MACHINE LEARNING MODELING OF WETTABILITY AND CONTACT ANGLE BEHAVIOR IN CO₂-WATER-ROCK SYSTEM

Abstract

The focus of this research project is to understand how machine learning algorithms work with regarding wettability and contact angles in CO₂-water-rock combinations. A broad dataset containing static contact angles along with advancing and receding contact angle measurements originated from published experimental studies that studied different types of rocks across a range of pressures, temperatures, and salinities. The analysis implemented six machine learning algorithms, including Decision Trees, Random Forests, XGBoost, Gradient Boosting Regressor, Support Vector Machines, and Artificial Neural Networks. Both training and testing datasets received evaluation through different error metrics, which assessed the performance of the models. The GBR model delivered optimum performance in static contact angle prediction for CO₂-water-rock systems by reaching R² =0.99 value during training and R² = 0.92 value during testing. The high accuracy value shows that GBR effectively identifies intricate non linear patterns in wettability patterns. The GBR model produced the most accurate results for testing dataset advancing and receding contact angles with a calculation error rate of R² =0.93 training and R² =0.88 testing. GBR model demonstrate excellent proficiency for understanding the changing behavior of liquid wetting in such systems. Different input conditions such as pressures and temperatures and rock types and salinities were subject to sensitivity analysis to check model prediction accuracy and to investigate the impact on the advancing, receding and static contact angles. The research shows that pressure produces the largest impact on contact angle measurement whereas temperature and salinity impacts differ based on the rock type. The predictive capabilities of developed models worked effectively in predicting the contact angles of silicate, clay, carbonate and basalt and effectively captured their complex non-linear relationships.