Field pump equipment system
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.
1. A method comprising:
receiving input that comprises time series data from pump equipment at a wellsite, wherein the wellsite comprises 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.
2. The method of claim 1 , wherein the first trained machine learning model comprises one or more of an autoencoder model, a clustering model, and a tree model.
3. The method of claim 1 , wherein the first trained machine learning model is trained using a normal behavior dataset for the pump equipment.
4. The method of claim 3 , wherein the first trained machine learning model is trained using unsupervised learning.
5. The method of claim 1 , wherein processing the input and the output comprises computing differences between the input and the output.
6. The method of claim 5 , wherein the time series data comprise time series data for multiple channels and wherein the differences comprise differences for each of the multiple channels.
7. The method of claim 1 , wherein the survival function indicates a probability of survival with respect to time for a number of days.
8. The method of claim 1 , wherein the second trained machine learning model is trained using the output of the trained first machine learning model for a normal behavior and abnormal behavior dataset for the pump equipment.
9. The method of claim 8 , wherein the second trained machine learning model is trained using supervised learning.
10. The method of claim 1 , wherein the second trained machine learning model comprises decision trees.
11. The method of claim 1 , wherein the second trained machine learning model comprises a time-dependent Cox model.
12. The method of claim 1 , wherein a computational device at the wellsite receives the input, processes the input to generate the output and processes the input and the output to generate the survival function.
13. The method of claim 1 , wherein the pump equipment comprises an electric submersible pump disposed in the wellbore and a surface control unit and wherein at least a portion of the time series data are received from one or more sensors coupled to the electric submersible pump.
14. The method of claim 1 , wherein the input corresponds to a time window greater than 30 minutes, wherein the input is updated according to a time interval, wherein the time interval is greater than 30 seconds and less than 30 minutes, and wherein the survival function is updated according to the time interval.
15. The method of claim 1 , comprising adjusting one or more operational parameters of the pump equipment based at least in part on the predicted survival function for the pump equipment.
16. The method of claim 15 , wherein the adjusting extends a remaining useful life of the pump equipment.
17. The method of claim 15 , wherein the adjusting is based at least in part on a digital twin of the pump equipment that predicts performance of the pump equipment responsive to implementation of the one or more operational parameters.
18. The method of claim 1 , comprising utilizing a remaining useful life of the pump equipment based at least in part on the survival function.