Our on-demand Webinars

Gain insight into key challenges in E&P data analytics by watching recordings of our previous webinars. These recordings provide valuable information for anyone interested in staying up-to-date on the latest developments in the industry.

Access recordings of past webinars on crucial E&P data analytics challenges.

On-demand Webinars

Webinar

[Webinar] Introducing Causal Models

Live Webinar + Q&A Session Introducing Causal Models: Accurate forecast on parent-child developments In this live webinar, you will learn how Novi’s new algorithm improves
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Webinar

Novi Insight Engine | Live demo and Q&A

In this demo, Novi Director of Product Management Ted Cross will walk you through the features and capabilities listed below that have been added to
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Webinar

Novi Model Engine Introduction | Live demo and Q&A session

In this demo, you will learn how Novi’s AI platform replaces the current outdated methods for A&D evaluation, asset optimization, and energy investment analysis.
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Webinar

Novi Presents :: Primexx’s Journey with Novi Webinar Replay

Unlocking Value Though Machine Learning Machine Learning is not new to the industry, but few oil and gas players fully leverage its power in their
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Webinar

Novi Presents :: Oil & Gas Cube Development Webinar Replay

Cube optimization was a popular development strategy in recent years, with the multizone developments and huge numbers of wells per section justifying soaring acreage valuations.
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Webinar

Novi Presents :: Cube Optimization Webinar Preview

  Join us Wednesday, July 14th at 11:00 AM – 12:00 PM CTL for a live webinar on cube development strategy in the context of
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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.