Application of robust intelligent schemes for accurate modelling interfacial tension of CO2 brine systems: Implications for structural CO2 trapping.

[Display omitted] • Various robust AI methods were implemented for predicting IFT of CO 2 -brine system. • A comprehensive database including more than 2184 experiment samples was employed for implementing the AI study. • The newly proposed random forest model for predicting IF outperforms the other AI models. Given the current global climate change, renewable energy sources, carbon capture, utilization, and storage (CCUS) are being considered as a potential solutions to this critical global issue. Structural and capillary processes can be used to store carbon dioxide (CO 2) in deep saline aquifers in a way that is safe and does not harm the environment. Due to this fact, the interfacial tension (IFT) of the CO 2 -brine system is an important factor influencing the capacity of storage formations to sequester CO 2. As a result, IFT is essential for conducting a thorough and accurate evaluation in order to determine the optimal strategy for CO 2 storage projects. This paper used intelligent models such as Gaussian Process Regression (GPR), Radial Basis Function (RBF), and Random Forest (RF) to forecast IFT in the CO 2 -brine system with high precision and substantial time saving. The results reveal that the constructed RF model could deliver excellent performance in predicting IFT with the lowest average absolute percent relative error (AAPRE = 1.9156 percent), highest coefficient of determination (R2 = 0.9907), and lowest root mean squared error (RMSE = 0.7279). Furthermore, a sensitivity analysis was performed to ascertain the most critical parameters in the RF model to be considered. The parametric analysis found that both pure and non-pure CO 2 systems had a significant impact on IFT prediction. Also, the RF model was used to assess the structural trapping capacity of a storage location in the Cuu Long Basin. The estimation results obtained in this study agreed perfectly with the previous ones. The findings of this study can aid in a better understanding of how machine learning models can be applied to predict IFT values for the evaluation of the structural CO 2 storage capacity. [ABSTRACT FROM AUTHOR]