Methods, systems and devices for modelling reservoir properties

Abstract

Aspects of the present disclosure may provide devices, systems and methods for modelling resource production for which there may be incomplete information and/or unknown parameters. In some embodiments, the method includes applying an analytical fracture model and reducing a the number of models to be matched in a set of potential subterranean formation models.

What is claimed is: 
     
         1 . A method of modelling hydrocarbon production rates for a subterranean formation, the method comprising:
 obtaining, by at least one processor, production data for at least one well in the subterranean formation;   based at least in part on geological data for the subterranean formation, identify, at the at least one processor, a range of potential values for each of a plurality of parameters, the plurality of parameters including at least one parameter representative of geological characteristics of the subterranean formation, and fracture parameters; where each set of values including a selection from each of the ranges for the plurality of parameters defining a potential subterranean formation model, and where sets of values including different combinations of values for the plurality of parameters define a set of potential subterranean formation models;   matching at least a portion of the set of potential subterranean formation models to the production data for the at least one well by iteratively:
 inputting, by the at least one processor, a set of parameter values selected from the ranges of potential values to an analytical fracture model to generate a production model for the subterranean formation for the particular subterranean formation model defined by the inputted set of values, the production model a function of a stimulated area value; 
 determining at least one stimulated area value for the production model, and comparing production values for the production model with the production data for the at least one well to generate an error value; and 
 selecting parameter values for inputting in a subsequent iteration based on a machine learning algorithm and past error values to reduce a number of analyzed subterranean formation models that do not fit the production profile; 
   identifying production models which fit the production profile from the production data within a defined error threshold;   with the identified production models which fit the production profile from the production data for the at least one well in the subterranean formation, selecting a range of stimulated area values from a subset of the identified models having the lowest generated error values; and   based on a frequency distribution of the stimulated area values from the subset of the identified models having the lowest productivity value error scores, creating a forecast production model for at least a portion of the subterranean resource, the forecast production model having input parameters representative of geological characteristics of at least the portion of the subterranean formation, and an input parameter associated with the stimulated area value and limited to the selected range.   
     
     
         2 . The method of  claim 1 , wherein the stimulated area value is a function of permeability and fracture area. 
     
     
         3 . The method of  claim 1 , wherein the analytical fracture model is a function of the stimulated area value. 
     
     
         4 . The method of  claim 1 , wherein the analytical fracture model includes dimensionless time and dimensionless pressure logs to generate the production model for a series of time steps. 
     
     
         5 . The method of  claim 1  wherein the production data is collected over a period of time. 
     
     
         6 . The method of  claim 1  wherein the geological data for the subterranean formation is collected from at least one sensing device or a petrophysical analysis of well logs. 
     
     
         7 . The method of  claim 1  wherein identifying the range of potential values for each of a plurality of parameters comprises identifying a granularity at which values can be selected within the range of potential values. 
     
     
         8 . The method of  claim 1  wherein the fracture parameters include parameters associated with a number of fractures and at least one fracture area dimension. 
     
     
         9 . The method of  claim 1  comprising: creating forecast models of different portions of the subterranean resource, each forecast production model having input parameters representative of geological characteristics of the respective portion of the subterranean formation. 
     
     
         10 . The method of  claim 9  comprising: using the forecast models, generating a visual map illustrating different production forecasts for the different portions of the subterranean formation. 
     
     
         11 . A system for modelling hydrocarbon production rates for a subterranean formation, the system comprising at least one processor configured for:
 obtaining production data for at least one well in the subterranean formation;   based at least in part on geological data for the subterranean formation, identify a range of potential values for each of a plurality of parameters, the plurality of parameters including at least one parameter representative of geological characteristics of the subterranean formation, and fracture parameters; where each set of values including a selection from each of the ranges for the plurality of parameters defining a potential subterranean formation model, and where sets of values including different combinations of values for the plurality of parameters define a set of potential subterranean formation models;   matching at least a portion of the set of potential subterranean formation models to the production data for the at least one well by iteratively:
 inputting a set of parameter values selected from the ranges of potential values to an analytical fracture model to generate a production model for the subterranean formation for the particular subterranean formation model defined by the inputted set of values, the production model a function of a stimulated area value; 
 determining at least one stimulated area value for the production model, and comparing production values for the production model with the production data for the at least one well to generate an error value; and 
 selecting parameter values for inputting in a subsequent iteration based on a machine learning algorithm and past error values to reduce a number of analyzed subterranean formation models that do not fit the production profile; 
   identifying production models which fit the production profile from the production data within a defined error threshold;   with the identified production models which fit the production profile from the production data for the at least one well in the subterranean formation, selecting a range of stimulated area values from a subset of the identified models having the lowest generated error values; and   based on a frequency distribution of the stimulated area values from the subset of the identified models having the lowest productivity value error scores, creating a forecast production model for at least a portion of the subterranean resource, the forecast production model having input parameters representative of geological characteristics of at least the portion of the subterranean formation, and an input parameter associated with the stimulated area value and limited to the selected range.   
     
     
         12 . The system of  claim 11 , wherein the stimulated area value is a function of permeability and fracture area. 
     
     
         13 . The system of  claim 11 , wherein the analytical fracture model is a function of the stimulated area value. 
     
     
         14 . The system of  claim 11 , wherein the analytical fracture model includes dimensionless time and dimensionless pressure logs to generate the production model for a series of time steps. 
     
     
         15 . The system of  claim 11  wherein the production data is collected over a period of time. 
     
     
         16 . The system of  claim 11  wherein the geological data for the subterranean formation is collected from at least one sensing device or a petrophysical analysis of well logs. 
     
     
         17 . The system of  claim 11  wherein identifying the range of potential values for each of a plurality of parameters comprises identifying a granularity at which values can be selected within the range of potential values. 
     
     
         18 . The system of  claim 11  wherein the fracture parameters include parameters associated with a number of fractures and at least one fracture area dimension. 
     
     
         19 . The system of  claim 11  wherein the at least one processor is configured for: creating forecast models of different portions of the subterranean resource, each forecast production model having input parameters representative of geological characteristics of the respective portion of the subterranean formation. 
     
     
         20 . The system of  claim 19  wherein the at least one processor is configured for: using the forecast models, generating a visual map illustrating different production forecasts for the different portions of the subterranean formation. 
     
     
         21 . A computer-readable medium or media having stored thereon computer-readable instructions which when executed by at least one processor configured the at least one processor for:
 obtaining, by the at least one processor, production data for at least one well in the subterranean formation;   based at least in part on geological data for the subterranean formation, identify, at the at least one processor, a range of potential values for each of a plurality of parameters, the plurality of parameters including at least one parameter representative of geological characteristics of the subterranean formation, and fracture parameters; where each set of values including a selection from each of the ranges for the plurality of parameters defining a potential subterranean formation model, and where sets of values including different combinations of values for the plurality of parameters define a set of potential subterranean formation models;   matching at least a portion of the set of potential subterranean formation models to the production data for the at least one well by iteratively:
 inputting, by the at least one processor, a set of parameter values selected from the ranges of potential values to an analytical fracture model to generate a production model for the subterranean formation for the particular subterranean formation model defined by the inputted set of values, the production model a function of a stimulated area value; 
 determining at least one stimulated area value for the production model, and comparing production values for the production model with the production data for the at least one well to generate an error value; and 
 selecting parameter values for inputting in a subsequent iteration based on a machine learning algorithm and past error values to reduce a number of analyzed subterranean formation models that do not fit the production profile; 
   identifying production models which fit the production profile from the production data within a defined error threshold;   with the identified production models which fit the production profile from the production data for the at least one well in the subterranean formation, selecting a range of stimulated area values from a subset of the identified models having the lowest generated error values; and   based on a frequency distribution of the stimulated area values from the subset of the identified models having the lowest productivity value error scores, creating a forecast production model for at least a portion of the subterranean resource, the forecast production model having input parameters representative of geological characteristics of at least the portion of the subterranean formation, and an input parameter associated with the stimulated area value and limited to the selected range.