An application of machine learning to the motion response prediction of floating assets

TL;DR

This paper introduces a machine learning framework that accurately predicts the nonlinear motion response of floating offshore assets in real-time, outperforming traditional methods and aiding operational decisions.

Contribution

It presents a novel supervised machine learning approach combining gradient boosting and a custom solver for real-time offshore vessel motion prediction, handling complex nonlinear responses.

Findings

Prediction errors less than 5% for mooring parameters

Vessel heading accuracy within 2.5 degrees

Outperforms traditional frequency-domain methods

Abstract

The real-time prediction of floating offshore asset behavior under stochastic metocean conditions remains a significant challenge in offshore engineering. While traditional empirical and frequency-domain methods work well in benign conditions, they struggle with both extreme sea states and nonlinear responses. This study presents a supervised machine learning approach using multivariate regression to predict the nonlinear motion response of a turret-moored vessel in 400 m water depth. We developed a machine learning workflow combining a gradient-boosted ensemble method with a custom passive weathervaning solver, trained on approximately $10^6$ samples spanning 100 features. The model achieved mean prediction errors of less than 5% for critical mooring parameters and vessel heading accuracy to within 2.5 degrees across diverse metocean conditions, significantly outperforming traditional frequency-domain methods. The framework has been successfully deployed on an operational facility, demonstrating its efficacy for real-time vessel monitoring and operational decision-making in offshore environments.