An ILI Based Program That Prevents Reoccurrence of Post ILI Failures Seen in Industry
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Notice bibliographique
Résumé
The pipeline industry has been using Inline Inspection (ILI) since the 1970s. High resolution tools have been available for inspecting corrosion from about the 1980s and related ILI-based programs have been evolving. In this study incident rate data from the last 30 to 40 years of experience was examined and trended. Corrosion related incident rates have reduced where ILI programs have been implemented. Significant changes in programs have shown related incident reductions or positive trends. Throughout this time there have been a few post-ILI incidents and by taking a closer look at these incidents and learning from the findings the ILI-based assessments and programs were further improved. In this study, all of the post-ILI corrosion related ruptures on the TransCanada system have been closely examined and trended. The effects of program changes and related changes to performance indicators have been examined. Some significant industry failures, where data is publicly available, have also been examined. These failures have been analyzed and trended to understand significant commonalities between these failures. Data was analyzed with the intention of learning from them and applying this learning to avoid similar failures in the future. By understanding the uncertainties, technology limitations, and limits of applicability as well as the types of programs used and where these have not identified probable failures practical solutions were derived. All of the failures have been examined (as allowed by the data available) to find approaches which would have proactively identified these events, so that similar events can be avoided in the future. ILI tools generate a wealth of information and appropriate use of this information has shown to be effective in managing pipelines. However, it is also important to understand the limitations of technologies, learn from the failures, and acknowledge uncertainties so that undesirable events can be avoided.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 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) | 0,001 | 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 découle