Understanding caprock integrity in underground hydrogen storage: A geochemical study of mineral alteration and sealing efficiency
Bibliographic record
Abstract
Underground hydrogen storage (UHS) in aquifers has emerged as a promising large-scale energy storage solution, crucial for enabling the transition to renewable energy systems. Ensuring the safety and long-term reliability of UHS is essential to support increased adoption and integration of renewable energy sources into existing energy infrastructures. However, the success of UHS systems depends not only on effective management of the physical properties of reservoirs but also on a comprehensive understanding of the geochemical interactions between hydrogen and the surrounding rock formations, especially the caprock. While existing literature predominantly addresses geochemical reactions within the reservoir, research focusing on hydrogen-induced mineral alteration that may affect caprock integrity remains limited. This study aims to bridge this critical gap by investigating the geochemical interactions between hydrogen and caprock minerals and their subsequent impact on caprock integrity and hydrogen leakage potential. Numerical simulations were performed to assess the effects of varying pH, temperature, pressure, and salinity on the porosity, permeability, and sealing capacity of the caprock. The results reveal that hydrogen interactions with key minerals, such as calcite and dolomite, induce significant alterations in the physical and chemical properties of the caprock, potentially compromising its sealing effectiveness. Under acidic conditions, mineral precipitation leads to a reduction in caprock porosity over 0.05 %, whereas under alkaline conditions, a slight increase in porosity is observed. Elevated temperatures further accelerate geochemical reactions, intensifying the changes in porosity. Pressure significantly influences geochemical processes with lower pressures facilitating mineral dissolution and increasing porosity, which increases leakage risk. In contrast, higher pressures suppress geochemical reactions, enhancing the caprock's sealing integrity. Additionally, salinity was found to influence caprock integrity, with higher salinity promoting halite precipitation, which reduces porosity by 0.2 % and has a related effect on permeability, thereby enhancing the sealing efficiency of the caprock and mitigating hydrogen leakage. These quantitative findings provide valuable insights for practical decision-making in UHS projects, highlighting the importance of managing geochemical conditions to enhance caprock stability and minimize leakage risks. Effective optimization of these conditions is essential for ensuring the long-term efficiency and viability of UHS systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".