Abstract: A method can include receiving results from a field device at a field site, where the results are generated using a trained machine learning model at the field site and real-time field equipment data from field equipment at the field site; issuing a signal responsive to an assessment of the results, where the signal indicates a performance-related issue of the trained machine learning model; updating training data with at least a portion of the real-time field equipment data to address the performance-related issue; and generating a new trained machine learning model for deployment to the field site using at least a portion of the updated training data.

Abstract: A method can include receiving input that includes time series data from pump equipment at a wellsite, where the wellsite includes a wellbore in contact with a fluid reservoir; processing the input using a first trained machine learning model as an anomaly detector to generate output; and processing the input and the output using a second trained machine learning model to predict a survival function for the pump equipment.