As the first decade of the US shale revolution continues, rod pump systems are still the prominent form of artificial lift. Technological advancements in diagnostic tools for rod pump systems, such as fluid level guns and dynamometers, have enabled operators to make better decisions on a well level. Larger scale tools, such as programmable logic controllers coupled with SCADA systems, allow for better decision-making on a multi-well level. While these solutions are proven within the industry, they can become increasingly difficult to keep up with as well counts increase and as equipment types vary.

As the shale revolution commenced, a data science revolution materialized. The concepts of big data, data mining, cloud computing, and predictive analytics continued to expand beyond the tech space into all industries. Naturally, some of these ‘big data’ concepts worked their way into the oil and gas space. While early iterations of big data implementation in oil and gas focused on large-scale projects, certain components now appear in US onshore operations.

This paper presents a case history on how a US onshore operator took a three-step approach to optimize over one hundred rod pump wells spanning across two states.

  1. Data consolidation: joining SCADA, well test, and well equipment databases

  2. Automated workflows: calculations using consolidated operational data to generate a ‘recommended optimization action’ for each rod pump system

  3. Interactive data visualization: interface for decision-makers to view aggregated results, with the ability to drill-down to specific well level details (using TIBCO Spotfire software)

This approach led to increased unit runtimes, decreased unit cycling, improved production/equipment surveillance, and increased staff productivity. The ultimate goal is to increase profitability by decreasing lifting costs and increasing operating efficiency.

The content in this paper is catered to teams working with assets that utilize rod pump artificial lift systems. Nevertheless, it may be beneficial to any teams who are looking to create more value from existing operational data.

You can access this article if you purchase or spend a download.