Artificial Neural Networks for the analysis of spread‐mooring configurations for floating production systems

• ANN meta-models for preliminary design stages of FPS are presented.
• They analyze arbitrarily defined asymmetrical spread-mooring configurations.
• Performance assessed by application to a FPS for actual deepwater scenario.
• ANNs provide fairly accurate values for the parameters of the mooring response.

This work presents the development of Artificial Neural Networks for the analysis of any arbitrarily defined spread-mooring configuration for floating production systems (FPS), considering a given scenario characterized by the water depth, metocean data, characteristics of the platform hull, and the riser layout. The methodology is applied to recent designs of deepwater semi-submersible platforms connected to a large number of risers with asymmetrical layout. In such cases, the design variables may include values for the azimuthal spacing and mooring radius varying along the corners of the platform, besides the pretension and material of the lines. The results of the case study indicated that, given any mooring configuration characterized by the combination of all these design variables, the ANNs provide fairly accurate values for the parameters of the response that are required for the design of mooring systems (typically platform offsets and line tensions).