Purpose -The purpose of this paper is to develop a risk-based integrity model for the optimal replacement of offshore process components, based on the likelihood and consequence of failure arising from time-dependent degradation mechanisms. Design/methodology/approach -Risk is a combination of the probability of failure and its likely consequences. Offshore process component degradation mechanisms are modeled using Bayesian prior-posterior analysis. The failure consequences are developed in terms of the cost incurred as a result of failure, inspection and maintenance. By combining the cumulative posterior probability of failure and the equivalent cost of degradations, the operational life-risk curve is produced. The optimal replacement strategy is obtained as the global minimum of the operational risk curve. Findings -The offshore process component degradation mechanisms are random processes. The proposed risk-based integrity model can be used to model these processes effectively to obtain an optimal replacement strategy. Bayesian analysis can be used to model the uncertainty in the degradation data. The Bayesian posterior estimation using an M-H algorithm converged to satisfactory results using 10,000 simulations. The computed operational risk curve is observed to be a convex function of the service life. Furthermore, it is observed that the application of this model will reduce the risk of operation close to an ALARP level and consequently will promote the safety of operation.Research limitations/implications -The developed model is applicable to offshore process components which suffer time-dependent stochastic degradation mechanisms. Furthermore, this model is developed based on an assumption that the component degradation processes are independent. In reality, the degradation processes may not be independent. Practical implications -The developed methodology and models will assist asset integrity engineers/managers in estimating optimal replacement intervals for offshore process components. This can reduce operating costs and resources required for inspection and maintenance (IM) tasks. Originality/value -The frequent replacement of offshore process components involves higher cost and risk. Similarly, the late replacement of components may result in failure and costly breakdown maintenance. The developed model estimates an optimal replacement strategy for offshore process components suffering stochastic degradation. Implementation of the developed model improves component integrity, increases safety, reduces potential shutdown and reduces operational cost.
There is a need for accurate modeling of mechanisms causing material degradation of equipment in process installation, to ensure safety and reliability of the equipment. Degradation mechanisms are stochastic processes. They can be best described using risk-based approaches. Risk-based integrity assessment quantifies the level of risk to which the individual components are subjected and provides means to mitigate them in a safe and cost-effective manner. The uncertainty and variability in structural degradations can be best modeled by probability distributions. Prior probability models provide initial description of the degradation mechanisms. As more inspection data become available, these prior probability models can be revised to obtain posterior probability models, which represent the current system and can be used to predict future failures. In this article, a rejection sampling-based Metropolis-Hastings (M-H) algorithm is used to develop posterior distributions. The M-H algorithm is a Markov chain Monte Carlo algorithm used to generate a sequence of posterior samples without actually knowing the normalizing constant. Ignoring the transient samples in the generated Markov chain, the steady state samples are rejected or accepted based on an acceptance criterion. To validate the estimated parameters of posterior models, analytical Laplace approximation method is used to compute the integrals involved in the posterior function. Results of the M-H algorithm and Laplace approximations are compared with conjugate pair estimations of known prior and likelihood combinations. The M-H algorithm provides better results and hence it is used for posterior development of the selected priors for corrosion and cracking.
The deterioration of the condition of process plants assets has a major negative impact on the safety of its operation. Risk based integrity modeling provides a methodology to quantify the risks posed by an aging asset. This provides a means for the protection of human life, financial investment and the environmental damage from the consequences of its failures. This methodology is based on modeling the uncertainty in material degradations using probability distributions, known as priors. Using Bayes theorem, one may improve the prior distribution to obtain a posterior distribution using actual inspection data. Although the choice of priors is often subjective, a rational consensus can be achieved by judgmental studies and analyzing the generic data from the same or similar installations. The first part of this paper presents a framework for a risk based integrity modeling. This includes a methodology to select the prior distributions for the various types of corrosion degradation mechanisms, namely, the uniform, localized and erosion corrosion. Several statistical tests were conducted based on the data extracted from the literature to check which of the prior distributions follows data the best. Once the underlying distribution has been confirmed, one can estimate the parameters of the distributions. In the second part, the selected priors are tested and validated using actual plant inspection data obtained from existing assets in operation. It is found that uniform corrosion can be best described using 3P-Weibull and 3P-Lognormal distributions. Localized corrosion can be best described using Type1 extreme value and 3P-Weibull, while erosion corrosion can best be described using the 3P-Weibull, Type1 extreme value, or 3P-Lognormal distributions.
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