Estimation of CO₂-Brine Interfacial Tension Based on an Advanced Intelligent Algorithm Model: Application for Carbon...

The emission reduction of the main greenhouse gas, CO₂, can be achieved via carbon capture, utilization, and storage (CCUS) technology. Geological carbon storage (GCS) projects, especially CO₂ storage in deep saline aquifers, are the most promising methods for meeting the net zero emission goal. The safety and efficiency of CO₂ saline aquifer storage are primarily controlled by structural and capillary trapping, which are significantly influenced by the interactions between fluid and solid phases in terms of the interfacial tension (IFT) between the injected CO₂ and brine at the reservoir site. In this study, a model based on the random forest (RF) model and the Bayesian optimization (BO) algorithm was developed to estimate the IFT between the pure and impure gas-brine binary systems for application to CO₂ saline aquifer sequestration. Then three heuristic algorithms were applied to validate the accuracy and efficiency of the established model. The results of this study indicate that among the four mixed models, the Bayesian optimized random forest model fits the experimental data with the smallest root-mean-square error (RMSE = 1.7705) and mean absolute percentage error (MAPE = 2.0687%) and a high coefficient of determination (R² = 0.9729). Then the IFT values predicted via this model were used as an input parameter to estimate the CO₂ sequestration capacity of saline aquifers at different depths in the Tarim Basin of Xinjiang, China. The burial depth had a limited influence on the CO₂ storage capacity.