The design of riser systems can be improved if structural reliability methods are used to assess their safety and integrity and confirm that such design meets a target annual probability of failure. TTRs are typically multi–bore assemblies involving several sub-assemblies. The failure of any of the components of a TTR under extreme or service environmental conditions can lead to an immediate failure of the entire assembly and impose a direct risk of damaging the wellheads, conductors, casing and tubing hangers, or other subsea equipment, because they are installed directly on top of the wellhead. However, the actual strength safety of the TTR cannot be examined unless after it is installed and examined under extreme events. Because of the numerous uncertainties associated with the design of TTRs, a probabilistic approach based on structural reliability methods can account for many of those uncertainties and serve as a basis for their reliable and cost-effective design. In turn, a comprehensive reliability assessment of a TTR requires extensive analysis that is considerably more complex and time consuming compared to a conventional deterministic-based analysis. This paper presents a probabilistic-based simplified methodology for the strength reliability assessment of TTR systems. In this method, marginal values on some uncertain model inputs are considered similar to the conventional analysis methods but, some key random variables related to environmental demands and component capacities are considered with their associated probability distributions. As a result, this method can be used to estimate the minimum level of safety of the TTR under extreme events. Additionally, results of the proposed method are discussed for integrity analysis and integrity-based optimal design of the TTR system, which compare the safety of the TTR components and estimate the component Optimality Factors for improving the design integrity and meeting a target minimum annual probability of failure.
A Comprehensive Approach to Account for Weather Uncertainties in Ship Route Optimization
