Intelligent Optimization Design of a Deepwater Semi-Submersible Platform Mooring System Based on Multi-Task Shared Neural Networks

Abstract With the increasing demand for key technologies in deepwater oil and gas exploration, the mooring system, a critical component for the positioning of deepwater floating platforms, plays a vital role in ensuring the stable operation of offshore oil and gas platforms under harsh working conditions. In this study, we propose an optimization method for mooring systems based on Enhanced Multi-task Genetic Optimization (EMGO). Taking a semi-submersible platform mooring engineering project in the South China Sea as an example, a multi-body coupled model of the semi-submersible platform and its mooring system was established in compliance with mooring system design specifications. A custom-developed program was utilized to generate 7,751 sets of dynamic mooring system data. Subsequently, an enhanced multi-task learning (MTL) neural network was designed to predict platform offsets and mooring line axial tensions under various working conditions with high accuracy. The optimal performance of the MTL model achieved a mean squared error (MSE) of approximately 0.044 and R-Square (R²) of 0.96. Combining the enhanced MTL model with a genetic algorithm, iterative optimization was performed to converge on an optimal mooring design parameter set tailored for the semi-submersible platform. The proposed method achieved a reduction of over 20% in maximum axial tension in mooring lines and a reduction of over 10% in maximum platform offset. The EMGO algorithm demonstrated excellent capability in high-dimensional nonlinear fitting and generalization. Validation of the optimized design was performed on taut mooring systems of three additional semi-submersible platforms, confirming the feasibility and effectiveness of the optimization method under extreme environmental conditions in the South China Sea. This study introduces an innovative intelligent design method for deepwater taut mooring systems, which significantly improves the safety and reliability of ultra-deepwater platforms. It provides valuable guidance for intelligent design in offshore and marine engineering fields.