Inverse Gaussian process-based corrosion growth model for energy pipelines considering the sizing error in inspection data

Abstract

This paper describes an inverse Gaussian process-based model to characterize the growth of the depth of corrosion defects on underground energy pipelines based on inspection data. The model is formulated in a hierarchical Bayesian framework, which allows consideration of uncertainties from different sources. The Markov Chain Monte Carlo (MCMC) simulation techniques are used to evaluate the probabilistic characteristics of the model parameters by incorporating the defect depths reported by multiple in-line inspections (ILIs) as well as the prior knowledge about these parameters. The bias and random scattering error associated with the ILI tool as well as the correlation between the random scattering errors associated with different ILI tools are considered in the analysis. An example involving real ILI data collected from an in-service pipeline is employed to illustrate the application of the growth model. 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.