Well Integrity for Fracturing and Re-Fracturing: What Is Needed and Why?
Bibliographic record
Abstract
Abstract Fracturing, multiple fracturing and refracturing often create peak loads for well design. The scope of this work is to identify areas of potential damage in older wells being considered for refracturing so that those wells can be flagged for further investigation and repair. Removing damaged well stock from consideration before a well integrity failure can occur protects the environment and also allows time for repairs that protect the future value of the well stock. New and older well conditions and stimulation methods are discussed with a view towards identifying peak-load factors. Laboratory work and literature study results are also cited to document the connection to previous work and relate literature findings to current stress load causes and isolation damage prevention. Well integrity investigation methods include pressure testing, cement evaluation tools (CET), cement and stimulation pump charts, downhole imaging devices (cameras, calipers, electrical logging tools), and other approaches. New well fracturing, and particularly multi-fracturing completions can produce stresses on the cement-to-pipe and cement-to-formation seals, although the damage, depending on specific conditions can be minor to moderate and changes to construction techniques can eliminate or minimize most problems. Re-fracturing of wells opens several new areas of concern including corrosion, erosion, and production related issues such as subsidence that may result in a higher risk to barrier elements. The results of the work illustrate construction methods that can produce wellbores capable of handling significant multi-fracturing stimulation. In addition, risks of re-fracturing damage on damage to older wells are rated with monitoring and remedial operations as part of the discussions. Finally, frac hits (a fracture from one well intersecting and damaging an adjacent well) will be discussed with examples of several factors that are common in frac hits.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".