How Industrial DataOps lays the foundation for operational efficiency at scale

Use existing data to boost operational efficiency

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:

• How can we approach unplanned deferments in a more grounded, data-driven way?
• How do we use analytics to better plan, manage, and communicate our maintenance activity?
• How do we make use of the investments made across the whole fleet?

A data foundation powered by Industrial DataOps

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:

• A tool that connects worker orders and risk factors (such as defective valves, integrity threats, and out-of-service equipment) to a 3D model to give a visual overview of a selected platform.
• The functionality to help plan maintenance campaigns by reassigning work orders into different campaigns.
• A drag-and-drop interface for sequencing work orders.
• Automatic reports to provide the user with visibility on performance in real time.

Operational efficiency at scale

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:

• Improved visibility and risk alerting of out-of-spec equipment status
• Better monitoring and mitigation planning of spill and gas leak risks
• Lower risk to personnel by clustering higher-risk maintenance work
• Lower unnecessary exposure to risk during lower-risk work orders
• Lower greenhouse gas emissions due to less frequent transport of personnel and parts
• Cuts in materials usage and waste from unnecessary, schedule-driven parts replacement