Beyond 3D: Extending life of subsea assets by applying advanced simulation and prediction

Proceedings Publication Date:

26 Jul 2016
Dr. Pawel Michalak
Pawel Michalak, Graham Viecelli
Part of the proceedings of
As humans, we see the world as one-, two- or three-dimensional. Many will recognize the fourth dimension (4D) as time, which - while not measured in terms of Euclidean space - can be represented by the detectable change in state of a given object between two points in time. In that sense we cannot see in 4D but as asset managers and operators we experience asset performance and risk implications from it, and in a geospatial context it is more commonly understood through the application of change detection. While 3D addresses the questions ‘What is the object and where is it?’, 4D asks ‘How did it change?’. Given the limitless potential of commoditized cloud computing, sophisticated deep learning computer algorithms, next generation data acquisition platforms, and access to the number of accurate real-time environmental data streams, we are now able to move subsea asset management into the fifth dimension (5D). This postulates all possible scenarios of change between two objects or locations. Now we are asking ‘How could it change?’. Offshore pipeline owners can now optimize their IRM management strategy to meet certain cost or risk targets by simulating environmental conditions and performing a risk based analysis. This consequently leads to narrowing down the inspection program to high risk areas based on pipeline structural information, subterrain data, live metocean stream, fishery activities, shipping lines traffic etc. Knowing the current status and predicting future behaviour of an infrastructure network is key to reducing the probability of failure. The method to realistically interpolate the state of assets between surveys and to reliably predict how assets and environment will change in the future is therefore of the highest importance. The onshore utilities benefit from 40% savings over traditional IRM methods (Sharma, 2016) when using these novel improvements. They also observe a corresponding reduction in the probability of failure and lead time for repairs and maintenance.

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