Abstract

Integrity of mooring system is of high importance in the offshore industry. In-service assessment of loads in the mooring lines is however very challenging. Direct monitoring of mooring line loads through load cells or inclinometers requires subsea installation work and continuous data transmission. Other solutions based on GPS and motion monitoring have been presented as solutions to overcome these limitations [1].

Monitoring solutions based on GPS and motion data provide good practical benefits, because monitoring can be conducted from accessible area. The procedure relies on accurate numerical models to model the relation between global motions and response of the mooring system. In this paper, validation of this monitoring approach for a single unit will be presented.

The unit under consideration is a turret-moored unit operating in Australia. In-service measurements of motions, GPS and line tensions are available. A numerical time-domain model of the mooring system was created. This model was used to simulate mooring line tensions due to measured FPSO motions. Using the measured unit response avoids the uncertainty resulting from a prediction of the hydrodynamic response.

Measurements from load cells in various mooring lines are available. These measurements were compared against the results obtained from the simulations for validation of the approach. Three different periods, comprising a total of five weeks of data, were examined in more detail. Two periods are mild weather conditions with different dominant wave directions. The third period features heavy weather conditions.

In this paper, the data set and numerical model are presented. A comparison between the measured and numerically calculated mooring line forces will be presented. Differences between the calculated and measured forces are examined.

This validation study has shown that in-service monitoring of mooring line loads through GPS and motion data provides a new opportunity for mooring integrity assessment with reduced monitoring system complexity.

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