Prediction of FPSO single point mooring line tension based on Bayesian optimization

Abstract

Oil exploitation requires offshore engineering equipment such as offshore platforms, positioning systems and drilling risers. As one of the positioning systems, the mechanical safety performance of mooring system is of great significance to offshore platforms. To ensure the safety of the mooring system under unknown sea conditions, predictive monitoring of mooring line tension magnitude is necessary. This paper uses business software Orcaflex to establish the single point mooring system model of FPSO, and then gives a mooring tension prediction method based on Training-data-set. The long short-term memory artificial neural network model is employed to establish the mooring line tension prediction model based on the parameters of the Bayesian optimization neural network model. The error analysis of the prediction results provides a valuable reference for the mooring system design.