A review on Bayesian modeling approach to quantify failure risk assessment of oil and gas pipelines due to corrosion
No Thumbnail Available
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A2 Katsausartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2022-12
Major/Subject
Mcode
Degree programme
Language
en
Pages
18
Series
International Journal of Pressure Vessels and Piping, Volume 200
Abstract
To forecast safety and security measures, it is vital to evaluate the integrity of a pipeline used to carry oil and gas that has been subjected to corrosion. Corrosion is unavoidable, yet neglecting it might have serious personal, economic, and environmental repercussions. To predict the unanticipated behavior of corrosion, most of the research relies on probabilistic models (petri net, markov chain, monte carlo simulation, fault tree, and bowtie), even though such models have significant drawbacks, such as spatial state explosion, dependence on unrealistic assumptions, and static nature. For deteriorating oil and gas pipelines, machine learning-based models such as supervised learning models are preferred. Nevertheless, these models are incapable of simulating corrosion parameter uncertainties and the dynamic nature of the process. In this case, Bayesian network approaches proved to be a preferable choice for evaluating the integrity of oil and gas pipeline models that have been corroded. The literature has no compilations of Bayesian modeling approaches for evaluating the integrity of hydrocarbon pipelines subjected to corrosion. Therefore, the objective of this study is to evaluate the current state of the Bayesian network approach, which includes methodology, influential parameters, and datasets for risk analysis, and to provide industry experts and academics with suggestions for future enhancements using content analysis. Although the study focuses on corroded oil and gas pipelines, the acquired knowledge may be applied to several other sectors.Description
Funding Information: The authors would like to thank Universiti Teknologi PETRONAS (UTP) Malaysia for giving the opportunity to conduct research under grant number 015LC0-381 for the project “Failure Prediction Model for Stress Corrosion Cracking Using Deep Learning Approach." Publisher Copyright: © 2022 Elsevier Ltd
Keywords
Bayesian, Bibliometric analysis, Corrosion, Integrity assessment, Probability of failure, Reliability
Other note
Citation
Soomro, A A, Mokhtar, A A, Kurnia, J C, Lashari, N, Sarwar, U, Jameel, S M, Inayat, M & Oladosu, T L 2022, ' A review on Bayesian modeling approach to quantify failure risk assessment of oil and gas pipelines due to corrosion ', International Journal of Pressure Vessels and Piping, vol. 200, 104841 . https://doi.org/10.1016/j.ijpvp.2022.104841