Predicting oil and gas reservoir production

Abstract

A method of predicting an output of oil and gas production in a hydrocarbon reservoir of a current and future producing well using a neural network model, includes receiving a data set comprising a plurality of parameters of the hydrocarbon reservoir at a wellsite. The method also includes using the data set to generate a plurality of simulation curves of the hydrocarbon reservoir, each parameter of the plurality of parameters has a range, and the range is adjustable, and the wellsite includes a wellbore penetrating a subterranean formation to extract reserves from the hydrocarbon reservoir. The method also includes performing a simulation, based on the range of each said parameter of the plurality of parameters, of the hydrocarbon reservoir. The method also includes downloading the plurality of simulation curves into a local server to prepare training data for training the neural network model.

What is claimed is: 
     
         1 . A method of predicting an output of oil and gas production in a hydrocarbon reservoir using a neural network model, comprising:
 receiving a data set comprising a plurality of parameters of the hydrocarbon reservoir at a wellsite, wherein the wellsite comprises a wellbore penetrating a subterranean formation to extract reserves from the hydrocarbon reservoir;   generating, using the data set, a plurality of simulation curves of the hydrocarbon reservoir, wherein each parameter of the plurality of parameters has a range, and the range is adjustable, and performing a simulation, based on the range of each said parameter of the plurality of parameters, of the hydrocarbon reservoir to generate a plurality of simulation curves;   downloading the plurality of simulation curves into a local server to prepare training data for training the neural network model;   calculating a plurality of key factors from a neighboring well for at least two wellsites from the simulation;   combining the plurality of key factors with the plurality of parameters to define input features of the neural network model; and   defining each of the plurality of simulation curves as output features of the neural network model.   
     
     
         2 . The method of  claim 1 , further comprising tuning the neural network model using a set of hidden layers between the input features and the output features, wherein the tuning comprises a plurality of tunings. 
     
     
         3 . The method of  claim 2 , further comprising retrieving, for each said tuning of the plurality of tunings, selected data, shuffling and splitting the selected data with K-fold cross validation, and scaling the selected data using a scaler to obtain scaled data. 
     
     
         4 . The method of  claim 3 , further comprising searching for a plurality of hyperparameters by fitting the scaled data in each said tuning of the plurality of tunings. 
     
     
         5 . The method of  claim 3 , further comprising applying an early stop function to prevent the training from overfitting the selected data to the neural network model. 
     
     
         6 . The method of  claim 2 , further comprising processing each said tuning of the plurality of tunings and calculating an average error of the neural network model, and saving the average error as a result. 
     
     
         7 . The method of  claim 6 , further comprising searching for a plurality of hyperparameters by fitting the scaled data in each said tuning of the plurality of tunings, and comparing a result and selecting an optimal set of hyperparameters of the plurality of hyperparameters belonging to the neural network model having a lowest validation error. 
     
     
         8 . The method of  claim 7 , further comprising:
 further training the neural network model having the lowest validation error with the optimal set of hyperparameters to obtain an optimized neural network model; and   uploading the optimized neural network model to a virtual server or a virtual private cloud.   
     
     
         9 . The method of  claim 1 , further comprising:
 uploading from a client firewall, by an analytical module, actual wellsite data comprising actual wellsite production data, actual wellsite pressure data, and actual wellsite parameter data; and   inputting the wellsite parameter data and selecting the range of each said parameter of the plurality of parameters to display the plurality of simulation curves generated from the neural network model in a virtual server or a virtual private cloud.   
     
     
         10 . The method of  claim 9 , further comprising:
 matching simulation production data and simulation pressure data from the plurality of simulation curves generated from the neural network model in the virtual server or the virtual private cloud with the actual wellsite production data and the actual wellsite pressure data to obtain a plurality of matching simulation curves;   displaying an outcome of the plurality of matching simulation curves on a display; and   storing the plurality of matching simulation curves and the plurality of parameters.   
     
     
         11 . The method of  claim 9 , further comprising:
 creating a plurality of hydrocarbon development scenarios in the analytical module user interface for drilling operation in an area of interest;   assigning the stored plurality of parameters to the wellsite in a hydrocarbon development scenarios of the plurality of hydrocarbon development scenarios; and   displaying the plurality of simulation curves generated from the neural network model in the virtual server or the virtual private cloud using the plurality of hydrocarbon development scenarios.   
     
     
         12 . The method of  claim 11 , further comprising calculating a probability distribution for an outcome of the plurality of simulation curves. 
     
     
         13 . The method of  claim 12 , further comprising:
 creating a plurality of decline curve models with an outcome of calculated probability distribution;   matching an outcome of the plurality of probability simulation curves to the plurality of decline curve models by adjusting a plurality of decline curve parameters; and   exporting the adjusted plurality of decline curve models for the current and the future producing wells into a user format for economic analysis.   
     
     
         14 . The method of  claim 11 , further comprising:
 re-selecting the range of each said parameter of the plurality of parameters and re-adjusting the hydrocarbon development scenarios for adjusting the probability distribution for the current and the future producing wells until achieving an optimal economic result; and   using the adjusted probability distribution to select a location to perform a drilling operation to drill another wellbore at the hydrocarbon reservoir.   
     
     
         15 . The method of  claim 1 , wherein the range has a low variable and a high variable. 
     
     
         16 . The method of  claim 1 , further comprising using a simulation module user interface to:
 adjust each said range of the plurality of parameters for the simulation to obtain an outcome of the base case simulation;   display a plurality of hydrocarbon producing wells and the reserve based on the adjusted range of the plurality of parameters and the outcome of the simulation;   display an outcome of the simulation in the plurality of simulation curves on a display in the simulation module; and   export and store the plurality of simulation curves into a database in a virtual server or a virtual private cloud.   
     
     
         17 . The method of  claim 1 , wherein the plurality of key factors comprises: neighboring well quantities and influence, spacing differences, timing differences, or FDI factors. 
     
     
         18 . The method of  claim 2 , wherein the tuning further comprises using: a number of nodes, activation functions, optimizer functions, learning rates, dropout rates, and regularization. 
     
     
         19 . A method of predicting an output of oil and gas production in a hydrocarbon reservoir using a neural network model, comprising:
 receiving, by a data collection module, a data set comprising a plurality of parameters of the hydrocarbon reservoir at a wellsite, wherein the wellsite comprises a wellbore penetrating a subterranean formation to extract reserves from the hydrocarbon reservoir;   generating, using the data set, a plurality of simulation curves of the hydrocarbon reservoir, wherein each parameter of the plurality of parameters has a range, and the range is adjustable;   performing a simulation, by a simulation module, based on the range of each said parameter of the plurality of parameters, of the hydrocarbon reservoir to generate a plurality of simulation curves;   downloading the plurality of simulation curves into a local server to prepare training data for training the neural network model;   calculating a plurality of key factors from a neighboring well for at least two wellsites from the simulation;   combining the plurality of key factors with the plurality of parameters to define input features of the neural network model; and   defining each of the plurality of simulation curves as output features of the neural network model.   
     
     
         20 . A computer device of predicting an output of oil and gas production in a hydrocarbon reservoir using a neural network model, comprising:
 a non-transitory computer readable medium configured to store computer executable instructions;   at least one processor, wherein in response to executing the computer executable instructions, the processor is configured to:
 receive a data set, using a graphic user interface (GUI), comprising a plurality of parameters of the hydrocarbon reservoir at a wellsite, wherein the wellsite comprises a wellbore penetrating a subterranean formation to extract reserves from the hydrocarbon reservoir; 
 generate, using the data set, a plurality of simulation curves of the hydrocarbon reservoir on the GUI, wherein each parameter of the plurality of parameters has a range, and the range is adjustable using the GUI; 
 perform a simulation, using the GUI, based on the range of each said parameter of the plurality of parameters, of the hydrocarbon reservoir; 
 download the plurality of simulation curves into a database to prepare training data for training the neural network model; 
 calculate a plurality of key factors from a neighboring well for at least two wellsites from the simulation; 
 combine the plurality of key factors with the plurality of parameters to define input features of the neural network model; and 
 define each of the plurality of simulation curves as output features of the neural network model.