AI-based Data Analytics

CMG strengthens its commitment to the digital transformation of the energy industry to improve efficiency, cut costs, and reduce risks by leveraging physics-driven data analytics

A new forecasting application that incorporates a production analysis system using neural network models learning from pre-run simulation data using CMG simulators to evaluate petroleum reservoir performance. It implements a patented scientific methodology for type well matching, optimization in well spacing and timing, as well as maximizing asset valuation and production performance.

Compelling Benefits include:

Significant Time Savings

AI-based analytics reduces data-intensive input, and quickly estimates the upside value of undeveloped drilling locations using a physics-based approach.

Scalable

Producers can run sensitivities on thousands of wells in minutes for field development optimization with high accuracy.
Reporting

Provides quality and integrity in SEC reserves reporting and addresses reservoir engineering challenges for unconventional reservoirs.

Financial Assessment

Facilitates well density studies and capital deployment evaluation to maximize new well economics.

“This technology allows operators to analyze the adverse effects on production from well interference rapidly and confidently, in ways difficult to achieve with conventional approaches. As a result, it supports improved production forecasts and leads to better asset value assessments.”

Dr. John Lee, a recognized expert in petroleum reserves evaluations and professor at Texas A&M University

"This technology has taken some of the best modeling tools available, numerical and analytical modeling, and innovated a cutting-edge approach that has provided us with physics-based results. It has given us the ability to evaluate a wide range of development scenarios in a short amount of time. The solution has proven to be very cost-effective for our project. I highly recommend and plan to utilize the expertise in upcoming projects."

Wade Wardlow, Sr. Reservoir Engineer at Contango Oil & Gas

“Accurate physics-based predictions of well performance such as these are key to enhanced profitability of future infill wells.”

Tor Meling, Sr. VP at William M. Cobb & Associates

“SBF is a must-have tool for any engineers working on unconventional reservoirs. Combining state-of-the art ML and reservoir simulation it allows the engineer to identify adequate development plans in a time effective manner. With the appropriate workflow, one can quickly understand the impact of completion, well spacing and geology to EUR Degradation and RF implications. Additionally, the team is always willing to go above and beyond to deliver custom-based solution that would fit your workflow. I highly recommend the integration of SBF.”

Carlos Brun – Senior Reservoir Engineer

“Using only publicly available data, I was able to history-match existing offset wells in a fraction of the time using physics-based modelling. CMG’s new scientific data analytics software allows you to run thousands of different scenarios and hone in on the most likely range of variables needed to match each well. I gained valuable insight into reservoir, fluid, and frac properties despite not having PVT reports, detailed frac information, DFITs, etc. I could then use data to create new type well and run quick sensitivities on gunbarrel configurations and timing considerations to quantify how changes improved or degraded EURs.”