Real-time feedback and machine learning system for downhole environments

Abstract

Disclosed are systems, apparatuses, methods, and computer readable medium for a real-time feedback and machine learning system for downhole environments. A method includes: receiving first measurement data from a first equipment submersed into a downhole environment during a run for extracting material from the downhole environment; determining a performance curve based on the first measurement data, the performance curve identifying an extraction rate of the material with respect to efficiency and head capacity; and providing the performance curve to an operator of the run for real-time feedback associated with performance of equipment in the downhole environment.

What is claimed is: 
     
         1 . A method of providing performance feedback of an extraction facility comprising:
 receiving first measurement data from a first equipment submersed into a downhole environment during a run for extracting material from the downhole environment;   determining a performance curve based on the first measurement data, the performance curve identifying an extraction rate of the material with respect to efficiency and head capacity; and   providing the performance curve to an operator of the run for real-time feedback associated with performance of equipment in the downhole environment.   
     
     
         2 . The method of  claim 1 , further comprising:
 predicting a second measurement from a second equipment submersed into the downhole environment during the run by using a first machine learning model, wherein the performance curve is further based on the second measurement.   
     
     
         3 . The method of  claim 2 , wherein the first machine learning model is at least partially trained based on measurement data from the downhole environment. 
     
     
         4 . The method of  claim 2 , wherein the first machine learning model is further trained based on downhole environments having different characteristics. 
     
     
         5 . The method of  claim 2 , wherein the first machine learning model comprises a multi-label classifier configured to identify classifications of the downhole environment and predict the second measurement based on training data similar to the downhole environment. 
     
     
         6 . The method of  claim 1 , further comprising:
 determining that a third equipment becomes unavailable during the run; and   predicting third measurement data associated with the third equipment during a time that the third equipment is unavailable.   
     
     
         7 . The method of  claim 1 , wherein calculating the performance curve based on the first measurement data comprises:
 predicting a minimum value, a maximum value, and an actual value of an efficiency of the equipment based on a flow rate of material,   wherein the minimum value, the maximum value, and the actual value of the efficiency are provided to the operator.   
     
     
         8 . The method of  claim 1 , wherein, before beginning the run, an initial performance curve is generated, and the performance curve changes based on measurement data during the run. 
     
     
         9 . The method of  claim 1 , wherein the initial performance curve of the first equipment is based on a standardized performance test of the first equipment using a calibration material. 
     
     
         10 . The method of  claim 1 , further comprising:
 estimating a recommended flow rate for the material to minimize deterioration of the equipment based on the downhole environment.   
     
     
         11 . A system for providing performance feedback of an extraction facility, comprising:
 a storage configured to store instructions;   a processor configured to execute the instructions and cause the processor to:
 receive first measurement data from a first equipment submersed into a downhole environment during a run for extracting material from the downhole environment; 
 determine a performance curve based on the first measurement data, the performance curve identifying an extraction rate of the material with respect to efficiency and head capacity; and 
 provide the performance curve to an operator of the run for real-time feedback associated with performance of equipment in the downhole environment. 
   
     
     
         12 . The system of  claim 11 , wherein the processor is configured to execute the instructions and cause the processor to:
 predict a second measurement from a second equipment submersed into the downhole environment during the run by using a first machine learning model, wherein the performance curve is further based on the second measurement.   
     
     
         13 . The system of  claim 12 , wherein the first machine learning model is at least partially trained based on measurement data from the downhole environment. 
     
     
         14 . The system of  claim 12 , wherein the first machine learning model is further trained based on downhole environments having different characteristics. 
     
     
         15 . The system of  claim 12 , wherein the first machine learning model comprises a multi-label classifier configured to identify classifications of the downhole environment and predict the second measurement based on training data similar to the downhole environment. 
     
     
         16 . The system of  claim 11 , wherein the processor is configured to execute the instructions and cause the processor to:
 determine that a third equipment becomes unavailable during the run; and   predict third measurement data associated with the third equipment during a time that the third equipment is unavailable.   
     
     
         17 . The system of  claim 11 , wherein the processor is configured to execute the instructions and cause the processor to:
 predict a minimum value, a maximum value, and an actual value of an efficiency of the equipment based on a flow rate of material,   wherein the minimum value, the maximum value, and the actual value of the efficiency are provided to the operator.   
     
     
         18 . The system of  claim 11 , wherein an initial performance curve is generated, and the performance curve changes based on measurement data during the run. 
     
     
         19 . The system of  claim 11 , wherein the initial performance curve of the first equipment is based on a standardized performance test of the first equipment using a calibration material. 
     
     
         20 . The system of  claim 11 , wherein the processor is configured to execute the instructions and cause the processor to:
 estimate a recommended flow rate for the material to minimize deterioration of the equipment based on the downhole environment.