 0.9991 for the training set, with an RMSE of 1.0303 and an R2 of 0.9845 for the testing set, outperforming traditional MMP prediction models in various performance metrics. The proposed methodology enables a transparent, efficient, and generalizable approach to MMP prediction, offering valuable insights for CO2-EOR strategy design and supporting more cost-effective and data-driven reservoir development.