Elevating Oil & Gas Investments through Proprietary Data Access

Novi's Data Sharing License

The reliability of public well data has long worried operators and investors, with accuracy posing a challenge in states like Texas and Louisiana where well-level production data isn’t reported. For that, Novi Labs has developed a solution by licensing data from operators and releasing it monthly , with the aim of enhancing data accuracy.

  • Monthly well-level production data and other well data types
  • >1 MM barrels of oil per day in Texas
  • Data is published in Insight Engine data products.
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Join the ranks of industry leaders that have overcome bad analytics data problems by using Novi Labs
  • Shell
  • Blue OX Resources
  • Bedrock Energy Partner
  • Endeavor Energy Resources

Leverage the power of proprietary data

Industry-Leading Data Accuracy

With Novi's Data Sharing license, customers can use highly accurate production data from exclusive sources not available anywhere else.

 

Operator-provided data

Unique dataset with exclusive well-level, daily production data and rarely-available completion data.

Fully Integrated Into the Novi Platform

All data has been merged, cleaned, and vetted by Novi reservoir engineers, and is now ready for use with just a single click.

Analytics & Data

Data Sharing License: Solving What Problem?

Dealing with inaccurate well data poses a significant challenge when trying to assess individual well performance or conduct detailed economic analyses. To address this issue, Novi Labs has acquired licenses for production data and other data types from both operators and royalty customers. This license allows Novi to release confidential monthly data provided directly by these companies. This, in turn, empowers customers to precisely analyze well performance, enhance investment decision-making, and minimize the uncertainties associated with noisy allocated data.

Leveraging Oil & Gas Proprietary Data

How does our data sharing license add value?

Enhanced development optimization, improved acquisitions & divestiture valuations, and streamlined planning and budgeting

Accurate Production Data from Proprietary Sources

Access the most accurate well data possible, using proprietary sources that cannot be accessed anywhere else.

Accurately analyze well performance, improve investment decisions, and minimize work forecasting around noisy allocated data.

Improved Lease Allocation Production Data

Combining our data science expertise with the NDN database, Novi offers the market's most accurate lease-to-well allocation algorithm. This is essential for maintaining precise production data at the well level.

Improved production forecasts and decreased planning risk

Accurately forecasting development designs and upcoming wells depends upon accurate well-level data. Issues like lease-to-well allocation can decrease your forecast accuracy. Our licence includes well-level data.

Unknown downtime can also decrease forecast accuracy, especially in early time. Novi's data license includes daily data, so you can accurately remove downtime from your training data.

In some jurisdictions like Alberta, the liquids content of production is often not available publicly. With the analytics data license, you can train a model that has access to proprietary liquids production data.

Gain an edge in A&D evaluations

BD teams are often at a disadvantage the moment they step away from their core areas because they lack proprietary datasets.

Novi Labs gives BD teams a proprietary dataset to use in A&D valuations. Generate highly accurate forecasts and look for optimization opportunities, even in basins you haven't worked before.

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INTRODUCING CAUSAL MODELS

Accurate forecast on parent-child developments

In this live webinar, you will learn how Novi’s new algorithm improves model sensitivity for spacing and parent-child scenarios, providing powerful results for previously difficult-to-analyze problems.

Ted Cross, our VP of Product Management, will show you how this update improves spacing and infill scenario analysis without sacrificing model accuracy.

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