Monitoring, diagnosing and optimizing electric submersible pump operations

• US 9,261,097 B2
• Filed: 09/10/2012
• Issued: 02/16/2016
• Est. Priority Date: 07/31/2012
• Status: Active Grant

• Current Assignee: Landmark Graphics Corporation (Halliburton Company)
• Original Assignee: Landmark Graphics Corporation (Halliburton Company)
• Inventors: Moricca, Giuseppe, Querales, Maiquel, Dhar, Joydeep, Carvajal, Gustavo, Cullick, Alvin S., Vellanki, Rama K., Villamizar, Miguel, Rodriguez, Jose
• Primary Examiner(s): BHAT, ADITYA S
• Application Number:
  US13/609,163
• Publication Number:
  US 20140039836A1
• Time in Patent Office: 1,254 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: E21B 43/12 Methods or apparatus for controlling the flow of the obtained fluid to or in wells E21B43/25 takes precedence; valve arrangements E21B34/00
• CPC Class Codes: E21B 43/128 Adaptation of pump systems with down-hole electric drives
• CPC Class Codes: F04D 13/10 adapted for use in mining bore holes
• CPC Class Codes: F04D 15/00 Control, e.g. regulation, of pumps, pumping installations or systems
• CPC Class Codes: F04D 15/0066 by changing the speed, e.g. of the driving engine
• CPC Class Codes: F04D 15/0088 Testing machines
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

The present disclosure describes systems and methods for monitoring, diagnosing and optimizing operation of an electric submersible pump (ESP). At least some illustrative embodiments include a method that includes collecting measured data representative of a state of an ESP within a well or of well conditions and storing the measured data, matching a nodal analysis model of the well to the well by matching one or more modeled values to corresponding measured data, and identifying one or more likely conditions of the ESP based at least in part on data generated by a matched model. The method further includes updating the matched model to reflect a selected correction of one of the likely conditions identified, generating ESP performance curves using the updated model, and presenting to a user an action recommended to achieve an ESP performance consistent with an ESP operating point selected from one of the ESP performance curves.

Claims

1. A method for monitoring, diagnosing and optimizing operation of an electric submersible pump (ESP) that comprises:

collecting measured data representative of a state of an ESP within a well or of well conditions, and further storing the measured data;

matching a nodal analysis model of the well to the well by matching one or more modeled values to corresponding measured data;

identifying one or more likely conditions of the ESP based at least in part on data generated by a matched nodal analysis model;

updating the matched nodal analysis model to reflect a selected correction of one of the one or more likely conditions identified;

generating a plurality of ESP performance curves using the updated matched nodal analysis model; and

presenting to a user an action recommended to achieve an ESP performance consistent with an ESP operating point selected from one of the plurality of ESP performance curves.

2. The method of claim 1, further comprising filtering and conditioning the measured data, wherein the corresponding measured data used by the matching comprises the filtered and conditioned data.

3. The method of claim 1, further comprising generating a plurality of recommended actions based on at least one of the one or more likely conditions identified, wherein the action recommended is selected from the plurality of recommended actions.

4. The method of claim 3, further comprising generating the one or more likely conditions at least in part by applying fuzzy logic to the measured data.

5. The method of claim 1, wherein the measured data comprises data selected from the group consisting of real-time data, recorded data and simulated data.

6. The method of claim 1, wherein the matching is performed iteratively using different model parameters for each iteration until the one or more modeled values are within a tolerance range of corresponding measured data values.

7. The method of claim 1, wherein the data representative of the well conditions comprises data selected from the group consisting of bottom hole pressure, bottom hole temperature, tube head pressure, tube head temperature, choke size, fluid flow rates, oil flow rates and water cuts.

8. The method of claim 1, wherein the data representative of the state of the ESP comprises data selected from the group consisting of intake pressure, outlet pressure, motor temperature, motor frequency and motor amperage.

9. The method of claim 8, further comprising presenting to at least one of one or more users the current status of the task tracking ticket.

10. The method of claim 8, further comprising determining if at least one of one or more users may view or update the task tracking ticket based upon an access permission structure.

11. The method of claim 1, wherein the identifying of the one or more likely conditions comprises calculating a probability for each of a plurality of conditions, ranking each of the plurality of conditions according to its probability and presenting to the user one or more of the plurality of conditions as a ranked condition list.

12. The method of claim 1, further comprising:

generating an advisory message if a value of the measured data is detected outside of an allowable range of values and sending out a corresponding notification to one or more contacts of a distribution list;

creating a task tracking ticket corresponding to the advisory message;

updating the task tracking ticket to include the action recommended and personnel assigned to implement the solution;

updating the task tracking ticket to document implementation of the solution and closing the task tracking ticket; and

generating an additional advisory message and sending out an additional corresponding notification to the one or more contacts each time the task tracking ticket is updated.

13. The method of claim 1, wherein the matched nodal analysis model outputs flow and pressure of multi-phase fluids within the borehole based on a set of inputs including well tubing friction.

14. The method of claim 13, wherein the set of inputs further include reservoir permeability.

15. An electric submersible pump (ESP) monitoring, diagnosing and optimizing system that comprises:

a memory having ESP monitoring, diagnosing and optimizing software; and

one or more processors coupled to the memory, the software causing the one or more processors to;

collect measured data representative of a state of an ESP within a well or of well conditions, and further store the measured data;

match a nodal analysis model of the well to the well by matching one or more modeled values to corresponding measured data;

identify one or more likely conditions of the ESP based at least in part on data generated by a matched nodal analysis model;

update the matched nodal analysis model to reflect a selected correction of one of the one or more likely conditions identified;

generate a plurality of ESP performance curves using the updated matched nodal analysis model; and

present to a user an action recommended to achieve an ESP performance consistent with an ESP operating point selected from one of the plurality of ESP performance curves.

16. The system of claim 15, wherein the software further causes the one or more processors to filter and condition the measured data, and wherein the corresponding measured data used to match the nodal analysis model to the well comprises the filtered and conditioned data.

17. The system of claim 15, wherein the software further causes the one or more processors to generate a plurality of recommended actions based on at least one of the one or more likely conditions identified, and wherein the action recommended is selected from the plurality of recommended actions.

18. The system of claim 17, wherein the software further causes the one or more processors to generate the one or more likely conditions at least in part by applying fuzzy logic to the measured data.

19. The system of claim 15, wherein the measured data comprises data selected from the group consisting of real-time data, recorded data and simulated data.

20. The system of claim 15, wherein the software further causes the one or more processors to match the nodal analysis model to the well iteratively using different model parameters for each iteration until the one or more modeled values are within a tolerance range of the corresponding measured data.

21. The system of claim 15, wherein the data representative of the well conditions comprises data selected from the group consisting of bottom hole pressure, bottom hole temperature, tube head pressure, tube head temperature, choke size, fluid flow rates, oil flow rates and water cuts.

22. The system of claim 15, wherein the data representative of the state of the ESP comprises data selected from the group consisting of intake pressure, outlet pressure, motor temperature, motor frequency and motor amperage.

23. The system of claim 15, wherein the software further causes the one or more processors to identify the one or more likely conditions at least in part by causing the processor to calculate a probability for each of a plurality of conditions, rank each condition according to its probability and present to the user one or more of the plurality of conditions as a ranked condition list.

24. A non-transitory information storage medium having electric submersible pump (ESP) monitoring, diagnosing and optimizing software that comprises:

a data collection and storage module that collects measured data representative of a state of an ESP within a well or of well conditions, and further stores the measured data;

a model match module that matches a nodal analysis model of the well to the well by matching one or more modeled values to corresponding measured data;

a condition identifier module that identifies one or more likely conditions of the ESP based at least in part on data generated by a matched nodal analysis model;

a model update module that updates the matched nodal analysis model to reflect a selected correction of one of the one or more likely conditions identified;

a performance curve module that generates a plurality of ESP performance curves using the updated matched nodal analysis model and accepts a selection of an ESP operating point on one of the plurality of ESP performance curves; and

a recommended action module that identifies and presents to a user one or more actions recommended to achieve an ESP performance consistent with the selected ESP operating point.

25. The storage medium of claim 24, wherein the software further comprises a filtering and conditioning module that filters and conditions the measured data, and wherein the corresponding measured data used to match the nodal analysis model to the well comprises the filtered and conditioned data.

26. The storage medium of claim 24, wherein the software further comprises a recommended action module that generates a plurality of recommended actions based on at least one of the one or more likely conditions identified, and wherein the action recommended is selected from the plurality of recommended actions.

27. The storage medium of claim 26, wherein the recommended action module further generates the one or more likely conditions at least in part by applying fuzzy logic to the measured data.

28. The storage medium of claim 24, wherein the measured data comprises data selected from the group consisting of real-time data, recorded data and simulated data.

29. The storage medium of claim 24, wherein the data representative of the well conditions comprises data selected from the group consisting of bottom hole pressure, bottom hole temperature, tube head pressure, tube head temperature, choke size, fluid flow rates, oil flow rates and water cuts.

30. The storage medium of claim 24, wherein the data representative of the state of the ESP comprises data selected from the group consisting of intake pressure, outlet pressure, motor temperature, motor frequency and motor amperage.

31. The storage medium of claim 24, wherein the software further comprises a condition likelihood module that identifies the one or more likely conditions at least in part by calculating a probability for each of a plurality of conditions, ranking each condition according to its probability and presenting to the user one or more of the plurality of conditions as a ranked condition list.