Hierarchical Bayesian Corrosion Growth Model Based on In Line Inspection Data

Abstract

A hierarchical Bayesian growth model is presented in this paper to characterize and predict the growth of individual metalloss corrosion defects on pipelines. The depth of the corrosion defects is assumed to be a powerlaw function of time characterized by two powerlaw coefficients and the corrosion initiation time, and the probabilistic characteristics of the these parameters are evaluated using Markov Chain Monte Carlo (MCMC) simulation technique based on inline inspection (ILI) data collected at different times for a given pipeline. The model accounts for the constant and nonconstant biases and random scattering errors of the ILI data, as well as the potential correlation between the random scattering errors associated with different ILI tools. The model is validated by comparing the predicted depths with the fieldmeasured depths of two sets of external corrosion defects identified on two real natural gas pipelines. The results suggest that the growth model is able to predict the growth of active corrosion defects with a reasonable degree of accuracy. The developed model can facilitate the pipeline corrosion management program.