Distributed machine learning control of electric submersible pumps

Patent metadata

Field	Value
Publication number	US-11480039-B2
Application number	US-201816494972-A
Country	US
Kind code	B2
Filing date	Dec 6, 2018
Priority date	Dec 6, 2018
Publication date	Oct 25, 2022
Grant date	Oct 25, 2022

Abstract

A motor of an electric submersible pump (ESP) is positioned in a wellbore. Measured data is received from one or more sensors. A first deep learning model running on a motor controller of the ESP determines first operating parameters or first operating conditions for the ESP based on the measured data. The motor controller sends the first operating parameters or first operating conditions to a centralized computer system. A second deep learning model running on the centralized computer system determines second operating parameters or second operating conditions associated with the ESP based on the first operating parameters or first operating conditions. The centralized computer system sends the second operating parameters or second operating conditions to the motor controller. The motor controller adjusts operation of the motor of the ESP based on the second operating parameters or second operating conditions.

First claim

What is claimed is:

1. A method comprising:
networking a plurality of electric submersible pumps (ESPs) with a centralized computing system, wherein each of the plurality of ESPs includes a motor controller and a deep learning model associated with each ESP, and wherein each of the plurality of ESPs are located in a different wellbore of a plurality of wellbores;
receiving, at the centralized computer system, predicted operating conditions for each of the different wellbores predicted by the deep learning model for each of the associated ESPs, wherein the predicted operating conditions are predicted by the associated deep learning model, based on measurement data of the associated ESP;
obtaining, from a central deep learning model executing on the centralized computing system for each of the plurality of ESPs, at least one operating parameter for the associated motor controller based on the predicted operating conditions for the different wellbores; and
sending motor adjustments to at least one motor controller associated with at least one ESP of the plurality of ESPs based on the at least one operating parameter for the at least one motor controller.

2. The method of claim 1 , wherein each deep learning model is unique to a wellbore in which the ESP is positioned and the central deep learning model comprises at least one of a reservoir model and a fluid flow model.

3. The method of claim 1 , wherein the motor adjustments include adjusting at least one of a frequency setpoint, an operation mode, a voltage, a voltage to frequency ratio, a pump speed, motor speed, a current, a temperature, and a pressure of the ESP.

4. The method of claim 1 , wherein the predicted operating condition is a future operating condition.

5. The method of claim 1 , wherein each deep learning model is a deep learning model trained on a training data set associated with the associated ESP, wherein receiving the operating conditions of each ESP of the plurality of ESPs, further comprises:
determining if the predicted operating conditions match a goal set; and
based on a determination that the predicted operating conditions do not match the goal set retraining each deep learning model.

6. The method of claim 1 , further comprising:
based on a determination that a measure of fluid production has changed, retraining the associated deep learning model according to the changed measure of fluid production.

7. The method of claim 1 , further comprising:
receiving, at the centralized computer system second operating conditions determined from a second deep learning model associated with a second ESP of the plurality of ESPs, the second operating conditions based on measurement data of at least the second ESP of the plurality of ESPs;
obtaining from the centralized deep learning model running on the centralized computing system second operating parameters based on at least the second operating conditions of the second ESP of the plurality of ESPs; and
sending motor adjustments to at least the motor controller associated with at least the second ESP of the plurality of ESPs based on the second operating parameters.

8. The method of claim 1 , wherein the predicted operating conditions comprise at least one of intake pressure, motor temperature, motor current, motor frequency, power consumption, ESP health indication and operation mode, wherein the operation mode comprises at least one of gas locked, gas bubbles, overheating, draw down, and normal operation, and wherein ESP health indication comprises either ESP healthy indication and ESP unhealthy indication.

9. The method of claim 4 , wherein obtaining the operating parameters comprises obtaining the operating parameters based on the future operating condition.

10. The method of claim 2 , wherein the reservoir model is a mathematical fluid flow model of fluid flows and geologic properties of a plurality of wellbores in which the network of the plurality of ESPs are deployed and flows and properties of reservoir rocks that connect the plurality of wellbores.

11. A system comprising:
a network of a plurality of electric submersible pumps (ESPs), wherein each of the plurality of ESPs includes a motor, a processor, a deep learning model, and at least one sensor, and wherein each of the plurality of ESPs are located in a different wellbore of a plurality of wellbores
a centralized computing system networked with the network of the plurality of ESPs, the centralized computing system having installed thereon a reservoir model;
program code stored in memory and executable by the processor on each ESP of the plurality of ESPs to:
generate by a deep learning model associated with the each ESP, predicted operating conditions for each ESP based on measurement data from at the at least the one sensor associated with each ESP;
send the predicted operating conditions to the centralized computing system; and
adjust operation of the motor of at least a first ESP of the plurality of ESPs based on operating parameters obtained from the central deep learning model executing on the centralized computing system;

program code stored in memory and executable by the centralized computing system to:
obtain from the reservoir model the operating parameters for at least the first ESP based on the predicted operating conditions, wherein the reservoir model is a mathematical model of fluid flows and geologic properties of a plurality of wellbores in which the network of the plurality of ESPs are deployed and flows and properties of reservoir rocks that connect the plurality of wellbores; and
send the operating parameters to at least the first ESP of the plurality of ESPs.

12. The system of claim 11 , wherein the program code to adjust operation of the motor comprises program code to adjust at least one of a frequency setpoint, an operation mode, a voltage, a voltage to frequency ratio, a pump speed, a motor speed, a current, a temperature, and a pressure of at least one ESP of the plurality of ESPs.

13. The system of claim 11 , further comprising program code to determine if the predicted operating conditions obtained from a first deep learning model match a goal data, and based on a determination that the predicted operating conditions do not match the goal data, retrain the first deep learning model.

14. The system of claim 11 , wherein the at least one sensor comprises a virtual sensor, and wherein measurement data from the virtual sensor comprises output from a mathematical model of multiphase fluid flow.

15. The system of claim 11 , further comprising:
a second ESP of the plurality of ESPs;
program code stored in memory and executable by the processor of the second ESP of the plurality of ESPs to:
based on measurement data from a second sensor associated with the second ESP, obtain from a second deep learning model associated with the second ESP second operating conditions of the second ESP;
send the second operating conditions of the second ESP to the reservoir model; and
adjust operation of the motor of the second ESP based on second operating parameters; and

wherein the program code stored in memory and executable by the centralized computing system further comprises program code to:
obtain from the reservoir model the second operating parameters for the second ESP based on the second operating conditions of the second ESP; and
send the second operating parameters to the second ESP.

16. The system of claim 15 , wherein program code executable by the centralized computing system further comprises program code to determine the second operating parameters for the second ESP b

Assignees

• Halliburton Energy Services Inc

Inventors

• Beck David C
• Janger Luke Golden
• Brown Donn J

Classifications

• F04D15/0088

  Testing machines · CPC title

• F04D15/0066

  by changing the speed, e.g. of the driving engine · CPC title

• F04D13/10

  adapted for use in mining bore holes · CPC title

• E21B43/128 Primary

  Adaptation of pump systems with down-hole electric drives · CPC title

• F04B2205/04

  Pressure in the outlet chamber · CPC title