Effective Improvements to Overcome “Blind Spots” in a Corrosion Management Program and Practical Measures to Verify Effectiveness
Notice bibliographique
Résumé
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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Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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