Mubasher: Several energy majors, including Shell, ExxonMobil and BP, have been adopting predictive maintenance technologies for assessing the condition of their operational equipment and help foresee maintenance needs, analytics firm GlobalData said.

Recent advancements in cloud-based data analytics and the emergence of digital twins in oil and gas businesses are making predictive maintenance techniques a reliable tool for monitoring asset integrity, the data provider GlobalData’s latest report “Predictive Maintenance in Oil & Gas” showed.

Shell has spearhead the adopting of predictive maintenance technologies to boost equipment reliability to extend the total operational life of its assets.

The Anglo-Dutch oil giant adopted artificial intelligence (AI) and machine learning in its predictive maintenance processes, in order to reduce operational costs, while mitigating environmental risks.

Chevron used the cloud-based data analytics approach for predictive equipment failure in its refinery operations, GlobalData said.

Russia’s Roseneft adopted predictive maintenance and other digital technologies to boost growth and sustainability. It collaborated with General Electric (GE) since 2013 to develop and implement Internet of Things (IoT) solutions for its liquefied natural gas (LNG).

In addition, AI-driven predictive maintenance has become a core part of French oil company Total’s global operations, as it uses such expertise for deploying predictive maintenance and other technologies on the cloud.

It agreed with Google Cloud to build and deploy AI-driven solutions for the geophysical analysis and equipment monitoring.

The GlobalData report highlighted other leading upstream companies which moved to adapt predictive maintenance technologies in oil and gas industry, such as Norway’s Equinor, Spain’s Repsol and US energy corporation ConocoPhilips.