Oil and gas producers, pipeline and electrical grid operators require up-to-the-second views of exactly how systems are performing. In most circumstances, the faster they can respond to an issue, the more efficiently they operate.

These types of operations share similar characteristics in terms of the volume, velocity, and complexity of their data.

Use cases include:

  • Oil & gas production analysis
  • Grid utilization monitoring
  • Profitability analysis
  • Capacity analysis
  • Project performance monitoring
  • Customer service monitoring for call centers

Optimize energy production and distribution

With Panopticon, operators can process, monitor, analyze, and visualize the massive amounts of operational data streaming in from sensors and other devices in real time. They can combine real-time streams with historical data, including time series data stored in high performance columnar databases, to make on-the-fly comparisons with previous activity and develop a comprehensive view of operations.

  • Reduce costs and increase productivity: Increase production while maintaining safe operations, distribute network loads based on real-time feedback from the grid, and deploy field service personnel based on up-to-the-minute data.
  • Improve customer experiences: Prevent outages by monitoring for anomalies in real-time data streams.
  • Ensure safety and compliance: Shutdown drilling systems before a small maintenance issue becomes a major problem and monitor for leaks in real time.

Real-time streaming analytics designed to handle energy industry requirements

Panopticon support the critical functions required to analyze and monitor the vast amounts of rapidly-changing data generated in energy production and utilities applications, including:

  • Subscribe to full and parameterized streams from real-time message buses and CEP engines
  • Dynamically query data warehouses and retrieve aggregated data sets
  • Federate static and real-time streaming data through joins and unions
  • Dynamically change hierarchies and aggregate values
  • Aggregation and netting
  • Interpolation between known values
  • Time window and time period analysis to identify deltas and changes between time periods
  • Time snapshot selection to drill into performance spikes
  • Visualize trends, clustering, correlations, and outliers