Numerical Model for Underground Hydrogen Storage in Cased Boreholes
Bibliographic record
Abstract
The decrease in generation costs of renewable energy, combined with advances in electrolyser technologies, suggest that green hydrogen production may be a viable option in the ongoing energy transition.Yet, a green hydrogen economy requires not only production solutions but also storage options, which prove to be challenging.An underexplored solution is the underground storage of hydrogen gas (H 2 ) in cased boreholes or shafts.Its integration would bring versatility in the implementation, and large applicability since it does not require a particular geological context.The objective of this paper is to evaluate the technical viability of this new storage technology.Accurate prediction of temperature and pressure variations is essential for design, materials selection and safety reasons.This work uses numerical models based on the mass and energy conservation equations to simulate hydrogen storage operations in cased boreholes.The study shows that the heat transfer at the cavity walls strongly affects temperature and pressure variations.This effect is accentuated by a borehole's geometry providing significant contact area.Thus, such technology mitigates extreme pressure and temperature variations and yields a higher hydrogen density than conventional caverns for a given pressure constraint.Results show that with a radius of 0.2 m, a hydrogen density of 30 kg m -3 can be attained at a maximum pressure of 50 MPa.The response of the system in terms of maximum temperature and pressure is relatively linear with an injection over 4 h but quickly becomes non-linear with a shorter injection time.The optimization of the initial storage conditions appears essential to minimize the cooling cost and maximize the storage mass.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".