A global oil and gas company with tens of thousands of employees in more than 70 countries delivers hundreds of thousands of barrels of oil per day to global markets. Each of its businesses is composed of multiple assets that include oil well basins, offshore platforms, refineries, pipelines and retail outlets. Each asset includes vast arrays of equipment that are orchestrated with the goal of maximizing the efficiency of oil production. At any point in time, any one of these components can fail—with potentially catastrophic results.
A single control valve failure in a refinery led to a $6 million loss due to maintenance costs and production losses. Losses of this kind can include the costs of both clean-up and asset downtime. With over 20,000 control valves in a single refinery and over 1 million in total, the company looked for proactive ways to discover and mitigate failures before they become catastrophic.
The company selected the BHC3™ AI Suite to develop and deploy a scalable predictive maintenance solution. Using this solution, they can now implement machine learning models at scale to predict the expected behavior of control valves within any downstream or upstream asset. The software flags any anomalous behavior to onsite engineers who can preemptively address failures.