THE USAGE OF ARTIFICIAL NEURAL NETWORKS IN HYDRODYNAMIC ANALYSIS OF FLOATING OFFSHORE PLATFORMS
Floating offshore structures, particularly floating oil production, storage and offloading systems (FPSOs) are still in great demand, both in small and large reservoirs, for deployment in deep water. The prediction of such vessels’ responses to her environmental loading over her lifetime is now often undertaken using response-based design methodology, although the approach is still in its early stages of development. Determining the vessel’s responses to hydrodynamic loads induced by long term sea environments is essential for implementing this approach effectively. However, it is often not practical to perform a complete simulation for every 3-hour period of environmental data being considered. Therefore, an Artificial Neural Networks (ANN) modelling technique has been developed for the prediction of FPSO’s responses to arbitrary wind, wave and current loads that alleviates this problem. Comparison of results obtained from a conventional mathematical model with those of the ANN-based technique for the case of a 200,000 tdw tanker demonstrates that the approach can successfully predict the vessel’s responses due to arbitrary loads.
Neural Network Meta-Models for FPSO Motion Prediction From Environmental Data With Different Platform Loads
The current design process of mooring systems for Floating Production, Storage, and Offloading units (FPSOs) depends on the availability of the platform’s mathematical model and the accuracy of dynamic simulations. These simulations then provide the FPSO’s time series motion which is evaluated according to design constraints. This process can be time-consuming and present inaccurate results due to the mathematical model’s limitations and the overall complexity of the vessel’s dynamics. We propose a Neural Simulator, called NeuroSim, a set of data-based surrogate models with environmental data as input, each model specialized in predicting different motion statistics relevant to mooring system design: Maximum Roll, Platform Offset, and Fairlead Displacements. The surrogate models are trained by current, wind, and wave data given in 3 hours periods at a Brazilian Offshore Basin from 2003 to 2010, and the associated dynamic response of a spread-moored FPSO is obtained through time-domain simulations using the Dynasim software. Hyperparameter Optimization techniques are performed to obtain optimal Artificial Neural Network (ANN) models specialized in different platform drafts. Finally, the proposed models are shown to correctly capture platform dynamics, providing good results when compared to motion statistics obtained from Dynasim. We conclude that an ANN surrogate model can be trained directly on actual measured metocean conditions and corresponding FPSO motion statistics to provide increased accuracy and reduced computational time over traditional methods based on dynamic simulation. Moreover, the proposed architecture can be integrated into an automated learning framework: The data-based surrogate models can be continuously fine-tuned and updated with newly measured data, improving accuracy over time.
DEVELOPMENT OF ENVIRONMENTAL COMBINATION DESIGN CRITERIA FOR FIXED PLATFORMS IN THE GULF OF MEXICO
The object of this ongoing API sponsored research study is to develop design criteria for selection of winds, waves, and currents in the Gulf of Mexico to be used in design of offshore structures. A probabilistic methodology is developed that leads to the joint probability distribution of hurricane induced winds, waves and currents for a generic site. Both analytic and statistical input is based on the historical hurricane information in the Gulf. Three different methods are compared for selection of the design combination criteria: 1) ignoring the platform response and simply focusing on the joint probability of the environmental events, 2) aiming for a target return period for the platform response, and 3) aiming for a target reliability for the platform. For fixed platforms, the results are shown to be similar based on the three selection methods; this conclusion may not be valid for other climate areas. The project Technical Advisory Committee's recommendation for fixed platforms is a combination of the 100-year maximum wave height, the 100-year wind, and 54% of the 100-year current or roughly a 2.0 knot current. This, on the average, leads to an annual safety index of 3.0 for the platform.