An integrated approach for optimizing geological hydrogen storage

In the pursuit of a sustainable energy transition and the achievement of net-zero emissions goals, Geological Hydrogen (H2) Storage (GHS) emerges as a critical component. GHS involves converting sustainable energy into H2 and injecting it into underground formations during low energy demand periods, then extracting it during high energy demand periods. Optimizing GHS performance necessitates rigorous investigation into the operational parameters, such as injection and production rates, tailored to each storage site’s characteristics—particularly reservoir heterogeneity and thickness. Understanding the interaction between reservoir properties and operational parameters is essential to enhance H2 recovery efficiency and minimize water production. Through thousands of reservoir-scale simulations, we identify how these variables influence key performance metrics: maximizing H2 recovery and minimizing water production. Multivariate adaptive regression spline (MARS) sensitivity analysis indicates that injection and production rates play a crucial role in determining cumulative H2 and water production, while reservoir heterogeneity is the primary factor influencing the H2 recovery factor (ratio of produced to injected H2). Response surface analysis reveals that optimal GHS performance is achieved in reservoirs characterized by low degree of heterogeneity (standard deviation < 1.2), a thickness of less than 55 m, and a minimum injection rate of 21,200 Kg/day, which can vary with reservoir size and boundary conditions. These findings highlight the necessity of establishing threshold injection rates and specific reservoir attributes for effective GHS operations. Additionally, we present performance estimation correlations derived from our simulations, providing a valuable framework for the screening and deployment of promising GHS sites.