Granted Patent B2

US 12,385,382 · App. 18/125,576 · Granted Aug 12, 2025

Inventors: Nazli Demirer (Tomball, TX); Robert P. Darbe (Tomball, TX); Alexander Mathew Keller (Austin, TX); Dongmei Chen (Austin, TX)

Assignees: HALLIBURTON ENERGY SERVICES, INC.; BOARD OF REGENTS, THE UNIVERSITY OF TEXAS SYSTEM

E21B44/00E21B7/06E21B2200/20E21B2200/22

Abstract

Claims (104)

1. A method of controlling a drill string having a steerable bit when drilling a wellbore through a substrate, comprising:

developing a deterministic model of a directional behavior of the drill string comprising:

a drill string state;

one or more drill parameters associated with the drill string; and

one or more substrate parameters associated with the substrate;

developing a stochastic differential model (SDM) of the directional behavior of the drill string by replacing the state and each of the parameters of the deterministic model with respective probability distributions and adding feedback;

reducing the SDM to a truncated stochastic model (TSM) using a stochastic approximation method;

creating a controller which incorporates operation of the TSM; and

controlling the drill string using a control input from the controller.

2. The method of claim 1 , wherein the drill string state comprises at least one state variable selected from the group of: a position of the bit, an inclination of the drill string at the bit, and a curvature of the wellbore at the bit.

3. The method of claim 2 , wherein a projected path of the drill string is identified by calculating at least one of a mean and a variance for at least one of the state variables along the projected path from a current position of the drill string.

4. The method of claim 3 , further comprising:

projecting the drill string state over a horizon using the TSM and a plurality of candidate control inputs;

calculating a respective cost function for each candidate control input of the plurality of candidate control inputs; and

selecting a candidate control input of the plurality of candidate control inputs based on a lowest cost function being associated with the selected candidate control input.

5. The method of claim 4 , wherein each of the respective cost functions comprises one or more of the group:

a difference between the mean of the projected path of the drill string and a portion of a reference path;

the variance of the at least one of the state variables along the projected path;

a difference between a projected drill string state and a first constraint; and

a difference between a candidate control input and a second constraint.

6. The method of claim 5 , wherein:

the first constraint comprises a respective maximum value for one or more of the selected state variables;

the candidate control input comprises one or more control variables selected from a group of a steering angle, a torque, and a weight; and

the second constraint comprises a respective maximum value for one or more of the selected control variables.

7. The method of claim 3 , wherein the controller is a stochastic model predictive controller (SMPC), further comprising:

selecting a new control input using the SMPC that comprises the TSM, a cost function, and a constraint; and

updating a current control input with the new control input upon occurrence of an event within the group of:

a difference between the current state of the drill string and a desired state of the drill string exceeds a first threshold;

a variance associated with the current state of the drill string exceeds a second threshold; and

a distance advanced by the drill string since a prior update of the control input exceeds a third threshold.

8. The method of claim 3 , further comprising:

selecting a new control input using a robust control method selected from a group of a robust Model Predictive Controller (MPC), a tube MPC, and a scenario-based MPC.

9. The method of claim 1 , wherein the stochastic approximation method is selected from a group of a generalized polynomial chaos (gPC) method and an arbitrary polynomial chaos (aPC) method.

10. The method of claim 1 , wherein reducing the SDM to a TSM comprises substituting a predetermined number of terms of expansion for each probability distribution in the stochastic approximation method and then evaluating one or more expectations.

11. A non-transitory computer-readable storage medium comprising instructions for controlling a drill string having a steerable bit when drilling a wellbore through a substrate that, when loaded into a processor, cause the processor to execute the instructions to:

develop a deterministic model of a directional behavior of the drill string comprising:

a drill string state;

one or more drill parameters associated with the drill string; and

one or more substrate parameters associated with the substrate;

develop a stochastic differential model (SDM) of the directional behavior of the drill string by replacing the state and each of the parameters of the deterministic model with respective probability distributions and adding feedback;

reduce the SDM to a truncated stochastic model (TSM) using a stochastic approximation method;

create a controller which incorporates operation of the TSM; and

control the drill string using a control input from the controller.

12. The storage medium of claim 11 , wherein the drill string state comprises at least one state variable selected from the group of: a position of the bit, an inclination of the drill string at the bit, and a curvature of the wellbore at the bit.

13. The storage medium of claim 12 , wherein a projected path of the drill string is identified by calculating at least one of a mean and a variance for at least one of the state variables along the projected path from a current position of the drill string.

14. The storage medium of claim 13 , further comprising instructions that cause the processor to:

project the drill string state over a horizon using the TSM and a plurality of candidate control inputs;

calculate a respective cost function for each candidate control input of the plurality of control inputs; and

select a candidate control input of the plurality of candidate control inputs based on a lowest cost function being associated with the selected candidate control input.

15. The storage medium of claim 14 , wherein each of the respective cost functions comprises one or more of the group:

a difference between the mean of the projected path of the drill string and a portion of a reference path;

the variance of the at least one of the state variables along the projected path;

a difference between a projected drill string state and a first constraint; and

a difference between a candidate control input and a second constraint.

16. The storage medium of claim 15 , wherein:

the first constraint comprises a respective maximum value for one or more of the selected state variables;

the candidate control input comprises one or more control variables selected from a group of a steering angle, a torque, and a weight; and

the second constraint comprises a respective maximum value for one or more of the selected control variables.

17. The storage medium of claim 13 , wherein the controller is a stochastic model predictive controller (SMPC), further comprising:

selecting a new control input using the SMPC that comprises the TSM, a cost function, and a constraint; and

updating a current control input with the new control input upon occurrence of an event within the group of:

a difference between the current state of the drill string and a desired state of the drill string exceeds a first threshold;

a variance associated with the current state of the drill string exceeds a second threshold; and

a distance advanced by the drill string since a prior update of the control input exceeds a third threshold.

18. The storage medium of claim 13 , further comprising:

selecting a new control input using a robust control method selected from a group of a robust Model Predictive Controller (MPC), a tube MPC, and a scenario-based MPC.

19. The storage medium of claim 11 , wherein the stochastic approximation method is selected from a group of a generalized polynomial chaos (gPC) method and an arbitrary polynomial chaos (aPC) method.

20. The storage medium of claim 11 , wherein reducing the SDM to a TSM comprises substituting a predetermined number of terms of expansion for each probability distribution in the stochastic approximation method and then evaluating one or more expectations.

21. A system for controlling a drill string having a steerable bit when drilling a wellbore through a substrate, comprising:

a processor; and

a non-transitory computer-readable storage medium coupled to the processor and comprising instructions that, when loaded into the processor, cause the processor to:

develop a deterministic model of a directional behavior of the drill string comprising:

a drill string state;

one or more drill parameters associated with the drill string; and

one or more substrate parameters associated with the substrate;

develop a stochastic differential model (SDM) of the directional behavior of the drill string by replacing the state and each of the parameters of the deterministic model with respective probability distributions and adding feedback;

reduce the SDM to a truncated stochastic model (TSM) using a stochastic approximation method;

create a controller which incorporates operation of the TSM; and

control the drill string using a control input from the controller.

22. The system of claim 21 , wherein the drill string state comprises at least one state variable selected from the group of: a position of the bit, an inclination of the drill string at the bit, and a curvature of the wellbore at the bit.

23. The system of claim 22 , wherein a projected path of the drill string is identified by calculating at least one of a mean and a variance for at least one of the state variables along the projected path from a current position of the drill string.

24. The system of claim 23 , wherein the storage medium comprises further instructions that cause the processor to:

project the drill string state over a horizon using the TSM and a plurality of candidate control inputs;

calculate a respective cost function for each candidate control input of the plurality of candidate control inputs; and

select a candidate control input of the plurality of candidate control inputs based on a lowest cost function being associated with the selected candidate control input.

25. The system of claim 24 , wherein each of the respective cost functions comprises one or more of the group:

a difference between the mean of the projected path of the drill string and a portion of a reference path;

the variance of the at least one of the state variables along the projected path;

a difference between a projected state of the drill string and a first constraint; and

a difference between a candidate control input and a second constraint.

26. The system of claim 25 , wherein:

the first constraint comprises a respective maximum value for one or more of the selected state variables;

the candidate control input comprises one or more control variables selected from a group of a steering angle, a torque, and a weight; and

the second constraint comprises a respective maximum value for one or more of the selected control variables.

27. The system of claim 23 , wherein the controller is a stochastic model predictive controller (SMPC) and the storage medium comprises further instructions that cause the processor to:

select a new control input using the SMPC that comprises the TSM, a cost function, and a constraint; and

update a current control input with the new control input upon occurrence of an event within the group of:

a difference between the current state of the drill string and a desired state of the drill string exceeds a first threshold;

a variance associated with the current state of the drill string exceeds a second threshold; and

a distance advanced by the drill string since a prior update of the control input exceeds a third threshold.

28. The system of claim 23 , wherein the storage medium comprises further instructions that cause the processor to:

select a new control input using a robust control method selected from a group of a robust Model Predictive Controller (MPC), a tube MPC, and a scenario-based MPC.

29. The system of claim 21 , wherein the stochastic approximation method is selected from a group of a generalized polynomial chaos (gPC) method and an arbitrary polynomial chaos (aPC) method.

30. The system of claim 21 , wherein reducing the SDM to a TSM comprises substituting a predetermined number of terms of expansion for each probability distribution in the stochastic approximation method and then evaluating one or more expectations.

Assignments (3)

Continuity (1)

References Cited (14)