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Condition Monitoring of Subsea Pipelines Considering Stress Observation and Structural Deterioration

Chen, Linying, Arzaghi, E, Abaei, MM, Garaniya, V ORCID: 0000-0002-0090-147X and Abbassi, R ORCID: 0000-0002-9230-6175 2018 , 'Condition Monitoring of Subsea Pipelines Considering Stress Observation and Structural Deterioration' , Journal of Loss Prevention in The Process Industries, vol. 51 , pp. 178-185 , doi: 10.1016/j.jlp.2017.12.006.

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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 thenetwork 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 Type: Article
Authors/Creators:Chen, Linying and Arzaghi, E and Abaei, MM and Garaniya, V and Abbassi, R
Keywords: Pipelines Maintenance Bayesian Modeling
Journal or Publication Title: Journal of Loss Prevention in The Process Industries
Publisher: Elsevier Sci Ltd
ISSN: 0950-4230
DOI / ID Number: 10.1016/j.jlp.2017.12.006
Copyright Information:

Copyright 2017 Elsevier

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