Risk Based Integrity Modeling of Gas Processing Facilities using Bayesian Analysis

Abstract The deterioration of a gas processing asset has a major impact on the continuous operation of the facility. Risk based integrity modeling renders a framework to quantify the risks posed by an ageing asset and hence to protect the human life, financial investment and the environment from the consequences of its likely failure. In risk based integrity assessments, the structural degradations are modeled using prior probability distributions. With the availability of inspection data, this prior can be updated to posterior using Bayes theorem. This posterior probability can then be used to estimate the likelihood of component failure, which would be subsequently used in quantifying the potential ageing risks to the facility. The first part of this paper presents a framework for risk based integrity modeling considering the various types of structural degradations; namely, corrosion and cracking. The stochastic degradation models for uniform, pitting, and erosion corrosion are developed, along with models describing stress corrosion, corrosion fatigue, and hydrogen induced cracking. The second part presents a methodology to develop the prior and likelihood models for various types of corrosion and cracking degradations applicable to components in gas processing facility. In the present study, several statistical tests were conducted based on the data extracted from literature to check which of the prior distributions best describes the data. Similarly, the likelihood distribution for corrosion has been established using the life inspection data from an ageing process facility. Once the underlying distribution has been confirmed, the parameters of the distributions are estimated using the method of maximum likelihood. The third part of this paper presents the development of posterior distributions using the selected priors and the likelihoods. The Metropolis-Hastings algorithm has been used to estimate the posteriors and the estimated parameters are compared with the values obtained from Laplace approximation method.