General corrosion vulnerability assessment using a Bayesian belief network model incorporating experimental corrosion data for X60 pipe steel

Abstract

External corrosion is one of the leading causes of pipe failure in the oil and gas industry. In this study, a Bayesian belief network (BBN) model has been developed using corrosion rate (CR) data obtained from experimental test results and analytical burst failure models. The BBN model for CR was coupled with a time marching simulation to obtain corrosion defects and quantify failure pressure capacity. Finally, in a reliability framework, the failure pressure capacity was coupled with operating pressure to obtain the probability of failure. Furthermore, the developed BBN model was used to perform a parametric study to identify the critical parameters for the CR. The outcome of the study indicated that the proposed BBN model can be useful to integrate experimental and analytical models to derive reliability of a pipeline operating under various conditions.