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Case Study: Operational Excellence through Predictive Maintenance

Driving Worker Safety and Equipment Uptime in Oil and Gas with Predictive Maintenance

Facing the double imperatives of maintaining operational efficiency and ensuring safety in harsh offshore environments, our client, a pioneer in the energy industry, recognized the need for a departure from traditional maintenance schedules.

To address the needs, Kalypso and Rockwell Automation implemented an innovative predictive maintenance solution adapted specifically for a critical semi-submersible drilling rig.

Real Results

  • Optimized equipment uptime by reducing unnecessary downtime

  • Enhanced operational safety and environmental compliance

  • Lowered overall maintenance costs by eliminating unneeded checks

The Predictive Maintenance Solution

The project commenced with a thorough analysis and a pilot program on a semi-submersible rig, crucial for year-round drilling operations.

The implementation involved several advanced technological interventions:

  1. Health Index Development: A dynamic health index was created to continuously evaluate the condition of the drilling equipment, providing a quantifiable measure of operational integrity.
  2. Anomaly Detection: Utilizing advanced statistical models, the system was designed to identify deviations from normal operation patterns, facilitating early detection of potential issues.
  3. Real-Time Monitoring and Decision Support: By integrating edge computing, the system enabled real-time data processing directly on the rig, minimizing latency and enhancing the responsiveness of the maintenance team.
  4. Intuitive Dashboards: An intuitive dashboard was developed, displaying critical health metrics and alerts to empower the rig operators with immediate and actionable insights.

Future Impact and Long-term Strategy

The predictive system proved its effectiveness by detecting potential issues early, triggering timely alerts to the maintenance teams to address problems before they impacted operations. This proactive approach maximized rig availability and productivity.

Encouraged by the pilot's success, the company plans to scale the predictive maintenance model across its offshore fleet to standardize efficiency and safety practices, focusing on:

Scalability: Applying the model to additional rigs and platforms to amplify the impact across the portfolio

Adaptive Enhancements: Continuously refining the system through machine learning and feedback loops to adapt to new challenges and data insights

Strategic Leadership: By pioneering advanced predictive maintenance, reinforcing the company’s leadership in deploying innovative technologies in the energy sector

This collaboration showcases Kalypso's vision of combining emerging industrial analytics capabilities with established technologies to drive operational excellence for capital-intensive industries.

Our work made it possible to transition from reactive to predictive maintenance. Enabled by real-time monitoring and edge computing, this energy leader can now optimize performance across critical offshore assets while upholding the highest standards of environmental stewardship.

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