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This thesis assesses the use of the measured motions of a semisubmersible oil platform as a basis for estimating on-site wave spectra. The inference method followed is based on the wave buoy analogy, which aims at solving the linear inverse problem: estimate the sea state, given the measured motions and the transfer function of the platform. Directional wave inference obtained from the records of vessels motions is a technique that has seen its application grow significantly over the last years. As a matter of fact, its applications in ships with forward speed and ship-shaped moored platforms (such as FPSOs) have provided good results. However, little research has been done regarding the use of semisubmersible platforms as wave sensors. This is due to the fact that these platforms are designed to present no significant responses when excited by waves. Notwithstanding this, the semisubmersible platforms are characterized by measurable small motions. Moreover, if compared with ship-shaped motion-based wave sensors, the responses of the semisubmersibles are in better agreement with the response characteristics estimations obtained by means of linear hydrodynamic models. In addition, the eminently linear characteristics of the responses often lasts even for severe wave conditions. This feature results in that the semisubmersible platforms stand as a promising wave sensor even for extreme sea states, conditions in which other types of sensors (i.e. buoys, radars) may face difficulties. Throughout the text, the main results of this work are presented and discussed. These results are mainly based on a dedicated experimental campaign, carried out with a scaled model of the Åsgar-B platform, which is a semisubmersible platform located in the Åsgard field offshore Norway. Regarding the sea states tested during the experiential campaign, they were estimated by means of a motion-based Bayesian inference method, which has been developed for more than then years at the EPUSP. In order to allow the adoption of the semisubmersible platforms as a motion based wave sensors, this thesis provides two significant improvements of the method: first, a method to obtain an estimation of the linearized equivalent external viscous damping is provided. This analytical methodology allows to reduce the uncertainty of the transfer function of the platform close to the resonances of the motions and, as a consequence, it increases the accuracy of the inference approach. The second relevant contribution is the development of an alternative prior distribution, which is adopted to introduce the prior beliefs regarding the sea state in the Bayesian inference approach. It is shown that although some aspects of this novel approach require further evaluation in future work, the prior distribution developed has potential to improve the accuracy of wave estimates, and, at the same time, it significantly simplifies the calibration procedures followed by other state-of-the-art Bayesian wave inference methods. Summing up, the inference approach proposed in this work provides the bases to use each semisubmersible oil platform, which stand as the most common type of oil platforms operated offshore Brasil, as a motion based wave sensor, thus contributing to the possible broadening of the Brazilian oceanographic measurement network.

Assessing the use of a semisubmersible oil platform as a motion-based sea wave sensor.