By using Cognite Data Fusion® to create a maintenance application and quickly scale it to 29 others, a major offshore oil operator reduced planned shutdowns by 30%, boosting production by approximately 7,000,000 barrels annually—an estimated value of $38 million.
30% fewer planned shutdowns
Production increased by 700,000 barrels annually
$38 million in estimated annual value
Maintenance management is a critical workflow that is ideal for digital optimization. Offshore oil and gas platforms must maintain thousands of components, including hundreds of complex rotating equipment components, from multiple vendors, with critical information stored in multiple data silos.
Moreover, the cost of being wrong is exorbitant for safety, environmental, and production. Planned and unplanned maintenance often results in deferred production, costing operators millions of dollars each year in unplanned events.
In one example, an operator of 30 oil platforms with more than 300 wells lacked a unified overview of maintenance activities within and between all assets. This prohibited the company from optimizing scheduling, realizing synergies between assets, communicating across organizational silos, and making data-driven decisions.
The operator’s goal was to make use of the intelligence hidden in equipment by using its existing data to develop solutions for operational efficiency.
The operator asked itself:
The operator used Cognite Data Fusion® to create a new way of managing data within the wider organization, enabling it to embrace growing data diversity and serve a growing population of data users.
With Cognite Data Fusion® as its data foundation, the operator then worked with Cognite to create an interactive maintenance planner application.
The app helps optimize efficiency and reduce waste by enabling efficient scoping, planning, and execution of maintenance work. The application is built around four core ideas:
By approaching the challenge with Industrial DataOps, the operator reduced the effort needed to deploy the maintenance planner app and scale it to 29 assets by 216 weeks.
Before implementing the app, the operator suffered from production losses and inefficient use of resources. Each platform was responsible for rolling out solutions, and the manual maintenance planning process included many people and touchpoints.
Now, AI models calculate maintenance plans based on real-time performance. This creates cross-asset synergies, cutting waste and lowering risk.
The maintenance app delivers: