Variability of Extreme Riser Responses due to Wave Frequency Motions of a Weather-Vaning FPSO

Abstract

The variability of extreme responses of a flexible riser due to wave frequency motions of weather-vaning FPSO is investigated numerically. The objective of this study is to examine such variability in isolation from that caused by the low frequency (slow drift) vessel motions and vessel offsets. Investigation of the extreme value distributions of flexible risers provides the statistical foundation for flexible riser Response Based Analysis (RBA) for use in system design; the determination of the statistical properties of extreme flexible riser responses provides a method for the prediction of extreme responses of offshore systems in cyclonic conditions. A case study conducted in OrcaFlex included an FPSO vessel with a Lazy-S configured riser system. Five riser responses were selected in critical locations including tension, heave, and curvature responses. The environmental cases included two cyclonic storms consisting of multiple half-hour intervals. For each interval, time domain simulations included 40 wave realizations in order to provide a dataset for robust fitting of the extreme value distributions in the Gumbel format. Once the short term interval distributions were established, response distributions in a storm were generated by multiplying the short term distributions and the most probable maximum (MPM) response in a storm computed. A comparison of maximum interval, storm and 3-hour MPMs is presented, which indicates to what extent the MPM response in a storm exceeds the corresponding maximum interval response. Differences between the tension and heave responses are compared with those observed in the curvature responses. This study was limited to riser excitation by waves, current and wave frequency motions of a turret moored FPSO and it is expected that further inclusion of low frequency motions would contribute to the response variability. The inclusion of such variability will ultimately enable the storm-based statistical approach to be used for the development of long-term distribution of the riser responses.