A quantitative risk assessment framework for fault reactivation in underground hydrogen storage: Coupled simulation and deep learning approach
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); University of North Dakota, Grand Forks, ND (United States)
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- University of North Dakota, Grand Forks, ND (United States)
Underground hydrogen storage (UHS) is emerging as a critical solution for large-scale energy storage. However, like all subsurface fluid injection activities, UHS poses the risk of injection-induced fault reactivation. Accurate risk assessment is essential to ensuring the safety and efficiency of UHS operations. This study presents the development of deep-learning surrogate models for fault reactivation prediction in UHS, trained on a comprehensive database of fully coupled fluid flow-geomechanics simulations. Our findings reveal that analytical models often yield unreliable estimates, with errors up to 54% in the allowable injection pressure, potentially leading to a 40% reduction in UHS operational capacity. The developed surrogate models were incorporated into a quantitative risk assessment (QRA) framework, enabling probabilistic evaluation of fault reactivation risk while accounting for uncertainties in the input variables. Site-specific features, such as horizontal stress gradients, fault’s dip and strike angles, and operational parameters like bottom-hole injection pressure and well-fault distance, were identified as the primary drivers of fault reactivation across various stress regimes. Whereas other hydraulic, geological, and poroelastic reservoir properties were found to have a secondary impact. Notably, we observed that the risk of fault reactivation for a critically oriented fault with a static friction coefficient greater than 0.55 remains below 10% in a normal faulting stress regime. However, the risk significantly increases as the stress regime transitions from normal to strike-slip and ultimately to reverse faulting conditions. These findings underscore the importance of rigorous site characterization and comprehensive QRA evaluations to optimize UHS performance and minimize geomechanical risks.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- 89233218CNA000001
- OSTI ID:
- 2588094
- Report Number(s):
- LA-UR--24-30089; 10.1016/j.jrmge.2025.05.025
- Journal Information:
- Journal of Rock Mechanics and Geotechnical Engineering, Journal Name: Journal of Rock Mechanics and Geotechnical Engineering; ISSN 1674-7755
- Publisher:
- Elsevier BVCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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