Tendering is always a stressful process: there never seems to be enough time and budget to do the work the way you would like to. Further, a lot of things are still unclear. However, it is extremely important, as it lays the foundation of the project and the companies’ project pipeline of works. Important aspects of the project are included in the tender, a critical one is the expected waiting on weather time (WoW). Should we offer our services on a lump sum, or on a day rate, and under what conditions? In which months should I be able to complete my work? What if I don’t make this deadline and the weather gets worse? The tender is the moment to identify risks and hedge said risks as much as possible in the T&C’s. If this is not done properly the project may become a bleeder. Many of us have been part of projects with big challenges due to inaccurate estimates in the tender phase.
One way to streamline the tender process is to automate as much as reasonably possible. This removes a lot of time-consuming manual work and also reduces human errors. At MO4 we have been working on software that gives an estimate of the WoW and further allows users to play with the input to better understand and define the operational profile. MO4 has a vessel database so you can identify the benefits of working with a bigger (and more costly) asset. For instance, how would a CTV perform versus an SOV? MO4 gives highly accurate insights into the expected performance of various assets, which helps tender managers to better assess costs and risks.
We have seen that often our clients receive a metocean report that contains hundreds of pages with weather statistics. It is preferred to use this data, as buying another dataset is at your own risk and is an additional cost. This is unfortunate, as the underlying data source of the metocean report (time trace data) is much richer in information and better suited for the analysis. An example of a wave scatter table is given below, showing how often a combination of significant wave height and peak period occur. The directionality and persistence (which sea states follows the current one?) are lost in this table. This figure is standard output of our MO4 tender module.
With this metocean data we perform a hydrodynamic analysis to find out how your ship responds to these sea states. To do this, we need to make several assumptions. What wave spectrum do we take? Do we include wave spreading? What loading condition do we expect? We do not know all these things, but we want to investigate their effect on weather downtime. Building a new model for each possibility is time-consuming and costly. We have automated all this. You only need to select a few relevant input parameters and the workability analysis is done instantly.
A screenshot of the results is shown below. The weather downtime for a certain heading is shown over the wave scatter. The effect of seasonality and heading is shown in the right part of the screen. These figures can be copied into the tender bid or the data can be exported to an excel sheet for further analysis.
Let’s say we perform an operation on the North Sea, we know that typically the wind seas have a strong directional spreading. The effect is shown in the two figures below, easily obtained by rerunning the analysis with different settings. The figures show the downtime versus the relative wave heading.
MO4 Tender makes it simple and fast to identify risks in weather downtime assessments during tendering. No difficult analyses are required, last-minute changes can be easily incorporated. Having a good assessment of weather downtime will make a difference in the profitability of an offshore or marine project.
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