Allocating fluid flow by controlling intelligent completion valves in a hydrocarbon well using machine learning

Abstract

A system for controlling intelligent completion valves (ICVs) used in a hydrocarbon well operation is disclosed. The ICVs can be located downhole in a wellbore, and an outflow of pressurized injection fluid from the wellbore may be used to drive hydrocarbons toward one or more offset producing wells. A trained machine-learning model can be generated by training a machine-learning model on training data comprising acoustic signal data generated by a pressurized injection fluid flowing through the ICVs at various flow rates and ICV positions. When applied to new acoustic sensing system sensor data associated with an ICV of multiple ICVs in the well, the trained machine-learning model can generate a result indicating a predicted flow rate of the pressurized injection fluid through the ICV. The result may be output and used to control the flow rate of pressurized injection fluid through the ICVs.

What is claimed is: 
     
         1 . A system comprising:
 a plurality of intelligent completion valves positionable downhole in a wellbore in a formation;   a distributed acoustic sensing system positioned in the wellbore;   a processor; and   a memory communicatively coupled to the processor, the memory including instructions that are executable by the processor to cause the processor to perform operations comprising:
 storing a trained machine-learning model using training data comprising acoustic signal data generated by a pressurized injection fluid flowing outward through the plurality of intelligent completion valves at various flow rates and at different intelligent completion valve positions; 
 receiving, from the distributed acoustic sensing system, sensor data associated with an intelligent completion valve of the plurality of intelligent completion valves; 
 applying the sensor data to the trained machine-learning model to generate a result indicating a predicted flow rate of the pressurized injection fluid through the intelligent completion valve; and 
 outputting the result that is useable to control a position of the intelligent completion valve or a pressure of the pressurized injection fluid to control the flow rate of the pressurized injection fluid through the intelligent completion valve. 
   
     
     
         2 . The system of  claim 1 , wherein the distributed acoustic sensing system includes an optical fiber cable located in the wellbore. 
     
     
         3 . The system of  claim 1 , wherein the training data further comprises acoustic signal data generated by the pressurized injection fluid flowing outward through the plurality of intelligent completion valves at different pressurized injection fluid pressures. 
     
     
         4 . The system of  claim 1 , wherein the training data is buildable by:
 operating an injection logging tool in the wellbore to record flow rates of the pressurized injection fluid at locations near each of the intelligent completion valves;   concurrently with operating the injection logging tool, operating the distributed acoustic sensing system to record acoustic signals generated at each of the intelligent completion valves; and   while operating the injection logging tool and the distributed acoustic sensing system, controlling the intelligent completion valves such that:
 the position of the one of the plurality of intelligent completion valves through which the pressurized injection fluid flows is changed in a step-wise fashion. 
   
     
     
         5 . The system of  claim 1 , wherein the operations further comprise processing the acoustic signal data by:
 truncating the acoustic signal data about a location of the intelligent completion valve;   filtering the acoustic signal data to maximize a signal-to-noise ratio;   stacking at least some acoustic signal data truncated recording channels to create a single channel;   converting the acoustic signal data from a time domain to a frequency domain by an integral transform;   down-sampling or low pass filtering a frequency content of the acoustic signal data to a lower frequency;   reducing a number of acoustic signal data samples by subjecting the acoustic signal data to a filterbank; and   generating engineered filterbank features having frequency-bound filterbank amplitudes, and converting the frequency-bound filterbank amplitudes to a decibel scale using a logarithmic transformation.   
     
     
         6 . The system of  claim 5 , wherein:
 the lower frequency is between approximately 200 Hz to approximately 2,000 Hz, with a corresponding Nyquist frequency of between approximately 100 Hz to approximately 1,000 Hz; and   the filterbank includes filters that are centered at approximately 5 Hz to approximately 20 Hz frequency intervals with an overlap of approximately 5 Hz to approximately 20 Hz between adjacent filters.   
     
     
         7 . The system of  claim 1 , wherein:
 a flow allocation model is configured to control a flow rate of the pressurized injection fluid through each of the plurality of intelligent completion valves; and   the operations further comprise automatically adjusting a position of a given intelligent completion valve to cause a flow rate of the pressurized injection fluid through the given intelligent completion valve to match a flow rate calculated for the given intelligent completion valve by the flow allocation model based on a predicted flow rate of the pressurized injection fluid through the given intelligent completion valve determined by the trained machine-learning model.   
     
     
         8 . A method, comprising:
 receiving, by a processor of a computing device, from a distributed acoustic sensing system, sensor data associated with an intelligent completion valve of a plurality of intelligent completion valves positionable downhole in a wellbore in a formation;   applying the sensor data to a trained machine-learning model using training data comprising acoustic signal data generated by a pressurized injection fluid flowing outward through the plurality of intelligent completion valves at various flow rates and at different intelligent completion valve positions;   generating a result indicating a predicted flow rate of pressurized injection fluid through the intelligent completion valve; and   outputting the result that is used to control a position of the intelligent completion valve or a pressure of the pressurized injection fluid to control the flow rate of the pressurized injection fluid through the intelligent completion valve.   
     
     
         9 . The method of  claim 8 , wherein:
 the training data further comprises acoustic signal data generated by the pressurized injection fluid flowing outward through the plurality of intelligent completion valves at different pressurized injection fluid pressures.   
     
     
         10 . The method of  claim 8 , wherein the trained machine-learning model continuously predicts the flow rate of pressurized injection fluid through the intelligent completion valve. 
     
     
         11 . The method of  claim 8 , wherein the training data is built by:
 operating an injection logging tool in the wellbore to record flow rates of the pressurized injection fluid at locations near each of the intelligent completion valves;   concurrently with operating the injection logging tool, operating the distributed acoustic sensing system to record acoustic signals generated at each of the intelligent completion valves; and   while operating the injection logging tool and the distributed acoustic sensing system, controlling the intelligent completion valves such that:
 the position of the one of the plurality of intelligent completion valves through which the pressurized injection fluid flows is changed in a step-wise fashion. 
   
     
     
         12 . The method of  claim 8 , further comprising processing the acoustic signal data by:
 truncating the acoustic signal data about a location of the intelligent completion valve;   filtering the acoustic signal data to maximize a signal-to-noise ratio;   stacking at least some acoustic signal data truncated recording channels to create a single channel;   converting the acoustic signal data from a time domain to a frequency domain by an integral transform;   down-sampling or low pass filtering a frequency of the acoustic signal data to a lower frequency;   reducing a number of acoustic signal data samples by subjecting the acoustic signal data to a filterbank; and   generating engineered filterbank features having frequency-bound filterbank amplitudes, and converting the frequency-bound filterbank amplitudes to a decibel scale using a logarithmic transformation.   
     
     
         13 . The method of  claim 12 , wherein:
 the lower frequency is between approximately 200 Hz to approximately 2,000 Hz, with a Nyquist frequency of between approximately 100 Hz to approximately 1,000 Hz; and   the filterbank includes filters that are centered at approximately 5 Hz to approximately 20 Hz frequency intervals with an overlap of approximately a 5 Hz to approximately 20 Hz between adjacent filters.   
     
     
         14 . The method of  claim 8 , wherein:
 a flow allocation model is configured to control a flow rate of the pressurized injection fluid through each of the plurality of intelligent completion valves; and   a position of a given intelligent completion valve is automatically adjusted to cause a flow rate of the pressurized injection fluid through the given intelligent completion valve to match a flow rate calculated for the given intelligent completion valve by the flow allocation model based on a predicted flow rate of the pressurized injection fluid through the given intelligent completion valve determined by the trained machine-learning model.   
     
     
         15 . A non-transitory computer-readable medium comprising instructions that are executable by a processor of a computing device, for causing the processor to perform operations comprising:
 receiving, by a processor of a computing device, from a distributed acoustic sensing system, sensor data associated with an intelligent completion valve of a plurality of intelligent completion valves positionable downhole in a wellbore in a formation;   applying the sensor data to a trained machine-learning model using training data comprising acoustic signal data generated by a pressurized injection fluid flowing outward through the plurality of intelligent completion valves at various flow rates and at different intelligent completion valve positions;   generating a result indicating a predicted flow rate of pressurized injection fluid through the intelligent completion valve; and   outputting the result that is useable to control a position of the intelligent completion valve or a pressure of the pressurized injection fluid to control the flow rate of the pressurized injection fluid through the intelligent completion valve.   
     
     
         16 . The non-transitory computer-readable medium of  claim 15 , wherein the training data further comprises acoustic signal data generated by the pressurized injection fluid flowing outward through the plurality of intelligent completion valves at different pressurized injection fluid pressures. 
     
     
         17 . The non-transitory computer-readable medium of  claim 15 , wherein the training data is buildable by:
 operating an injection logging tool in the wellbore to record flow rates of the pressurized injection fluid at locations near each of the intelligent completion valves;   concurrently with operating the injection logging tool, operating the distributed acoustic sensing system to record acoustic signals generated at each of the intelligent completion valves; and   while operating the injection logging tool and the distributed acoustic sensing system, controlling the intelligent completion valves such that:
 the position of the one of the plurality of intelligent completion valves through which the pressurized injection fluid flows is changed in a step-wise fashion. 
   
     
     
         18 . The non-transitory computer-readable medium of  claim 15 , wherein the operations further comprise processing the acoustic signal data by:
 truncating the acoustic signal data about a location of the intelligent completion valve;   filtering the acoustic signal data to maximize a signal-to-noise ratio;   stacking at least some acoustic signal data truncated recording channels to create a single channel;   converting the acoustic signal data from a time domain to a frequency domain by an integral transform;   down-sampling or low pass filtering a frequency content of the acoustic signal data to a lower frequency;   reducing a number of acoustic signal data samples by subjecting the acoustic signal data to a filterbank; and   generating engineered filterbank features having frequency-bound filterbank amplitudes, and converting the frequency-bound filterbank amplitudes to a decibel scale using a logarithmic transformation.   
     
     
         19 . The non-transitory computer-readable medium of  claim 18 , wherein:
 the lower frequency is between approximately 200 Hz to approximately 2,000 Hz, with a corresponding Nyquist frequency of between approximately 100 Hz to approximately 1,000 Hz; and   the filterbank includes filters that are centered at approximately 5 Hz to approximately 20 Hz frequency intervals with an overlap of approximately 5 Hz to approximately 20 Hz between adjacent filters.   
     
     
         20 . The non-transitory computer-readable medium of  claim 15 , wherein:
 a flow allocation model is configured to control a flow rate of the pressurized injection fluid through each of the plurality of intelligent completion valves; and   the operations further comprise automatically adjusting a position of a given intelligent completion valve to cause a flow rate of the pressurized injection fluid through the given intelligent completion valve to match a flow rate calculated for the given intelligent completion valve by the flow allocation model based on a predicted flow rate of the pressurized injection fluid through the given intelligent completion valve determined by the trained machine-learning model.