Influence-Based Consequence Assessment of Subsea Pipeline Failure under Stochastic Degradation

Sidum Adumene, Rabiul Islam, Ibitoru Festus Dick, Esmaeil Zarei, Morrison Inegiyemiema and Ming Yang ()
Additional contact information
Sidum Adumene: School of Ocean Technology, Marine Institute, Memorial University of Newfoundland, St. John’s, NL A1C 5R3, Canada
Rabiul Islam: National Centre for Ports and Shipping (NCPS), Australian Maritime College (AMC), University of Tasmania, Launceston, TAS 7250, Australia
Ibitoru Festus Dick: Department of Marine Engineering, Rivers State University, Port Harcourt PMB 5080, Nigeria
Esmaeil Zarei: Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
Morrison Inegiyemiema: Department of Marine Engineering, Rivers State University, Port Harcourt PMB 5080, Nigeria
Ming Yang: Safety and Security Science Section, Department of Values, Technology, and Innovation, Faculty of Technology, Policy, and Management, Delft University of Technology, 2628 BX Delft, The Netherlands

Energies, 2022, vol. 15, issue 20, 1-10

Abstract: The complexity of corrosion mechanisms in harsh offshore environments poses safety and integrity challenges to oil and gas operations. Exploring the unstable interactions and complex mechanisms required an advanced probabilistic model. The current study presents the development of a probabilistic approach for a consequence-based assessment of subsea pipelines exposed to complex corrosion mechanisms. The Bayesian Probabilistic Network (BPN) is applied to structurally learn the propagation and interactions among under-deposit corrosion and microbial corrosion for the failure state prediction of the asset. A two-step consequences analysis is inferred from the failure state to establish the failure impact on the environment, lives, and economic losses. The essence is to understand how the interactions between the under-deposit and microbial corrosion mechanisms’ nodes influence the likely number of spills on the environment. The associated cost of failure consequences is predicted using the expected utility decision theory. The proposed approach is tested on a corroding subsea pipeline (API X60) to predict the degree of impact of the failed state on the asset’s likely consequences. At the worst degradation state, the failure consequence expected utility gives 1.0822 × 10 8 USD . The influence-based model provides a prognostic tool for proactive integrity management planning for subsea systems exposed to stochastic degradation in harsh offshore environments.

Keywords: subsea pipeline; under-deposit corrosion; influential risk factors; Bayesian probabilistic network; microbial corrosion; expected utility decision theory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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