Inverse wave estimation from measured FPSO motions through artificial neural networks

This study develops a method to inversely estimate wave heights, periods, and directions from hull motion data through an Artificial Neural Network. Hull motion data, measured for approximately 2.5 years from a Floating Production, Storage, and Offloading vessel currently operating at the Liza field, are collected. Corresponding wave, current, and wind data are also collected. Statistical data, i.e., hourly mean and standard deviation of 6-degree-of-freedom motions along with hourly wind, current, and draft information, are selected as potential inputs for the ANN. A correlation matrix, which shows the correlation between parameters, is used to discover the correct combination of inputs. Then, different input combinations are taken into account to identify a correct combination of inputs with the highest estimation accuracy, which provides the best results when using highly correlated inputs rather than using all variables as inputs. An R-squared of 0.72 is achieved for significant wave height, with the estimated peak period and wave direction resulting in the Root Mean Square Errors of 1.45 s and 7.7 deg, respectively. The results demonstrate the practicability of the developed inverse estimation tool in offshore platform operations.