A Novel Failure Mangement Framework for SAGD Well Integrity
Bibliographic record
Abstract
Abstract Bow-tie approach was originally implemented for safety management system. The theory behind the bow-tie approach can be found in the Swiss cheese model by British psychologist James T. Reason (1990). The bow-tie has become popular as a structured method to assess risk where a qualitative approach is not possible or desirable. Bow-tie method can accommodate multiple outcomes and simultaneous multiple failure events. Bow-tie method avoids repeating the barrier analysis which is the old version of bow-tie method for multiple scenarios. In another word bow-tie method assimilates an accident scenario to a sequence of events, and barriers are mitigating the event results. Although Bow-tie method has been used in many industries, it is an unknown technique in oil and gas. The features attributes to bow-tie diagram are mostly designed for failures which are not attributed to location as depth or timing as operation versus abandonment timing. Due to these down sides the failure management diagram (FMD) is suggested which handles these challenges with bow-tie diagram. In this method the incidents causing the well shut-in or regulatory actions is presented as incidents and failures which yield to these incidents such as buckled casing of micro-annulus in cement which is not treated as failure events and things which may cause these failure events are called hazards. In this study the framework of FMD is presented for different well elements (i.e., casing, cement, and liner) during different stage of its life. Also the risk assessment analysis using fuzzy sets theory and Dempster-Shafer theory (DST) is discussed and results are discussed for different well elements.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".