Cenovus Energy Creates Oil Well Efficiencies with AWS Data Lab

To assess operating conditions and optimize performance of its oil wells, Cenovus Energy Inc. continuously measures temperature distribution inside production wells, a process called Distributed Temperature Sensing (DTS). Cenovus analyzes DTS data for each oil well independently through 3rd party vendor systems and each vendor system generates its own unique data formats. Cenovus’s existing process for extracting and analyzing large volumes of data from these systems was manual, inefficient, and time consuming. The Cenovus team needed to rebuild and automate the DTS process within a consolidated data store for analysts to reference for both ad-hoc queries and more in-depth analytics needs for the complete DTS data for all production wells.

With the support of the AWS Data Lab, Cenovus Energy Inc. built a working prototype of a data lake on Amazon Simple Storage Service (S3) that can ingest a variety of datasets from multiple vendors through a streamlined process. The team also built an event-driven data pipeline to transform ingested datasets into a common data model with AWS Glue, which will allow users and downstream applications to query DTS data that spans different vendors, facilities, and oil wells. With 1 to 10 million files existing per DTS system vendor, efficient data management and speed of transformations are critical to Cenovus’ solution. Over the course of the four day Build Lab, the solution Cenovus built was able to process 1.5 billion rows of DTS data across all vendors in only 1 hour. 

The Cenovus team left the AWS Data Lab with a custom-fit, cloud-based solution that will allow production engineers and other analysts to derive insights into oil well performance faster without having to worry about storage capacity. By moving to this centrally-managed data platform, Cenovus can ingest, process, and analyze billions of rows of DTS data, enabling the business to immediately access and generate actionable insights into oil well performance and optimize costs. By leveraging guidance from the AWS Data Lab to build this solution, Cenovus was able to accelerate their prototype to production in only 5 months after joining the lab. Since launching in production, the company has seen increased utilization of DTS data through advanced analytics and has standardized on a single DTS viewer.