Approximately 17,000 wells in the customer's portfolio have beam pump artificial lift technology. While beam pump technology is relatively inexpensive compared to other artificial lift technology, beam pumps fail frequently, at rates ranging from 66% to 95% per year. Unexpected failures result in weeks of lost production, emergency maintenance expenses, and costly equipment replacements.
One of the largest oil and gas producers in the U.S. with an upstream portfolio consists of more than 22,000 wells distributed across 10 countries in North America, South America, and the Middle East.
As part of the C3 IoT analytic software suite, C3 Predictive Maintenance employs machine learning-based algorithms to enhance failure prediction and diagnostic capabilities. The application augments traditional systems by continuously monitoring all instrument signals, tracking complex failure modes, and detecting operating anomalies associated with impending equipment failures for a large range of assets. In this deployment, C3 IoT integrated daily sensor readings from in-field equipment and unstructured data from maintenance work orders. This comprehensive data integration and analysis gives service teams a comprehensive weeks-ahead view of emerging equipment maintenance requirements, with detailed supporting data and diagnostic tools to support maintenance decision making. Hardware Components - Daily sensor
|Daily sensor readings from in-field equipment, unstructured data including field notes and operator comments from maintenance work orders|
|Emerging (technology has been on the market for > 2 years)|