Subsurface storage capacity in structural traps in underexplored sedimentary basins: hydrogen (H <sub>2</sub> ) and carbon dioxide (CO <sub>2</sub> ) storage on the Irish Atlantic margin
Bibliographic record
Abstract
Methodologies for storage assessment developed for basins with dense data coverage are typically not optimally applicable to underexplored sedimentary basins. To address this, a methodology and workflow for storage assessment in underexplored basins is presented which uses existing datasets to identify structural traps and populate a Fluid-in-Place equation which can be used for a variety of gases including CO 2 and H 2 . This is then applied to the Irish Atlantic margin; Jurassic, Triassic and Carboniferous reservoirs are investigated to understand their reservoir quality and extent, and related seals. Structural trap types are described and the theoretical capacities of three candidate sites with varying data coverage are calculated. The results highlight the potential for underexplored sedimentary basins on the Irish Atlantic margin to support offshore renewable energy projects and reduce Ireland's CO 2 emissions. This workflow is applicable to a variety of underexplored sedimentary basins and emphasizes the utility of legacy hydrocarbon datasets for early-stage subsurface storage assessment. Other aspects of energy storage are also discussed, including anthropogenic salt caverns, other candidate reservoir–seal pairs, and the potential for collaborative infrastructure development with CO 2 emitters and renewable energy projects.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".