In recent years, offshore reservoirs have been developed in deeper and deeper water environments, where floating production, storage and offloading (FPSO), semi-submersibles, spars and TLPs are considered to be the most economically viable platforms. Steel Catenary Risers (SCRs) are being considered for these production units in deepwater development such as Northern North Sea. A variety of uncertainties are associated with material behaviour, environmental loading, hydromechanics modelling, structural modelling, and fatigue / corrosion / wear characteristics, especially at hang-off and touch down points. To improve the understanding of SCR behaviour and increase the confidence in the design of such systems in deep water environment, a probabilistic reliability-based methodology that systematically accounts for the inherent uncertainties is needed. By using a probabilistic mechanics approach, the existing deterministic design/analysis methods are improved with introduction of uncertainties in model parameters. This paper concentrates on the probability of failure associated with the current design practice of fatigue analysis of SCRs. A probabilistic methodology for fatigue reliability is developed, which utilizes deterministic cumulative fatigue damage indicators calculated from DeepC, namely the stress levels and cycles associated with the various sea states and the fatigue strength of the members. Uncertainties in structural load and material properties are accounted for by assigning probability distributions and standard deviations to the deterministic stress levels. Furthermore, fatigue strength parameters, Miner’s indices, and capacities are modelled as random variables. First order reliability method (FORM) and second order reliability method (SORM) are employed for estimating fatigue reliability. The methodology is applied to two deterministic case studies, involved either a semi-submersible or a FPSO platform. The effect of uncertainties in parameters on fatigue reliabilities is investigated. Additional insight is gained from the parametric sensitivity studies on the fatigue strength parameters.

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