Abstract

Mooring systems that are used to secure position keeping of floating offshore oil and gas facilities are subject to deterioration processes, such as pitting corrosion and fatigue crack growth. Past investigations show that pitting corrosion has a significant effect on reducing the fatigue resistance of mooring chain links. In situ inspections are essential to monitor the development of the corrosion condition of the components of mooring systems and ensure sufficient structural safety. Unfortunately, offshore inspection campaigns require large financial commitments. As a consequence, inspecting all structural components is unfeasible. This article proposes to use value of information analysis to rank identified inspection alternatives. A Bayesian Network is proposed to model the statistical dependence of the corrosion deterioration among chain links at different locations of the mooring system. This is used to efficiently update the estimation of the corrosion condition of the complete mooring system given evidence from local observations and to reassess the structural reliability of the system. A case study is presented to illustrate the application of the framework.

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