With the advent of IIoT enabled Edge Analytics, and its ability to run Machine Learning based inference at the extremities of a production network, it has become essential to enable Operators and Subject Matter Experts to transfer their knowledge to Edge Computing Devices.

This paper discusses the application of Edge Analytics enabled Augmented Intelligence for wells operated by Electric Submersible Pumps, where Machine Learning and Pattern Recognition Models help detect anomalous events in multivariate time-series data. These Models runs on Edge Computing Devices where identify newly discovered and well known ESP performance patterns that can be labelled by a Subject Matter Expert. Once these patterns are identified and tagged, the Models are retrained and pushed back to the Edge Computing Device, where they continue to detect and predict patterns in real-time.

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