Cloud-based management of a hydraulic fracturing operation in a wellbore

Abstract

The method includes receiving raw data at a cloud service relating to a hydraulic fracturing operation. The raw data can be streamed to the cloud service. The method further includes pre-processing the raw data to generate pre-processed data. The pre-processed data can be ingestible by a cloud-based dashboard. Additionally, the method includes identifying at least one parameter relating to the hydraulic fracturing operation using the pre-processed data. The method can further include determining a difference between the at least one parameter and at least one optimized parameter. Further, the method can include adjusting the hydraulic fracturing operation based the difference between the at least one parameter and the at least one optimized parameter.

What is claimed is: 
     
         1 . A system comprising:
 a processor; and   a memory device that includes instructions executable by the processor for causing the processor to perform operations comprising:
 receiving, at a cloud service, raw data streamed to the cloud service from a hydraulic fracturing operation; 
 pre-processing, via the cloud service, the raw data to generate pre-processed data ingestible by a cloud-based dashboard of the cloud service; 
 identifying, via the cloud service, at least one parameter relating to the hydraulic fracturing operation using the pre-processed data; 
 determining, via the cloud service, a difference between the at least one parameter and at least one optimized parameter, the at least one optimized parameter determined by the cloud service based on historical data or theoretical data; and 
 adjusting the hydraulic fracturing operation based on the difference between the at least one parameter and the at least one optimized parameter. 
   
     
     
         2 . The system of  claim 1 , further comprising displaying the at least one parameter on the cloud-based dashboard. 
     
     
         3 . The system of  claim 2 , wherein the at least one parameter comprises pumping hours over time, number of gear changes per treatment, pumped rate over time, proppant concentration over time, pressure over time, duration of cavitation per treatment, other diagnostic parameters of hydraulic fracturing equipment, or a combination thereof. 
     
     
         4 . The system of  claim 1 , wherein the operation of pre-processing, via the cloud service, the raw data to generate the pre-processed data ingestible by the cloud-based dashboard further comprises:
 receiving the raw data at a data lake in a plurality of formats;   performing at least one operation on the raw data and combining the raw data with preexisting data to generate the pre-processed data; and   extracting a subset of the pre-processed data to generate a diagnostic indicator for adjusting the hydraulic fracturing operation.   
     
     
         5 . The system of  claim 1 , wherein the operation of receiving, by the cloud service, the raw data related to the hydraulic fracturing operation further comprises:
 identifying a subset of raw data, the subset of raw data related to an unexpected behavior of the hydraulic fracturing operation; and   generating an alert for display at the cloud-based dashboard for the unexpected behavior of the hydraulic fracturing operation based on the subset of raw data.   
     
     
         6 . The system of  claim 1 , wherein the operation of adjusting the hydraulic fracturing operation based on the difference between the at least one parameter and the at least one optimized parameter further comprises:
 adjusting the hydraulic fracturing operation in real-time or near real-time.   
     
     
         7 . The system of  claim 1 , wherein the operation of receiving, by the cloud service, the raw data relating to the hydraulic fracturing operation further comprises:
 assigning a first pre-processing priority level to a first subset of raw data from the raw data;   assigning a second pre-processing priority level to a second subset of raw data from the raw data; and   pre-processing, by the cloud service, the first subset of raw data prior to pre-processing the second subset of raw data based on the first pre-processing priority level exceeding the second pre-processing priority level.   
     
     
         8 . The system of  claim 1 , wherein the operation of pre-processing the raw data or the operation of identifying the at least one parameter further comprises executing a machine learning model that predicts data with a highest impact on the hydraulic fracturing operation. 
     
     
         9 . A computer-implemented method comprising:
 receiving, at a cloud service, raw data streamed to the cloud service from a hydraulic fracturing operation;   pre-processing, via the cloud service, the raw data to generate pre-processed data ingestible by a cloud-based dashboard of the cloud service;   identifying, via the cloud service, at least one parameter relating to the hydraulic fracturing operation using the pre-processed data;   determining, via the cloud service, a difference between the at least one parameter and at least one optimized parameter, the at least one optimized parameter determined by the cloud service based on historical data or theoretical data; and   adjusting the hydraulic fracturing operation based on the difference between the at least one parameter and the at least one optimized parameter.   
     
     
         10 . The computer-implemented method of  claim 9 , further comprising displaying the at least one parameter on the cloud-based dashboard. 
     
     
         11 . The computer-implemented method of  claim 10 , wherein the at least one parameter comprises pumping hours over time, number of gear changes per treatment, duration of cavitation per treatment, pumped rate, proppant concentration, pressure over time, proppant concentration over time, or any combination thereof. 
     
     
         12 . The computer-implemented method of  claim 9 , wherein pre-processing, via the cloud service, the raw data to generate the pre-processed data ingestible by the cloud-based dashboard further comprises:
 receiving the raw data at a data lake in a plurality of formats;   performing at least one operation on the raw data and combining the raw data with preexisting data to generate the pre-processed data; and   extracting a subset of the pre-processed data to generate a diagnostic indicator for adjusting the hydraulic fracturing operation.   
     
     
         13 . The computer-implemented method of  claim 9 , wherein receiving, by the cloud service, the raw data related to the hydraulic fracturing operation further comprises:
 identifying a subset of raw data, the subset of raw data related to an unexpected behavior of the hydraulic fracturing operation; and   generating an alert for display at the cloud-based dashboard for the unexpected behavior of the hydraulic fracturing operation based on the subset of raw data.   
     
     
         14 . The computer-implemented method of  claim 9 , wherein adjusting the hydraulic fracturing operation based on the difference between the at least one parameter and the at least one optimized parameter further comprises:
 adjusting the hydraulic fracturing operation in real-time or near real-time.   
     
     
         15 . The computer-implemented method of  claim 9 , wherein receiving, by the cloud service, the raw data related to the hydraulic fracturing operation further comprises:
 assigning a first pre-processing priority level to a first subset of raw data from the raw data;   assigning a second pre-processing priority level to a second subset of raw data from the raw data; and   pre-processing, by the cloud service, the first subset of raw data prior to pre-processing the second subset of raw data based on the first pre-processing priority level exceeding the second pre-processing priority level.   
     
     
         16 . A non-transitory computer-readable medium comprising instructions that are executable by a processing device for causing the processing device to perform operations comprising:
 receiving, at a cloud service, raw data streamed to the cloud service from a hydraulic fracturing operation;   pre-processing, via the cloud service, the raw data to generate pre-processed data ingestible by a cloud-based dashboard of the cloud service;   identifying, via the cloud service, at least one parameter relating to the hydraulic fracturing operation using the pre-processed data;   determining, via the cloud service, a difference between the at least one parameter and at least one optimized parameter, the at least one optimized parameter determined by the cloud service based on historical data or theoretical data; and   adjusting the hydraulic fracturing operation based on the difference between the at least one parameter and the at least one optimized parameter.   
     
     
         17 . The non-transitory computer-readable medium of  claim 16 , further comprising displaying the at least one parameter on the cloud-based dashboard. 
     
     
         18 . The non-transitory computer-readable medium of  claim 17 , wherein the at least one parameter comprises pumping hours over time, number of gear changes per treatment, duration of cavitation per treatment, pumped rate, proppant concentration, pressure over time, proppant concentration over time, or any combination thereof. 
     
     
         19 . The non-transitory computer-readable medium of  claim 16 , wherein the operation of pre-processing, via the cloud service, the raw data to generate the pre-processed data ingestible by the cloud-based dashboard further comprises:
 receiving the raw data at a data lake in a plurality of formats;   performing at least one operation on the raw data and combining the raw data with preexisting data to generate the pre-processed data; and   extracting a subset of the pre-processed data to generate a diagnostic indicator used to adjust the hydraulic fracturing operation.   
     
     
         20 . The non-transitory computer-readable medium of  claim 16 , wherein the operation of receiving, by the cloud service, the raw data related to the hydraulic fracturing operation further comprises:
 identifying a subset of raw data, the subset of raw data related to an unexpected behavior of the hydraulic fracturing operation; and   generating an alert for display at the cloud-based dashboard for the unexpected behavior of the hydraulic fracturing operation based on the subset of raw data.