“Waiting on weather” is a costly restraint on offshore vessel operability. Vessel operating windows are determined based on the relationships between the weather and vessel movement, and uncertainties in these predictions may result in vessel operations being ceased prematurely. To improve the efficiency of offshore operations, existing assumptions and calculations based on conventional response amplitude operators (RAOs) should be challenged and improved. A machine learning approach is presented as a means of enriching these conventional RAOs with data. The machine learning model uses sea state forecasts to predict vessel response spectra. The model is cleverly formulated to use any existing RAO as a fallback solution in the absence of sufficient data. When applied to a comprehensive real-world scenario, the model predominantly outperforms the “best” available existing RAO. The results can be used not only to improve wave-vessel response predictions, but also to improve our understanding of existing RAOs and their shortcomings. Ultimately, the work can contribute to reducing overconservatism in weather-based restrictions on offshore vessel operability.

Estimating wellhead loads from lower stack motion measurements is a practical and cost-effective approach. In this paper, a new method is proposed, which is based on system identification techniques rather than Newtonian mechanics, thus omitting reliance on uncertain and variable quantities such as lowerflex joint stiffness/damping, riser and drill pipe tension etc. The proposed method is simple and easy to apply, while maintaining accuracy. Both simulation and real-world measurement data are utilized to demonstrate and evaluate the method.

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