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Tamr Insights
Tamr Insights
AI-native MDM
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Updated
August 6, 2019
| Published

In Oil and Gas, it’s Time to Imagine How to Integrate and Interpret Data to Improve Wells Mastering

Tamr Insights
Tamr Insights
AI-native MDM
In Oil and Gas, it’s Time to Imagine How to Integrate and Interpret Data to Improve Wells Mastering

Finding and producing oil and gas is technically challenging and fundamentally uncertain, from environmental, safety, and economic stand points. But what if every oil and gas company could unify and track a wide array of reservoirs, machinery, vendors, personnel, and competitor performance? What if the industry had new technologies and approaches to integrate and interpret data to drive faster, smarter, more accurate, and less risky decisions? 

Wells Mastering: Crucial to Success in Oil and Gas

One of the most important activities in oil and gas production is wells mastering, or wells harmonization in industry-speak. Wells mastering is the process of capturing and analyzing the data that’s captured about a well. Each well that’s drilled requires extensive machinery, measurement devices, and people—all of which produce huge amount of structured and unstructured data. The volume, variety, and velocity of data being captured is substantial. 

Wells mastering is crucial because oil wells are expensive to develop. They are expensive to operate, too. It’s essential to maximize production of each well and run it efficiently and safely. Analytical approaches can greatly affect the success rate of finding and running each well to make energy more affordable, safer, and environmentally conscious.

Although energy companies today know they need to harness their wells data, most of them face a big problem. Much of the information they need comes from third parties or disparate vendors using a variety of applications and systems. The upshot is that many energy producers are not certain how to unify, clean, and analyze the data from all these sources to make smarter decisions. 

One Imaginative Company’s Approach

One American crude oil and natural gas company found a way to understand relationships among well attributes across data sources. It implemented an innovative, machine-learning based approach to unify data sources and improve wells mastering to incorporate information from multiple vendors. As a result, the company can identify the wells with the most potential and the least risk.

The ability to make sense of datasets was not easy in the beginning. Like most oil and gas producers, the company has a large variety and volume of data coming in at rapid velocity, and new data sources from many vendors are being added all the time. That won’t change any time soon. What has changed is the way the organization coalesces and analyzes its data influx.

Previously, the company was reliant on a deterministic, rules-based Master Data Management (MDM) system to unify the data needed for wells harmonization. Every source of data that needed to be added required many rules and copious amounts of time. It took six months or more and a lot of IT involvement to add every new data source. Even then, there were duplicate, “dirty” data sources that could not be trusted. Few people could access and collaborate on the information because it was too dispersed and presented in too many different formats.

Enabling Data Unification and Data Sharing

Task number one was to curate data and create golden records representing the best instance of each data point. The goal was to give users access the most correct, complete information. Second, the energy producer needed to allow multiple users to access and analyze the data for better decision-making.

To solve these issues, the company adopted a probabilistic, machine-learning based approach to data unification. The new method enabled the company to integrate and use trusted data to make better operational decisions. In less than one day, the organization had loaded and associated the datasets for 19 vendors. Then, over the course of a week, subject-matter experts reviewed the data and provided input to help ensure accuracy and train the model.

The Benefits of Imagining What’s Possible

On the technical side, the advantages of the effort to improve wells mastering are evident: there is less work for IT and far less time required to make data accessible to users. Instead, the right people can collaborate with data seamlessly from a single source. Unified data allows the organization to share master well data from multiple vendor datasets and identify high-interest wells to add to the company watch list.

The business results include a unified view of wells based on accurate data from a variety of vendors. Data at the company today is treated as a live feed versus a previously stagnant report as a result of Tamr’s unification efforts. Teams can now generate daily reports that consumers at all levels within the organization can use to gain insights and answer business-critical questions.

Imagine the Art of the Possible

This oil and gas producer is just one example of a company that imagined the art of the possible and gained a tremendous edge by taking full advantage of its data. To learn more about the art of the possible and what it can mean to your organization, reach out to us or schedule a demo. 

We’d love the opportunity to demonstrate what our customers refer to as the ‘art of the possible’—in person. Schedule a meeting with us here.

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