System and method for using ai-based model to predict borehole size in horizontal carbonate wells

Abstract

Some implementations provide a method that includes: accessing a stream of input data from logging tools in a first well-bore, wherein the stream of input data comprises measurements of bore sizes inside the first well-bore; splitting the stream of input data into a training set of input data and a testing set of input data; training a machine learning model using the training set of input data, wherein the machine learning model is configured to predict a bore size parameter based on input features of the training set of input data; evaluating the machine learning model using the testing set of input data; and in response to evaluating the machine learning model as satisfactory, applying the machine learning model to a newly received stream of input data from a second well-bore such that the bore size parameter of the second well-bore is determined independent of measurements of bore sizes inside the second well-bore.

What is claimed is: 
     
         1 . A computer-implemented method, comprising:
 accessing a stream of input data from logging tools in a first well-bore, wherein the stream of input data comprises measurements of bore sizes inside the first well-bore;   splitting the stream of input data into a training set of input data and a testing set of input data;   training a machine learning model using the training set of input data, wherein the machine learning model is configured to predict a bore size parameter based on input features of the training set of input data;   evaluating the machine learning model using the testing set of input data; and   in response to evaluating the machine learning model as satisfactory, applying the machine learning model to a newly received stream of input data from a second well-bore such that the bore size parameter of the second well-bore is determined independent of measurements of bore sizes inside the second well-bore.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein the machine learning model comprises at least one of: a Random Forest (RF) model, or a XGBoost (eXtreme Gradient Boosting) model. 
     
     
         3 . The computer-implemented method of  claim 2 , further comprising:
 selecting the input features for the machine learning model.   
     
     
         4 . The computer-implemented method of  claim 1 , wherein evaluating the machine learning model comprises:
 computing a Root Mean Square Error (RMSE) between the predicted bore size parameter and an actual measurement; and   comparing the RMSE with a pre-determined threshold.   
     
     
         5 . The computer-implemented method of  claim 4 , further comprising:
 in response to evaluating the machine learning model as unsatisfactory, refining the machine learning model.   
     
     
         6 . The computer-implemented method of  claim 5 , wherein refining the machine learning model comprises at least one of: providing at least one additional input feature to the machine learning model, or replacing at least one input feature with a different input feature. 
     
     
         7 . The computer-implemented method of  claim 5 , wherein refining the machine learning model comprises:
 adjusting at least one parameter of the machine learning model.   
     
     
         8 . The computer-implemented method of  claim 1 , wherein the stream of input data comprises logging data encoding a resistivity, a density, a neutron recording, and a gamma ray recording. 
     
     
         9 . The computer-implemented method of  claim 1 , wherein the bore size parameter comprises at least one of: a maximum size, or a minimum size. 
     
     
         10 . A computer system comprising one or more hardware processors configured to perform operations of:
 accessing a stream of input data from logging tools in a first well-bore, wherein the stream of input data comprises measurements of bore sizes inside the first well-bore;   splitting the stream of input data into a training set of input data and a testing set of input data;   training a machine learning model using the training set of input data, wherein the machine learning model is configured to predict a bore size parameter based on input features of the training set of input data;   evaluating the machine learning model using the testing set of input data; and   in response to evaluating the machine learning model as satisfactory, applying the machine learning model to a newly received stream of input data from a second well-bore such that the bore size parameter of the second well-bore is determined independent of measurements of bore sizes inside the second well-bore.   
     
     
         11 . The computer system of  claim 10 , wherein the machine learning model comprises at least one of: a Random Forest (RF) model, or a XGBoost (eXtreme Gradient Boosting) model. 
     
     
         12 . The computer system of  claim 11 , wherein the operations further comprise:
 selecting the input features for the machine learning model.   
     
     
         13 . The computer system of  claim 11 , wherein evaluating the machine learning model comprises:
 computing a Root Mean Square Error (RMSE) between the predicted bore size parameter and an actual measurement; and   comparing the RMSE with a pre-determined threshold.   
     
     
         14 . The computer system of  claim 13 , wherein the operations further comprise:
 in response to evaluating the machine learning model as unsatisfactory, refining the machine learning model.   
     
     
         15 . The computer system of  claim 13 , wherein refining the machine learning model comprises at least one of: providing at least one additional input feature to the machine learning model, or replacing at least one input feature with a different input feature. 
     
     
         16 . The computer system of  claim 15 , wherein refining the machine learning model comprises:
 adjusting at least one parameter of the machine learning model.   
     
     
         17 . The computer system of  claim 10 , wherein the stream of input data comprises logging data encoding a resistivity, a density, a neutron recording, and a gamma ray recording. 
     
     
         18 . The computer system of  claim 10 , wherein the bore size parameter comprises at least one of: a maximum size, or a minimum size. 
     
     
         19 . A non-transitory computer-readable medium comprising software instructions which, when executed by one or more computer processors, cause the one or more computer processors to perform operations of:
 accessing a stream of input data from logging tools in a first well-bore, wherein the stream of input data comprises measurements of bore sizes inside the first well-bore;   splitting the stream of input data into a training set of input data and a testing set of input data;   training a machine learning model using the training set of input data, wherein the machine learning model is configured to predict a bore size parameter based on input features of the training set of input data;   evaluating the machine learning model using the testing set of input data; and   in response to evaluating the machine learning model as satisfactory, applying the machine learning model to a newly received stream of input data from a second well-bore such that the bore size parameter of the second well-bore is determined independent of measurements of bore sizes inside the second well-bore.   
     
     
         20 . The non-transitory computer-readable medium of  claim 19 , wherein evaluating the machine learning model comprises:
 computing a Root Mean Square Error (RMSE) between the predicted bore size parameter and an actual measurement; and   comparing the RMSE with a pre-determined threshold.