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.

Abstract: A method may include receiving imagery data from a wellsite; analyzing the imagery data to detect movement; determining a risk to a human at the wellsite based on the detected movement; and, responsive to the determining, issuing an instruction to reduce the risk.

Abstract: A method can include, receiving by a computational device at a wellsite, real-time, time series data from pump equipment at the wellsite, where the wellsite includes a wellbore in contact with a fluid reservoir; using the computational device, processing the time series data as input to a trained machine learning model to detect a performance issue of the pump equipment; and issuing a signal responsive to detection of the performance issue.

Abstract: Methods and systems are provided for monitoring the operation of a sucker rod pump (SRP), which involves a workflow that processes surface operational data and downhole operational data related to the operation of the SRP. The surface operational data is derived from real-time measurements performed by surface-located sensors, while the downhole operational data is derived from real-time measurements performed by downhole sensors. The surface operational data is processed to generate input data for supply to a first machine learning model (e.g., Surface Data Classifier) and the downhole operational data is processed to generate input data for supply to a second machine learning model (e.g., Downhole Data Classifier). The output of at least one of the first and second machine learning models is used to characterize an operational condition or status of the SRP.

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.