Hydraulic Fracturing in a Subsurface Formation

Abstract

Systems and methods for hydraulic fracturing a subsurface formation include obtaining a reservoir model representing a subsurface formation; obtaining a baseline hydraulic fracture model for one or more stages of a well in the subsurface formation based on the reservoir model. Additional hydraulic fracture models for one or more additional stages in one or more wells in the subsurface formation are generated using a machine learning model trained based on the baseline hydraulic fracture model. The additional hydraulic fracture models are integrated into the reservoir model. Hydrocarbon production from the subsurface formation is simulated using the reservoir model with the integrated additional hydraulic fracture models; and a well spacing for the one or more wells or a cluster spacing for hydraulic fractures in the one or more wells is determined based on the simulated hydrocarbon production.

What is claimed is: 
     
         1 . A method for hydraulic fracturing a subsurface formation, the method comprising:
 obtaining a reservoir model representing a subsurface formation;   obtaining a baseline hydraulic fracture model for one or more stages of a well in the subsurface formation based on the reservoir model;   generating additional hydraulic fracture models for one or more additional stages in one or more wells in the subsurface formation using a machine learning model trained based on the baseline hydraulic fracture model;   integrating the additional hydraulic fracture models into the reservoir model;   simulating hydrocarbon production from the subsurface formation using the reservoir model with the integrated additional hydraulic fracture models; and   determining a well spacing for the one or more wells or a cluster spacing for hydraulic fractures in the one or more wells based on the simulated hydrocarbon production.   
     
     
         2 . The method of  claim 1 , further comprising drilling one or more wells in the subsurface formation based on the determined well spacing. 
     
     
         3 . The method of  claim 1 , further comprising performing a hydraulic fracturing operation on the one or more wells based on the determined cluster spacing. 
     
     
         4 . The method of  claim 1 , wherein obtaining the baseline hydraulic fracture model comprises generating the baseline hydraulic fracture model by performing a geomechanics simulation of the subsurface formation based on geomechanical and geophysical properties of the subsurface formation. 
     
     
         5 . The method of  claim 1 , wherein the machine learning model comprises an affine transformation or an artificial neural network. 
     
     
         6 . The method of  claim 5 , further comprising training the machine learning model wherein training data to train the machine learning model comprises input features comprising geomechanical and geophysical properties of the subsurface formation and labeled output comprising the obtained hydraulic fracture model. 
     
     
         7 . The method of  claim 1 , wherein inputs to the machine learning model comprises one or more of geomechanical properties, geophysical properties, and time-series data for a hydraulic fracturing operation. 
     
     
         8 . The method of  claim 1 , wherein integrating the additional hydraulic fracture models into the reservoir model comprises using local grid refinement, an unstructured grid, or embedded discrete fracture models to represent the fractures in the reservoir model. 
     
     
         9 . The method of  claim 1 , wherein determining the well spacing or the cluster spacing comprises performing a sensitivity analysis by iteratively simulating hydrocarbon production from the subsurface formation while altering spacing parameters of wells or clusters in the reservoir model. 
     
     
         10 . A system for hydraulic fracturing a subsurface formation, the system comprising:
 at least one processor and a memory storing instructions that when executed by the at least one processor cause the at least one processor to perform operations comprising:
 obtaining a reservoir model representing a subsurface formation; 
 obtaining a baseline hydraulic fracture model for one or more stages of a well in the subsurface formation based on the reservoir model; 
 generating additional hydraulic fracture models for one or more additional stages in one or more wells in the subsurface formation using a machine learning model trained based on the baseline hydraulic fracture model; 
 integrating the additional hydraulic fracture models into the reservoir model; 
 simulating hydrocarbon production from the subsurface formation using the reservoir model with the integrated additional hydraulic fracture models; and 
 determining a well spacing for the one or more wells or a cluster spacing for hydraulic fractures in the one or more wells based on the simulated hydrocarbon production. 
   
     
     
         11 . The system of  claim 10 , wherein the instructions further comprise drilling one or more wells in the subsurface formation based on the determined well spacing or performing a hydraulic fracturing operation on the one or more wells based on the determined cluster spacing. 
     
     
         12 . The system of  claim 10 , wherein obtaining the baseline hydraulic fracture model comprises generating the baseline hydraulic fracture model by performing a geomechanics simulation of the subsurface formation based on geomechanical and geophysical properties of the subsurface formation. 
     
     
         13 . The system of  claim 10 , wherein the instructions further comprise training the machine learning model wherein training data to train the machine learning model comprises input features comprising geomechanical and geophysical properties of the subsurface formation and labeled output comprising the obtained hydraulic fracture model. 
     
     
         14 . The system of  claim 13 , wherein the machine learning model comprises an affine transformation or an artificial neural network. 
     
     
         15 . The system of  claim 10 , wherein integrating the additional hydraulic fracture models into the reservoir model comprises using local grid refinement, an unstructured grid, or embedded discrete fracture models to represent the fractures in the reservoir model. 
     
     
         16 . The system of  claim 10 , wherein determining the well spacing or the cluster spacing comprises performing a sensitivity analysis by iteratively simulating hydrocarbon production from the subsurface formation while altering spacing parameters of wells or clusters in the reservoir model. 
     
     
         17 . One or more non-transitory, machine-readable storage devices storing instructions for hydraulic fracturing a subsurface formation, the instructions being executable by one or more processors, to cause performance of operations comprising:
 obtaining a reservoir model representing a subsurface formation;   obtaining a baseline hydraulic fracture model for one or more stages of a well in the subsurface formation based on the reservoir model;   generating additional hydraulic fracture models for one or more additional stages in one or more wells in the subsurface formation using a machine learning model trained based on the baseline hydraulic fracture model;   integrating the additional hydraulic fracture models into the reservoir model;   simulating hydrocarbon production from the subsurface formation using the reservoir model with the integrated additional hydraulic fracture models; and   determining a well spacing for the one or more wells or a cluster spacing for hydraulic fractures in the one or more wells based on the simulated hydrocarbon production.   
     
     
         18 . The one or more non-transitory, machine-readable storage devices of  claim 17 , wherein the instructions further comprise drilling one or more wells in the subsurface formation based on the determined well spacing or performing a hydraulic fracturing operation on the one or more wells based on the determined cluster spacing. 
     
     
         19 . The one or more non-transitory, machine-readable storage devices of  claim 17 , wherein obtaining the baseline hydraulic fracture model comprises generating the baseline hydraulic fracture model by performing a geomechanics simulation of the subsurface formation based on geomechanical and geophysical properties of the subsurface formation. 
     
     
         20 . The one or more non-transitory, machine-readable storage devices of  claim 17 , wherein the instructions further comprise training the machine learning model wherein training data to train the machine learning model comprises input features comprising geomechanical and geophysical properties of the subsurface formation and labeled output comprising the obtained hydraulic fracture model.