Machine learning technology to extend offshore asset life

The productive life of an oil and gas platform could be significantly extended through a transformative machine learning solution that will contribute to maximising economic recovery from the UK Continental Shelf (UKCS) by predicting equipment failure.

Spartan Solutions Ltd, a world-class software company, is leading a project to develop a machine learning software solution that provides real-time analysis on asset equipment, designed to make physical objects ‘intelligent' and eliminate errors. The project is being delivered in partnership with the Oil & Gas Technology Centre (OGTC), the University of Strathclyde and Repsol Sinopec.

By combining equipment maintenance and repair records with sensor data, Spartan's PHALANX digital operations platform and advanced algorithms aim to deliver a fully integrated solution for predicting equipment failure and managing successful early intervention. The goal of the technology development is to increase production by reducing unexpected equipment failure and downtime, extend the lifespan of equipment and reduce costs by eliminating unnecessary maintenance.

A recent study which was published by the Technology Leadership Board (TLB), the OGTC and the OGA in September 2018 highlighted that in 2017 failure of critical equipment offshore resulted in lost production of around 110 million barrels of oil equivalent – a significant amount that could be prevented through the use of technology like PHALANX.

The digital landscaping study further added that by adopting data analytics and digital technologies offshore asset maintenance and operations could increase output and reduce maintenance leading to additional revenue worth £1.5 billion per year from the UKCS.

John Glen, Chief Operating Officer, Spartan Solutions, said:

“Working with our project partners, we aim to predict failure probability across a range of sub-systems that account for a large percentage of unplanned downtime. The software will not only predict failure but will alert a subject matter expert and provisionally schedule an engineer to prevent the problem occurring.”

“We will use our PHALANX software platform to integrate with back-office systems to rapidly plan, execute and record the necessary interventions to maximise uptime. We will then use this fully digital field service data to continuously educate the machine learning predictor to further increase accuracy.”

Stephen Ashley, Digital Transformation Solution Centre Manager at the Oil & Gas Technology Centre, said:

“A small number of North Sea and international operators are already benefitting from data analytics technologies, with case study examples including a 65% reduction in system outages and annual maintenance savings of more than £1 million.

“Spartan's machine learning technology further contributes to the oil and gas industry's move towards a more focused data driven environment and we're looking forward to supporting them with their technology development.”

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North SeaUnited Kingdom Continental Shelfpredictive maintenancepredictive analysisOGTCRepsolMaintenance and Reliability