AI-based models for predicting rock/mineral-hydrogen-brine contact angles using experimental data
Hajibolouri E. Shafiei A.
7 November 2025Elsevier Ltd
International Journal of Hydrogen Energy
2025#186
In this paper, six intelligent models were developed and tested using various artificial intelligence algorithms for accurate prediction of contact angle (CA) for 10 rock/mineral types in rock/mineral-H2-brine system. A comprehensive database with 1085 experimental data that covers a wide range of geological conditions was used. Both static and dynamic CAs were considered in the modeling to tackle the uncertainty associated with common CA measurement methods. Monte-Carlo SHAP analysis showed that organic acid concentration, pressure, and mineral type are the most sensitive variables in this process. The proposed models can accurately predict wettability characteristics under various geological conditions. The CatBoost and XGB models achieved a great performance with coefficients of determination of 0.979 and 0.978 and root mean square errors of 3.186 and 3.199, respectively. This research work provides a reliable tool for CA prediction which is required for adequate assessment of hydrogen storage capacity.
Contact angle , H2 geo-storage , Machine learning , Rock/mineral-H2-brine , Wettability
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Petroleum Engineering Program, School of Mining & Geosciences, Nazarbayev University, Astana, 010000, Kazakhstan
Petroleum Engineering Program
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