CO₂ storage technology is crucial in addressing climate change by controlling the greenhouse effect. This technology involves the injection of captured CO₂ into deep saline aquifers, where it undergoes a series of reactions, such as structure binding, dissolution, and mineralization, enabling long-term storage. Typically, the CO₂ is maintained in a supercritical state, enhancing its storage efficiency. However, the efficiency can be influenced by the CO₂-water-rock reactions. Many minerals exist in rock, like calcite, dolomite, kaolinite, etc. This study introduces some chemical reactions that occur during the dissolution and mineralization of CO₂. The relationship between solubility and pressure was obtained through solubility fitting. We obtained the initial parameters of the CO₂-water-rock reaction experiment by fitting the data. These parameters can be applied to the mechanism model. This study employs the GEM module of CMG software, integrating physical parameters from the Ordos Basin's deep saline aquifers to develop a mechanism model. In this model, CO₂ injection started from the first year and continued for 20 years. This study simulated a total of 80 years of CO₂ storage. This study has elucidated how reservoir conditions and injection schemes affect the dissolution and mineralization of CO₂. This study creatively combines practical experiments and numerical simulations and uses numerical simulations to compensate for the manpower and material resources consumed in actual experiments. The research results indicate that permeability should not be too high, and an increase in porosity is beneficial for storage. As the injection rate increases, the amount of CO₂ storage increases. Top layer perforation yields lower efficiency compared to full, middle, or bottom layer perforation, with the latter providing the higher efficiency in CO₂ dissolution and mineralization. Bottom perforation is the most favorable perforation position for CO₂ storage.

The minimum miscibility pressure (MMP) is a crucial parameter in assessing the miscibility of CO₂ displacement and evaluating the effectiveness of oil displacement. Traditional methods for calculating MMP are intricate and time-consuming, involving numerous related parameters. Therefore, precise and efficient determination of MMP is highly significant in the development of CO₂-driven reservoirs. This study first utilized the Pearson correlation coefficient to analyse the correlation factor mechanism of 36 sets of fine-tube experimental data. Subsequently, the physical information neural network prediction model was employed with reservoir temperature, crude oil composition, and injected gas type as input parameters. The PRI state equation and Glaso correlation equation drove the model, with parameter optimization and training conducted under both physical and data driving. The model demonstrates high prediction accuracy and strong generalization ability. Finally, Validation of the model was performed using fine-tube experimental data from 5 other wells, revealing a relatively small relative deviation between calculated and experimental values, with an average coefficient of determination of 0.95 and an average relative error of 4.42%. The prediction accuracy was improved by about 75% compared to other machine learning algorithms. This model holds potential for application in on-site reservoir development, enhancing the measurement accuracy of the minimum miscible pressure of pure CO₂ flooding, greatly shortening the design cycle of reservoir development, expediting the process of reservoir development, and providing technical guidance for improving oil and gas recovery and pure CO₂ flooding exploration and development.

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 (T_cm) 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.