Optimal design of mooring systems for floating production units based on metamodel-assisted Differential Evolution

This work presents a new approach to the optimal design of mooring systems for oil & gas floating production units (FPU). The proposed optimization procedure integrates an efficient optimization algorithm, the epsilon-Constrained Differential Evolution, with Artificial Neural Networks (ANNs) as metamodels that evaluate the candidate solutions along the optimization loop, effectively replacing the computationally expensive Finite Element nonlinear time-domain simulations. The ANNs are trained with data from time-domain simulations of representative sample configurations. This integration of a metamodel and an optimization algorithm into a unified framework for the optimal design of FPU spread mooring systems comprises an innovative tool for such applications. Other innovative aspects include enhancements in the modeling of the optimization problem by defining design variables, objective function and constraints that are more simple, direct, and appropriate to actual taut leg and semi-taut leg configurations. A case study considering a real VLCC-sized spread-moored FPSO operating in a deep-water field is presented to illustrate the practical application of the optimization tool. The results indicate that, with virtually no human supervision, the tool can provide feasible solutions with noticeable overall cost reductions (in terms of the CAPEX of the system), and with drastic reductions of total computational requirement.