Ensuring Safe CO2 Storage - Measurement, Monitoring and Verification (MMV) Approach for the Blue Horizons CCS Project, Leveraging Industry Best Practices and Cutting-Edge Technologies
Bibliographic record
Abstract
This work presents a comprehensive approach to build a site-specific, risk-based Measurement, Monitoring, and Verification (MMV) plan for effective, long-term CO2 storage for the Blue Horizons Project in the Sultanate of Oman. The MMV supports the safeguarding of a low-carbon hydrogen (H2) and ammonia (NH3) production facility enabled by Carbon Capture and Storage (CCS). The MMV plan ensures that injected CO2 behaves as predicted by subsurface models and allows for timely corrective actions if irregularities occur. The work follows a site-specific and adaptable approach, addressing potential scenarios leading to loss of containment. The approach rationale includes the selection of prevention controls, monitoring, and corrective measures to ensure there is no significant leakage that would compromise the safety and effectiveness of the storage operation. The Blue Horizons CCS project aims to provide a sustainable solution for CO2 storage in saline aquifers, based on existing industry best practices and thorough site characterization and risk assessment, supporting low-carbon hydrogen and ammonia production. Ensuring the integrity of the storage complex and preventing CO2 leakage are paramount for the project's success. This study focuses on the approach to develop a comprehensive MMV plan that leverages industry best practices and cutting-edge technologies to ensure safe and effective long-term CO2 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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".