A critical and bibliometric review of life cycle cost analysis integration into decision support systems for pipeline asset integrity management
Notice bibliographique
Résumé
• Maintaining pipeline integrity is essential for safety, environmental protection, and energy security. • Traditional pipeline management is reactive, leading to high costs, safety risks, and inefficiencies. • Life Cycle Cost Analysis-based Decision Support Systems (LCCA-DSS) improve pipeline management by optimizing costs and risks. • There is limited research on integrating LCCA and DSS for pipeline integrity, highlighting a major gap. • North America leads research in this field, while South America and Africa have minimal contributions. Pipelines play an important role in the worldwide oil and gas industry, allowing hydrocarbons to be transported over long distances. Maintaining their integrity is critical to environmental preservation, energy security, and community safety. Traditional pipeline assets management has been mainly reactive, addressing faults after they occur, resulting in inefficiencies, safety issues, and increased costs. The challenges are worsened by aging pipeline infrastructure, emphasizing the importance of a proactive approach throughout the pipeline’s life cycle. Life Cycle Cost Analysis-Based Decision Support Systems (LCCA-DSS) provide a novel solution that combines advanced data analytics, risk assessment, and optimization algorithms. By taking into consideration the cost of construction, operation, maintenance, and decommissioning, these systems enable proactive decision-making. A bibliometric review using Elsevier’s Scopus and Web of Science databases found extensive research activities on DSS with 127,719 and 14,450 documents identified respectively. Similarly, and LCCA has 3,951 documents in Scopus and 2,128 in web of science. However, only 77 documents in Scopus and 5 Web of science addressed the integration of LCCA and DSS. Regarding DSS and pipeline integrity management, 29 documents were found in Scopus, while none in Web of science. Likewise, integration of LCCA and pipeline integrity management revealed only one document in Scopus and none in web of science at the time the data was collected. Indicating a limited research effort in this domain. The Study reveal that North America, Europe and Asia are the main contributors, with the United State leading with 19 contributions, followed by Canada with 14, and China with 10, while South America and Africa are the regions that shows minimal research activity in this field. By integrating LCCA-based DSS into reality, pipeline asset integrity management will be transformed, and oil and gas infrastructure will have a reliable, economical, and sustainable future. Based on this, a comprehensive LCCA-based DSS framework was developed, it is anticipated that the implementation of this framework can increase pipeline management effectiveness, lower costs, and improve safety by addressing technical, financial, and operational challenges. Moreover, more research is required, since this study highlights the gaps in the current body of knowledge.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,130 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,086 | 0,119 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,008 | 0,007 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».