Jet lift has no moving components downhole, which makes it a higher reliability lift method for accelerating initial production from unconventional tight oil wells. The surface jet lift power fluid pump frequently becomes the primary limitation of overall system reliability and uptime. This paper will review several field case studies of novel wireless instrumentation being applied in combination with machine learning techniques to increase the runtime of multiplex plunger pumps and horizontal surface pumps used as power fluid pumps.

Wireless instrumentation was installed on multiple wells to capture critical power fluid pump equipment health data (vibration, temperature, magnetic flux, etc.). This instrumentation was installed on all critical components of the surface pump system, and then combined with electrical and site instrumentation data connected to the pump's variable speed drive. The entire data feed was then captured through a local gateway and into a cloud data historian. The data was processed to establish an acceptable performance baseline. Equipment health models were created using machine learning techniques to compare the real-time data feed against reliability prediction algorithms. The equipment health status was then used to evaluate time-based maintenance versus condition-based maintenance, improving overall system uptime performance.

The field case studies indicated that overall jet lift system reliability is improved by carefully monitoring, modeling, and predicting power fluid pump performance. When combined with condition-based maintenance and field response protocols, overall jet lift system uptime can be improved. Safety risks are also reduced due to less frequent site visits.

Most machine learning and AI papers focus on applying new analytics methods to existing instrumentation and data. Maximizing the benefit of analytics requires more robust data and an integrated field instrumentation approach. This paper will present the increased benefit of developing and deploying additional instrumentation to capture equipment health data designed to increase the performance of cloud analytics.

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