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SAIBOK Phase 2

Project Summary

In Phase 2, the Technology Project aims at building the recommended platform for an extensive library of subsea images to apply AI driven solutions to revolutionise subsea inspection across the oil & gas industry and beyond. The Technology Project will focus on defect localisation and recognition on pipeline videos/images using machine learning algorithms. Such a framework will accelerate and speed up the development of AI driven solutions on other subsea assets. Typical examples will include identifying corrosion in pipes, recognising objects of interest, remote inspections and others.
Supported by data and algorithms provided by sponsoring operators, this proposal aims to deliver a working prototype solution that displays the potential of machine learning in subsea pipeline inspection. A successful proof of concept will accelerate the development and deployment of machine learning applications to improve subsea inspection in the oil & gas industry.


OUTCOMES

The value generation expected from a one-time technology deployment is £350,000 with a potential to deliver £7 million annually across the UKCS and an overall value of £70 million over a 10-year period.

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