Risk Evaluation and Management of CO2 Storage in the Bahrain Oil Field
Bibliographic record
Abstract
Abstract This paper presents a risk-focused assessment of CO2 storage in the Mauddud reservoir of the Bahrain Oil Field, emphasizing subsurface uncertainty and technical risk management. A detailed geological model was constructed using core analysis, well log interpretation, and formation micro-imaging (FMI), with the secondary gas cap (248,000 acre-ft) identified as the primary containment unit for CO2 injection. A volumetric-based storage estimation was coupled with a Monte Carlo simulation using Oracle Crystal Ball, resulting in a mean CO2 storage potential of 37.57 million metric tons (MMt), with P10 = 26.88 MMt and P90 = 49.47 MMt. A sensitivity analysis, conducted using both rank correlation and a machine learning-based Random Forest model, revealed that storage efficiency, porosity, and formation volume factor were the dominant parameters influencing storage capacity, while irreducible water saturation exhibited minimal impact. A Bowtie risk assessment framework was used to identify and evaluate technical risks, including caprock integrity under pressure buildup, fault reactivation near injection zones, potential wellbore leakage due to aging completions, and uncontrolled plume migration. Targeted mitigation strategies such as zonal isolation, corrosion-resistant completions, and real-time pressure monitoring were proposed to address these risks. This integrated approach, combining probabilistic modeling, machine learning insights, and a structured risk evaluation framework, enhances confidence in safe, long-term CO2 storage and offers a replicable model for future carbon capture and storage (CCS) projects in similar carbonate reservoirs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".