BOREHOLE PRESSURE MANAGEMENT METHODS AND SYSTEMS WITH ADAPTIVE LEARNING

• US 20160222741A1
• Filed: 11/15/2013
• Published: 08/04/2016
• Est. Priority Date: 11/15/2013
• Status: Active Grant

• Current Assignee: Halliburton Energy Services Incorporated (Halliburton Company)
• Original Assignee: Halliburton Energy Services Incorporated (Halliburton Company)
• Inventors: Poletzky, Isabe Cristina, Davis, Nancy Suzan, Lovorn, James Randolph, Saeed, Saad
• Granted Patent: US 10,294,742 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: E21B 21/08 Controlling or monitoring pressure or flow of drilling fluid, e.g. automatic filling of boreholes, automatic control of bottom pressure valve arrangements therefor E21B21/10
• CPC Class Codes: E21B 34/00 Valve arrangements for boreholes or wells
• 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 47/06 Measuring temperature or pressure
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G05B 13/048 using a predictor

Abstract

Various managed pressure drilling tools, systems, and methods are disclosed. An example method includes obtaining a model-based pressure prediction and sensor-based pressure measurements during borehole drilling operations. The method also includes determining a real-time learning calculation using the model-based pressure prediction and at least some of the sensor-based pressure measurements. The method also includes updating a formation pressure model based at least in part on the real-time learning calculation. The method also includes controlling annular pressure for the borehole drilling operations based on a pressure predicted by the updated formation pressure model.

Claims

1. A method of managing pressure in a borehole, the method comprising:

obtaining a model-based pressure prediction and sensor-based pressure measurements during operations in the borehole;

determining real-time learning calculations based at least in part on a difference between the model-based pressure prediction and at least some of the sensor-based pressure measurements;

updating a formation pressure model based at least in part on the real-time learning calculations; and

controlling annular pressure for the operations based on a pressure predicted by the updated formation pressure model.

2. The method of claim 1, wherein determining the real-time learning calculations comprises:

comparing a model-based pore pressure curve prediction with a sensor-based pore pressure measurement curve; and

comparing a model-based fracture pressure curve prediction with a sensor-based fracture pressure measurement curve.

3. The method of claim 1, further comprising filtering the obtained sensor-based pressure measurements using an adaptive learning module and determining the real-time learning calculations based on a filtered set of sensor-based pressure measurements.

4. The method of claim 3, wherein the adaptive learning module includes a neural network.

5. The method of claim 3, wherein the filtered set of sensor-based pressure measurements includes look-ahead log data and look-around log data.

6. The method of claim 3, wherein the filtered set of sensor-based pressure measurements includes log data selected from the group consisting of:

resistivity log data, acoustic log data, density log data, neutron density log data, gamma ray log data, and seismic log data.

7. The method of claim 1, further comprising generating a log that displays pore pressure and at least one logging tool measurement as a function of depth, wherein an operator updates the formation pressure model based at least in part on the generated log.

8. The method of claim 1, wherein the updated formation pressure model is based at least in part on compaction trend data filtered by an adaptive learning module.

9. The method of claim 1, wherein the updated formation pressure model is based at least in part on historical pressure test data filtered by an adaptive learning module.

10. The method of claim 1, wherein the updated formation pressure model is based at least in part on real-time pressure test data filtered by an adaptive learning module.

11. The method of claim 1, wherein the updated formation pressure model is based at least in part on event signature data filtered by the adaptive learning strategy.

12. The method of claim 1, further comprising repeating said obtaining steps, said determining step, said updating step, and said controlling step, during the borehole operations.

13. A system for managing pressure in a borehole, comprising:

a controllable choke unit to adjust pressure in the borehole; and

a processor that;

determines real-time learning calculations based at least in part on a difference between a model-based pressure prediction and sensor-based pressure measurements obtained during borehole operations;

updates a formation pressure model based at least in part on the real-time learning calculations;

determines a pressure setpoint using the updated formation pressure model; and

provides the pressure setpoint to the controllable choke unit.

14. The system of claim 13, wherein the processor determines the real-time learning calculations by comparing a model-based pore pressure curve prediction with a sensor-based pore pressure measurement curve, and by comparing a model-based fracture pressure curve prediction with a sensor-based fracture pressure measurement curve.

15. The system of claim 13, wherein the processor filters sensor-based pressure measurements using an adaptive learning module, and determines the real-time learning calculations based on a filtered set of sensor-based pressure measurements.

16. The system of claim 13, wherein the sensor-based pressure measurements correspond to look-ahead look-around log data filtered by an adaptive learning module.

17. The system of claim 13, wherein the sensor-based pressure measurements correspond to log data filtered by an adaptive learning module, the log data selected from the group consisting of:

resistivity log data, acoustic log data, density log data, neutron density log data, gamma ray log data, and seismic log data.

18. The system of claim 13, wherein the processor generates a log that displays pore pressure in real-time and at least one logging tool measurement as a function of depth, and updates the formation pressure model in real-time based at least in part on operator input in response to the generated log.

19. The system of claim 13, wherein the updated formation pressure model is based at least in part on data filtered by an adaptive learning module, the data selected from the list consisting of:

compaction trend data, historical pressure test data, real-time pressure test data, and event signature data.