Evaluation of the Potential for Gas and CO2 Leakage Along Wellbores
Bibliographic record
Abstract
Abstract Implementation of carbon dioxide storage in geological media requires a proper assessment of the risk of CO2 leakage from storage sites. Leakage pathways may exist through and along wellbores which may penetrate or be near to the storage site. One method of assessing the potential for CO2 leakage through wells is by mining databases that usually reside with regulatory agencies These agencies collect data concerning wellbore construction, oil and gas production, and other regulated issues for existing wells. The Alberta Energy and Utilities Board (EUB), the regulatory agency in Alberta, Canada, collects and stores information about more than 315,000 oil, gas and injection wells in the province of Alberta, Canada. The EUB also records well leakage at the surface as surface casing vent flow (SCVF) through wellbore annuli and gas migration (GM) outside casing, as reported by industry. The evaluation of a leakage pathway through wellbore casing or annuli and what causes these wellbore leaks is a first step in determining what factors may contribute to wellbore leakage from CO2 storage sites. By utilizing available data, major factors which contribute to wellbore leakage were identified. Data analysis shows that there is a correlation between these SCVF/GM and economic activity, technology changes, geographic location and regulatory changes regarding well completion and abandonment. Further analysis indicates a relationship between low annular cement top, external corrosion, casing failure and wellbore leakage (SCVF/GM). Other factors that could affect the presence of wellbore leakage, such as wellbore deviation, surface casing depth and wellbore density, were also investigated. This paper presents the findings of the data analysis and a method to evaluate the potential for leakage along wells in an area where CO2 storage is intended. This information is useful not only for future operations of CO2 storage in geological media, but also for current operations relating to the exploration and production of hydrocarbons.
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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 | 0.001 |
| 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 | 0.000 |
| 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".