New flow simulation framework for underground hydrogen storage modelling considering microbial and geochemical reactions
Bibliographic record
Abstract
The widespread use of hydrogen as an energy source relies on efficient large-scale storage techniques. Underground Hydrogen Storage (UHS) is a promising solution to balance the gap between renewable energy production and constant energy demand. UHS employs geological structures like salt caverns, depleted reservoirs, or aquifers for hydrogen storage, enabling long-term and scalable storage capacity. Therefore, robust and reliable predictive tools are essential to assess the risks associated with geological hydrogen storage. This paper presents a novel reactive transport model called “Underground Gas Flow simulAtions with Coupled bio-geochemical reacTions” or “UGFACT”, designed for various gas injection processes, accounting for geochemical and microbial reactions. The flow module and geochemical reactions in the UGFACT model were verified against two commercial reservoir simulators, E300 and CMG-GEM, showing excellent agreement in fluid flow variables and geochemical behaviour. A major step forward of this model is to integrate flow dynamics, geochemical reactions and microbial activity. UGFACT was used to conduct a simple storage cycle in a 1D geometry across three different reservoirs, each with different mineralogies and water compositions: Bentheimer sandstone, Berea sandstone, and Grey Berea sandstone, under three microbial conditions (“No Reaction”, “Moderate Rate”, “High Rate”). The findings suggest that Bentheimer sandstone and Berea sandstone sites may experience severe effects from ongoing microbial and geochemical reactions, whereas Grey Berea sandstone shows no significant H 2 loss. Additionally, the model predicts that under the high-rate microbial conditions, the hydrogen consumption rate can reach to as much as 11 mmol of H 2 per kilogram of water per day ( m m o l / k g · d a y ) driven by methanogenesis and acetogenesis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".