Workflow of inflow performance relationship for a reservoir using machine learning technique

Abstract

A machine learning model is trained to facilitate determination of transient inflow performance relationship for a reservoir. A type reservoir model for the reservoir is developed and run multiple times with different input parameters to generate multiple production simulations for the reservoir. The input parameters and the results of the multiple production simulations for the reservoir are used to train a machine learning model. The trained machine learning model facilitates determination of transient inflow performance relationship for the reservoir by providing time-series prediction of average pressure, production rate, and absolute open flow of the reservoir.

What is claimed is: 
     
         1 . A system for determining inflow performance relationship for a reservoir, the system comprising:
 one or more physical processors configured by machine-readable instructions to:
 obtain type reservoir model information, the type reservoir model information defining a type reservoir model for the reservoir; 
 generate multiple production simulations for the reservoir based on different values of input parameters for the type reservoir model; 
 generate training data for a machine learning model based on the multiple production simulations for the reservoir; 
 train the machine learning model using the training data, wherein the trained machine learning model provides prediction of transient production in the reservoir and facilitates determination of transient inflow performance relationship for the reservoir; and 
 store the trained machine learning model in a non-transitory storage medium. 
   
     
     
         2 . The system of  claim 1 , wherein the one or more physical processors are further configured by the machine-readable instructions to:
 obtain production parameter scenario information, the production parameter scenario information defining a scenario of production parameter for the reservoir;   determine transient production prediction based on inputting the scenario of production parameter for the reservoir into the trained machine learning model; and   determine the transient inflow performance relationship for the reservoir based on the transient production prediction.   
     
     
         3 . The system of  claim 2 , wherein the scenario of production parameter for the reservoir includes a time series of flowing bottom hole pressure. 
     
     
         4 . The system of  claim 2 , wherein the determination of the transient inflow performance relationship for the reservoir includes determination of separate inflow performance relationships for separate times. 
     
     
         5 . The system of  claim 4 , wherein the determination of the transient inflow performance relationship for the reservoir includes determination of transient oil inflow performance relationship or transient gas condensate inflow performance relationship. 
     
     
         6 . The system of  claim 1 , wherein the machine learning model includes a recurrent neural network. 
     
     
         7 . The system of  claim 1 , wherein the training data for the machine learning model includes pairings of corresponding values of the input parameters and values of production rate. 
     
     
         8 . The system of  claim 1 , wherein the input parameters include one or more of fracture geometry, flowing bottom hole pressure, and number of fracture clusters. 
     
     
         9 . The system of  claim 1 , wherein output of the trained machine learning model includes multiple sets of average pressure, production rate, and/or absolute open flow of the reservoir at different times, wherein the multiple sets of the average pressure, the production rate, and/or the absolute open flow of the reservoir at different times includes a first set of the average pressure, the production rate, and/or the absolute open flow of the reservoir at a first time and a second set of the average pressure, the production rate, and/or the absolute open flow of the reservoir at a second time. 
     
     
         10 . The system of  claim 1 , wherein the type reservoir model information is obtained based on history matching. 
     
     
         11 . A method for determining inflow performance relationship for a reservoir, the method comprising:
 obtaining type reservoir model information, the type reservoir model information defining a type reservoir model for the reservoir;   generating multiple production simulations for the reservoir based on different values of input parameters for the type reservoir model;   generating training data for a machine learning model based on the multiple production simulations for the reservoir;   training the machine learning model using the training data, wherein the trained machine learning model provides prediction of transient production in the reservoir and facilitates determination of transient inflow performance relationship for the reservoir; and   storing the trained machine learning model in a non-transitory storage medium.   
     
     
         12 . The method of  claim 11 , further comprising:
 obtaining production parameter scenario information, the production parameter scenario information defining a scenario of production parameter for the reservoir;   determining transient production prediction based on inputting the scenario of production parameter for the reservoir into the trained machine learning model; and   determining the transient inflow performance relationship for the reservoir based on the transient production prediction.   
     
     
         13 . The method of  claim 12 , wherein the scenario of production parameter for the reservoir includes a time series of flowing bottom hole pressure. 
     
     
         14 . The method of  claim 12 , wherein determining the transient inflow performance relationship for the reservoir includes determining separate inflow performance relationships for separate times. 
     
     
         15 . The method of  claim 14 , wherein determining the transient inflow performance relationship for the reservoir includes determining transient oil inflow performance relationship or transient gas condensate inflow performance relationship. 
     
     
         16 . The method of  claim 11 , wherein the machine learning model includes a recurrent neural network. 
     
     
         17 . The method of  claim 11 , wherein the training data for the machine learning model includes pairings of corresponding values of the input parameters and values of production rate. 
     
     
         18 . The method of  claim 11 , wherein the input parameters include one or more of fracture geometry, flowing bottom hole pressure, and number of fracture clusters. 
     
     
         19 . The method of  claim 11 , wherein output of the trained machine learning model includes multiple sets of average pressure, production rate, and/or absolute open flow of the reservoir at different times, wherein the multiple sets of the average pressure, the production rate, and/or the absolute open flow of the reservoir at different times includes a first set of the average pressure, the production rate, and/or the absolute open flow of the reservoir at a first time and a second set of the average pressure, the production rate, and/or the absolute open flow of the reservoir at a second time. 
     
     
         20 . The method of  claim 11 , wherein the type reservoir model information is obtained based on history matching. 
     
     
         21 . A method for determining inflow performance relationship for a reservoir, the method comprising:
 obtaining production parameter scenario information, the production parameter scenario information defining a scenario of production parameter for the reservoir;   obtaining a trained machine learning model, the trained machine learning model providing prediction of transient production in the reservoir and facilitating determination of transient inflow performance relationship for the reservoir, wherein training data for the trained machine learning model is generated based on multiple production simulations for the reservoir, the multiple production simulations for the reservoir generated based on different values of input parameters for a type reservoir model for the reservoir;   determining transient production prediction based on inputting the scenario of production parameter for the reservoir into the trained machine learning model; and   determining the transient inflow performance relationship for the reservoir based on the transient production prediction.