Using distributed sensor data to control cluster efficiency downhole

Abstract

A system for determining real time cluster efficiency for a pumping operation in a wellbore includes a pump, a surface sensor, a downhole sensor system, and a computing device. The pump can pump slurry or diverter material in the wellbore. The surface sensor can be positioned at a surface of the wellbore to detect surface data about the pump. The downhole sensor system can be positioned in the wellbore to detect downhole data about an environment of the wellbore. The computing device can receive the surface data from the surface sensor, receive the downhole data from the downhole sensor system, apply the surface data and the downhole data to a long short-term memory (LSTM) neural network to produce a predicted cluster efficiency associated with operational settings of the pump, and control the pump using the operational settings to achieve the predicted cluster efficiency.

What is claimed is: 
     
         1 . A system comprising:
 a pump in operable communication with a wellbore having multiple stages, to pump slurry or diverter material into the wellbore;   a surface sensor positionable at a surface of the wellbore to detect surface data about the pump;   a downhole sensor system positionable in the wellbore to detect downhole data about an environment of the wellbore; and   a computing device to communicate with the pump, the surface sensor, and the downhole sensor system, the computing device being operable to:   receive the surface data from the surface sensor;
 receive the downhole data from the downhole sensor system; 
 apply the surface data and the downhole data to a long short-term memory (LSTM) neural network to produce a predicted cluster efficiency associated with operational settings of the pump; and 
 control the pump using the operational settings to achieve the predicted cluster efficiency. 
   
     
     
         2 . The system of  claim 1 , wherein the LSTM neural network is a deep recurrent neural network (DRNN) that is trained using a subset of the surface data and of the downhole data. 
     
     
         3 . The system of  claim 1 , wherein the surface data includes a pump pressure from the pump and flow rate of slurry or diverter material. 
     
     
         4 . The system of  claim 1 , wherein the downhole sensor system is a distributed acoustic sensing system or a distributed temperature sensing system implemented by a fiber optic cable. 
     
     
         5 . The system of  claim 1 , wherein the downhole data includes flow rate percentage at different depth ranges in the wellbore. 
     
     
         6 . The system of  claim 1 , wherein the predicted cluster efficiency represents a measurement of how uniformly that slurry or diverter material is distributed among perforation clusters in the wellbore. 
     
     
         7 . The system of  claim 1 , wherein the computing device is operable to control the pump using the operational settings to achieve the predicted cluster efficiency substantially in real time with respect to receiving the surface data and the downhole data. 
     
     
         8 . A method comprising:
 receiving surface data from a surface sensor positioned at a surface of a wellbore to detect surface data about a pump;   receiving downhole data from a downhole sensor system disposed in the wellbore to detect the downhole data about an environment of the wellbore;   applying the surface data and the downhole data to a long short-term memory (LSTM) neural network to produce a predicted cluster efficiency associated with operational settings of the pump; and   controlling the pump using the operational settings to achieve the predicted cluster efficiency.   
     
     
         9 . The method of  claim 8 , wherein the LSTM neural network is a deep neural network (DRNN) that is trained using a subset of the surface data and of the downhole data. 
     
     
         10 . The method of  claim 8 , wherein the surface data includes a pump pressure from the pump and flow rate of slurry or diverter material. 
     
     
         11 . The method of  claim 8 , wherein the downhole sensor system is a distributed acoustic sensing system or a distributed temperature sensing system implemented by a fiber optic cable. 
     
     
         12 . The method of  claim 8 , wherein the downhole data includes flow rate percentage at different depth ranges in the wellbore. 
     
     
         13 . The method of  claim 8 , wherein the predicted cluster efficiency represents a measurement of how uniformly that slurry or diverter material is distributed among perforation clusters in the wellbore. 
     
     
         14 . The method of  claim 8 , wherein controlling the pump using the operational settings to achieve the predicted cluster efficiency comprises controlling the pump substantially in real time with respect to receiving the surface data and the downhole data. 
     
     
         15 . A non-transitory computer-readable medium that includes instructions that are executable by a processing device for causing the processing device to perform operations comprising:
 receiving surface data from a surface sensor positioned at a surface of a wellbore to detect surface data about a pump;   receiving downhole data from a downhole sensor system disposed in the wellbore to detect the downhole data about an environment of the wellbore;   applying the surface data and the downhole data to a long short-term memory (LSTM) neural network to produce a predicted cluster efficiency associated with operational settings of the pump; and   controlling the pump using the operational settings to achieve the predicted cluster efficiency.   
     
     
         16 . The non-transitory computer-readable medium of  claim 15 , wherein the LSTM neural network is a deep recurrent neural network (DRNN) that is trained using a subset of the surface data and of the downhole data. 
     
     
         17 . The non-transitory computer-readable medium of  claim 15 , wherein the surface data includes a pump pressure from the pump and flow rate of slurry or diverter material, wherein the downhole data includes flow rate percentage at different depth ranges in the wellbore. 
     
     
         18 . The non-transitory computer-readable medium of  claim 15 , wherein the downhole sensor system is a distributed acoustic sensing system or a distributed temperature sensing system implemented by a fiber optic cable. 
     
     
         19 . The non-transitory computer-readable medium of  claim 15 , wherein the predicted cluster efficiency represents a measurement of how uniformly that slurry or diverter material is distributed among perforation clusters in the wellbore. 
     
     
         20 . The non-transitory computer-readable medium of  claim 15 , wherein the operation of controlling the pump using the operational settings to achieve the predicted cluster efficiency comprises controlling the pump substantially in real time with respect to receiving the surface data and the downhole data.