Can a hydrodynamic model predict the flow evolution of a hydrogen plume in a depleted natural gas reservoir?
Bibliographic record
Abstract
Hydrogen storage in depleted natural gas reservoirs is a promising solution for storing excess renewable energy on a timescale longer than can be accommodated by salt cavern storage. However, commercial exploitation of the technology in question awaits the resolution of key challenges and uncertainties. Chief among these from a fluid mechanics point of view is to resolve the connection between hydrogen leakage and the mixing of hydrogen and cushion gas. Reduced-order-models examining this question have been developed i.e. by Sheikhi and Flynn (2024), however their work relies much more heavily on fluid mechanics than it does on thermodynamics. It is therefore unclear the extent to which their model predictions are accurate when compared with e.g. the numerical output from reservoir-level simulation packages. Addressing this knowledge gap is the key focus of the present study. To this end, we use OpenGoSim and CMG to numerically resolve hydrogen injection directly below an interbed layer of reduced, but still finite, permeability. The resulting comparison demonstrates that the theoretical model predicts, with generally good accuracy, the overall shape of the gravity current plus the amount of hydrogen that dispersively mixes into the surrounding cushion gas. However, reduced-order-model fidelity suffers when the injection time is long, the draining layer is thin and the interbed layer admits a relatively large drainage. This observation highlights areas of future improvement for the reduced-order-model, which can otherwise be applied, with great computational efficiency, in screening candidate reservoirs for hydrogen storage.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".