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

Abstract

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 CO2-brine 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.