The importance of predictive maintenance has increased in oil and gas operations against the backdrop of ageing infrastructure and volatile crude pricing, according to data and analytics company GlobalData
The company’s latest thematic report: ‘Predictive Maintenance’ reveals that the adoption of predictive maintenance technologies helps companies reduce operational expenditure by optimising maintenance scheduling and driving productivity.
Ravindra Puranik, oil and gas analyst at GlobalData, said, “The insights gained from predictive maintenance programme enable decision makers to schedule maintenance activities without disrupting routine production operations. These insights can also be used to evaluate if any machinery or infrastructure requires a major overhaul on priority, and accordingly decide whether to use the available capital expenditure for new projects and expansion plans or divert it for the upgrading of existing facilities.”
Predictive maintenance is a fairly mature concept and has been widely used in the oil and gas industry over the last 20 years. Predictive maintenance tools evaluate the condition of operational equipment and predict maintenance requirements to achieve optimum performance and prevent malfunction.
Predictive maintenance uses automated condition monitoring and advanced data analytics to gather vital equipment statistics – such as vibration, temperature, sound, and electrical current – and compares them with historical records of similar equipment to detect signs of deterioration.
GlobalData’s thematic research identifies multinational oil and gas companies, such as Shell, ExxonMobil, Chevron, BP, Rosneft and Equinor as the leaders in the digital oilfield theme. The research also identifies oilfield service providers, such as GE-Baker Hughes, Schlumberger, Halliburton, Aker Solutions, Weatherford and National Oilwell Varco among the leading players in this theme.
“Recent advancements in cloud-based data analytics and the rise of the digital twin in oil and gas operations are extending the boundaries of predictive maintenance technologies, making it a reliable tool for monitoring asset integrity,” Puranik concluded.
