SCC Detection and Mitigation Based on In-Line Inspection Tools
Bibliographic record
Abstract
The focus of the Enbridge Integrity Management System is to prevent leaks or ruptures caused by all duty-related pipe deterioration including SCC. As with all pipe defect types, ongoing monitoring programs are employed to determine whether SCC has occurred. Where it has, preventative maintenance programs are employed to mitigate the SCC. Where required, Enbridge employs high-resolution crack in-line inspection (ILI) as the most precise method for managing SCC. As a member of the Canadian Energy Pipeline Association (CEPA), Enbridge participated in the development of a basic framework for SCC management programs and has adopted this framework as the basis for the Enbridge program. Ultrasonic crack detection ILI, capable of detecting SCC, has been employed on over 3000 km of Enbridge pipe and several hundred investigative excavations have been conducted in relation to the ILI data. The results gathered from these investigations have been trended to define the effectiveness of crack detection ILI to detect, size, and discriminate SCC defects. This paper and presentation describes Enbridge’s experience utilizing ultrasonic crack detection ILI for SCC management. The Enbridge trends have shown that ILI can be reliably utilized to detect SCC but, additional innovation is required for defect sizing. While ILI sizing is limited, trends developed from field inspection data have provided the ability to categorize ILI signals into general classifications that ensure all relevant SCC features are highlighted. The categorization is accurate but added precision would reduce the number of investigative excavations, which currently, are also conducted on many sub-relevant features. Coincident with SCC activities driven by ILI data, trends were also developed for peripheral aspects such as field NDT technology, fitness-for-purpose equations, and SCC initiation and growth causes. Observations and trends related to these activities are also described herein.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".