Hybrid machine learning modeling for evaluating hydraulic fracture conductivity using strain data from fiber optics

Abstract

Systems and methods described herein provide for hydraulic fracturing conductivity evaluation with respect to producer wells in a field. An exemplary method includes measuring fracture-related data corresponding to a hydraulic fracturing operation performed with respect to one or more producer wells in a field, where the fracture-related data include strain data measured using one or more optical fibers deployed within one or more offset wells in the field. The method also includes extracting deep convolutional neural network (DCNN)-based features, physics-based features, and statistics-based features using the fracture-related data, as well as training a hybrid machine learning model for evaluating hydraulic fracture conductivity corresponding to the hydraulic fracturing operation, where the training is performed using the DCNN-based features, the physics-based features, and the statistics-based features. The method further includes applying the trained hybrid machine learning model to generate a well spacing plan for the field.

What is claimed is: 
     
         1 . A method for hydraulic fracturing conductivity evaluation with respect to a producer well, wherein the method is executed via a processor of a computing system, and wherein the method comprises:
 measuring fracture-related data corresponding to a hydraulic fracturing operation performed with respect to least one producer well in a field, wherein the fracture-related data comprise strain data measured using at least one optical fiber deployed within at least one offset well in the field;   extracting deep convolutional neural network (DCNN)-based features, physics-based features, and statistics-based features using the fracture-related data;   training a hybrid machine learning model for evaluating hydraulic fracture conductivity corresponding to the hydraulic fracturing operation, wherein the training is performed using the DCNN-based features, the physics-based features, and the statistics-based features; and   applying the trained hybrid machine learning model to generate a well spacing plan for the field.   
     
     
         2 . The method of  claim 1 , wherein the fracture-related data further comprise radioactive proppant tracer data measured using at least one spectral gamma ray logging tool deployed within the at least one offset well in the field. 
     
     
         3 . The method of  claim 1 , wherein the fracture-related data further comprise pressure depletion data measured using distributed pressure gauges deployed within the at least one offset well in the field. 
     
     
         4 . The method of  claim 1 , wherein extracting the DCNN-based features using the fracture-related data comprises performing deep-learning-based feature extraction by:
 generating two-dimensional (2D) strain maps from the strain data;   converting each 2D strain map to a strain rate map;   averaging each strain rate map to a time series of averaged strain rate;   calculating a Gramian Angular Field (GAF) map for each time series;   loading each GAF map into a pretrained DCNN;   removing top layers of the DCNN to generate an array of features;   mapping the array of features to at least two principal components; and   outputting the at least two principal components as at least a portion of the DCNN-based features.   
     
     
         5 . The method of  claim 4 , comprising pretraining the DCNN using an open image-based training dataset. 
     
     
         6 . The method of  claim 1 , wherein extracting the physics-based features using the fracture-related data comprises:
 generating two-dimensional (2D) strain maps from the strain data; and   calculating at least a portion of the physics-based features from the 2D strain maps.   
     
     
         7 . The method of  claim 1 , comprising training the hybrid machine learning model by performing transfer learning using a pretrained DCNN, in combination with the DCNN-based features, the physics-based features, and the statistics-based features. 
     
     
         8 . The method of  claim 1 , wherein training the hybrid machine learning model comprises:
 ranking each of the DCNN-based features, the physics-based features, and the statistics-based features based on an importance score; and   utilizing a specified number of features with highest importance scores to train the hybrid machine learning model.   
     
     
         9 . The method of  claim 1 , wherein training the hybrid machine learning model comprises performing extreme gradient boosting (XGB), and wherein the trained hybrid machine learning model comprises an XGB model. 
     
     
         10 . The method of  claim 1 , wherein applying the trained hybrid machine learning model to generate the well spacing plan for the field comprises:
 utilizing the trained hybrid machine learning model to calculate at least one of fracture conductivity or proppant arrival for the at least one producer well; and   generating the well spacing plan based on the at least one of the fracture conductivity or the proppant arrival.   
     
     
         11 . The method of  claim 1 , further comprising causing the generated well spacing plan to be applied to the field. 
     
     
         12 . The method of  claim 11 , wherein causing the generated well spacing plan to be applied to the field comprises at least one of:
 causing the at least one producer well to be completed in the field according to the well spacing plan; or   causing at least one additional producer well to be drilled in the field according to the well spacing plan.   
     
     
         13 . A method for developing a field of producer wells, comprising:
 executing a hydraulic fracturing operation for a producer well in a field;   generating fracture-related data corresponding to the hydraulic fracturing operation for the producer well using an offset well, wherein the fracture-related data comprise strain data measured using an optical fiber deployed within the offset well;   extracting deep convolutional neural network (DCNN)-based features, physics-based features, and statistics-based features using the fracture-related data;   training a hybrid machine learning model for evaluating hydraulic fracture conductivity corresponding to the hydraulic fracturing operation, wherein the training is performed using the DCNN-based features, the physics-based features, and the statistics-based features;   applying the trained hybrid machine learning model to generate a well spacing plan for the field; and   developing the field according to the generated well spacing plan.   
     
     
         14 . The method of  claim 13 , wherein the fracture-related data further comprise radioactive proppant tracer data measured using at least one spectral gamma ray logging tool deployed within the at least one offset well in the field. 
     
     
         15 . The method of  claim 13 , wherein the fracture-related data further comprise pressure depletion data measured using distributed pressure gauges deployed within the at least one offset well in the field. 
     
     
         16 . The method of  claim 13 , wherein extracting the DCNN-based features using the fracture-related data comprises performing deep-learning-based feature extraction by:
 generating two-dimensional (2D) strain maps from the strain data;   converting each 2D strain map to a strain rate map;   averaging each strain rate map to a time series of averaged strain rate;   calculating a Gramian Angular Field (GAF) map for each time series;   loading each GAF map into a pretrained DCNN;   removing top layers of the DCNN to generate an array of features;   mapping the array of features to at least two principal components; and   outputting the at least two principal components as at least a portion of the DCNN-based features.   
     
     
         17 . The method of  claim 13 , comprising training the hybrid machine learning model by performing transfer learning using a pretrained DCNN, in combination with the DCNN-based features, the physics-based features, and the statistics-based features. 
     
     
         18 . The method of  claim 13 , wherein training the hybrid machine learning model comprises performing extreme gradient boosting (XGB), and wherein the trained hybrid machine learning model comprises an XGB model. 
     
     
         19 . A computing system, comprising:
 a processor; and   a non-transitory, computer-readable storage medium, comprising code configured to direct the processor to:
 access fracture-related data corresponding to a hydraulic fracturing operation performed with respect to least one producer well in a field, wherein the fracture-related data comprise strain data measured using at least one optical fiber deployed within at least one offset well in the field; 
 extract deep convolutional neural network (DCNN)-based features using the fracture-related data by performing deep-learning-based feature extraction, wherein performing the deep-learning-based feature extraction comprises:
 generating two-dimensional (2D) strain maps from the strain data; 
 converting each 2D strain map to a strain rate map; 
 averaging each strain rate map to a time series of averaged strain rate; 
 calculating a Gramian Angular Field (GAF) map for each time series; 
 loading each GAF map into a pretrained DCNN; 
 removing top layers of the DCNN to generate an array of features; 
 mapping the array of features to at least two principal components; and 
 outputting the at least two principal components as at least a portion of the DCNN-based features; 
 
 extract physics-based features using the fracture-related data by calculating at least a portion of the physics-based features from the 2D strain maps; 
 extract statistics-based features using the fracture-related data; 
 train a hybrid machine learning model for evaluating hydraulic fracture conductivity corresponding to the hydraulic fracturing operation, wherein the training is performed using the pretrained DCNN, in combination with the DCNN-based features, the physics-based features, and the statistics-based features; and 
 apply the trained hybrid machine learning model to generate a well spacing plan for the field. 
   
     
     
         20 . The computing system of  claim 19 , wherein the non-transitory, computer-readable storage medium further comprises code configured to direct the processor to cause the well spacing plan to be applied to the field.