CMG SHALEIQ
Data Analytics Forecasting Tool

ShaleIQ

ShaleIQ™ is a physics-driven advanced forecasting solution tailored specifically for unconventional reservoirs. 

Improve efficiency and reduce costs by leveraging existing simulation data to achieve the fastest physics-driven production forecasts. 

Machine Learning Approach

Proprietary technology for type well matching

Harness the power of analytics and AI to perform type well matching, analyze parent-child well interactions, optimize well spacing and timing, and maximize asset valuation and production performance of unconventional resources. 

shaleiq for unconventional reservoirs
oil and gas operator
Analytical & Empirical Methods

Speedy analysis and well-density research

Experience the accuracy and robustness of physics-based numerical methods with the speed and efficiency of analytical and empirical methods. Construct physics-based type well profiles using AI to accurately and quickly predict and model well-to-well interference.

Train AI models by creating and running thousands of different simulation runs to capture the effects of physics-based parameters and phenomena like well interference and pressure depletion for quicker time to decision. 

Optimize Production Forecasts

Enhance decision-making with AI models

Maximize business efficiency through informed decision-making based on readily accessible data. Efficiently access public data or customer data inputs to tune models that match historical production, predict production forecasts, and optimize well spacing, infill well density, and timing.

data team on computer

Benefits

Key Features

How does ShaleIQ work?

Predicting primary and infill well performance has always been a critical process for unconventional assets and organizations often have to trade off accuracy for speed (or vice versa). 

By constructing physics-based type well profiles using AI models trained with numerical reservoir simulations, Shale IQ improves on traditional approaches by delivering the accuracy and robustness of physics-based models in a fraction of the time.

Check out our quick video to learn more.