System Identification of Offshore Jack-up Platform

As demand for offshore exploration and production of oil and gas continues to increase, structural health monitoring (SHM) for offshore structures has become an increasingly important R&D topic. The objective of this study is to develop a robust and efficient numerical strategy for system identification of offshore structures, using jack-up platform as an example. The strategy based on Genetic Algorithms (GA) is used involving search space reduced method (SSRM). In order to reasonably capture the structural status under an uncertain sea state, structural parameters are identified together with the hydrodynamic coefficients. The offshore industry makes extensive use of self-elevating drilling platforms. Known as jack-up units, these platforms are composed of a floatable hull and a number of vertical legs, and the common type is independent three legged jack-up platform. Jack-up units which are primarily used in shallow water are recently considered to be used in deeper water and for longer durations as production platforms. This study focuses on identifying the jack-up platform under the random sea state. The external forces are water forces including wave and current as the main source of external excitations over the contribution from wind. Linear wave theory, Pierson-Moskowitz (P-M) wave spectrum and Morison's equation are used to calculate the wave forces according to the situations in which the offshore structures are involved. The equation of motion is derived to represent the dynamic system of the offshore structure and applied to the identification based on GA. Some numerical results are derived by SSRM, which can enhance the optimization procedure over using the improved GA based on migration and artificial selection (iGAMAS) alone by properly rescaling the search range before each iGAMAS run. Noise effects are also considered in the identification. Due to the large size of the jack-up platform, a "divide-and-conquer" strategy is adopted by means of substructural identification (SSI) to improve the computational accuracy and efficiency.

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