Major oil refiners and petrochemical companies are deploying AI systems to predict maintenance failures, detect safety hazards, and guide operator decisions in real time—shifting from reactive repairs to proactive risk management.
In October 2024, Chevron and Honeywell announced a collaboration on AI-assisted alarm management solutions designed to help operators respond more effectively to operational events, reduce process safety incidents, and protect workers and infrastructure.
What happened
Oil and gas companies including Chevron, ExxonMobil, Phillips 66, and others are embedding AI systems into refining and petrochemical operations to predict maintenance needs, detect hazards, and guide operator responses. In October 2024, Chevron and Honeywell announced a collaboration on AI-assisted alarm management solutions that provide operators with specific guidance on how to respond to alarms and operational events.
Why it matters
Rather than waiting for equipment to fail or relying on manual safety inspections, these AI systems continuously analyze sensor data and video feeds to spot emerging risks—allowing companies to shift from reactive crisis response to predictive intervention. For refining professionals, this means fewer process safety incidents, reduced downtime, and lower environmental risk, while protecting workers and communities from harm.
What to watch
The new Chevron-Honeywell solutions are expected to include an Alarm Guidance application that reduces lost profit opportunities and process safety incidents. Other firms like ExxonMobil use machine learning and computer vision to analyze real-time refinery footage, while Marathon Petroleum uses Flyscan, an AI-driven leak-detection technology deployed from patrol planes to monitor pipeline corridors for hydrocarbon leaks and equipment threats with greater sensitivity than human observers.
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The use of AI in refining and petrochemical safety represents a fundamental shift in how these safety-critical industries manage operational risk. Traditionally, maintenance relied on fixed schedules or manual inspections—an approach prone to missing emerging problems. The new generation of AI systems continuously ingest sensor data and historical performance records, allowing them to predict failures before they occur. This transition from reactive to predictive maintenance is particularly important in refining, where a single undetected equipment failure can trigger process safety incidents, environmental releases, or worker harm.
The October 2024 Chevron-Honeywell collaboration exemplifies this trend. By pairing alarm management with AI-guided operator responses, the solution targets a critical human-machine interface: the moment when an operator must decide what to do in response to a system alert. Providing specific, data-backed guidance at that moment reduces both the risk of wrong decisions and the cognitive load on personnel. Similarly, the adoption of machine vision by ExxonMobil and leak-detection from aircraft by Marathon Petroleum extends AI's reach beyond sensor networks into visual and spatial monitoring—domains where human observers have inherent limitations in attention span and pattern recognition.
Across the industry, the shared presence of major operators (ExxonMobil, Chevron, Phillips 66, Flint Hills Resources, LyondellBasell, BASF) implementing these technologies signals that AI safety tools are moving from experimental pilots to standard practice. The quote from Lara Swett, Vice President of Technical and Safety Programs at AFPM (American Fuel & Petrochemical Manufacturers), underscores that the industry recognizes these systems work best not as replacements for human judgment but as force multipliers—freeing skilled operators to focus on higher-level decision-making and problem-solving rather than routine hazard detection.
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