Blog
July 22, 2018 by

Data before delivery: putting the cart before the horse?

I have now heard dozens of stories from friends and colleagues about failed BI and analytics initiatives. They range in costs from hundreds of thousands to tens of millions of dollars. It’s a problem that I see several companies at risk of repeating…and the motivation for today’s blog. A common thread to most of these stories is trying to “fix up our data before we select or implement an analytics tool”. How can anyone possibly understand the data needs, the use cases, and the possible data issues without providing a means to use the data, view the data and identify issues? It’s like trying to anticipate what part of a car a mechanic should fix without test driving it first. Consider starting with the data you have, take it for a test drive. See how the business wants to use it and evolve your data quality and architectural initiatives incrementally. You’ll realize value on the way and better focus your efforts. Why do they fail? Lack of focus: Hyped up terms like “big data”, “data lakes” and “cloud” distract us from the pragmatic task of delivering information ­­­— getting reliable, current information into the hands of business users in a form...

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May 7, 2018 by

Machine Learning: Is it really a Black Box?

Machine Learning isn’t the “black box” that many perceive it to be. On complex data sets, the use of Machine Learning with a rigorous process and supporting visualizations can yield far more transparency than other methods. What is a “Black Box”? Machine learning models are sometimes characterized as being Black Boxes due to their powerful ability to model complex relationships between inputs and outputs without being accompanied by a tidy, intuitive description of how exactly they do this. A “Black Box” is “a device, system or object which can be viewed in terms of inputs and outputs without any knowledge of its internal workings” (Source: Wikipedia). Black Boxes (and Machine Learning models) exist everywhere We tend to label things as “Black Boxes” when we don’t trust them more than when we don’t understand them. Machine Learning models aren’t unique in having an element of “mystery” in how they work – there are all sorts of things we trust all around us for which we don’t fully understand the inner workings. GPS, search engines, car engines, step counters, even the curve fitting algorithms in Excel are examples where we trust what’s happening inside because we’re able to see and, with experience,...

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Presentations

Machine Learning: Practical Use in Upstream Oil & Gas

There’s an enormous amount of discussion about the possibilities of Machine Learning but far less practical information about the impacts it can have on Oil & Gas today. In this presentation, we articulate some of most valuable prizes Machine Learning offers for upstream Oil & Gas, including feature importance and sensitivity analysis, and the power of predictive Machine Learning models. The presentation outlines two recent case studies – one focused on reservoir properties and the other focused on drilling and completion optimization – that bring the material to life. This presentation was originally delivered May 31, 2018 as part of the SPE Oil & Gas Breakfast Series in Calgary.

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Visual Analytics in the New Normal: Past, Present & Future

Over the last decade, the collective impact of changes in production technologies, the shift in commodity prices and a reduced workforce is defining a ‘new normal’ in oil & gas. This presentation provides a visual overview of evolving production and market trends and explores the role data and technology will play as the industry adapts to its changed landscape. “Visual Analytics in the New Normal: Past, Present & Future” was originally presented as part of the geoLOGIC Adapting to the New Normal technical showcase on Nov. 16th, 2017.

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Client Stories

Producer uncovers millions in operational improvements while establishing visual analytics culture

One of our favourite things is when our clients are able to get more value out of VERDAZO than they originally expected. We love watching visual analytics cultures take hold, as one project inspires another and users across organizations discover new uses for our product. At one intermediate Canadian producer, a project focused on minimizing downtime yielded a $12 million operational improvement on just a single group of wells. But that was just the start of what they did with VERDAZO. Here’s their story.

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How a producer started its analytics journey

A producer in Calgary used our VERDAZO software as part of an initiative to improve their Well Review process. Their results went beyond their expectations: they were able to free up their engineers from days of manual Excel analysis in advance of a well review, saving time that exceeded $175,000 in annual costs. That initial project also spawned something the producer couldn’t have initially predicted: a commitment to analytics that eventually spread across the organization. New projects were created and an analytical culture took hold as the producer uncovered new efficiencies and value in unexpected places.

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Product Infosheets

Machine Learning

Predict production performance, reservoir properties and optimize well location & completion design.

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Public Data

Explore public data. Identify business opportunities by discovering insights into companies, plays and completion technologies.

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Financial Data

Analyze your accounting data. Optimize financial performance by identifying operating cost issues and revenue opportunities.

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