Abstract
Hydrogen storage is a crucial technology for ensuring a sustainable energy transition. Underground Hydrogen Storage (UHS) in depleted hydrocarbon reservoirs, aquifers, and salt caverns provides a viable large-scale solution. However, hydrogen dispersion in cushion gases such as nitrogen (N(2)), methane (CH(4)), and carbon dioxide (CO(2)) lead to contamination, reduced purity, and increased purification costs. Existing experimental and numerical methods for predicting hydrogen dispersion coefficients (K(L)) are often limited by high costs, lengthy processing times, and insufficient accuracy in dynamic reservoir conditions. This study addresses these challenges by integrating experimental data with advanced machine learning (ML) techniques to model hydrogen dispersion. Various ML models-including Random Forest (RF), Least Squares Boosting (LSBoost), Bayesian Regression, Linear Regression (LR), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs)-were employed to quantify K(L) as a function of pressure (P) and displacement velocity (U(m)). Among these methods, RF outperformed the others, achieving an R(2) of 0.9965 for test data and 0.9999 for training data, with RMSE values of 0.023 and 0.001, respectively. The findings highlight the potential of ML-driven approaches in optimizing UHS operations by enhancing predictive accuracy, reducing computational costs, and mitigating hydrogen contamination risks.