A sequential downtime analysis method is presented, which is applied to the prediction of the expected duration of a typical offshore installation campaign made up of a series of tasks. Accounting for typical limiting sea states for each step of the installation sequence, it is shown how a sequential downtime analysis using a hindcast metocean time series can be used to estimate the likely duration to complete the series of tasks.

The analysis is based on typical stages in the installation of an FPSO mooring and riser system, including set-up before and re-positioning between tasks, combined with multi-year hindcast metocean data. Each task has a set of limiting sea states associated with it, as well as an expected duration and the likely variation in the duration due to delays. The effect of delays in each task on the duration of the complete sequence of operations is accounted for by a Monte Carlo type method.

The results of the analysis show that a sequential downtime analysis can be used in a number of ways to optimise an offshore installation campaign. Firstly, it can identify the best times of the year at a particular site for wave conditions which will minimise weather related delays. Secondly, by correctly accounting for realistic variations in the duration of each task due to delays, the potential total duration of the offshore operation can be more accurately estimated, along with likely costs. Finally, examples will be presented of how this analysis technique can be used to test the economics of employing more expensive equipment and/or vessels with improved capabilities to increase the limiting wave conditions for particular operations, thereby reducing total cost.

The methods presented in the paper are considered to represent a significant improvement over typical methods for planning and costing offshore operations. By allowing the likelihood and magnitude of potential delays to be better quantified, informed decisions on the potential benefits of using more capable vessels and/or equipment can be made and costs connected to the duration of the operation better predicted.

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