Systems and methods of predictive decline modeling based on time-dependent depletion function

Abstract

Systems and method for predicting production decline for a target well include receiving a first data set representing a target well; generating a static model of the target well based on the first data set; receiving a second data set representing one or more neighboring wells; generating a dynamic model of the target well based on the static model and based on a time-dependent depletion function, wherein the time-dependent depletion function is based on distances between the target well and the one or more neighboring wells, and the time-dependent depletion function based on the second data set and predictions of cumulative production of the one or more neighboring wells; and generating, based on the dynamic model, one or more time series values of a production profile of the target well.

What is claimed is: 
     
         1 . A method for predictive decline modeling for reservoir development, the method comprising:
 receiving a first data set representing a target well;   generating a static model of the target well based on the first data set;   receiving a second data set representing one or more neighboring wells;   generating a dynamic model of the target well based on the static model and based on a time-dependent depletion function, wherein the time-dependent depletion function depends on distances between the target well and the one or more neighboring wells, the second data set, and one or more predictions of cumulative production of the one or more neighboring wells; and   generating, based on the dynamic model, one or more time series values of a production profile of the target well.   
     
     
         2 . The method of  claim 1 , wherein the dynamic model of the target well is generated based on the time-dependent depletion function that combines or integrates the one or more predictions of cumulative production of the one or more neighboring wells. 
     
     
         3 . The method of  claim 2 , wherein the one or more predictions of cumulative production of the one or more neighboring wells are combined or integrated using an influence function that is based, at least in part, on the distances between the target well and the one or more neighboring wells. 
     
     
         4 . The method of  claim 1 , wherein generating the dynamic model of the target well comprises iteratively predicting cumulative production at respective time intervals for the target well and the one or more neighboring wells. 
     
     
         5 . The method of  claim 1 , wherein the dynamic model of the target well is generated by:
 applying inputs comprising the time-dependent depletion function to a machine learning (ML) model to provide the dynamic model and output the one or more time series values of a production profile of the target well,   wherein time series representing neighboring well production rates and includes training outputs comprising other time series representing target well production rates.   
     
     
         6 . The method of  claim 1 , wherein the static model is generated with supervised machine learning using the first data set that includes historical production data as feature inputs and a target variable being an initial resource production rate. 
     
     
         7 . The method of  claim 1 , wherein generating the production profile of the target well includes recursive calculations generating predicted production rates for the target well and the one or more neighboring wells for a series of time intervals. 
     
     
         8 . The method of  claim 1 , wherein the first data set represents one or more of a geological feature, well completion parameters, reservoir properties, production data, injection data, and fluid data. 
     
     
         9 . The method of  claim 1 , wherein the dynamic model of the target well is generated with a neural network using historical production data and dynamic well data as feature inputs and a target variable being resource production rate at time (t). 
     
     
         10 . The method of  claim 1 , further comprising:
 using the one or more time series values of the production profile of the target well to generate asset intelligence corresponding to an asset life cycle stage; and   recommending, based on the asset intelligence, risk-mitigation strategy associated with reservoir depletion.   
     
     
         11 . The method of  claim 1 , further comprising:
 repeating, for potential locations of the target well, the steps of generating the static model and generating the dynamic model of the target well at each of the potential locations; and   comparing the production profile of the target well at the potential locations.   
     
     
         12 . 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:
 receiving a first data set representing a target well;   generating a static model of the target well based on the first data set;   receiving a second data set representing one or more neighboring wells;   generating a dynamic model of the target well based on the static model and based on a time-dependent depletion function, wherein the time-dependent depletion function is based on distances between the target well and the one or more neighboring wells, and the time-dependent depletion function based on the second data set and one or more predictions of cumulative production of the one or more neighboring wells; and   generating, based on the dynamic model, one or more time series values of a production profile of the target well.   
     
     
         13 . The one or more tangible non-transitory computer-readable storage media of  claim 12 , wherein the dynamic model of the target well is generated based on the time-dependent depletion function that combines or integrates the one or more predictions of cumulative production of the one or more neighboring wells. 
     
     
         14 . The one or more tangible non-transitory computer-readable storage media of  claim 12 , wherein the one or more predictions of cumulative production of the one or more neighboring wells are combined or integrated using an influence function that is based, at least in part, on the distances between the target well and the one or more neighboring wells. 
     
     
         15 . The one or more tangible non-transitory computer-readable storage media of  claim 12 , wherein generating the dynamic model of the target well comprises iteratively predicting cumulative production at respective time intervals for the target well and the one or more neighboring wells. 
     
     
         16 . The one or more tangible non-transitory computer-readable storage media of  claim 12 , wherein the dynamic model of the target well is generated by:
 applying inputs comprising the time-dependent depletion function to a machine learning (ML) model to provide the dynamic model and output the one or more time series values of a production profile of the target well,   wherein the ML model has been trained using training data that includes training inputs comprising time series representing neighboring well production rates and includes training outputs comprising other time series representing target well production rates.   
     
     
         17 . The one or more tangible non-transitory computer-readable storage media of  claim 12 , wherein performing the computer process on a computing system in accordance with the one or more tangible non-transitory computer-readable storage media further comprises:
 using the one or more time series values of the production profile of the target well to generate asset intelligence corresponding to an asset life cycle stage; and   recommending, based on the asset intelligence, risk-mitigation strategy associated with reservoir depletion.   
     
     
         18 . The one or more tangible non-transitory computer-readable storage media of  claim 12 , wherein performing the computer process on a computing system in accordance with the one or more tangible non-transitory computer-readable storage media further comprises:
 repeating, for potential locations of the target well, the steps of generating the static model and generating the dynamic model of the target well at each of the potential locations; and   comparing the production profile of the target well at the potential locations.   
     
     
         19 . A system for predictive decline modeling for an oil well, the system comprising:
 a predictive decline modeling system configured to generate a predicted well production profile for a target well, the predicted well production profile generated by:
 receiving a first data set representing a target well; 
 generating a static model of the target well based on the first data set; 
 receiving a second data set representing one or more neighboring wells; 
 generating a dynamic model of the target well based on the static model and based on a time-dependent depletion function, wherein the time-dependent depletion function is based on distances between the target well and the one or more neighboring wells, and the time-dependent depletion function based on the second data set and one or more predictions of cumulative production of the one or more neighboring wells; and 
 generating, based on the dynamic model, one or more time series values of a production profile of the target well. 
   
     
     
         20 . The system of  claim 19 , wherein the dynamic model of the target well is generated based on the time-dependent depletion function that combines or integrates the one or more predictions of cumulative production of the one or more neighboring wells.