Leading Industrial DataOps platform can now stream synthetic data from simulators and deploy physics-guided machine learning for sensitive production use cases

Cognite, a global leader in industrial software, today announced the launch of native simulator integration and Hybrid AI workflow capabilities for its leading Industrial DataOps platform, Cognite Data Fusion®. These hybrid AI workflows combine the best of data-driven machine learning and physics-based modeling to deploy advanced digital solutions at scale, including solutions for sensitive production use cases that can reduce costs and increase proactive decision-making that can optimize production.

With Cognite Data Fusion®, industrial organizations can automate routine simulator studies or monitor problem cases by automatically running a simulator with live data, collecting the results, triggering actions, and visualizing output. Cognite Data Fusion® is vendor-agnostic and can integrate with industry-leading simulations such as Unisim, Olga, Turbulent Flux, Multiflash, and Hysys. Additionally, Cognite recently announced a strategic partnership with SLB (formally Schlumberger), a global industry simulator, software, and services leader, to simplify and supercharge access to Hybrid AI solutions for energy industry customers.

Neptune Energy, the operator of Cygnus, leveraged Cognite Data Fusion® to develop a Hybrid AI well modeling and monitoring application. Through a native integration with PROSPER, a well and pipeline modeling simulator, Cognite Data Fusion® delivers automated well modeling and near-real-time well monitoring, eliminating manual processes and achieving higher volumes of broader surveillance and higher quality assessments. As a result of the Hybrid AI workflows in Cognite Data Fusion®, Neptune saw an:

• 80% reduction in time spent on data mining and manipulation
• 67% reduction in time spent on data analysis and validation
• Increased proactive decision-making due to near real-time surveillance in a common application that allows for multi-disciplinary collaboration

"Cognite Data Fusion provides our engineers with a wealth of efficiencies by delivering mined, contextualized, modeled, and visualized data for them to act on. Now, they can dedicate more time to interpreting the results and making collaborative, data-driven decisions,” said Alan Bibb, Global IT Business Partner Manager at Neptune Energy. “With the Hybrid AI workflows in Cognite Data Fusion, we’re able to focus on more holistic engineering optimization and improve time to value."

"Digital transformation starts and succeeds with simple access to complex industrial data. Today's announcement ensures that Cognite Data Fusion provides one central, contextualized source of truth for an organization's data for both Hybrid AI models and the subject matter experts supervising the decision-making process, which is critical for asset and production optimization," said Geir Engdahl, co-founder and CTO, Cognite.

Cognite Data Fusion® makes asset data more accessible and meaningful to humans and machines, delivering trusted data and insights to unlock opportunities in real-time, reduce costs, and improve the integrity and sustainability of your operations. To learn more about Cognite Data Fusion visit: cognite.com/why-cognite-data-fusion

About Cognite

Cognite is a global industrial SaaS company that was established with a clear vision: to rapidly empower industrial companies with contextualized, trustworthy, and accessible data to help drive the full-scale digital transformation of asset-intensive industries worldwide. Our core Industrial DataOps platform, Cognite Data Fusion®, enables industrial data and domain users to collaborate quickly and safely to develop, operationalize, and scale industrial AI solutions and applications to deliver both profitability and sustainability. Visit us at www.cognite.com and follow us on Twitter and LinkedIn.

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"Today's announcement ensures that Cognite Data Fusion provides one central, contextualized source of truth for an organization's data for both Hybrid AI models and the subject matter experts supervising the decision-making process, which is critical."