FPSO/LNG hawser system lifetime assessment by Gaidai multivariate risk assessment method

Abstract

Floating Production Storage and Offloading (FPSO) unit being an offshore vessel, storing and producing crude oil, prior to crude oil being transported by accompanying shuttle tanker. Critical mooring/hawser strains during offloading operation have to be accurately predicted, in order to maintain operational safety and reliability. During certain types of offloading, excessive hawser tensions may occur, causing operational risks. Current study examines FPSO vessel’s dynamic reactions to hydrodynamic wave-induced loads, given realistic in situ environmental conditions, utilizing the AQWA software package. Current study advocates novel multi-dimensional spatiotemporal risks assessment approach, that is particularly well suited for large dataset analysis, based on numerical simulations (or measurements). Advocated multivariate reliability methodology may be useful for a variety of marine and offshore systems that must endure severe environmental stressors during their intended operational lifespan. Methodology, presented in this study provides advanced capability to efficiently, yet accurately evaluate dynamic system failure, hazard and damage risks, given representative dynamic record of multidimensional system’s inter-correlated critical components. Gaidai risk assessment method being novel dynamic multidimensional system’s lifetime assessment methodology. In order to validate and benchmark Gaidai risk assessment method, in this study it was applied to FPSO and potentially LNG (i.e., Liquid Natural Gas) vessels dynamics. Major advantage of the advocated approach is that there are no existing alternative risk assessment methods, able to tackle unlimited number of system’s dimensions. Accurate multi-dimensional risk assessment had been carried out, based on numerically simulated data, partially verified by available laboratory experiments. Confidence intervals had been given for predicted dynamic high-dimensional system risk levels.