System for optimizing drilling in real time

• US 20060173625A1
• Filed: 02/01/2005
• Published: 08/03/2006
• Est. Priority Date: 02/01/2005
• Status: Active Grant

• Current Assignee: Smith International Incorporated (Schlumberger NV)
• Original Assignee: Smith International Incorporated (Schlumberger NV)
• Inventors: Moran, David P.
• Granted Patent: US 7,142,986 B2
• Time in Patent Office: Days
• US Class Current: 702/9
• 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 optimizing drilling parameters includes obtaining previously acquired data, querying a remote data store for current well data, determining optimized drilling parameters, and returning optimized parameters for a next segment to the remote data store. Determining optimized drilling parameters may include correlating the current well data to the previously acquired data, predicting drilling conditions for the next segment, and optimizing drilling parameters for the next segment.

Claims

1. A method for optimizing drilling parameters, comprising:

obtaining previously acquired data;

querying a remote data store for current well data;

determining optimized drilling parameters for a next segment; and

returning optimized parameters for a next segment to the remote data store.

2. The method of claim 1, wherein the determining the optimized drilling parameters comprises:

correlating the current well data to the previously acquired data;

predicting drilling conditions for the next segment; and

optimizing drilling parameters for the next segment.

3. The method of claim 2, further comprising:

predicting drilling conditions to a planned depth; and

optimizing drilling parameters to a planned depth.

4. The method of claim 3, wherein the optimizing the drilling parameters to a planned depth comprises updating a previous optimization using at least one selected from the group consisting of updated data and newly available data.

5. The method of claim 2, wherein the optimizing drilling parameters for the next segment is performed with the use of a trained artificial neural network.

6. The method of claim 5, wherein the correlating data it performed with a second artificial neural network, and the predicting the drilling conditions is performed with a third artificial neural network.

7. The method of claim 2, wherein the correlating the current well data comprises:

obtaining current well formation properties; and

correlating the formation properties to offset well properties.

8. The method of claim 7, wherein the obtaining current well formation properties comprises at least one selected from the group consisting of determining the current well formation properties based on the current well data and querying the data store for the current well formation properties.

9. The method of claim 2, wherein the correlating the current well data to the previously acquired data comprises using a fitting algorithm.

10. The method of claim 9, wherein the using the fitting algorithm comprises minimizing an error function.

11. The method of claim 2, wherein the optimizing the drilling parameters comprises estimating a dulling off of the drill bit that has occurred.

12. The method of claim 11, wherein the optimizing the drilling parameters comprises predicting a dulling off of the drill bit that will occur while drilling the next segment.

13. The method of claim 12, wherein the optimizing the drilling parameters comprises predicting a dulling off of the drill bit that will occur while drilling to a planned depth.

14. The method of claim 13, further comprising predicting a number of hours of remaining bit life.

15. The method of claim 2, wherein the optimizing the drilling parameters is performed based on a set of drilling priorities.

16. The method of claim 15, wherein the set of drilling priorities includes at least one selected from the group consisting of a well path, a vibration problem, a drilling economics, a bit life, and a rate of penetration.

17. The method of claim 1, wherein the determining the optimized parameters is performed with an artificial neural network.

18. The method of claim 1, wherein the querying the remote data store, the determining the optimized drilling parameters, and the returning the optimized parameters are performed in real-time.

19. The method of claim 1, wherein the drilling parameters comprise at least one selected from the group consisting of weight on bit, torque on bit, rotary speed, and mud flowrate.

20. The method of claim 1, wherein the previously acquired data comprise data measured from an offset well.

21. The method of claim 1, wherein the previously acquired data comprise data from at least one selected from the group consisting of data from a nearby previously drilled well and data from a well drilled in a geologically similar area.

22. The method of claim 1, wherein the remote data store uses a WITSML data transfer standard.

23. The method of claim 1, further comprising:

communicating the optimized drilling parameters to an automated drilling system at a drilling site; and

controlling the drilling parameters using the automated drilling system.

24. A method for optimizing drilling parameters in real-time, comprising:

obtaining previously acquired data;

querying a remote data store for current well data;

determining current well formation properties;

correlating the current well formation properties to formation properties determined from the previously acquired data;

predicting formation properties for a next segment;

optimizing the drilling parameters for the next segment; and

returning the optimized drilling parameters to the remote data store.

25. A method of drilling, comprising:

measuring current drilling parameters;

uploading the current drilling parameters and the lagged data to a data store;

querying the remote data store for optimized drilling parameters; and

controlling the drilling according to the optimized drilling parameters.

26. The method of claim 25, further comprising:

measuring lagged data; and

uploading the lagged data to the data store.

27. The method of claim 26, further comprising repeating querying the remote data store for updated optimized drilling parameters.