Predicting Interfacial Tension in CO₂/Brine Systems: A Data-Driven Approach and Its Implications for Carbon Geostorage

CO₂ Interfacial Tension (IFT) and the reservoir rock-fluid interfacial interactions are critical parameters for successful CO₂ geological sequestration, where the success relies significantly on the rock-CO₂-brine interactions. IFT behaviors during storage dictate the CO₂/brine distribution at pore scale and the residual/structural trapping potentials of storage/caprocks. Experimental assessment of CO₂-Brine IFT as a function of pressure, temperature, and readily available organic contaminations on rock surfaces is arduous because of high CO₂ reactivity and embrittlement damages. Data-driven machine learning (ML) modeling of CO₂brine IFT are less strenuous and more precise. They can be conducted at geo-storage conditions that are complex and hazardous to attain in the laboratory. In this study, we have applied three different machine learning techniques, including Random Forest (RF), XGBoost (XGB), and Adaptive Gradient Boosting (AGB), to predict the interfacial tension of the CO₂ in brine system. The performance of the ML models was assessed through various assessment tests, such as cross-plots, average absolute percentage error (AAPE), root mean square error (RMSE), and coefficient of determination (R2). The outcomes of the predictions indicated that the XGB outperformed the RF, and AdaBoost. The XGB yielded remarkably low error rates. With optimal settings, the output was predicted with 97% accuracy. The proposed methodology can minimize the experimental cost of measuring rheological parameters and serve as a quick assessment tool.