A Compliance-based Approach to Well Integrity Management
Bibliographic record
Abstract
Abstract This paper describes a process for actively managing well integrity throughout the development life cycle with a particular focus on the production operations / well maintenance phase. Although developed in relation to ConocoPhillips UK North Sea development wells, most of the features of the process have application to all development well operations (both onshore & offshore) under any regulatory regime. The original well integrity management process was developed in order to demonstrate compliance with the UK Offshore Installations & Wells (Design & Construction, etc) Regulations 1996 (DCR). Since that time the original process has continued to be modified to improve effectiveness & efficiency. As well as describing the current process, this paper also describes its development and how we see that the process might evolve in the future. DCR regulations form a part of the UK ‘safety case’ regime. The ‘safety case’ regime places a strong focus on the development of site-specific performance standards; it also utilises the principles of independent examination & verification in order to demonstrate compliance. The process described in this paper represents a significant new contribution to the management of major accident hazards in well operations since it focuses on the use of performance standards and independent examination in order to ensure well integrity.
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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 | 0.041 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".