Method for determining preferred drill bit design parameters and drilling parameters using a trained artificial neural network, and methods for training the artificial neural network

• US 6,424,919 B1
• Filed: 06/26/2000
• Issued: 07/23/2002
• Est. Priority Date: 06/26/2000
• Status: Expired due to Term

• Current Assignee: Smith International Incorporated (Schlumberger NV)
• Original Assignee: Smith International Incorporated (Schlumberger NV)
• Inventors: Robertson, James A., Moran, David P.
• Primary Examiner(s): McElheny, Jr., Donald E.
• Application Number:
  US09/603,321
• Time in Patent Office: 757 Days
• Field of Search: 702/9, 702/6, 703/10
• US Class Current: 702/6
• CPC Class Codes: E21B 2200/22 Fuzzy logic, artificial intelligence, neural networks or the like
• 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

Abstract

A method for selecting a design parameter for a drill bit is disclosed. The method includes entering a value of at least one property of an earth formation to be drilled into a trained neural network. The neural network is trained by selecting data from drilled wellbores. The data comprise values of the formation property for formations through which the drilled wellbores have penetrated. Corresponding to the values of formation property are values of at least one drilling operating parameter, the drill bit design parameter, and values of a rate of penetration and a rate of wear of a drill bit used on each of the formations. Data from the wellbores are entered into the neural network to train it, and the design parameter is then selected based on output of the trained neural network.

Claims

1. A method for selecting a design parameter for a drill bit, comprising:

entering a value of at least one property of an earth formation to be drilled by said bit into a trained neural network, said neural network trained by selecting data from drilled wellbores, said data comprising values of said at least one formation property for formations through which said drilled wellbores penetrated, and corresponding thereto values of at least one drilling operating parameter, said drill bit design parameter, and values of a rate of penetration and a rate of wear of a drill bit used on each said formation;

entering said data from said wellbores into said neural network; and

selecting said design parameter based on output of said trained neural network.

2. The method as defined in claim 1 wherein said at least one property of said earth formation comprises a property selected from the group of rock mineral composition, porosity, compressive strength, abrasiveness, natural gamma ray radiation, electrical resistivity and acoustic velocity.

3. The method as defined in claim 1 wherein said design parameter comprises a cutting element type.

4. The method as defined in claim 1 wherein said design parameter comprises a cutting element count.

5. The method as defined in claim 1 wherein said design parameter comprises an hydraulic nozzle configuration.

6. The method as defined in claim 1 wherein said design parameter comprises IADC code of said drill bit.

7. The method as defined in claim 1 wherein said neural network is trained by selecting data from drilled wellbores, said data comprising values of said at least one formation property for formations through which said drilled wellbores penetrated, and corresponding thereto values of at least one drilling operating parameter, values of said drill bit design parameter, and values of at least one drilling performance parameter;

and

entering said data from said wellbores into said neural network.

8. A method for optimizing an economic performance of a drill bit, comprising:

entering a value of at least one property of an earth formation to be drilled by said bit into a trained neural network;

entering at least one design parameter of said drill bit into said trained neural network; and

adjusting a value of at least one drilling operating parameter in response to output of said trained neural network so as to optimize a value of a parameter related to said economic performance of said bit.

9. The method as defined in claim 8 wherein said at least one formation property comprises a property selected from the group of rock mineral composition, porosity, compressive strength, abrasiveness, acoustic velocity, natural gamma radiation and electrical resistivity.

10. The method as defined in claim 8 wherein said at least one design parameter is selected from the group of bit type, IADC code, cutting element type, cutting element count and hydraulic nozzle configuration.

11. The method as defined in claim 8 wherein said economic performance parameter comprises wear rate of said drill bit.

12. The method as defined in claim 8 wherein said drilling operating parameter comprises a parameter selected from the group of weight on bit and rotary speed of said bit.

13. The method as defined in claim 8 wherein said drilling operating parameter comprises drilling fluid circulating pressure.

14. The method as defined in claim 13 wherein said drilling operating parameter further comprises an amplitude and a frequency of a pressure variation component of said fluid circulating pressure, said variation component related to operation of a drilling hammer.

15. The method as defined in claim 8 wherein said value of said at least one formation property and said at least one drilling operating parameter are entered into said neural network during drilling of said wellbore, and said value of said at least one drilling operating parameter is adjusted in response to an output of said trained neural network so as to optimize said value of said economic performance parameter.

16. The method as defined in claim 15 wherein said value of said at least one formation property is determined by logging-while-drilling instrumentation.

17. The method as defined in claim 15 wherein said value of said formation property is determined by analysis of formation cuttings.

18. The method as defined in claim 8 wherein said neural network is trained by selecting data from drilled wellbores, said data comprising values of said at least one formation property for formations through which said drilled wellbores penetrated, and corresponding thereto values of said at least one drilling operating parameter, said at least one drill bit design parameter, and values of said economic performance parameter;

and

entering said data from said wellbores into said neural network.

19. The method as defined in claim 8 further comprising determining said value of said at least one formation property during drilling of a wellbore, and adjusting said value of said at least one drilling operating parameter in response to changes in said value of said at least one formation property, said value of said at least one formation property determined during drilling by entering values of said at least one formation property with respect to depth from nearby wellbores into said neural network so as to train said neural network to calculate expected values of said at least one formation property in said wellbore being drilled at corresponding stratigraphic depths therein.

20. The method as defined in claim 8 wherein said economic performance parameter comprises a cost to drill a selected portion of a wellbore.

21. The method as defined in claim 8 wherein said economic performance parameter comprises a distance drilled by a single drill bit.

22. The method as defined in claim 8 wherein said economic performance parameter comprises an amount of damage to a producing earth formation.

23. The method as defined in claim 8 wherein said economic performance parameter comprises degree of departure from a planned wellbore trajectory.

24. The method as defined in claim 8 further comprising changing said at least one drill bit design parameter in response to the output of said trained neural network so as to optimize said value of said parameter related to economic performance.

25. A method for estimating change in economic performance of a drill bit in response to change in an input parameter, comprising:

entering a value of at least one property of an earth formation to be drilled by said bit into a trained neural network;

entering at least one design parameter of said bit into said trained neural network;

entering at least one drilling operating condition into said trained neural network; and

varying at least one of said at least one property of said earth formation, said at least one design parameter and said at least one drilling operating condition and determining a change in a value of at least one parameter related to said economic performance of said bit.

26. The method as defined in claim 25 wherein said at least one formation property comprises a property selected from the group of rock mineral composition, porosity, compressive strength, abrasiveness, acoustic velocity, electrical resistivity and natural gamma radiation.

27. The method as defined in claim 25 wherein said at least one design parameter comprises a parameter selected from the group of cutting element type, cutting element count and hydraulic nozzle configuration.

28. The method as defined in claim 25 wherein said at least one drilling operating parameter comprises a parameter selected from the group of weight on bit, rotary speed of said bit and drilling fluid flow rate.

29. The method as defined in claim 25 wherein said at least one drilling operating parameter comprises drilling fluid circulating pressure.

30. The method as defined in claim 29 wherein said at least one drilling operating parameter further comprises an amplitude and a frequency of a pressure variation component of said fluid circulating pressure, said variation component related to operation of a drilling hammer.

31. The method as defined in claim 25 wherein said at least one economic performance parameter comprises a wear rate of said drill bit.

32. The method as defined in claim 25 wherein said neural network is trained by selecting data from drilled wellbores, said data comprising values of said at least one formation property for formations through which said drilled wellbores penetrated, and corresponding thereto values of said at least one drilling operating parameter, said at least one drill bit design parameter, and values of said at least one economic performance parameter;

and

entering said data from said wellbores into said neural network.

33. The method as defined in claim 25 wherein said economic performance parameter comprises cost to drill a selected portion of a wellbore.

34. The method as defined in claim 25 wherein said economic performance parameter comprises a distance drilled by a single drill bit.

35. The method as defined in claim 25 wherein said economic performance parameter comprises an amount of damage to a producing earth formation.

36. The method as defined in claim 25 wherein said economic performance parameter comprises degree of departure from a planned wellbore trajectory.

37. The method as defined in claim 25 further comprising changing said at least one drill bit design parameter in response to the output of said trained neural network so as to optimize said value of said parameter related to economic performance.