Date of Award
May 2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Petroleum Engineering
First Advisor
Sven Egenhoff
Abstract
The global transition to a carbon-neutral energy future necessitates the rapid deployment of large-scale, long-duration energy storage systems to balance supply and demand, improve grid reliability, and facilitate renewable resource integration. Subsurface energy storage, including geological carbon storage (GCS), underground hydrogen storage (UHS), and other emerging technologies, provides unmatched capacity and longevity. Nevertheless, large-scale injection and withdrawal operations modify pore pressures and stress distributions, which may compromise mechanical integrity and long-term containment.A multiscale, physics-based framework for risk assessment and containment assurance in subsurface energy storage systems is developed. This framework integrates hydraulic, mechanical, and monitoring analyses across three complementary scales: basin, reservoir, and field. By linking performance, stability, and assurance within a unified conceptual structure, the research combines numerical simulations, analytical formulations, laboratory experiments, and machine-learning models to address critical challenges at each scale and to provide a generalizable methodology for safe and efficient subsurface energy storage.
At the basin scale, compositional flow simulations combined with material-balance and rate-transient analyses quantify pressure interference among concurrent CO₂ storage projects. The findings indicate that operational parameters, especially well spacing and injection rate, govern interference patterns. These results highlight the necessity for coordinated basin-scale pressure management to maintain injectivity and storage capacity.
At the reservoir scale, an integrated physics and machine-learning framework is established to evaluate fault reactivation risk during cyclic hydrogen storage. Coupled flow and geomechanics simulations generate a database that links pore pressure, stress evolution, and fault slip potential, which is then used to train deep-learning surrogate models. The resulting models were integrated into a quantitative risk assessment tool, enabling rapid probabilistic evaluation of mechanical stability and facilitating the identification of safe operating envelopes for pressure and fault proximity.
At the field scale, a geomechanics-informed monitoring framework is introduced to assess Above Injection Zone (AIZ) pressure monitoring as a viable, physics-based method for containment assurance. The study demonstrates that subtle pressure responses in the overburden can serve as early indicators of leakage, positioning AIZ monitoring as a valuable complement to conventional monitoring programs in geological storage projects.
Recommended Citation
Chellal, Hichem Aymene Katib, "On The Risk Assessment And Containment Assurance For Subsurface Applications" (2026). Theses and Dissertations. 13935.
https://commons.und.edu/theses/13935