Machine Learning Modeling of Saudi Arabian basalt/CO₂/brine Wettability Prediction: Implications for CO₂ geo-storage

CO2 wettability and the reservoir rock-fluid interfacial interactions are crucial parameters that regulates the successful CO2 geological sequestration. This study implemented the feed-forward neural network to model the wettability behavior of Saudi Arabian (SA) basaltic rocks in a ternary system of basaltic rocks, CO2, and brine under different operating conditions. To gain higher accuracy of the machine learning models, a sufficient dataset was utilized that was recorded by conducting a large number of laboratory experiments under a realistic pressure range, 0-25 MPa and the temperatures range, 298-343 K. Different graphical exploratory data analysis techniques, such as heatmaps, violin plots, and pair plots were used to analyze the experimental dataset. The machine learning models were trained to predict the receding and advancing contact angles of SA basalt/CO2/brine systems. Both statistical evaluation and graphical analyses were performed to show the reliability and performance of the developed models. The results showed that the implemented ML model accurately predicted the wettability behavior under various operating conditions.