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Zoback Introduces Frac Fingerprint and Viscoplastic Stress Relaxation Theory at URTeC 2022
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Know and Manage Your Stress Barriers
Frac Model and Drainage—how the pieces fit
Pad Development: Less Can Mean More
Petro.ai’s Drilling Spacing Unit Design Service (DSUDS) is the New Required Modeling for Shale
Planning your Drilling Spacing Unit in any shale reservoir requires an understanding of stress. Tight spacing, Parent/Child issues, uncertain economics, lean teams, and the need to drill to value top the list of questions our clients bring to the Petro.ai Drilling Spacing Unit Design Service (DSUDS) for actionable decisions. Foundation technologies like Frac Fingerprint, a geomechanical understanding of stress, and Viscoplastic Stress Relaxation (VSR) Theory, conceptualizing stress in changing lithography, deliver a high level of consistent accuracy in modeling any shale reservoir.
A Quick Look at Viscoplastic Stress Relaxation (VSR) Theory from the Upcoming Zoback/Petro.ai/Henry URTeC Presentation
Plan to join Dr. Mark Zoback, Professor of Geoscience Stanford University, and Petro.ai at the Houston URTeC Conference on Monday, June 20, 2022, 1:50 PM, Exhibition Hall, Station B for Zoback’s presentation of Lithologically-Controlled Variations of the Least Principal Stress with Depth and Resultant Frac Fingerprints During Multi-Stage Hydraulic Fracturing.
Lithologically-Controlled Variations of the Least Principal Stress with Depth and Resultant Frac Fingerprints During Multi-Stage Hydraulic Fracturing
An expository paper featuring the science behind Petro.ai, Lithologically-Controlled Variations of the Least Principal Stress with Depth and Resultant Frac Fingerprints During Multi-Stage Hydraulic Fracturing, authored by Petro.ai and Stanford University’s Dr. Mark Zoback, will be featured at the upcoming URTeC Conference in Houston, Texas on June 20-22, 2022.
Bringing Certainty to Shale
How does Petro.ai bring certainty to shale? Richard Gaut, CFO of Petro.ai leads us into the discussion, “The biggest problem with shale development historically is over promising and under delivering. People assumed that new wells would perform as well as the old wells, or the next well would perform as well as the previous well.
Blind Tests Reveal High Accuracy of Petro.ai
How can a client company be certain of the accuracy of the algorithms used by Petro.ai? “We use blind tests as a part of every process with every company,” Richard Gaut, CFO of Petro.ai answers with a quick confidence. “The client chooses the analog pads, meaning the areas that are most geologically similar to what they’re going to be developing. And the most similar from an engineering standpoint to how they’re going to develop that pad, meaning 2 benches or 3 benches or some other spacing. You find the most similar pad or most similar DSU in your basin and then we run blind tests on that.
Reboot Your Confidence in Shale: Exploit Your Reservoir with Petro.ai
With the new approach of the Petro.ai geophysical models, how should we be looking at well spacing in the industry?
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