Data & Analytics Case Study

Azure Pilot Program for a National Natural Gas Company

Client

Our client is among the largest natural gas and natural gas liquids producers in the United States. In addition to exploration and production, they market and transport natural gas, natural gas liquids, and oil, optimizing delivery through market intelligence and robust transportation networks. Their commercial and asset management teams collaborate to ensure efficient transport and maximize value, with significant contributions from oil, condensate, and natural gas liquids production.

CLIENT PROFILE
EMPLOYEES: 1,900+
INDUSTRY: Energy
FOUNDED: 1929

Project Scope

Testing the waters with a new cloud program

The organization had an established, mature data & analytics program with an existing data warehouse on-prem but were looking to move to a cloud-based solution like Azure. However, they were not completely clear on how to accomplish this and needed to evaluate capabilities of Azure data services by migrating few workloads to Azure. The goal of the project was to prove that Azure can serve as their future data & analytics platform. They previously tried to accomplish this with another group but were unsuccessful.

Key Challenges

Carefully managing multiple data sources

During any project, the Smartbridge team inevitably encounters a few challenges which provides an opportunity for our consultants to demonstrate their skills and ingenuity. Here are a few obstacles the team faced in this particular project:

Microsoft Data & AI Solutions Partner
Microsoft Digital & App Innovation Solutions Partner

  • As part of evaluating Azure capabilities the client wanted to implement Microsoft’s recommended approach of “Data Lake First” to consume and manage data from various data sources. This meant gathering all the data from various sources and placing it in the Data Lake before processing the data through the various zones (Bronze, Silver, and Gold) and making it available for traditional data analytics and advanced analytics using AI/ML techniques. Though not a difficult challenge, it was still a new approach the team had to adjust to.

  • Gathering data from Pason was a second challenge faced by the team. The dataset for Pason which was hosted in the cloud was a rather large data set with over 30 million records (up to the minute telemetry from the wells). The team built notebooks using Python and leveraged Azure’s capabilities to parallelize data loads . Smartbridge could load the 30 million records in roughly 8 hours and could setup incremental loads to consume daily updates which took less than 1 hour every day.

  • The client wanted to map their data to Microsoft’s oil & gas industry standard template, however, these templates don’t fit every company and their needs perfectly, as was the case here. Smartbridge took advantage of the base templates available and made adjustments to meet the clients needs.

The Smartbridge Solution

Showcasing Azure is the perfect fit

To address the client’s need to evaluate Azure as a future data and analytics platform, Smartbridge devised a comprehensive approach that ensured a seamless migration of a few selected workloads. The Smartbridge team set up the Azure environment and The Smartbridge team worked closely with the client to consume data from three key systems: Pason, WellView, and P2. This required detailed data mapping and integration, ensuring that the data flowed smoothly from its original sources into the Azure Data Lake.

In the end, Smartbridge successfully demonstrated that Azure was more than capable of handling the client’s data and analytics workloads. By leveraging Azure’s robust infrastructure, the client could now move forward confidently, knowing that a cloud-based data platform could deliver the scalability, speed, and agility they needed to optimize their analytics and decision-making processes across all their operations. With this pilot successfully completed, the client is well-positioned to implement the solution at full scale.

Technologies Used:

Microsoft Azure
python
Azure data lake storage

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