October 29 2018 0Comment

The influence of sea state modelling on operability

In this blog post we investigate how we currently model sea states and what the implication on workability of using these models is. As engineering takes place well before the operation, the engineering team has to make assumptions. One of these assumptions lies in how we model a sea state, as detailed wave forecasts are not yet available. A case study in this blog shows that simplifying the sea state comes at a great cost on workability, and a more efficient alternative is provided.

It is common in offshore engineering to model a sea state with a standard wave spectrum, such as the well-know JONSWAP spectrum that is used for modelling North Sea waves. These spectra make it possible to simulate offshore operations well before they take place. The main parameters for a wave spectrum are significant wave height and peak period.  Reducing a sea state to only a few parameters comes at a cost of accuracy. An actual sea state commonly consists of wind and swell components, with energy spread over direction. The best way to capture this is with a 2D spectrum: energy over period and direction.


A small case study is executed to quantify the effect of the sea state modelling on workability. A heavy lift operation taking place in the North Sea is modeled with hindcast data (2D spectra) of October 2016 to March 2017. In order to keep the example simple, it is assumed that the operation takes 10 hours to complete and it is limited by vertical crane tip motions. A vertical velocity above 0.75m/s is not allowed in the crane tip, as this leads to unsafe situations and possible damage.

The conventional method provides a workability table, that consists of allowable significant wave height per peak period and wave direction. This table is derived by assessing the motions of the crane tip with a JONSWAP spectrum in various significant wave heights, peak periods and directions.

In order to determine the workability of the operation, we will investigate every next hour if there is a weather window of at least 10 hours in our dataset. If the expected wave heights in the window are equal to or lower than the allowable value, the operation can be executed. In total there were 3802 weather windows in this example, of which 961 were workable: 25%. In other words, we would be waiting on weather 118 of the 158 days.

We can also use the 2D wave data to calculate the crane tip motions as they would have taken place. Instead of comparing significant wave heights, we now compare computed motions to the aforementioned limit. We find 1327 workable weather windows, or 35% of the total windows: an increase of 40%. Waiting on weather has reduced by 15 days to 103 days.

So far we’ve learned that a significant number of weather windows are not utilized. The opposite is also true: sometimes the operation takes place based on the workability table, but in reality the motion limits would have been exceeded. In this example, out of the 961 workable weather windows, 77 would have exceeded the crane limits. Most commonly this happens because of swells in the forecast which are not accounted for in engineering. Exceeding limits does not automatically lead to unsafe situations as many safety factors are present, but it is still worrisome.

For precisely the reasons mentioned in this blog post we have decided to use 2D wave forecasts in MO4. It greatly improves the accuracy of the motion forecasts.