Listen to this articleOAG Analytics’ technology accelerates collaboration between data scientists and subject matter experts. Oil & gas companies use OAG’s exploration and planning workflows to de-risk exploration, reduce costs, and increase revenue by rapidly connecting new insights with decision makers. Below is our recent interview with Luther Birdzell, the Founder and CEO of OAG Analytics:
Q: For those who haven’t heard of it, what is the way to describe OAG Analytics?
A: OAG Analytics’ AI and machine learning technology accelerates collaboration between data scientists and subject matter experts. Oil & gas companies use OAG’s exploration and planning workflows to de-risk exploration, reduce costs, and increase revenue by rapidly connecting new insights with decision makers.
Q: Why do we need to improve oil & gas forecasting and planning? Why now?
A: Estimating oil, gas, and water production before wells are drilled is complex and lacks precision. Current methods are based on average production of other wells, typically across areas that are over 100 square miles and too often resulting in inaccurate forecasts. This problem escalated from important to critical in 2019 because the cost of capital to the upstream industry increased substantially, requiring teams to forecast with higher precision to survive.
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Q: Can you give us insights into your solutions?
A: OAG’s extensible SaaS data science workflows provide a clickable interface for configuring and QCing data, enriching data with features, training and validating ML models, running simulations, and visualizing insights. Our workflows are extensible, allowing customers to deploy their own proprietary algorithms and interfaces without having to build the infrastructure to host them. OAG integrates with leading data visualization software like TIBCO Spotfire and runs on AWS.
Q: Could you tell us something more about your technology? What makes it unique?
A: OAG’s portfolio of innovative feature algorithms are how we combine domain expertise with machine learning. OAG uses automated feature engineering with highly effective results. In the world of data science, a feature is an individual measurable property or characteristic of the problem you are analyzing. Choosing informative, discriminating, and independent features is a crucial step for building useful models. OAG’s proprietary features are a key ingredient in our mission to accelerate data science, which also includes web-based software for orchestrating scalable data science workflows using a blend of proprietary and public data.
Subscriptions to OAG include solutions to challenging problems like well spacing and subsurface mapping. In 2019 we introduced our extensible SaaS model, giving our customers, “the best of both worlds.” Our customers enjoy the strategic value of accelerated time to market at a much lower cost than developing these workflows in-house. Extensible SaaS also empowers customers to create their own data science workflows by adding their proprietary algorithms and interfaces, again much more efficiently than developing the cloud expertise and workflow software in-house. Many customers are finding this combination ideal – enjoying the acceleration of a proven solution and enhancing it with their own IP.
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Q: What can we expect from OAG Analytics in the future?
A: Continue to invest heavily in R&D and diversify into adjacent industrial markets to help accelerate data science for mining, refining, petrochemicals, transportation & logistics, and power.
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