Stochastic Life Cycle Approach to Assess Wellbore Integrity for CO2 Geological Storage
Bibliographic record
Abstract
Abstract Storage sites associated with depleted oil and gas reservoirs may contain many abandoned wellbores in addition to potentially unidentified wellbores. These wellbores have historically variable quality and quantity of cement that will have undergone ranging degrees of degradation. Wellbore performance in a single wellbore is dependent on the wellbore events (i.e. pressure and temperature changes) that occur within the life of the wellbore (Fourmaintraux et al., 2005; Gray et al., 2007). There is significant uncertainty surrounding the integrity of existing wellbores due to a lack of data. Successful implementation of carbon capture and storage (CCS) will depend on solving the small-scale leakage problem associated with localized flow along wellbores. Our knowledge of oil wellbore performance under different life stages of a well is still weak. Consequently, each wellbore is unique and general conclusions about well integrity are difficult to ascertain from analyzing only a single well. Each wellbore is considered as a risk and robust tools are needed to allow for the assessment of the performance for wellbores and investigate wellbore leakage mechanism. In this paper, a full lifecycle methodology is proposed to assess wellbore integrity as a measure of the risk of leakage. The methodology identifies the key elements to model the wellbore element and incorporates the use of a statistical approach to better understand the uncertainty in the risk estimation and interaction between various parameters controlling the model.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".