Risk analysis on corrosion of submarine oil and gas pipelines based on hybrid Bayesian network

Abstract

Submarine pipeline is currently the safest and most economical transportation of subsea oil and gas. Corrosion is one of the main threats to submarine pipelines because of the corrosive of the sea. Therefore, it is significative to evaluate the corrosion risk of the submarine pipelines. In this work, a fault tree model is first employed to identify the corrosion hazards in the transportation process. To overcome the limitations of the fault tree analysis for the states of the events, a Bayesian network (BN) is established through mapping from the fault tree model. Because of the limitations of objective data, experts’ judgment combining fuzzy set theory are used to determine the parameters in the BN. The dynamic nodes in the BN are divided into real-time and discrete nodes, and the dynamic characteristics of them are displayed by the evidence nodes. In this way, when a new observation occurs, the real-time risk analysis of the corroded submarine pipeline is realized. Finally, a case study is carried out to verify the risk level variation with the observing events at different moments. Results shows that the proposed model can dynamically characterize the corrosion risk of the submarine pipeline and provide suggestions for the risk reduction.