Verdazo AI

Practical machine learning for Oil & Gas

Deep technical and domain expertise

We combine expertise in machine learning and analytics with real world Oil & Gas experience.

Industry’s fastest time to value

Our team delivers practical results you can act upon, in weeks rather than months.

Transparent, pragmatic approach

We identify and quantify the key factors that contribute to asset performance.

How Verdazo AI clients are using machine learning

Our solutions are applicable to several key areas in Oil & Gas. They’re
practical, understandable, and actionable.

Incorporate more data

Understand the impacts and importance of all your data.

  • Apply more data, analyses, and interpretations
  • Quantify the effect of all inputs on well performance
  • Get a more complete and strategic understanding of your assets

Explain well performance

Fortify your understanding and challenge your assumptions.

  • Understand feature importance and what drives production
  • Assess performance by production area and stratigraphic zone
  • Quantify all of these outputs to drive clear action

Usability and interpretability

Get usable, data-driven deliverables you can act on.

  • Test out hypotheses using an interactive predictive model
  • Explore visualizations that build model understanding and provide deeper insights
  • Access data and results with Verdazo BI or other visual analytics software

How we partner with you to deliver machine learning

We’re transparent, collaborative, and highly practical when working with our clients.
You’ll get results you can use and models you can trust.

Engage in a collaborative process

Our committed, expert team will work with you throughout the process and will continue to provide support as you require.

Deepen understanding of outcomes

Get improved, data-driven explanation of well performance, and an understanding of how better predictions can influence the way that you approach your assets.

Get usable, trusted, and interpretable results

Utilize predictive models to assess feature importance and test hypotheses, new datasets you can explore, and visualizations that bring it all to life.

Explore our industry-leading analytics content

April 14, 2019 by

Anton Biryukov of Verdazo Analytics places third in global machine learning competition

Verdazo Analytics Data Scientist Anton Biryukov took home third prize in a machine learning competition in March of this year. “Turn up the Zinc” was a crowdsourcing challenge to augment Glencore‘s efforts to predict zinc recovery at their McArthur River mine in Australia. The participants used the Unearthed platform and consisted of 229 global innovators from 17 countries forming 61 teams and submitting 1286 model variations over one month. “The Unearthed platform provides a great opportunity to polish my data science skills, while solving applied problems in the oil and gas and mining industries,” said Anton, who worked on the problem during his spare time. While most contest submissions were team collaborations, Anton chose to tackle the problem with the help of just one junior data resource. “For my team, third prize in this competition means motivation to try harder in the next challenge, plus new doggy treats for Junior Data Analyst Lumi.” Lumi and Anton discuss strategy For more information on “Turn up the Zinc,” click here.

Read More
March 29, 2019 by

Brian Emmerson delivering presentation at Houston Machine Learning in Oil & Gas conference

Verdazo Analytics’ Director of Data Science Brian Emmerson will deliver a presentation titled “Applied Geosciences and Machine Learning – A Case Study from the Spirit River” at the Machine Learning in Oil & Gas conference in Houston, Texas on April 18, 2019. The presentation takes place at 11:45 a.m at the JW Marriott Galleria. Emmerson’s presentation will outline how the integration of geoscience and engineering data can quantify well feature impact and importance. He’ll also outline how delivering usable models can help user adoption and how the application of interpretable machine learning techniques can build trust. This is the fourth annual Machine Learning in Oil and Gas Conference. It takes place April 17-18. To learn more or register, click here.

Read More

Spirit River Machine Learning Study

This machine learning study incorporates GLJ’s consistent petrophysical evaluation of the entire Spirit River play. Coupled with production and completion information, this geoscience data equips Verdazo’s machine learning team & technology to deliver a predictive model, robust interpretive visualizations, and tools that can help explain, feature by feature, the drivers of production performance for each well. This presentation provides an overview of this study and was presented at the SPE Subsurface Analytics Workshop on February 27th, 2019. If you would like to learn more about purchasing the detailed Spirit River machine learning study and predictive model click here.

Read More Download