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

Sidum Adumene, Rabiul Islam, Ibitoru Festus Dick, Esmaeil Zarei, Morrison Inegiyemiema, M. Yang

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
970 Downloads (Pure)


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 (Formula presented.) The influence-based model provides a prognostic tool for proactive integrity management planning for subsea systems exposed to stochastic degradation in harsh offshore environments.

Original languageEnglish
Article number7460
Pages (from-to)1-10
Issue number20
Publication statusPublished - 2022


  • subsea pipeline
  • under-deposit corrosion
  • influential risk factors
  • Bayesian probabilistic network
  • microbial corrosion
  • expected utility decision theory


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