Geomechanics of geological storage of hydrogen: Knowledge gaps and future directions
Bibliographic record
Abstract
Underground hydrogen storage is critical for supporting the transition to renewable energy systems, addressing the intermittent nature of solar and wind power. Despite its promise as a carbon-neutral energy carrier, there remains limited understanding of the geomechanical behavior of subsurface reservoirs under hydrogen storage conditions. This knowledge gap is particularly significant for fast-cycling operations, which have yet to be implemented on a large scale. This review evaluates current knowledge on the geomechanics of underground hydrogen storage, focusing on risks and challenges in geological formations such as salt caverns, depleted hydrocarbon reservoirs, saline aquifers, and lined rock caverns. Laboratory experiments, field studies, and numerical simulations are synthesized to examine cyclic pressurization, induced seismicity, thermal stresses, and hydrogen-rock interactions. Notable challenges include degradation of rock properties, fault reactivation, micro-seismic activity in porous reservoirs, and mineral dissolution/precipitation caused by hydrogen exposure. While salt caverns are effective for low-frequency hydrogen storage, their behavior under fast-cyclic loading requires further investigation. Similarly, the mechanical evolution of porous and fractured reservoirs remains poorly understood. Key findings highlight the need for comprehensive geomechanical studies to mitigate risks and enhance hydrogen storage feasibility. Research priorities include quantifying cyclic loading effects on rock integrity, understanding hydrogen-rock chemical interactions, and refining operational strategies. Addressing these uncertainties is essential for enabling large-scale hydrogen integration into global energy systems and advancing sustainable energy solutions. This work systematically focuses on the geomechanical implications of hydrogen injection into subsurface formations, offering a critical evaluation of current studies and proposing a unified research agenda.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".