Assessing Hydrogen Leakage in Underground Hydrogen Storage: Insights from Parametric Analysis
Bibliographic record
Abstract
Hydrogen plays a vital role in renewable energy systems and has a significant environmental impact. Storing hydrogen in underground geological formations offers an efficient and safe solution to balance production and consumption. However, due to hydrogen’s unique properties, there is a risk of leakage through the caprock of underground aquifers, potentially causing serious issues such as groundwater contamination, reduced storage efficiency, and explosion hazards. This study employs numerical simulations to investigate hydrogen leakage from caprock during underground storage, focusing on key parameters. These parameters include injection and production rates, cycle duration, hydrogen molecular diffusion, aquifer pressure, injection and production depths, well types, aquifer dip angle, caprock permeability, and capillary entry pressure. By examining these factors, the study provides an in-depth comprehensive analysis of hydrogen leakage from aquifers, addressing a critical gap in existing research. The results indicate that a significant amount of the total injected hydrogen leaks into the caprock after eight years of injection and storage cycles. This leakage can have significant environmental and economic impacts. The study also reveals that caprock permeability is crucial in influencing hydrogen leakage with higher permeability leading to increased leakage rates. Moreover, vertical caprock permeability has a more pronounced effect on leakage rates than horizontal permeability. Additionally, factors such as aquifer pressure, aquifer dip angle, injection and production depths, and hydrogen injection duration contribute to a higher hydrogen leakage from the caprock. The findings underscore the importance of carefully selecting underground hydrogen storage sites to mitigate the potential risks of hydrogen leakage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 teacher head, 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".