OIL services heavyweight Wood has helped develop a high-tech inspection tool that it is reckoned could slash the cost of completing checks on offshore facilities.
The tool utilises artificial intelligence to automate checks on the footage obtained by inspection devices ending the need for technicians to travel to potentially hazardous locations to complete such work.
Aberdeen-based Wood developed the Augmented Machine Vision Solution in partnership with National Energy Resources Australia (NERA), which underlined the potential of the technology to deliver improvements in inspection work along with big cost savings.
The organisation said: “With the reduction in safety risks and the associated process improvements the solution has the potential to create savings of AUD$2.8 billion (£1.4bn) per year for the offshore energy industry alone.”
It added: “Historically, inspections have required technicians to travel offshore to manually review numerous hours of footage recorded by inspection devices.”
READ MORE: Aberdeen and Glasgow engineering operations key to Wood's growth plans
Azad Hessamodini, President of Growth & Development at Wood, said: “The AMVS will deliver a safer and faster inspection approach which can provide operators with more accurate and up-to-date information to help maximise the output of their assets. It’s a game changer for inspections which we know are susceptible to human-error and inconsistencies.”
Wood has invested heavily in developing its technology capability in support of a drive to reduce its reliance on engineering work related to North Sea oil and gas facilities.
Led by chief executive Robin Watson, the group has expanded into sectors such as renewables and hydrogen production while extending its geographic reach.
Wood also worked with NERA on a project to improve subsea equipment design.
Funded by the Australian government, NERA says it is working with partners to catalyse low emissions and digital technologies to support the energy transition and to develop innovative firms and supply chains.
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