Neural-network-based prediction of mooring forces in floating production storage and offloading systems

This paper describes the development of a neural-network-based prediction of mooring forces of a deep-sea oil exploitation production process. The evolvement of a neural network simulator for analysis of the dynamic behavior of a system consisting of a turret-floating production storage and offloading (FPSO) system and a shuttle ship in tandem configuration is described. The turret-FPSO is a vessel with a cylindrical anchoring system fixed to the sea bed my mooring lines and a shuttle ship is connected during the oil transference. This system has quite complex dynamics owing to interactions of the forces and moments due to current, wind, and waves. In general, the mathematical model that represents the dynamics of these connected floating units involves a set of nonlinear equations requiring several parameters difficult to be obtained. In order to deal with such complexities, a neural network has been devised to simulate an FPSO tandem system. This approach opens new horizons for maintenance of mooring lines, preventing collisions of the ships.