A criteria-driven approach to the CO2 storage site selection of East Mey for the acorn project in the North Sea
Bibliographic record
Abstract
Carbon Capture and Storage (CCS) is an essential tool in the fight against climate change. Any prospective storage site must meet various criteria that ensure the effectiveness, safety and economic viability of the storage operations. Finding the most suitable site for the storage of the captured CO2 is an essential part of the CCS chain of activity. This work addresses the site selection of a second site for the Acorn CCS project, a project designed to develop a scalable, full-chain CCS project in the North Sea (offshore northeast Scotland). This secondary site has been designed to serve as a backup and upscaling option for the Acorn Site, and has to satisfy pivotal project requirements such as low cost and high storage potential. The methodology followed included the filtering of 113 input sites from the UK CO2Stored database, according to general and project-specific criteria in a multi-staged approach. This criteria-driven workflow allowed for an early filtering out of the less suitable sites, followed by a more comprehensive comparison and ranking of the 15 most suitable sites. A due diligence assessment was conducted of the top six shortlisted sites to produce detailed assessment of their storage properties and suitability, including new geological interpretation and capacity calculations for each site. With the new knowledge generated during this process, a critical comparison of the sites led to selection of East Mey as the most suitable site, due to its outstanding storage characteristics and long-lasting hydrocarbon-production history, that ensure excellent data availability to risk-assess storage structures. A workshop session was held to present methods and results to independent stakeholders; feedback informed the final selection criteria. This paper provides an example of a criteria-driven approach to site selection that can be applied elsewhere.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".