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Condition monitoring of subsea pipelines considering stress observation and structural deterioration


Chen, L and Arzaghi, E and Abaei, MM and Garaniya, V and Abbassi, R, Condition monitoring of subsea pipelines considering stress observation and structural deterioration, Journal of Loss Prevention in The Process Industries, 51 pp. 178-185. ISSN 0950-4230 (2018) [Refereed Article]

Copyright Statement

Copyright 2017 Elsevier

DOI: doi:10.1016/j.jlp.2017.12.006


The increasing demand by the world for energy has prompted the development of offshore oil and gas pipelines as the mode of transportation for hydrocarbons. The maintenance of these structures has also gained much attention for research and development with novel methodologies that can increase the efficiency of integrity management. This paper presents a probabilistic methodology for monitoring the condition of offshore pipelines and predicting the reliability when consideration is given to structure deterioration. Hydrodynamic simulations are carried out for an offshore pipeline to obtain the time history data from which the stress ranges are computed using a rainflow counting algorithm. To model the fatigue damage growth, a Bayesian Network (BN) is established based on a probabilistic solution of Paris’ law. Corrosion effects are also incorporated into the network providing a more realistic prediction of the degradation process. To demonstrate the application of the proposed methodology, a case study of a Steel Catenary Riser (SCR) subjected to fatigue cracks and corrosion degradation is studied. This method provided the growth rate of a crack during its lifetime during which the safety of operation can be assessed and efficient maintenance plans can be scheduled by the asset managers. The proposed method can also be applied by the designer to optimize the design of pipelines for specific environments.

Item Details

Item Type:Refereed Article
Keywords:pipelines, maintenance, Bayesian modeling, rainflow counting, fatigue crack, subsea pipeline, risk analysis
Research Division:Engineering
Research Group:Environmental engineering
Research Field:Air pollution modelling and control
Objective Division:Mineral Resources (Excl. Energy Resources)
Objective Group:Environmentally sustainable mineral resource activities
Objective Field:Environmentally sustainable mineral resource activities not elsewhere classified
UTAS Author:Chen, L (Ms Linying Chen)
UTAS Author:Arzaghi, E (Dr Ehsan Arzaghi)
UTAS Author:Abaei, MM (Mr Mohammad Abaei)
UTAS Author:Garaniya, V (Associate Professor Vikram Garaniya)
UTAS Author:Abbassi, R (Dr Rouzbeh Abbassi)
ID Code:123143
Year Published:2018
Web of Science® Times Cited:16
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2017-12-18
Last Modified:2018-11-23

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