Probabilistic characterisation of metal-loss corrosion growth on underground pipelines based on geometric Brownian motion process

Abstract

This article describes a geometric Brownian motion process-based model to characterise the growth rate of the depth of corrosion defects on underground steel pipelines based on inspection data subjected to measurement uncertainties. To account for the uncertainties from different sources, the hierarchical Bayesian method is used to formulate the growth model, and the Markov Chain Monte Carlo simulation techniques are used to numerically evaluate the probabilistic characteristics of the model parameters. The growth model considers the bias and random scattering error associated with the in-line inspection (ILI) tool as well as the correlations between the random scattering errors associated with different ILI tools. The application of the growth model is illustrated through an example involving real ILI data collected from an in-service pipeline in Canada. The results indicate that the model in general can predict the growth of corrosion defects reasonably well. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.

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“…Other application examples of hierarchical Bayesian approach include (Zhang et al, 2014, Zhang, 2014. The approach has also been similarly applied in stochastic processes such as geometrical Brownian motion (Zhang and Zhou, 2015) and inverse Gaussian process (Zhang et al, 2013), both for pipeline degradation modelling.…”