Through Tubing Casing Break Detection: An EM Thickness Tool Application
Bibliographic record
Abstract
Abstract Maintaining healthy well integrity in heavy-oil thermal wells is a challenge. Steam causes significant variation in temperature along the wellbore resulting in excessively high stresses that can result in parted casing or even a complete casing failure. Determining casing break/failure condition is an important part of managing thermal well integrity. Identifying potential risks based on findings from electromagnetic (EM) casing inspection logs is essential to plan mitigation actions. EM casing inspection logs can also be run prior to putting wells on steam injection to establish a baseline and regularly evaluate well integrity via a time-lapse methodology. In this paper, we have outlined the successful application of Pulsed Eddy Current (PEC) Electromagnetic casing inspection technology in thermal well integrity. One of the major benefits of PEC casing inspection technology is a reliable method to locate casing breaks. It provides casing inspection without retrieving tubing (first pipe) that saves time and costly workover. This paper briefly explains the PEC technology, how it has been deployed, and the methodology developed to quickly and clearly identify casing breaks because one of the evaluation challenges is that the typical thermal casing break occurs in the vicinity of the casing collar. We have demonstrated in the paper validation of the PEC technology for detecting casing breaks. It includes downhole comparison with a traditional multi-finger caliper log after the tubing is pulled. The paper includes some case study examples of how PEC has been used to successfully identify casing breaks and conclude with a summary collected over five years of PEC application with a 100% success rate in more than 200 Canadian heavy oil wells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".