Assessment of Kinetic Geochemical Reactions during Hydrogen Storage in Salt Caverns
Bibliographic record
Abstract
Previous studies have demonstrated that abiotic redox reactions between salt rocks and hydrogen (H 2 ) are unlikely to occur. However, when storing H 2 in salt caverns, microbial activities may exist and induce redox reactions such as methanogenesis and sulfate reduction, leading to byproduct generation and affecting H 2 impurity. In contrast to earlier simplified models, this study presents a more realistic geochemical modeling by incorporating actual mineral data and brine composition from the Lotsberg Formation. We combine kinetic mineral dissolution with microbial reaction pathways to simulate H 2 -brine-salt systems under subsurface conditions (50 °C, 20 MPa) over a two-year storage period. The model accounts for mineral-specific reactivity and microbial redox kinetics and is validated against published experimental data. In single-mineral systems, silicates and clays show geochemical stability, while carbonates and anhydrite exhibit significant microbial reactivity, producing methane (CH 4 ) and hydrogen sulfide (H 2 S). To reflect real-world complexity, we extend the modeling to mixed-mineral systems representing three salt-rock types with varying halite and impurity contents. Our results reveal that H 2 loss is strongly linked to rock composition. Halite-dominated systems show minimal reactivity (H 2 loss ∼ 0.08%), while systems with carbonates and anhydrite exhibit higher H 2 consumption (∼0.16 to 1.0%). Anhydrite, with its rapid dissolution and fast reduction rates, proves to be one of the most problematic minerals, reacting with H 2 to produce H 2 S. Despite this, overall hydrogen loss remains low. The findings suggest that, within the defined parameters of our study, salt caverns could be considered stable and efficient options for long-term hydrogen storage, demonstrating minimal hydrogen loss and geochemical stability under similar conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".