AUSTIN, Texas, March 23, 2020 /PRNewswire-PRWeb/ --Novi Labs ("Novi") today announced the release of Novi Prediction Engine version 2.0. This provides critical economic data to E&P workflows such as well planning or acquisition & divestitures. Novi customers can now run a wide range of large-scale scenarios in minutes and get immediate feedback on the economic feasibility of each plan. As price headwinds face the industry, having the ability to quickly and easily evaluate hundreds of scenarios allows operators to efficiently allocate capital.
In addition to the economic outputs, Novi Prediction Engine 2.0 also includes new features targeting enhanced usability and increased efficiency. Novi is now publishing confidence intervals as a standard output for every prediction. This allows customers to understand how confident the model is of each prediction it makes, which is critical decision-making criterion. A video demonstration of Novi Prediction Engine version 2.0 is available at https://novilabs.com/prediction-engine-v2/.
"With the integration of economic outputs and confidence intervals into Novi Prediction Engine, customers have increased leverage, transparency and certainty in what the Novi models are providing in support of their business decisions. This form of rapid scenario driven testing that is unlocked by the Novi platform is vital in today's uncertain market," said Scott Sherwood, Novi's CEO.
About Novi Labs Novi Labs, Inc. ("Novi") is the leading developer of artificial intelligence driven business applications that help the oil & gas industry optimize the economic value of drilling programs and acquisition & divestiture decisions. Leveraging cutting-edge data science, Novi delivers intuitive analytics that simplify complex decisions with actionable data and insights needed optimize capital allocation. Novi was founded in 2014 and is headquartered in Austin, TX. For more information, please visit http://www.novilabs.com.
SOURCE Novi Labs
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Novi Releases v2.0 of Prediction Engine, Adding Critical Economics to Its Machine Learning Outputs - Benzinga
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