Oil’s Next Efficiency Play? Letting Data Do the Watching

At ADIPEC, practical AI tools showed how data-driven maintenance is reshaping risk, reliability, and daily operations in upstream oil and gas

30 Jan 2026

Across oil and gas fields in the Middle East, a shift is taking place that has little to do with drilling technology and much to do with data. Artificial intelligence, once confined to small trials, is increasingly embedded in daily upstream operations, reshaping how companies maintain equipment and manage production risk.

The change was evident at the ADIPEC energy conference, where predictive maintenance drew sustained attention from operators, service companies and policymakers. Demonstrations showed AI systems analyzing live data from field equipment to detect early signs of wear or failure, often sooner than traditional time-based maintenance would allow. For an industry where downtime can be costly, the appeal was straightforward. Maintenance, long reactive or schedule-driven, is becoming more anticipatory.

FutureMain was among the technology providers presenting applied examples. Its ExRBM platform processes continuous data from assets such as pumps and compressors, flagging abnormal behavior in real time. According to company descriptions, the goal is to help operators prioritize maintenance based on actual equipment condition rather than fixed inspection cycles. That approach, condition-based decision making, is particularly attractive in upstream environments, where assets are capital-intensive and often operate in remote locations.

The broader industry context reinforces the momentum. Major producers, including Saudi Aramco, have publicly positioned artificial intelligence and advanced analytics as central to efficiency and digital transformation efforts. While such statements typically encompass a wide range of applications, predictive maintenance is widely regarded by analysts and operators as one of the most practical near-term uses. Where systems are successfully deployed, they have been associated with reductions in unplanned outages and maintenance costs, though results vary by asset and operating context.

Obstacles remain. Many facilities rely on legacy control systems that are difficult to integrate with modern analytics platforms, and concerns about data governance, cybersecurity and trust in algorithm-driven recommendations persist. Advisers note that adoption depends as much on workforce engagement and organizational readiness as on technical performance. Even so, interest in pilot projects is growing, collaboration with technology providers is deepening, and regulators are paying closer attention to digital standards, developments that could shape how upstream operations are run in the years ahead.