Data-driven methods to determine position of a moving object in a wellbore

Abstract

A computer-implemented method for determining the position of a downhole component with a neural network model is provided. The computer-implemented method can include acquiring real-time or characteristic data including values for one or more input variables associated with one or more time steps in a cementing operation, training the neural network to minimize a loss function and estimate a value for the position of the downhole component and an uncertainty at one or more time steps, estimating the value at the one or more time steps, estimating an uncertainty in the value at the one or more time steps, determining an operation position of the downhole component when an operation is to be performed, and determining the time step when the operation is to be performed.

What is claimed is: 
     
         1 . A computer-implemented method for determining a position of one or more downhole components with a neural network model, the computer-implemented method comprising:
 acquiring data including values for one or more input variables associated with one or more time steps in a cementing operation;   training a neural network model to minimize a loss function and estimate a value for a position of one or more downhole components and an uncertainty at the one or more time steps;   estimating the value for the position of the one or more downhole components at the one or more time steps via the trained neural network model;   estimating the uncertainty in the value at the one or more time steps;   determining an operation position of the one or more downhole components when an operation is to be performed; and   determining the time step when the operation is to be performed.   
     
     
         2 . The computer-implemented method of  claim 1 , the computer-implemented method further comprising estimating values of at least one controllable variable to perform the operation. 
     
     
         3 . The computer-implemented method of  claim 2 , wherein the at least one controllable variable comprises a cement flow rate, and the operation comprises increasing or decreasing a displacement rate of the one or more downhole components. 
     
     
         4 . The computer-implemented method of  claim 1 , wherein the one or more input variables comprise pressure on the one or more downhole components. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein the one or more downhole components includes a cement plug and/or a wiper cup on the cement plug. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein the neural network model comprises a Bayesian recurrent neural network (RNN). 
     
     
         7 . The computer-implemented method of  claim 1 , wherein the neural network model is trained using training data, wherein the training data is generated by a training data method comprising:
 providing a physics based model of the one or more downhole components based on one or more parameters;   applying a probability distribution for each of the one or more parameters for a first training data set;   applying a random sampling technique to the probability distribution for each parameter for the first training data set;   simulating component position of the one or more downhole components at each time step based on a pressure at each time step for each sample in the first training data set utilizing the physics based model;   ensembling the component position of the one or more downhole components as a function of time for each sample in the first training data set; and   outputting a mean position of the one or more downhole components at each time step and a standard deviation of the position at each time step for the first training data set.   
     
     
         8 . The computer-implemented method of  claim 7 , wherein the one or more parameters are rubber stiffness and/or friction coefficient. 
     
     
         9 . The computer-implemented method of  claim 7 , wherein the pressure at each time step, the position of the one or more downhole components at each time step, and the standard deviation of the position at each time step are preprocessed prior to being used to train the neural network model. 
     
     
         10 . The computer-implemented method of  claim 1 , the computer-implemented method further comprising outputting the position of the one or more downhole components at the one or more time steps and the time step when the operation is to be performed on a display. 
     
     
         11 . A system comprising:
 at least one processor; and   a memory coupled to the at least one processor having instructions stored therein, which when executed by the at least one processor, cause the at least one processor to perform a plurality of functions, including functions to:   acquire data including values for one or more input variables associated with one or more time steps in a cementing operation;   train a neural network model to minimize a loss function and estimate a value for a position of one or more downhole components and an uncertainty at the one or more time steps;   estimate the value for the position of the one or more downhole components at the one or more time steps via the trained neural network model;   estimate the uncertainty in the value at the one or more time steps via the trained neural network model;   determine an operational position of the one or more downhole components when an operation is to be performed; and   determine the time step when the operation is to be performed.   
     
     
         12 . The system of  claim 11 , wherein the functions further include functions to estimate values of at least one controllable variable to perform the operation. 
     
     
         13 . The system of  claim 12 , wherein the at least one controllable variable comprises a cement flow rate, and the operation comprises increasing or decreasing a displacement rate of the one or more downhole components. 
     
     
         14 . The system of  claim 11 , wherein the one or more input variables comprise pressure on the one or more downhole components. 
     
     
         15 . The system of  claim 11 , wherein the one or more downhole components includes a cement plug and/or wiper cup on the cement plug. 
     
     
         16 . The system of  claim 11 , wherein the neural network model comprises a Bayesian recurrent neural network (RNN). 
     
     
         17 . The system of  claim 11 , wherein training the neural network model comprises generating training data, wherein the training data is generated by a function to:
 provide a physics based model of the one or more downhole components based on one or more parameters;   apply a probability distribution for each of the one or more parameters for a first training data set;   apply a random sampling technique to the probability distribution for each parameter for the first training data set;   simulate component position of the one or more downhole components at each time step based on a pressure at each time step for each sample in the first training data set utilizing the physics based model;   ensemble the component position of the one or more downhole components as a function of time for each sample in the first training data set; and   output a mean position of the one or more downhole components at each time step and a standard deviation of the position at each time step for the first training data set.   
     
     
         18 . The system of  claim 17 , wherein the one or more parameters are rubber stiffness and/or friction coefficient. 
     
     
         19 . The system of  claim 17 , wherein the pressure at each time step, the position of the one or more downhole components at each time step, and the standard deviation of the position at each time step are preprocessed prior to being used to train the neural network model. 
     
     
         20 . The system of  claim 11 , wherein the functions further include functions to output the position of the one or more downhole components at the one or more time steps and the time step when the operation is to be performed on a display.