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
We combine expertise in machine learning and analytics with real world Oil & Gas experience.
Our team delivers practical results you can act upon, in weeks rather than months.
We identify and quantify the key factors that contribute to asset performance.
Our solutions are applicable to several key areas in Oil & Gas. They’re
practical, understandable, and actionable.
We’re transparent, collaborative, and highly practical when working with our clients.
You’ll get results you can use and models you can trust.
Our committed, expert team will work with you throughout the process and will continue to provide support as you require.
Get improved, data-driven explanation of well performance, and an understanding of how better predictions can influence the way that you approach your assets.
Utilize predictive models to assess feature importance and test hypotheses, new datasets you can explore, and visualizations that bring it all to life.
Verdazo Analytics’ Director of Data Science Brian Emmerson will deliver a talk on interpretable machine learning techniques in Houston, Texas on June 5th. The one-day forum is presented by Darcy Partners and is focused on analytics and the internet-of-things. Brian’s talk will focus on the ability of robust visualization techniques to investigate machine learning model predictions, with examples from North American unconventional resource plays. To learn more about the conference or to register, click here.
There are three ways to see Verdazo’s team in action at geoconvention 2019 in Calgary. Marcelo Guarido will be presenting Machine Learning Strategies to Perform Facies Classification Tuesday May 14th at 9:00 am in the Glenn Room 206. Verdazo Analytics’ Director of Data Science Brian Emmerson will co-present The Importance of Geoscience in using Machine Learning to Predict and Optimize Well Performance – Case Study from the Spirit River Formation Wednesday May 15th at 10:35 in the Telus Room 104-105. You can also catch members of the Verdazo team at Booth 503 to learn more about our visual analytics and machine learning capabilities.
The Montney Formation, located in the Western Canadian Sedimentary Basin, is developed in a multi-zone stack throughout the fairway. Unfortunately, these refined target zones are not captured in public data. For analysis, it is important to differentiate Montney Wells into multiple target zones because they vary significantly in reservoir properties both vertically and laterally. Identifying the target zone based on sequence stratigraphy is a valuable process but can be time-consuming. In this blog we show a quick method to differentiate target zones using a depth-based approach that is helpful when you have limited time and resources. This workflow can be applied to any map-based data to derive a data set suitable for well production analysis. For contour-based geologic data to be useful for well production analysis, we need a map-derived value for each individual well. To accomplish this, we started with a publicly available Geologic map of the Montney Top in Meters Subsea (BC OGC, 2012). Figure 1: Digitized and interpolated Montney Top Subsea TVD (True Vertical Depth) Structure Map, (OGC, 2012). The Montney Top Structure map contours were digitized (Figure 1), so that interpolated well-values could be derived. Using point-sampling, the Montney Top Depth was extracted at the intersection...