Operational safety assessment of offshore pipeline with multiple MIC defects

Abstract Microbiologically influenced corrosion (MIC) creates multiple defects. The interaction of MIC defects and their time dependence need to be considered for robust safety assessment of the asset. This paper presents a methodology for the dynamic safety assessment of the assets under the influence of MIC. The methodology is built by integrating the Bayesian Network (BN)–Markov Mixture (MM) technique with Monte Carlo simulation. The integration of BN and MM provides an empirical model to probabilistically predict the effective defect growth rate based on the multiple defects’ interaction. A rate-dependent stochastic formulation is also developed for the remaining strength and safe operating pressure prediction using the Monte Carlo simulation. The proposed methodology dynamically predicts and captures the evolving effect of corrosion defects’ interaction and effective defect growth rate on the remaining strength and survival likelihood of an in-service corroding asset. The methodology is tested on an offshore pipeline, and the dynamic effects of corrosion influencing parameters and defects’ interaction on the pipeline survivability were predicted. Critical safety influencing factors of the pipeline under complex microbial biofilm architecture were identified. The proposed methodology provides a parametric-based condition monitoring tool for effective management of MIC and ensuring safety in offshore systems.