Artificial intelligence-based prediction of hydrogen adsorption in various kerogen types: Implications for underground hydrogen storage and cleaner production

Abstract

Hydrogen offers significant potential as a sustainable energy source. However, its storage and transportation pose challenges due to its volatility and low density. Subsurface geological formations, such as shale, sandstone, and coal, have been investigated as potential storage sites for hydrogen. Precise estimation of hydrogen adsorption in these formations necessitates a thorough understanding of kerogens, organic-rich sedimentary rocks prevalent in unconventional formations. In this study, we introduce a novel approach employing Extreme Gradient Boosting (XGBoost), Random forest (RF), Light gradient Boosting (LGBM), and Natural gradient boosting (NGBM) algorithms to intelligently predict hydrogen adsorption in various kerogen types. Among these, the NGBM algorithm, utilizing three input features (temperature, pressure, and kerogen types), demonstrated the highest predictive accuracy, achieving an R2 value of 0.989, RMSE of 0.062, and MAE of 0.037 for all data samples. SHAP diagram analysis identified kerogen types as the most influential parameter within the NGBM model. The presented approach has implications beyond energy storage, highlighting the significance of advanced technologies in addressing complex energy challenges. Precise hydrogen adsorption estimation in subsurface formations is vital for developing sustainable energy solutions, and our approach has the potential to expedite progress in this field. Interdisciplinary collaboration among geologists, chemists, and data scientists is crucial for devising innovative solutions for sustainable energy.