An integrated dynamic failure assessment model for offshore components under microbiologically influenced corrosion

The microbiologically influenced corrosion (MIC) is a serious issue that should be considered for effective riskbased integrity management of offshore systems under MIC. This paper presents a proper methodology by using a hybrid Bayesian network (BN) and Markov process to predict the MIC rate, failure probability, and critical failure year of an internally corroded subsea pipeline. The BN model is developed to probabilistically obtain the MIC rate, considering the dynamic non-linearity and interdependency among vital input factors. The effects of the nonlinear interactions of various prominent factors are evaluated, and their degree of influence is explored. The Markov process is employed to predict the failure probability, critical failure year, and the time evolution MIC pit depth distribution using the predicted MIC rate as a transition intensity. The developed model is adaptive and captures the evolving impact of MIC. The proposed integrated methodology is tested on a case study, and the most critical parameters that influence the MIC rate and system failure are identified. The proposed approach would provide an early warning guide for a timely intervention to prevent total failure of corroded subsea pipelines and associated consequences.