Systems and methods for modeling of dynamic waterflood well properties

Abstract

Implementations described and claimed herein provide systems and methods for dynamic waterflood forecast modeling utilizing deep thinking computational techniques to reduce the processing time for generating the forecast model and improving the accuracy of resulting forecasts. In one particular implementation, a dataset of a field may be restructured into the spatio-temporal framework and data driven deep neural networks may be utilized to learn the nuances of data interactions to make more accurate forecasts for each well in the field. Further, the generated model may forecast a single time segment and build the complete forecast through recursive prediction instances. The temporal component of the restructured data may include all or a portion of the production history of the field divided into spaced time intervals. The spatial component of the restructure data may include, within each epoch, a computed or estimated spatial relationships of all existing wells.

What is claimed is: 
     
         1 . A method for generating a forecast model of a well field, the method comprising:
 generating a divided input dataset by dividing an input dataset comprising field data into a plurality of timeframes;   restructuring the divided input dataset based on a spatial component associated with a target well of the well field;   training, based on the input dataset and utilizing a deep learning computing technique, a plurality of production forecast models; and   generating an optimized production forecast model from the plurality of trained production forecast models.   
     
     
         2 . The method of  claim 1  further comprising:
 extracting the input dataset from a database of raw field data comprising production data from a plurality of wells of the well field. 
 
     
     
         3 . The method of  claim 2 , wherein the plurality of wells comprise at least one oil well and at least one injector well. 
     
     
         4 . The method of  claim 2 , wherein restructuring the divided input dataset comprises:
 establishing one or more distance rings defining a distance from the target well; and   determining one or more wells of the well field are located within the one or more distance rings.   
     
     
         5 . The method of  claim 4 , wherein a first defined distance of a first distance ring of the one or more distance rings is larger than a second defined distance of a second distance ring of the one or more distance rings. 
     
     
         6 . The method of  claim 1 , wherein at least two of the plurality of timeframes encompasses a different duration. 
     
     
         7 . The method of  claim 1 , further comprising:
 recursively executing the optimized production forecast model to generate a production prediction of a well of the well field, the optimized production forecast model receiving measured production data from the field data.   
     
     
         8 . The method of  claim 1 , further comprising:
 recursively executing the optimized production forecast model to generate a production forecast of a well of the well field, the optimized production forecast model receiving assumed production data from the field data.   
     
     
         9 . The method of  claim 8 , further comprising:
 displaying, on a user interface, the generated production forecast of the well of the well field.   
     
     
         10 . The method of  claim 1 , further comprising:
 generating and displaying a map of infill locations associated with the well field, the infill locations generated by the optimized production forecast model.   
     
     
         11 . One or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing a computer process on a computing system, the computer process comprising:
 dividing an input dataset comprising field data into a plurality of timeframes;   restructuring the input dataset based on a spatial component associated with a target well of a well field;   training, based on the input dataset and utilizing a deep learning computing technique, a plurality of production forecast models; and   generating an optimized production forecast model from the plurality of trained production forecast models.   
     
     
         12 . The one or more tangible non-transitory computer-readable storage media of  claim 11 , the computer process further comprising:
 extracting the input dataset from a database of raw field data comprising production data from a plurality of wells of the well field.   
     
     
         13 . The one or more tangible non-transitory computer-readable storage media of claim of  claim 12 , wherein the plurality of wells comprise at least one oil well and at least one injector well. 
     
     
         14 . The one or more tangible non-transitory computer-readable storage media of claim of  claim 12 , wherein restructuring the divided input dataset comprises:
 establishing one or more distance rings defining a distance from the target well; and   determining one or more wells of the well field are located within the one or more distance rings.   
     
     
         15 . The one or more tangible non-transitory computer-readable storage media of  claim 11 , wherein at least two of the plurality of timeframes encompasses a different duration. 
     
     
         16 . The one or more tangible non-transitory computer-readable storage media of  claim 11 , the computer process further comprising:
 recursively executing the optimized production forecast model to generate a production prediction of a well of the well field, the optimized production forecast model receiving measured production data from the field data.   
     
     
         17 . The one or more tangible non-transitory computer-readable storage media of  claim 11 , the computer process further comprising:
 recursively executing the optimized production forecast model to generate a production forecast of a well of the well field, the optimized production forecast model receiving assumed production data from the field data.   
     
     
         18 . The one or more tangible non-transitory computer-readable storage media of claim of  claim 17 , the computer process further comprising:
 rendering the generated production forecast of the well of the well field for display on a user interface.   
     
     
         19 . The one or more tangible non-transitory computer-readable storage media of  claim 11 , the computer process further comprising:
 generating and displaying a map of infill locations associated with the well field, the infill locations generated by the optimized production forecast model.   
     
     
         20 . A system for generating a forecast model of a well field, the system comprising:
 a waterflood modeling system having at least one processor configured to generate a plurality of production forecast models trained based on an input dataset and using a deep learning computing technique, the input dataset divided into a plurality of timeframes and restructured based on a spatial component associated with a target well of the well field, the waterflood modeling system generating an optimized production forecast model from the plurality of trained production forecast models.