Fuel a greener future with Seeq AI for oil and gas

Marathon Pipe Line shows how they used Seeq’s full toolkit to scale asset health monitoring across critical centrifugal pumps, combining efficiency modeling, scaling tables, machine learning, and Vantage workflows to focus experts on the changes that matter most. The session follows how Marathon built pump-specific models, tuned training windows and running conditions, and used efficiency deviation signals to surface a real equipment issue that other tools had missed, allowing the team to inspect the pump under controlled downtime before a more costly failure occurred. Framed around both operational reliability and business value, the approach highlights how a single early detection event was worth roughly a quarter of a million dollars, with an estimated annual impact of about $5 million when expanded across the pump fleet.

PBF shares how they built an enterprise energy sustainability platform that turns complex operational data into real-time KPIs for operators and leaders across six refinery sites. Using Seeq, Datalab, asset trees, and Power BI, they show how the team tied performance to lost profit opportunity, scaled to 337 KPIs across 114 process units and about 4,500 leaf signals, and used AI-assisted development to accelerate delivery and embed energy visibility into daily refinery operations.