Optimizing the operational efficiency of the underground hydrogen storage scheme in a deep North Sea aquifer through compositional simulations
Bibliographic record
Abstract
In this study, we evaluate the technical viability of storing hydrogen in a deep UKCS aquifer formation through a series of numerical simulations utilising the compositional simulator CMG-GEM. Effects of various operational parameters such as injection and production rates, number and length of storage cycles, and shut-in periods on the performance of the underground hydrogen storage (UHS) process are investigated in this study. Results indicate that higher H2 operational rates degrade both the aquifer's working capacity and H2 recovery during the withdrawal phase. This can be attributed to the dominant viscous forces at higher rates which lead to H2 viscous fingering and gas gravity override of the native aquifer water resulting in an unstable displacement of water by the H2 gas. Furthermore, analysis of simulation results shows that longer and less frequent storage cycles lead to higher storage capacity and decreased H2 retrieval. We conclude that UHS in the studied aquifer is technically feasible, however, a thorough evaluation of the operational parameters is necessary to optimise both storage capacity and H2 recovery efficiency.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".