Verdazo Analytics Launches Machine Learning Division

January 17, 2018 by

Verdazo Analytics entered the field of Artificial Intelligence today with the launch of its Machine Learning Division, which will deliver targeted and accessible Machine Learning solutions for Oil & Gas. Verdazo Analytics will initially deliver Machine Learning solutions through a consulting offering, with a software product to follow later in 2018. Machine Learning capabilities will eventually be fully integrated with the company’s flagship VERDAZO visual analytics software.

The Machine Learning Division of Verdazo Analytics will be led by Tyler Schlosser, the company’s new Chief Data Scientist. Tyler joins the company from GLJ Petroleum Consultants, where he was Director, Economics & Risk. Tyler brings a background as a petroleum engineer, as well as over a decade of focus in the Machine Learning and Artificial Intelligence spaces.

At GLJ, Tyler developed an industry-wide reputation as a trusted advisor on specific Oil & Gas topics, including reservoir engineering, economic modelling and energy markets.

“Machine Learning is a key pillar for the future of Oil & Gas analysis,” says Verdazo Analytics President Bertrand Groulx. “Tyler will be working directly with our clients to target specific industry opportunities with our new capabilities. Ultimately, Machine Learning will further enable our clients to generate value across their organizations and add millions to their bottom lines.”

Tyler is particularly excited to expand the value Verdazo Analytics can offer to its clients in both Canada and the U.S. through Machine Learning.

“Because we’re so adept at integrating data sources into our software, we have the opportunity to bring a more expansive set of data into a Machine Learning environment,” says Schlosser. “That means we can deliver our clients deeper insights, drawing from more and better data, faster than any of our competitors.”

Recent applications of Verdazo Analytics’ Machine Learning capabilities include:

  1. Completion design analysis that builds an understanding of which factors contribute the most to completion effectiveness and fuel optimal completion design decisions.
  2. Developing predictive models for rock properties (e.g., porosity, water saturation) using petrophysical data, drilling data and core analysis results. The predictive model will be deployed to improve development planning, completion design and reserves estimation.

Following the company’s acquisition by Pason Systems, Inc. in 2017, Machine Learning is the next major milestone for Verdazo Analytics, as it deepens its focus on helping Oil & Gas clients optimize all aspects of their business.

Schlosser: “Producers are looking to remove the complexity of data access. They want analytics for the entire well life cycle, including actionable predictions and the ability to understand which data features are most important across a variety of use cases. Our ability to accelerate this capability for our clients is a game changer.”

To learn more about Verdazo’s Machine Learning capabilities, contact Tyler at tyler.schlosser@verdazo.com.