Intelligence for Shale

Intelligence for Shale

Unlock insights with a 3D view of the subsurface and connected analytics built for the future of energy.

  • Single view of asset and curated data feed

    • Priced per well on AFE • Load and QC technical field data for the pad and view in the browser • Integrates with vendors Time: 1 week

    Key Features

    • Interactive portal for all well data
    • Navigate in 3D
    • Review stage performance
    • Share by URL
    • Easy downloads
    AI and other aspects of digitalization will make engineers’ jobs easier, not replace them.
    Dr. Troy Ruths
    Dr. Troy Ruths Founder & CEO of

Built differently. Revolutionary pricing.


Team of Experts

Team of Experts uses AI and Machine Learning to empower domain experts, data scientists, and executives with instant information in the right context, energizing teams and transforming data into action.

  • Microsoft Azure
  • MongoDB
  • Amazon Web Services
  • Open Subsurface Data Universe™

Learn More is a built-for-purpose oil and gas software platform bringing geoscience and engineering data types into one system for any shale workflow.


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Latest Articles

Frac Model and Drainage—how the pieces fit

Frac Model and Drainage—how the pieces fit

Kyle LaMotta, VP of Analytics explains, “The analysis below depicts the Drainage Model also called the Frac Fingerprint or the Frac Model. This is what we’re predicting for each well. The gun barrel view on the right represents the predicted percentage of rock stimulated around each well. That has a direct correlation to the drainage based on the dimensions of this stimulation.

Rosemary Jackson
Our Gratitude Grows with Your Success

Our Gratitude Grows with Your Success

We have one goal at We want to make your oil & gas patch a billion-dollar success. We want your story to be built with the real shale narrative, with the real translation of the rock, with a real way to grasp your opportunities in your space, in this time.

Rosemary Jackson
Frac Simulator in

Frac Simulator in

As part of our multi-step process for calibrating and validating the Digital Twin, one of the key steps is the Drainage Model. We use that as a feature when we’re predicting the well productivity. “And consistently it’s one of, if not the highest in importance of all the features. We have our own way of creating this drainage area that uses geomechanics and some machine learning processes. That’s heavily built off diagnostic data collected in the field. Microseismic data is also used to inform the shape.

Rosemary Jackson
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