Control variable determination to maximize a drilling rate of penetration

• US 9,085,958 B2
• Filed: 09/18/2014
• Issued: 07/21/2015
• Est. Priority Date: 09/19/2013
• Status: Active Grant

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Laing, Moray, Pope, David, Holdaway, Keith R., Duarte, James
• Primary Examiner(s): Lau, Tung S
• Application Number:
  US14/490,189
• Publication Number:
  US 20150081222A1
• Time in Patent Office: 306 Days
• Field of Search: 702/9
• US Class Current: 1/1
• CPC Class Codes: E21B 44/00 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 Class Codes: E21B 45/00 Measuring the drilling time or rate of penetration
• CPC Class Codes: E21B 49/003 by analysing drilling variables or conditions E21B49/005 takes precedence; systems specially adapted for monitoring a plurality of drilling variables or conditions E21B44/00
• CPC Class Codes: G01V 13/00 Manufacturing, calibrating, cleaning, or repairing instruments or devices covered by groups G01V1/00 – G01V11/00
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

A method of determining an optimal value for a control of a drilling operation is provided. Drilling data from a drilling operation is received. The drilling data includes a plurality of values measured for each of a plurality of drilling control variables during the drilling operation. An objective function model is determined using the received drilling data. The objective function model maximizes a rate of penetration for the drilling operation. Measured drilling data is received that includes current drilling data values for a different drilling operation. An optimal value for a control of the different drilling operation is determined by executing the determined objective function model with the measured drilling data that includes the current drilling data values for the different drilling operation as an input. The determined optimal value for the control of the different drilling operation is output.

Claims

1. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:

receive historical drilling data from a previous drilling operation, wherein the historical drilling data includes a plurality of values measured for each of a plurality of drilling control variables during the previous drilling operation;

determine an objective function model using the received historical drilling data and a neural network model, wherein the objective function model maximizes a rate of penetration for the previous drilling operation;

receive measured drilling data that includes current drilling data values for a different drilling operation, wherein the different drilling operation means that a well-bore of the different drilling operation is different from a well-bore of the previous drilling operation;

determine an optimal value for a control of the different drilling operation by executing the determined objective function model with the measured drilling data that includes the current drilling data values for the different drilling operation as an input;

output the determined optimal value for the control of the different drilling operation;

receive additional drilling data, wherein the additional drilling data includes a second plurality of values measured for each of the plurality of drilling control variables, wherein the additional drilling data is measured after outputting the determined optimal value for the control of the different drilling operation;

determine a second objective function model using the received additional drilling data, wherein the second objective function model maximizes a rate of penetration of the previous drilling operation;

receive second measured drilling data that includes second current drilling data values for the different drilling operation;

determine a second optimal value for the control of the different drilling operation by executing the determined second objective function model with the second measured drilling data that includes the second current drilling data values for the different drilling operation as an input; and

output the determined second optimal value for the control of the different drilling operation.

2. The non-transitory computer-readable medium of claim 1, wherein the objective function model is determined by evaluating a plurality of neural network model configurations.

3. The non-transitory computer-readable medium of claim 1, wherein determining the objective function model comprises:

defining a training dataset as a first portion of the received historical drilling data;

defining a validation dataset as a second portion of the received historical drilling data;

defining a first neural network model configuration;

training a first neural network model using the defined training dataset based on the defined first neural network model configuration;

predicting model output data with the defined validation dataset as an input to the trained first neural network model;

comparing the predicted model output data to output data of the validation dataset; and

determining a first validity score for the trained first neural network model based on comparing the predicted model output data to output data of the validation dataset.

4. The non-transitory computer-readable medium of claim 1, wherein a predictive model is determined using the received historical drilling data, wherein the predictive model predicts a value of a control variable input to the determined objective function model.

5. The non-transitory computer-readable medium of claim 1, wherein the received additional drilling data includes a third plurality of values measured for each of the plurality of drilling control variables during the different drilling operation.

6. The non-transitory computer-readable medium of claim 1, wherein the objective function model further minimizes a mechanical specific energy for the previous drilling operation.

7. The non-transitory computer-readable medium of claim 1, wherein the objective function model further optimizes the wellbore stability of the previous drilling operation.

8. The non-transitory computer-readable medium of claim 1, wherein the determined optimal value is output to a display device.

9. The non-transitory computer-readable medium of claim 1, wherein the determined optimal value is output to a control device associated with adjusting a set point of the control.

10. The non-transitory computer-readable medium of claim 3, wherein determining the objective function model further comprises:

defining a second neural network model configuration;

training a second neural network model using the defined training dataset based on the defined second neural network model configuration;

predicting second model output data with the defined validation dataset as an input to the trained second neural network model;

comparing the predicted second model output data to the output data of the validation dataset; and

determining a second validity score for the trained second neural network model based on comparing the predicted second model output data to the output data of the validation dataset.

11. The non-transitory computer-readable medium of claim 4, wherein the computer-readable instructions further cause the computing device to determine a current predicted value of the control variable input to the determined objective function model, wherein the determined objective function model is further executed with the current predicted value as an input.

12. The non-transitory computer-readable medium of claim 4, wherein the predictive model is determined using a decision tree model.

13. The non-transitory computer-readable medium of claim 6, wherein the objective function model further optimizes the wellbore stability for the previous drilling operation.

14. The non-transitory computer-readable medium of claim 10, wherein the objective function model is determined as the trained first neural network model or the trained second neural network model based on a comparison between the determined first validity score and the determined second validity score.

15. A computing device comprising:

a processor; and

a non-transitory computer-readable medium operably coupled to the processor, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the processor, cause the computing device to

receive historical drilling data from a previous drilling operation, wherein the historical drilling data includes a plurality of values measured for each of a plurality of drilling control variables during the previous drilling operation;

determine an objective function model using the received historical drilling data and a neural network model, wherein the objective function model maximizes a rate of penetration for the previous drilling operation;

receive measured drilling data that includes current drilling data values for a different drilling operation;

determine an optimal value for a control of the different drilling operation by executing the determined objective function model with the measured drilling data that includes the current drilling data values for the different drilling operation as an input;

output the determined optimal value for the control of the different drilling operation;

receive additional drilling data, wherein the additional drilling data includes a second plurality of values measured for each of the plurality of drilling control variables, wherein the additional drilling data is measured after outputting the determined optimal value for the control of the different drilling operation;

determine a second objective function model using the received additional drilling data, wherein the second objective function model maximizes a rate of penetration of the previous drilling operation;

receive second measured drilling data that includes second current drilling data values for the different drilling operation;

determine a second optimal value for the control of the different drilling operation by executing the determined second objective function model with the second measured drilling data that includes the second current drilling data values for the different drilling operation as an input; and

output the determined second optimal value for the control of the different drilling operation.

16. The computing device of claim 15, wherein a predictive model is determined using the received historical drilling data, wherein the predictive model predicts a value of a control variable input to the determined objective function model.

17. The computing device of claim 15, wherein the objective function model further minimizes a mechanical specific energy for the previous drilling operation.

18. The computing device of claim 17, wherein the objective function model further optimizes the wellbore stability of the previous drilling operation.

19. The computing device of claim 16, wherein the computer-readable instructions further cause the computing device to determine a current predicted value of the control variable input to the determined objective function model, wherein the determined objective function model is further executed with the current predicted value as an input.

20. The computing device of claim 19, wherein the predictive model is determined using a decision tree model.

21. A method of determining an optimal value for a control of a drilling operation, the method comprising:

receiving historical drilling data from a previous drilling operation, wherein the historical drilling data includes a plurality of values measured for each of a plurality of drilling control variables during the previous drilling operation;

determining, by a computing device, an objective function model using the received historical drilling data and a neural network model, wherein the objective function model maximizes a rate of penetration for the previous drilling operation;

receiving measured drilling data that includes current drilling data values for a different drilling operation;

determining, by the computing device, an optimal value for a control of the different drilling operation by executing the determined objective function model with the measured drilling data that includes the current drilling data values for the different drilling operation as an input; and

outputting, by the computing device, the determined optimal value for the control of the different drilling operation;

receiving additional drilling data, wherein the additional drilling data includes a second plurality of values measured for each of the plurality of drilling control variables, wherein the additional drilling data is measured after outputting the determined optimal value for the control of the different drilling operation;

determining, by the computing device, a second objective function model using the received additional drilling data, wherein the second objective function model maximizes a rate of penetration of the previous drilling operation;

receiving second measured drilling data that includes second current drilling data values for the different drilling operation;

determining, by the computing device, a second optimal value for the control of the different drilling operation by executing the determined second objective function model with the second measured drilling data that includes the second current drilling data values for the different drilling operation as an input; and

outputting, by the computing device, the determined second optimal value for the control of the different drilling operation.

22. The method of claim 21, wherein a predictive model is determined using the received historical drilling data, wherein the predictive model predicts a value of a control variable input to the determined objective function model.

23. The method of claim 21, wherein the objective function model further minimizes a mechanical specific energy for the previous drilling operation.

24. The method of claim 22, wherein the computer-readable instructions further cause the computing device to determine a current predicted value of the control variable input to the determined objective function model, wherein the determined objective function model is further executed with the current predicted value as an input.

25. The method of claim 22, wherein the predictive model is determined using a decision tree model.

26. The method of claim 23, wherein the objective function model further optimizes the wellbore stability of the previous drilling operation.