FUTURE DIGITAL TWIN
Incremental digital transformations that reduce time to value The iPhone 15 has just launched to similar fanfare as its predecessors - and the usual queues outside Apple Stores. There are some improvements, some new features but mostly it’s still a phone with access to the internet.
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o transformation, no disruption. Yet incremental improvements still generate excitement because they improve our lives in some way. Not unlike the changes we see in digital oil and gas. Thoughtfully-designed digital solutions that help engineers do more: make better decisions, work more productively, get ahead of risks and issues. Real digital transformation is less about the hype associated with new technologies, which are merely the inputs to change, and far more about the outcomes, the impact of change - what people can do today to improve their lives. At Eigen, we are experts in digitalisation and industrial systems, with more than sixteen years building solutions that deliver value for offshore oil and gas operators. Since our early beginnings, supporting bp’s operations in the Caspian Sea in the design, implementation and support (to this day!) of networks and digital systems, Eigen continues to innovate. With today’s Cloud and Knowledge Graph digital twin technology, we are focused on helping operators maximise value from their data and preventing those data from becoming an unmanageable monster.
knowledge graph technology to save a senior engineer a week every month. A knowledge graph, unlike a data lake, links to source data, bringing with it context and meaning. No data is moved or copied requiring minimal disruption to existing databases or systems and a faster time to value. For the operator, each time the plant was depressurised, owing to a trip, a report was required to verify safe blowdown in all 61 blowdown segments. An engineer had to manually pull data generated by 150 sensors, run analyses and calculations in Excel, build charts and prepare reports to be emailed to management confirming plant safety. The operator recorded that the entire activity took six days of a senior engineer’s time. By building a knowledge graph using open source Neo4j with Eigen’s Python library of standard code, our team was able to automate the data collection and integration, with calculations and visualisations to enable the engineer to spend just one day focused on the high value analyses and assurance before sending a link to a dashboard-based report to management highlighting areas for intervention. “[The Blowdown Analysis Tool] gives us a much better understanding of the performance and how it develops over time. And it has already proven useful in troubleshooting
Building a knowledge graph We built an Automated Blowdown Verification solution for one Norwegian operator, which used
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