Comprehensive workflow to model naturally fractured reservoirs

Abstract

A method includes modeling a reservoir using a lab scale set of models and a field scale set of models. The reservoir is modeled using the lab scale set of models by scanning a sample of the reservoir into the lab scale set of models to create modeled fractures, estimating hydraulic properties of the modeled fractures, estimating multi-phase dynamic properties of the modeled fractures, and determining characteristics of a flow regime of a fluid flowing through the modeled fractures. The reservoir is modeled using the field scale set of models by modeling a discrete fracture network of the reservoir, upscaling the discrete fracture network, and calibrating the discrete fracture network. An enhanced oil recovery operation is designed and performed on the reservoir using the calibrated discrete fracture network.

What is claimed: 
     
         1 . A method comprising:
 modeling a reservoir having natural fractures using a lab scale set of models and a field scale set of models, wherein modeling the reservoir using the lab scale set of models comprises:
 scanning a sample of the reservoir into the lab scale set of models to create modeled fractures that represent the natural fractures; 
 estimating hydraulic properties of the modeled fractures using analytical models or artificial intelligence based models; 
 estimating multi-phase dynamic properties of the modeled fractures based on geometric properties of the modeled fractures and the estimated hydraulic properties; and 
 determining characteristics of a flow regime of a fluid flowing through the modeled fractures using the estimated hydraulic properties and the estimated multi-phase dynamic properties; 
   wherein modeling the reservoir using the field scale set of models comprises:
 modeling a discrete fracture network of the reservoir using the modeled fractures, the estimated hydraulic properties, the estimated multi-phase dynamic properties, and the characteristics of the flow regime using artificial intelligence and stochastic methods to capture an anisotropy and heterogeneity of each individual modeled fracture; 
 upscaling the discrete fracture network by determining a shape factor using a physics-informed neural network; and 
 calibrating the discrete fracture network using machine learning algorithms to create a calibrated discrete fracture network; and 
   designing and performing an enhanced oil recovery operation on the reservoir using the calibrated discrete fracture network.   
     
     
         2 . The method of  claim 1 , wherein designing and performing the enhanced oil recovery operation on the reservoir using the calibrated discrete fracture network further comprises pumping a fluid into the reservoir using an injection well. 
     
     
         3 . The method of  claim 2 , wherein designing and performing the enhanced oil recovery operation on the reservoir using the calibrated discrete fracture network further comprises flowing the fluid through the natural fractures of the reservoir towards a production well. 
     
     
         4 . The method of  claim 3 , wherein a location of the injection well is based on the calibrated discrete fracture network. 
     
     
         5 . The method of  claim 1 , wherein modeling the reservoir using the lab scale set of models further comprises calibrating the modeled fractures using a reservoir model using single-phase and multi-phase near-wellbore radial and cartesian models. 
     
     
         6 . The method of  claim 1 , wherein modeling the reservoir using the field scale set of models further comprises quantifying and ranking static and dynamic properties of the discrete fracture network. 
     
     
         7 . The method of  claim 6 , wherein quantifying and ranking the static and dynamic properties of the discrete fracture network further comprises determining a geometric connectivity and the heterogeneity of the modeled fractures based on a static Lorenz coefficient. 
     
     
         8 . The method of  claim 1 , wherein modeling the reservoir using the field scale set of models further comprises modeling localized flow mechanisms in an interface between the modeled fractures and a matrix of the reservoir using numerical methods. 
     
     
         9 . The method of  claim 1 , wherein estimating the estimated hydraulic properties of the modeled fractures using the analytical models or the artificial intelligence based models further comprises estimating a stress-dependent permeability under effective normal stress and coupled flow-normal shear conditions. 
     
     
         10 . The method of  claim 1 , wherein estimating the estimated multi-phase dynamic properties of the modeled fractures based on the geometric properties of the modeled fractures and the estimated hydraulic properties further comprises using a Leverett J-function corresponding to different rock-fracture types. 
     
     
         11 . A system comprising:
 an enhanced oil recovery system having an injection well and a production well drilled into a reservoir having natural fractures;   at least one sample of the reservoir; and   a computer system configured to model the reservoir using a lab scale set of models and a field scale set of models, wherein the computer system is configured to model the reservoir using the lab scale set of models by:
 scanning a sample of the reservoir into the lab scale set of models to create modeled fractures that represent the natural fractures; 
 estimating hydraulic properties of the modeled fractures using analytical models or artificial intelligence based models; 
 estimating multi-phase dynamic properties of the modeled fractures based on geometric properties of the modeled fractures and the estimated hydraulic properties; and 
 determining characteristics of a flow regime of a fluid flowing through the modeled fractures using the estimated hydraulic properties and the estimated multi-phase dynamic properties; 
   wherein the computer system is configured to model the reservoir using the field scale set of models by:
 modeling a discrete fracture network of the reservoir using the modeled fractures, the estimated hydraulic properties, the estimated multi-phase dynamic properties, and the characteristics of the flow regime using artificial intelligence and stochastic methods to capture an anisotropy and heterogeneity of each individual modeled fracture; 
 upscaling the discrete fracture network by determining a shape factor using a physics-informed neural network; and 
 calibrating the discrete fracture network using machine learning algorithms to create a calibrated discrete fracture network. 
   
     
     
         12 . The system of  claim 11 , wherein the injection well is configured to pump a fluid into the reservoir. 
     
     
         13 . The system of  claim 12 , wherein the fluid is configured to flow through the natural fractures of the reservoir towards the production well. 
     
     
         14 . The system of  claim 13 , wherein a location of the injection well is based on the calibrated discrete fracture network. 
     
     
         15 . The system of  claim 11 , wherein modeling the reservoir using the lab scale set of models further comprises calibrating the modeled fractures using a reservoir model using single-phase and multi-phase near-wellbore radial and cartesian models. 
     
     
         16 . The system of  claim 11 , wherein modeling the reservoir using the field scale set of models further comprises quantifying and ranking static and dynamic properties of the discrete fracture network. 
     
     
         17 . The system of  claim 16 , wherein quantifying and ranking the static and dynamic properties of the discrete fracture network further comprises determining a geometric connectivity and the heterogeneity of the modeled fractures based on a static Lorenz coefficient. 
     
     
         18 . The system of  claim 11 , wherein modeling the reservoir using the field scale set of models further comprises modeling localized flow mechanisms in an interface between the modeled fractures and a matrix of the reservoir using numerical methods. 
     
     
         19 . The system of  claim 11 , wherein estimating the estimated hydraulic properties of the modeled fractures using the analytical models or the artificial intelligence based models further comprises estimating a stress-dependent permeability under effective normal stress and coupled flow-normal shear conditions. 
     
     
         20 . The system of  claim 11 , wherein estimating the estimated multi-phase dynamic properties of the modeled fractures based on the geometric properties of the modeled fractures and the estimated hydraulic properties further comprises using a Leverett J-function corresponding to different rock-fracture types.