A Unified Approach to Well Integrity Evaluation Led to Better Decision Making for Workovers in Mature Wells in the Waddell Ranch, West Texas
Bibliographic record
Abstract
Abstract The often referenced Norwegian standard D-0101 describes well integrity as the "application of technical, operational and organizational solutions to reduce the risk of uncontrolled release of formation fluids throughout the life cycle of a well". Well integrity can therefore be considered in terms of the structural soundness of the well, its casing, cement and wellhead; and how it reduces the risk of the uncontrolled flow of formation fluids to other formations or surface. Well integrity is a critical issue when working over and recompleting mature wells. This paper describes common integrity issues and develops a workflow for managing well integrity information during the design, execution, and evaluation of workover operations. It was found that no one measurement can give a complete well integrity analysis; instead a unified systematic approach considering a variety of measurements will lead to successful, economical, and well executed workovers. There is a great deal of value in obtaining certainty about the status of a wellbore from a clear logging and testing regime. The type of logging package will depend on the economics of the recompletion campaign, but the cost of logging operations pales in comparison to a lost hydraulic fracture or series of expensive squeezes. The evaluation and quantification of workover rates, reserves and economics in this field are not within the scope of this paper, and will not be addressed here.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".