Verification and abnormal state detection
Driving 20X productivity gains by connecting data from multiple sources into real-time reports
Overview
The manual verification of process safety conditions such as blowdown events, safety valve travel time and shutdown sequences can take significant effort and cost.
The Eigen Analytics Platform is used to automate the process, from events signalling a safety related event to the automated verification of process safety quality criteria.
We build knowledge graphs to connect up data from complex, distributed systems to automate verification and provide early warnings before issues arise.
One week
Read the Lundin case study
Context
Instrumentation and sensing are widespread in oil and gas operations, but operators struggle to integrate data into reports, workflows and alert systems.
Safety and Environmental Critical Elements (SECEs) – playing a key role in containment, integrity, emergency shutdowns, etc. – must meet strict performance standards or risk the licence to operate.
Consequences:
- Weak signals that may provide early warnings are often missed.
- Compliance with regulation is challenging and onerous.
- Discontinuous data streams and manually intensive process for data manipulation; heavy reliance on Excel and static reports.
- Huge effort in data cleansing and the reworking of analysis for each SECE and asset.
Goals
Impact
- Continuous monitoring of safety critical equipment and operations, with real-time verification and abnormal state detection.
- Shift to proactive state means less fire-fighting for engineering staff.
- Lower cost through better use of resources: less time hunting for data and preparing manual reports; less offshore repetitive and intrusive effort.
- Reduced dependence on copying and pasting data into Excel.
Perspective
90%
290,000 +
2 million +
Miles Daffin
Miles is an Applied Mathematics and Theoretical Physics graduate, with 5 years experience in developing advanced visualisation solutions for Eigen's most demanding Oil & Gas customers. His abstract thinking training serves him well in transforming equipment and tag lists into the Neo4j graph model at the heart of Eigen Ingenuity's digital twin.
“I love to deliver working solutions to our customers. I particularly enjoyed developing the python script that automatically verifies all blowdown events. The python plug-in provides so much power to automate workflows!”
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Eigen Analytics Platform
The Eigen Analytics Platform enables a more holistic, integrated system-wide view, ensuring safe, compliant operations.
- Knowledge graph integrates data from multiple sources along with their context meta-data.
- Python-enabled workflows to translate, optimise and automate manual workflows.
- Rules engine initiates alerts only when specific criteria met or triggered.
- User-configurable tools, so engineers can customise to meet their needs.
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