Analysis of Metal-loss Corrosion on Energy Pipelines Based on ILI Data
A Bayesian Approach
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This book describes the models of calibrating the high-resolution in-line inspection (ILI) tools for sizing metal-loss corrosion defects and to characterize the growth of individual defects on energy pipelines. The models are developed in a Bayesian Framework. The calibration of ILI tools is carried out by comparing the field-measured depths and ILI-reported depths for a set of static defects. And the probabilistic characteristics of the parameters involved in the growth model are evaluated using Markov Chain Monte Carlo (MCMC) simulation technique based on ILI data collected at different time...
This book describes the models of calibrating the high-resolution in-line inspection (ILI) tools for sizing metal-loss corrosion defects and to characterize the growth of individual defects on energy pipelines. The models are developed in a Bayesian Framework. The calibration of ILI tools is carried out by comparing the field-measured depths and ILI-reported depths for a set of static defects. And the probabilistic characteristics of the parameters involved in the growth model are evaluated using Markov Chain Monte Carlo (MCMC) simulation technique based on ILI data collected at different times for a given pipeline. Moreover, a methodology is described to evaluate the time-dependent system reliability of a segment of a pressurized pipeline containing multiple active corrosion defects based on ILI data. Both the conventional Monte Carlo simulation and MCMC simulation techniques are employed in the methodology to evaluate the failure probability of the pipeline. The methodology considers three distinctive failure modes, namely small leak, large leak and rupture, and incorporates the hierarchical Bayesian power-law growth model for the depth of individual corrosion defect.
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