Hydraulic fracturing, completion, and diverter optimization method for known well rock properties

Abstract

A method for optimizing hydraulic fracturing includes characterizing a fracture induced by pumping fracturing fluid into a subsurface formation. The characterizing includes analyzing properties of reflected tube waves detected in a well. Change in expected characterization of the subsurface formation is modeled with respect to a modeled change in at least one parameter of the pumping fracturing fluid. The modeled change is compared to a measured change in the characterization with respect to an actual change in the at least one parameter. The modeled change and the measured change are used to train a machine learning algorithm to determine an optimized change in the at least one parameter.

What is claimed is: 
     
         1 . A method for optimizing hydraulic fracturing, comprising:
 characterizing a fracture induced by pumping fracturing fluid into a subsurface formation, the characterizing comprising analyzing properties of reflected tube waves detected in a well;   modeling change in expected characterization of the subsurface formation with respect to a modeled change in at least one parameter of the pumping fracturing fluid;   comparing the modeled change to a measured change in the characterization with respect to an actual changes in the at least one parameter; and   using the modeled change and the measured change to train a machine learning algorithm to determine an optimized change in the at least one parameter.   
     
     
         2 . The method of  claim 1  wherein the at least one parameter comprises at least one of pad volume, fracture fluid viscosity, fracture fluid density, propping agent type, propping agent volume, fracturing fluid Injection rate and fracture fluid volume. 
     
     
         3 . The method of  claim 1  wherein the characterizing a fracture comprises analyzing a far field fracture system property and a near field fracture system property. 
     
     
         4 . The method of  claim 3  wherein the analyzing the far field fracture property comprises analyzing pressure decay after completing pumping the fracturing fluid. 
     
     
         5 . The method of  claim 3  wherein the analyzing the near field fracture property comprises analyzing at least amplitude and arrival time of the reflected tube waves. 
     
     
         6 . The method of  claim 5  wherein the tube waves are induced by inducing a pressure change in the well. 
     
     
         7 . The method of  claim 1  wherein the machine learning algorithm comprises a recursive feature elimination. 
     
     
         8 . The method of  claim 7  wherein the recursive feature elimination comprises a ridge regression model for each of a plurality of dimensions of hydraulic fractures. 
     
     
         9 . The method of  claim 8  wherein the plurality of dimensions comprises at least one of, fracture length, fracture height, near field fracture conductivity and far field fracture conductivity. 
     
     
         10 . The method of  claim 7  wherein the recursive feature elimination comprises a random forest model for each of a plurality of parameters of a fracture treatment. 
     
     
         11 . The method of  claim 10  wherein the plurality of fracture treatment parameters comprises at least one of fluid pumping rate, fluid pumping pressure, proppant type, proppant size, proppant size distribution and stage length of a fracture treatment state. 
     
     
         12 . The method of  claim 1  wherein the at least one parameter comprises at least one of diverter type and diverter amount. 
     
     
         13 . A non-transitory computer readable medium comprising logic operable to cause a programmable computer to perform actions comprising:
 characterizing a fracture induced by pumping fracturing fluid into a subsurface formation, the characterizing comprising analyzing properties of reflected tube waves detected in a well;   modeling change in expected characterization of the subsurface formation with respect to a modeled change in at least one parameter of the pumping fracturing fluid;   comparing the modeled change to a measured change in the characterization with respect to an actual changes in the at least one parameter; and   using the modeled change and the measured change to train a machine learning algorithm to determine an optimized change in the at least one parameter.   
     
     
         14 . The non-transitory computer readable medium of  claim 13  wherein the at least one parameter comprises at least one of pad volume, fracture fluid viscosity, fracture fluid density, propping agent type, propping agent volume, fracturing fluid Injection rate and fracture fluid volume. 
     
     
         15 . The non-transitory computer readable medium of  claim 13  wherein the characterizing a fracture comprises analyzing a far field fracture system property and a near field fracture system property. 
     
     
         16 . The non-transitory computer readable medium of  claim 15  wherein the analyzing the far field fracture property comprises analyzing pressure decay after completing pumping the fracturing fluid. 
     
     
         17 . The non-transitory computer readable medium of  claim 15  wherein the analyzing the near field fracture property comprises analyzing at least amplitude and arrival time of the reflected tube waves. 
     
     
         18 . The non-transitory computer readable medium of  claim 17  wherein the tube waves are induced by inducing a pressure change in the well. 
     
     
         19 . The non-transitory computer readable medium of  claim 13  wherein the machine learning algorithm comprises a recursive feature elimination. 
     
     
         20 . The non-transitory computer readable medium of  claim 19  wherein the recursive feature elimination comprises a ridge regression model for each of a plurality of dimensions of hydraulic fractures. 
     
     
         21 . The non-transitory computer readable medium of  claim 20  wherein the plurality of dimensions comprises at least one of, fracture length, fracture height, near field fracture conductivity and far field fracture conductivity. 
     
     
         22 . The non-transitory computer readable medium of  claim 19  wherein the recursive feature elimination comprises a random forest model for each of a plurality of parameters of a fracture treatment. 
     
     
         23 . The non-transitory computer readable medium of  claim 22  wherein the plurality of fracture treatment parameters comprises at least one of fluid pumping rate, fluid pumping pressure, proppant type, proppant size, proppant size distribution and stage length of a fracture treatment state. 
     
     
         24 . The method of  claim 13  wherein the at least one parameter comprises at least one of diverter type and diverter amount.