Abstract

A long-distance pipeline is a crucial connection for oil and gas (O&G) producing areas and demand locations. Pipeline failure may be caused by various factors, which can escalate into a disastrous event, causing huge loss of life and property. Reliable failure assessment is urgently needed to avoid the occurrence of pipeline failure and reduce losses effectively. Owing to a lack of historical pipeline data and the impact of various uncertainties, this study presents a model that systematically integrates Bayesian network (BN), fuzzy theory, and analytic hierarchy process (AHP) to analyze the probability of pipeline failure. In the proposed model, factors contributing to O&G pipeline failure are obtained from the literature, database searches, and expert questionnaires. AHP is used to establish a hierarchical network represented by a causality diagram. The hierarchical network is mapped to a BN structure, and the conditional probability of nodes in the BN is obtained by AHP and expert judgment. The prior probabilities of basic factors are analyzed by expert opinion with fuzzy theory in the absence of historical data. The model can be used for the quantitative analysis of pipeline failure. Moreover, diagnostic analysis can also be performed by updating the probabilities in the BN model with new information. The feasibility and rationality of the model are validated in a gas pipeline, which indicates that the proposed model can provide effective decision-making for pipeline managers to prevent and manage pipeline failures.

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Data Availability Statement

All data, models, and code generated or used during the study appear in the published article.

Acknowledgments

The authors gratefully acknowledge the support provided by the Strategic Cooperation Technology Projects of CNPC and CUPB (ZLZX2020-05). Thanks are also given to the experts for their input and anonymous reviewers their helpful suggestions, which improved the manuscript.

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Journal of Pipeline Systems Engineering and Practice
Volume 14Issue 1February 2023

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Received: Feb 2, 2021
Accepted: Aug 18, 2022
Published online: Oct 31, 2022
Published in print: Feb 1, 2023
Discussion open until: Mar 31, 2023

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College of Mechanical and Transportation Engineering, China Univ. of Petroleum-Beijing, Fuxue Rd., Changping District, Beijing 102249, China. ORCID: https://orcid.org/0000-0002-2842-0862. Email: [email protected]
Lei Hou, Dr.Eng. [email protected]
Professor, College of Mechanical and Transportation Engineering, China Univ. of Petroleum-Beijing, Fuxue Rd., Changping District, Beijing 102249, China (corresponding author). Email: [email protected]
College of Mechanical and Transportation Engineering, China Univ. of Petroleum-Beijing, Fuxue Rd., Changping District, Beijing 102249, China. Email: [email protected]
College of Mechanical and Transportation Engineering, China Univ. of Petroleum-Beijing, Fuxue Rd., Changping District, Beijing 102249, China. Email: [email protected]
Kai Yang, Dr.Eng. [email protected]
College of Mechanical and Transportation Engineering, China Univ. of Petroleum-Beijing, Fuxue Rd., Changping District, Beijing 102249, China. Email: [email protected]
Jiaquan Liu, Dr.Eng. [email protected]
College of Mechanical and Transportation Engineering, China Univ. of Petroleum-Beijing, Fuxue Rd., Changping District, Beijing 102249, China. Email: [email protected]

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