Optimization of dynamically changing downhole tool settings

Patent metadata

Field	Value
Publication number	US-9587478-B2
Application number	US-201113154921-A
Country	US
Kind code	B2
Filing date	Jun 7, 2011
Priority date	Jun 7, 2011
Publication date	Mar 7, 2017
Grant date	Mar 7, 2017

Abstract

A computer-assisted method for optimizing a drilling tool assembly, the method comprising defining a desired drilling plan; determining current drilling conditions; determining current drilling tool parameters of at least two drilling tool assembly components; analyzing the current drilling conditions and the current drilling tool parameters to define a base drilling condition; comparing the base drilling condition to the desired drilling plan; determining a drilling tool parameter to adjust to achieve the desired drilling plan; and adjusting at least one drilling tool parameter of at least one of the two drilling tool assembly components based on the comparing the base drilling condition to the desired drilling plan.

First claim

What is claimed:

1. A computer-assisted method for optimizing a drilling tool assembly, the method comprising:
defining a desired drilling plan;
determining current drilling conditions within a wellbore;
determining current drilling tool parameters of at least two drilling tool assembly components within the wellbore;
determining a wear potential of a component of the drilling tool assembly;
analyzing the current drilling conditions, the current drilling tool parameters, and the wear potential of a component using a processor located within the wellbore to define a base drilling condition;
comparing, using the processor, the base drilling condition to the desired drilling plan;
determining, using the processor, a drilling tool parameter to adjust to achieve the desired drilling plan;
transmitting instructions to adjust the drilling tool parameters of the drilling tool assembly components through an intelligent drilling string; and
adjusting at least one drilling tool parameter of at least one of the two drilling tool assembly components based on the comparing the base drilling condition to the desired drilling plan.

2. The method of claim 1 , wherein the determining, analyzing, comparing, and adjusting occurs in real time.

3. The method of claim 1 , wherein the determining the drilling tool parameter to adjust comprises:
determining the drilling tool parameter to adjust to drill a segment of a wellbore with an optimized rate of penetration.

4. The method of claim 3 , wherein the determining the drilling tool parameter to adjust further comprises:
determining an optimized drilling tool parameter based on the wear potential and the optimized rate of penetration that results in an optimized wear pattern.

5. The method of claim 1 , wherein the determining the drilling tool parameter to adjust comprises:
determining an optimized drilling tool parameter to drill a segment of a wellbore with a desired well path trajectory.

6. The method of claim 5 , wherein the determining the drilling tool parameter to adjust further comprises:
determining the optimized drilling tool parameter to drill the segment of a wellbore to mitigate drilling tool assembly damage while drilling a well with the desired well path trajectory.

7. The method of claim 1 , wherein the determining the drilling tool parameter to adjust comprises:
determining an optimized drilling tool parameter to drill a segment of a wellbore to mitigate a destructive vibration condition.

8. The method of claim 1 , wherein the analyzing, comparing, and determining the drilling tool assembly parameter to adjust is performed by an artificial neural network.

9. The method of claim 1 , further comprising:
adjusting at least one drilling tool parameter of at least two drilling tool assembly components.

10. A computer-assisted method for optimizing a drilling tool assembly, the method comprising:
determining a wear potential of a component of the drilling tool assembly;
disposing a drilling tool assembly in a wellbore, the drilling tool assembly comprising an artificial neural network and an intelligent drill string;
drilling a portion of the wellbore;
determining current drilling conditions and current drilling tool parameters;
transmitting the current drilling conditions, current drilling tool parameters, and the wear potential to the artificial neural network with the intelligent drill string;
analyzing the current drilling conditions, the current drilling tool parameters, and the wear potential with the artificial neural network;
identifying first and second drilling tool assembly components to adjust;
determining, based on the analyzing, an optimized drilling tool parameter value for at least one of the first and second drilling tool assembly components; and
adjusting a drilling tool parameter of at least one of the first and second drilling tool assembly components based on the determined optimized drilling tool parameter value.

11. The method of claim 10 , wherein the analyzing comprises:
determining optimized operating parameters to drill a segment of the wellbore having an optimized rate of penetration and optimized wear.

12. The method of claim 11 , wherein the analyzing further comprises:
determining the optimized operating parameters to maintain a planned well path trajectory.

13. The method of claim 12 , wherein the analyzing further comprises:
determining the optimized operating parameters to decrease destructive vibrations.

14. The method of claim 13 , wherein the analyzing further comprises:
determining the optimized operating parameters to mitigate drilling tool assembly damage.

15. The method of claim 14 , wherein the determining the optimized drilling tool parameter value comprises:
comparing the drilling tool parameters of the first and second drilling tool assembly components.

16. The method of claim 15 , further comprising:
adjusting at least one drilling tool parameter value of at least one of the first and second drilling tool assembly components based on the comparing the first and second drilling tool assembly components.

17. A drilling tool assembly comprising:
a first drilling tool assembly component;
a second drilling tool assembly component;
an intelligent drill string; and
an artificial neutral network in communication with the first and second drilling tool assembly components, the artificial neural network comprising a processor and a storage medium, the artificial neural network comprising instructions for:
determining current drilling conditions;
determining current drilling tool assembly parameters;
analyzing the current drilling conditions and the current drilling tool assembly parameters; and
controlling the first and second drilling tool assembly components to drill a desired wellbore, by transmitting the instructions through the intelligent drill string;
controlling the first and second drilling tool assembly components based on determining a rate of penetration of the drilling tool assembly;
determining a wear potential of a component of the drilling tool assembly;
determining the effect of adjusting the drilling tool assembly parameter on a well path trajectory; and
determining the effect of adjusting the drilling tool assembly parameter on a vibration of the drilling tool assembly.

Assignees

• Moran David P
• Oliver Stuart R
• Smith International

Inventors

• Moran David P
• Oliver Stuart R

Classifications

• E21B2200/22

  Fuzzy logic, artificial intelligence, neural networks or the like · CPC title

• E21B44/00 Primary

  Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions · CPC title

• E21B2041/0028

  Fixed Constructions · mapped topic