Deep Learning-Based Prediction of Hydrogen Dynamics and Mixing Phenomenon in Fractured Aquifers for Underground Hydrogen Storage
Bibliographic record
Abstract
Underground Hydrogen Storage (UHS) in aquifers is a promising solution. Some aquifers contain natural fractures that enhance permeability, improving injection and recovery. However, these fractures may also intensify mixing and channeling, reducing overall storage efficiency and hydrogen purity. To address these challenges, designing suitable UHS scenarios is essential to minimize hydrogen mixing and uneven distribution within the aquifer. Numerical simulations help optimize UHS operations, yet their high computational cost necessitates efficient alternatives. This study develops a grid-based proxy model using U-Net and Modified U-Net architectures to predict mixing maps and fluid flow dynamics without solving complex physical equations. The model achieves over 96% accuracy in capturing key flow behaviors like channeling and overriding while significantly reducing computational time. Results demonstrate that the optimized Modified U-Net reduces training time while maintaining prediction accuracy. The proposed framework enables rapid evaluation of different scenarios, enhancing decision-making for UHS optimization. It is applicable across various aquifer conditions, including different heterogeneities and operational settings, making it a cost-effective alternative to conventional numerical simulations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".