Field equipment system

Abstract

A method may include receiving data from a pump system at a field site; processing the data to generate card format data; detecting an operational condition of the pump system using a machine learning model and the card format data; and, responsive to the detecting, controlling operation of the pump system at the field site.

What is claimed is: 
     
         1 . A method comprising:
 receiving data from a pump system at a field site;   processing the data to generate card format data;   detecting an operational condition of the pump system using a machine learning model and the card format data; and   responsive to the detecting, controlling operation of the pump system at the field site.   
     
     
         2 . The method of  claim 1 , wherein the pump system comprises a sucker rod pump system. 
     
     
         3 . The method of  claim 1 , wherein the card format data comprises image data. 
     
     
         4 . The method of  claim 1 , wherein the machine learning model comprises a convolution neural network. 
     
     
         5 . The method of  claim 1 , wherein the detecting comprises using an inference generated by the machine learning model. 
     
     
         6 . The method of  claim 5 , wherein the detecting comprises assessing the inference in combination with at least a portion of the data. 
     
     
         7 . The method of  claim 1 , comprising generating a graphical user interface that comprises at least one card format image associated with the operational condition. 
     
     
         8 . The method of  claim 1 , wherein the detecting occurs in real-time. 
     
     
         9 . The method of  claim 1 , wherein the controlling occurs in real-time. 
     
     
         10 . The method of  claim 1 , wherein the data comprise streaming data and wherein the processing comprises processing the streaming data according to one or more cycle criteria that correspond to a cycle of the pump system. 
     
     
         11 . The method of  claim 10 , wherein the cycle comprises an up stroke and a down stroke. 
     
     
         12 . The method of  claim 10 , wherein the cycle comprises a cycle length determined by a pumping rate in strokes per minute. 
     
     
         13 . The method of  claim 12 , wherein the pumping rate comprises a pumping rate greater than 0.1 strokes per minute and less than 50 strokes per minute. 
     
     
         14 . The method of  claim 1 , comprising training the machine learning model. 
     
     
         15 . The method of  claim 14 , wherein training comprises performing feature engineering to engineer features for the card format data. 
     
     
         16 . The method of  claim 15 , wherein the features correspond to regions within the card format data indicative of one or more operational conditions. 
     
     
         17 . The method of  claim 14 , comprising generating training data utilizing one or more data augmentation processes. 
     
     
         18 . The method of  claim 17 , wherein the one or more data augmentation processes comprise a physics-informed interpolation process for smoothing data. 
     
     
         19 . A system comprising:
 a processor;   memory accessible to the processor; and   processor-executable instructions stored in the memory to instruct the system to:
 receive data from a pump system at a field site; 
 process the data to generate card format data; 
 detect an operational condition of the pump system using a machine learning model and the card format data; and 
 responsive to detection of the operational condition, controlling operation of the pump system at the field site. 
   
     
     
         20 . One or more computer-readable storage media comprising processor-executable instructions to instruct a wellsite computing system to:
 receive data from a pump system at a field site;   process the data to generate card format data;   detect an operational condition of the pump system using a machine learning model and the card format data; and   responsive to detection of the operational condition, controlling operation of the pump system at the field site.