Predicting Frackable Intervals

Abstract

A computer implemented method that enables predicting frackable intervals. The method includes extracting rock fabric data from well logs and integrating rock fabric data with well drilling data and corresponding historical performance data to create labeled rock fabric data. The method also includes training a machine learning model to predict a probability of being a successful fracture for at least one interval using the labeled rock fabric data.

What is claimed is: 
     
         1 . A computer-implemented method of training a neural network for predicting frackable intervals comprising:
 extracting, using at least one hardware processor, rock fabric data from well logs;   integrating, using the at least one hardware processor, rock fabric data from well logs with well drilling data and corresponding historical performance data to create labeled rock fabric data; and   training, using the at least one hardware processor, a machine learning model to predict a probability of being a successful fracture for at least one interval using the labeled rock fabric data.   
     
     
         2 . The computer implemented method of  claim 1 , comprising identifying patterns or clusters of intervals where frackability issues were encountered, and training the machine learning model using data corresponding to the clusters. 
     
     
         3 . The computer implemented method of  claim 1 , wherein labeled rock fabric data is created by assessing well log parameters for each perforation interval from different disciplines and labeling respective parameters as frackable or unfrackable. 
     
     
         4 . The computer implemented method of  claim 1 , comprising automatically bypassing intervals when a probability of being an unsuccessful frac satisfies a predetermined threshold. 
     
     
         5 . The computer implemented method of  claim 1 , wherein the machine learning model is trained to predict a probability of being an unsuccessful frac for discretized intervals. 
     
     
         6 . The computer implemented method of  claim 1 , comprising assessing well log data comprising parameters for each perforation interval from different disciplines against discretized historical frackability results to minimize parameters integrated with historical performance data or to select the most relevant parameters. 
     
     
         7 . The computer implemented method of  claim 1 , comprising inputting new, unseen well log data to the trained machine learning model to predict a probability of being a successful frac for discretized intervals. 
     
     
         8 . An apparatus comprising a non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
 extracting rock fabric data from well logs;   integrating rock fabric data from well logs with well drilling data and corresponding historical performance data to create labeled rock fabric data; and   training a machine learning model to predict a probability of being a successful fracture for at least one interval using the labeled rock fabric data.   
     
     
         9 . The apparatus of  claim 8 , comprising identifying patterns or clusters of intervals where frackability issues were encountered, and training the machine learning model using data corresponding to the clusters. 
     
     
         10 . The apparatus of  claim 8 , wherein labeled rock fabric data is created by assessing well log parameters for each perforation interval from different disciplines and labeling respective parameters as frackable or unfrackable. 
     
     
         11 . The apparatus of  claim 8 , comprising automatically bypassing intervals when a probability of being an unsuccessful frac satisfies a predetermined threshold. 
     
     
         12 . The apparatus of  claim 8 , wherein the machine learning model is trained to predict a probability of being an unsuccessful frac for discretized intervals. 
     
     
         13 . The apparatus of  claim 8 , comprising assessing well log data comprising parameters for each perforation interval from different disciplines against discretized historical frackability results to minimize parameters integrated with historical performance data or to select the most relevant parameters. 
     
     
         14 . The apparatus of  claim 8 , comprising inputting new, unseen well log data to the trained machine learning model to predict a probability of being a successful frac for discretized intervals. 
     
     
         15 . A system, comprising:
 one or more memory modules;   one or more hardware processors communicably coupled to the one or more memory modules, the one or more hardware processors configured to execute instructions stored on the one or more memory modules to perform operations comprising:   extracting rock fabric data from well logs;   integrating rock fabric data from well logs with well drilling data and corresponding historical performance data to create labeled rock fabric data; and   training a machine learning model to predict a probability of being a successful fracture for at least one interval using the labeled rock fabric data.   
     
     
         16 . The system of  claim 15 , comprising identifying patterns or clusters of intervals where frackability issues were encountered, and training the machine learning model using data corresponding to the clusters. 
     
     
         17 . The system of  claim 15 , wherein labeled rock fabric data is created by assessing well log parameters for each perforation interval from different disciplines and labeling respective parameters as frackable or unfrackable. 
     
     
         18 . The system of  claim 15 , comprising automatically bypassing intervals when a probability of being an unsuccessful frac satisfies a predetermined threshold. 
     
     
         19 . The system of  claim 15 , wherein the machine learning model is trained to predict a probability of being an unsuccessful frac for discretized intervals. 
     
     
         20 . The system of  claim 15 , comprising assessing well log data comprising parameters for each perforation interval from different disciplines against discretized historical frackability results to minimize parameters integrated with historical performance data or to select the most relevant parameters.