Effective Improvements to Overcome “Blind Spots” in a Corrosion Management Program and Practical Measures to Verify Effectiveness
Bibliographic record
Abstract
The recent industry wide post-ILI pipeline ruptures due to external corrosion happened in a relatively short period of time after the ILI using high-resolution Magnetic Flux Leakage (MFL) technology. Failure investigations show that the critical defects that caused these pipeline ruptures are generally long and complex corrosion, which typically consist of a number of deep corrosion pits (i.e. localized metal-loss) within an overall shallower, but relatively large corrosion area (i.e. generalized metal-loss). This has led us to investigate the gaps and “blind spots” of the ILI-based corrosion management program particularly to find out why existing methods fail to effectively identify and remediate such critical defects before they fail. Learning from these post-ILI failures, TransCanada has developed many assessment methods and criteria for identifying challenging areas. The many types of criteria account for blind spots from different perspectives in a multi-faceted manner. The traditional ILI based corrosion management programs calculate a deterministic failure pressure ratio (FPR) and maximum anomaly depth and ensure these do not reach a limiting value. However, this strictly deterministic assessment does not acknowledge the uncertainties, particularly the significant uncertainties in the ILI measurements, assessment models, and material properties. When all uncertainties are accounted for and a probabilistic excavation criterion is used, the excavations reveal that certain anomalies are found to be near-critical in the field even though the deterministic FPR based criteria did not identify these. The probabilistic criteria identifies longer shallower anomalies, with non-critical ILI based FPR values, as anomalies that have a higher probability of exceeding the FPR criteria in-the-ditch (where the uncertainties are minimized). This is because the probabilistic criterion acknowledges that longer anomalies are more sensitive to the depth measurement error and have a higher probability of becoming critical in-the-ditch. This “blind spot” in the deterministic method was overcome by incorporating a probabilistic criterion into the corrosion management program. The effectiveness of these new measures is discussed by examining excavation results of this program and subsequent ILI results. This paper discusses the approach to corrosion management where new learning and knowledge as well as new-found uncertainties are readily accommodated. The approach is also transparent and documented; so that new information can be incorporated into the assessment and post-ILI failures can be prevented more effectively.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".