Guidelines to Develop Fitness-for-Service Assessments in Oil Pipelines Exposed to Corrosive and Geotechnical Environments
Notice bibliographique
Résumé
Industry standards (i.e. API 1160, ASME B31.4 and B31.8S-2001, CSA-Z662-2003) and regulations (i.e. US DOT 49 Parts 195-2002 and 192-2003, and NEB On-shore 99) have delineated the risk-based elements of oil and gas pipeline integrity management programs. A Fitness-For-Service Assessment is part of an overall Integrity Management Program that is implemented for the pipeline system depending on the required pipeline operational conditions, severity of integrity threats, and their impact or consequences to the public, environment and employees. This paper provides guidelines for pipeline operators of oil pipeline systems exposed to corrosive and geotechnical sensitive environments and high consequence areas to develop long term integrity plans. In this case, the pipeline integrity plans were prepared based on the integration of data and assessments such as metal loss, geometry and strain in-line inspections, product corrositivity, cathodic protection, geotechnical hazard identification, and pipe class location/high consequence areas. Guidelines for developing near-term integrity plans are herein provided based on best industry practices and regulations. In 2002, Oleoducto Central S.A. (Ocensa) and CC Technologies initiated the Phase 1 of the Fitness-for-Service assessment of 698 km of NPS 16/30 crude oil pipeline from Cupiagua to Coven˜as. Phase 1 was comprised of an internal corrosion study to assess the corrosivity of the product and its impact in the future. Corrosivity of the crude oil was determined from laboratory testing and correlated to the pipeline operational and topographical conditions. In 2003, the Phase 2 of the Fitness-for-Service assessment was comprised of a review of the near-term maintenance program and the development of the long-term maintenance program. The long-term integrity plan program for corrosion features was developed using a deterministic and probabilistic corrosion growth modeling to determine excavation/repair and re-inspection interval alternatives. The corrosion growth modeling took into account the in-line inspection tool accuracy based on the field validation program. The most cost effective alternative was identified by using a cost benefit analysis technique. This implemented approach contributed to timely schedule maintenance activities. In addition, the selected excavations confirmed with high confidence the results from the Ocensa-CC Technologies Canada predictability model. Geometry features reported by the geometry/inertial in-line inspection were initially evaluated, and correlated to the corrosion in-line inspection data, and the geotechnical hazard study to identify potential locations of slope instability, river-crossing scouring for assessing internal corrosion criticality. Strain areas were also assessed and correlated to pipe wall deformation and potential areas of land movement. Pipe class location limits were determined based on latest dwelling locations and distribution, and then correlated to the reported corrosion features for verifying minimum safety factors. The long-term maintenance program was integrated from all assessments performed on the identified integrity threats. As a result, guidelines were prepared for implementing technically sound and economically-optimized long-term inline inspection, excavation and repair plans.
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Comment cette classification a été obtenuedéplier
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) | 0,000 | 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».