Unlock insights with a 3D view of the subsurface and connected analytics built for the future of energy.
• 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
AI and other aspects of digitalization will make engineers’ jobs easier, not replace them.
A risk-free start on your Connected Energy Analytics journey, bringing cross-disciplinary teams together for a single view of the most important drivers of unconventional production.
Petro.ai Earth Technology delivers a single view of public subscription data and private geotechnical data, offering insights to previously hidden optimization opportunities.
Accelerate digital transformation and create powerful analytics across the complete well lifecycle.
Deliver the next wave of optimization in shale development with insights from modern geomechanics.
Petro.ai is a built-for-purpose oil and gas software platform bringing geoscience and engineering data types into one system for any shale workflow.
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.
We have one goal at Petro.ai. 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.
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.