Wellbore Efficiency Model for CO2 Geological Storage Part I: Theory and Wellbore Element
Bibliographic record
Abstract
Abstract Storing carbon dioxide (CO2) in deep geological formations is one part of the carbon capture and storage (CCS) process. The process of storing CO2 into deep geological formations is defined as geological CO2 sequestration or CO2 geo-sequestration. Injecting CO2 into a reservoir does not guarantee safe storage because CO2 could leak back to the surface and/or may contaminate specific strata where other energy, mineral and/or groundwater resources are present. Two mechanisms control assurance of storage integrity, which are containment within the geological structure and minimizing the potential for wellbore leakage (Espie, 2005). This study is concerned with describing the wellbore leakage mechanism utilizing wellbore element, which will be extended to the whole wellbore system. To get a better chance of success in practice, successful CCS depends on the small-scale leakage problem associated with localized flow along wellbores. Our knowledge of wellbore performance for storage purposes is still weak. The paper is the first of a series to model wellbore element analytically by introducing wellbore sealing efficiency index. Wellbore sealing efficiency can be used as a ranking criteria between different wellbore elements in the same well and/or different wells in the same locality. Moreover, safety performance of wellbore element can be assessed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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 teacher head, 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".