Dynamic cost-based integrity assessment of oil and gas pipeline suffering microbial induced stochastic degradation

Abstract

This research paper presents an adaptive methodology that integrates the Bayesian network (BN) with the Monte Carlo algorithm for the cost-based assessment of an offshore asset in a harsh corrosive environment. The BN explores the various integrity influential factors from design to failure of an offshore pipeline, considering the dynamic interrelationships among core elements. This is defined based on the unstable and stochastic nature of the degradation influential variables, such as those in microbial corrosion mechanisms. The model captures the variability in the cost parameters using the dynamic discretization formalism in the BN structure to predict the corrosion integrity cost for predefined offshore operations. The Monte Carlo algorithm is used to forecast the failure characteristics of the asset based on the formulated failure functions. The cost function output is used as the baseline cost rate estimator for the multiple failure modes' cost prediction for the period under consideration. The proposed methodology is demonstrated with a subsea pipeline. The outcomes reveal the effect of the cost elements’ variability and the corrosivity index on the overall integrity management strategy. The proposed adaptive approach provides a cost-based asset integrity management tool for offshore operations suffering stochastic degradation.