Neural Network Meta-Models for FPSO Motion Prediction From Environmental Data

Abstract

The current design process of mooring systems for FPSOs is highly dependent on the availability of the platform’s mathematical model and accuracy of dynamic simulations, through which resulting time series motion is evaluated according to design constraints. This process can be time-consuming and present inaccurate results due to the mathematical model’s limitations and overall complexity of the vessel’s dynamics. We propose a Neural Simulator, a set of data-based surrogate models with environmental data as input, each specialized in the prediction of different motion statistics relevant to mooring system design: Maximum Roll, Platform Offset and Fairlead Displacements. The meta-models are trained by real current, wind and wave data measured in 3h periods at the Campos Basin (Brazil) from 2003 to 2010 and the associated dynamic response of a spread-moored FPSO obtained through time-domain simulations using the Dynasim software. A comparative analysis of different model architectures is conducted and the proposed models are shown to correctly capture platform dynamics, providing good results when compared to the statistical analysis of time series motion obtained from Dynasim.