Digging deep and solving problems: Well Data Labs applies machine learning to oil and gas challenges

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When CEO Josh Churlik co-founded Well Data Labs in 2014, he was acutely aware of a bizarre dichotomy in his industry: For oil and gas companies, “downhole” innovation (that is, what happens underground) far exceeds the pace of data and analysis innovation. The data systems used then were relics of the 1990s – more homages to history than helpful to the people who needed them.

Like many others in the industry, Josh and the Well Data Labs team were frustrated with the inability to access information that would have made frontline engineers’ jobs much easier. While the industry plodded along with spreadsheets, Churlik and his team saw an opportunity to build a modern software company around the rapid advancements in cloud computing.

The resulting company, Well Data Labs, describes itself as “a modern web application built to give operators the fastest and simplest way to manage, analyze, and report on their internal data.” In other words, Well Data Labs efficiently handles the messy time-series data created during operations—capturing, normalizing, structuring, and enabling analysis on that data—all within a web-based app.

With what Well Data Labs offers, engineers can make faster, more informed decisions—decisions that have a direct and immediate impact on the cost and success of the operations. The company has replaced manual front-end data collection and analysis with custom-developed machine learning (ML) models running on AWS, so that Well Data Labs’ customers can monitor field operations in real-time.

The AWS tech stack powers this solution. Churlik explained, “When we were getting started, we did a bakeoff between other cloud providers and AWS. Even though we’re a .NET stack and SQL database, AWS was significantly more performant.” So, AWS was their choice; to this day, Well Data Labs uses AWS for all their cloud needs. “What we’ve liked about AWS is we can always scale. We’ve been able to continuously build and grow,” Churlik added. “AWS was and still is ahead of its industry peers on technology services.”

Well Data Labs leverages the seamless integration between AWS services to power their robust solution. Currently, the Well Data Labs architecture includes Amazon Elastic Compute Cloud (Amazon EC2) for all of their managed servers (to power their applications), Amazon S3 to store the various data artifacts without worrying about storage limitations, Amazon Simple Queue Service (Amazon SQS) to create a distributed system, and Amazon Virtual Private Cloud (VPC) and AWS Identity and Access Management (IAM) to keep its infrastructure secure. In addition to all of those core services, Well Data Labs uses Amazon SageMaker in their Machine Learning (ML) workloads.

Churlik recalls that he started a data science team to begin exploratory R&D with ML about a year ago. “We asked ourselves, ‘what is the value that it [ML] could be providing to our customers?’ And then we started experimenting.”

Now, the team uses Amazon SageMaker to deploy trained models on custom Docker containers via their proprietary SaaS application. The Amazon SageMaker models and SageMaker endpoint features enable Well Data Labs to integrate ML into the SaaS application and thereby bring frontline engineering workers real-time data for event detection and notification during operations. Well Data Labs set the precedent by bringing ML to the oil and gas market in this way.

Using AWS to build and host many of their solutions means the Well Data Labs team can focus on R&D and developing new product features, rather than on managing infrastructure. Well Data Labs data scientists can deploy new prediction models as soon as they are ready and iterate on new versions rapidly. The quick integration and deployment of ML functionality into the SaaS application in turn enables frontline users to benefit from data science advances right away. The first set of models that Well Data Labs built immediately saved their customers up to an hour a day of manual data entry.

Achieving that kind of success right out of the gates is exciting, and this is only the beginning. Well Data Labs pioneered the “digital oilfield” (where technology, data, automation, and people in the oil and gas industry all intersect), and their customers affirm that this small but mighty Denver-based company is ushering in a new era for the oil and gas industry.


About the Author

Digging deep and solving problems: Well Data Labs applies machine learning to oil and gas challenges 1Marisa Messina is on the AWS ML marketing team, where her job includes identifying the most innovative AWS-using customers and showcasing their inspiring stories. Prior to AWS, she worked on consumer-facing hardware and then university-facing cloud offerings at Microsoft. Outside of work, she enjoys exploring the Pacific Northwest hiking trails, cooking without recipes, and dancing in the rain.

 

 

 

 

 



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