On-line Monitoring System Of The FPSO Soft Arm Single Point Mooring

Oil and gas exploration in some areas of shallow sea oilfield employing,Floating Production Storage and Offloading(FPSO)has become a relatively mature development model.The mooring device,as an essential component of the FPSO,has a direct impact on the FPSO operating status.At present,in the actual application of FPSO in Bohai Sea,single-point mooring online monitoring system is installed to monitor the force of single-point mooring system.For the situation that the FPSO has suffered many accidents due to overloading of single point system in recent years,The method of using generalized regression neural network(GRNN)to establish a single point mooring force forecasting model is proposed to predict the mooring force in order to reduce the risk of overloading the single point mooring system.The main contents of this thesis are as follows.Firstly,the On-line monitoring system of the FPSO soft arm single point mooring,including summarizing its structural composition,system functions,field installation as well as usage analysis,has been studied.Based on this,it is proposed to establish mooring force forecasting model to achieve accurate prediction of mooring force to solve the problem of how to improve the early warning capability of single point mooring online monitoring system.Then,for the actual application,the method of GRNN network was used to establish the mooring force forecasting model.The target model of mooring force forecasting model is determined according to the working principle of soft arm single point mooring.And the model’s input and output vectors were determined.Based on GRNN in MATLAB the mooring force forecasting model was established.Finally,the mooring force prediction model established by GRNN is used to predict the mooring force.The forecast results are compared with the measured results.The forecasting error is less than 10%,which can meet the forecasting needs of the project and verify the feasibility of this method.