Risk-based Integrity Modeling for the Optimal Replacement Decisions of Offshore Process Components

摘要

Ageing of components is a major threat to asset integrity in offshore process facilities. A robust replacement strategy mitigates the effects of age-based structural degradations and reduces the threat of failure. Failure caused by structural degradations is a stochastic process. This has to be taken into consideration in the design of the optimal replacement strategy. Risk based integrity modeling (RBIM) is a newly-developed methodology that aims at the protection of human life, financial investment, and environment against the consequences of failure. RBIM quantifies the risk to which individual components are subjected and uses this as a basis for the design of a replacement strategy. The major age-based degradations to be dealt with are identified as uniform corrosion, pitting, erosion corrosion, stress corrosion cracking, corrosion fatigue cracking, and hydrogen induced cracking. The degradation processes are modeled using Bayesian prior-posterior analysis. Field non-destructive test (NDT) data is used in the analysis. The consequences of failure are modeled using engineering economic analysis by estimating the costs of failure, inspection and maintenance. The cost of failure includes loss of breakdown, loss of shutdown, cost of spill cleanup, loss caused by environmental damage and liability. The annual equivalent of failure cost, known as the failure recovery cost, decreases with the age of component. The annual inspection and maintenance cost increases with age due to strength degradations and subsequent wall loss. The total annual equivalent cost (AEC) of owning and operating facility is the summation of annual equivalent costs of failure, inspection and maintenance. For all identified degradations, the cumulative posterior failure probability is combined with AEC to produce the operational risk curve for components. Since the overall risk curve is convex function of service life, the optimum replacement interval is the global minimum point. The Bayesian analysis with Monte Carlo simulations is used to model uncertainty in the analysis.