VIRTUAL MULTIPHASE FLOW METERING AND SAND DETECTION

• US 20150377667A1
• Filed: 06/10/2015
• Published: 12/31/2015
• Est. Priority Date: 06/30/2014
• Status: Abandoned Application

• Current Assignee: Saudi Arabian Oil Company (Government of Saudi Arabia)
• Original Assignee: Saudi Arabian Oil Company (Government of Saudi Arabia)
• Inventors: AHMAD, TALHA JAMAL, Black, Michael John, Noui-Mehidi, Mohamed Nabil
• Application Number:
  US14/735,217
• Publication Number:
  US 20150377667A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: E21B 47/107 using acoustic means
• CPC Class Codes: G01F 1/74 Devices for measuring flow of a fluid or flow of a fluent solid material in suspension in another fluid
• CPC Class Codes: G01N 29/14 using acoustic emission techniques echo of particles G01N29/046; measuring mechanical vibrations or acoustic waves in solids in general G01H1/00

Abstract

Virtual and non-invasive multiphase metering is performed for recognition of multiphase flow regimes of hydrocarbons and other fluids, flow rate, presence of sand, and other multiphase flow parameters. A passive acoustical detector system receives acoustical flow information in the form of acoustic emission signals, and a data processor processes and classifies the acoustical patterns. A statistical signal processing methodology is used. Acoustic models are provided for various flow regimes and flow patterns, using Artificial Intelligence methods including Hidden Markov Models and Artificial Neural Networks along with automated learning procedures. The metering can be used for downhole, top side and surface applications.

Claims

1. An apparatus for determining flow parameters of multiphase flow of fluids in a flow conduit, comprising:

(a) a transducer sensing acoustic emissions from the multiphase flow in the flow conduit;

(b) a converter transforming the sensed acoustic emissions into digital acoustic emission signals;

(c) a computer comprising a data memory storing a database of acoustic models of flow regime data in the flow conduit; and

(d) the computer further comprising a processor forming measures of the flow parameters from the digital acoustic emission signals and the acoustic models of the flow regime data to determine an acoustic model of the flow parameters of the multiphase flow, the processor performing the computer implemented steps of;

(1) segmenting the sensed acoustic emission signals into a sequence of digital acoustic emission segments;

(2) determining a feature vector for the digital acoustic emission segments of the sequence of digital acoustic emission segments;

(3) processing the feature vectors to determine a model of flow parameters of the multiphase flow.

2. The apparatus of claim 1, further including the data memory storing a database of actual multiphase flow conditions in the database of acoustic models.

3. The apparatus of claim 2, wherein the processor in processing the feature vectors to determine a model of flow parameters receives as inputs actual multiphase flow conditions data from the database.

4. The apparatus of claim 1, wherein the processor in processing the feature vectors to determine a model of flow parameters performs Hidden Markov modeling.

5. The apparatus of claim 1, wherein the processor in processing the feature vectors to determine a model of flow parameters performs the step of:

determining a model of flow parameters of the multiphase flow based on the determined flow vectors for the sequence of digital acoustic segments and the stored acoustic models of flow regime data in the flow conduit.

6. The apparatus of claim 5, wherein the processor in determining a model of flow parameters determines a most probable model of flow parameters of the multiphase flow based on the determined flow vectors for the sequence of digital acoustic segments and the stored acoustic models of flow regime data in the flow conduit.

7. The apparatus of claim 1, wherein the processor in processing the feature vectors to determine a model of flow parameters performs Artificial Neural Network modeling.

8. The apparatus of claim 1, wherein the processor in processing the feature vectors to determine a model of flow parameters performs the steps of:

(a) receiving the feature vectors as input states for Artificial Neural Network processing;

(b) performing the Artificial Neural Network processing based on the input states to determine flow parameters of the model of multiphase flow; and

(c) providing as output states the determined flow parameters of the model of multiphase flow.

9. The apparatus of claim 1, wherein the processor in processing the feature vectors to determine a model of flow parameters performs Hidden Markov modeling and Artificial Neural Network modeling.

10. The apparatus of claim 1, wherein the processor further forms a training model for performing the step of processing the feature vectors.

11. The apparatus of claim 10, wherein the processor further stores the formed training model in the memory.

12. The apparatus of claim 1, wherein the processor further provides the determined model of flow parameters of the multiphase flow for display.

13. A computer implemented method of determining with a processor of the computer flow parameters of multiphase flow of fluids in a flow conduit based on acoustic emissions from the multiphase flow and acoustic models of flow regime data in the conduit stored in a database of the computer, comprising the computer processing steps of:

(a) segmenting the acoustic emission signals into a sequence of digital acoustic emission segments;

(b) determining a feature vector for each of the sequence of digital acoustic emission segments;

(c) processing the feature vectors to determine a model of flow parameters of the multiphase flow.

14. The computer implemented method of claim 13, wherein the computer data memory stores a database of actual multiphase flow conditions, and wherein the step of processing the feature vectors to determine a model of flow parameters is performed based on actual multiphase flow conditions data from the database.

15. The computer implemented method of claim 13, wherein the step of processing the feature vectors to determine a model of flow parameters comprises Hidden Markov modeling.

16. The computer implemented method of claim 13, wherein the step of processing the feature vectors to determine a model of flow parameters comprises the step of:

determining a model of flow parameters of the multiphase flow based on the determined flow vectors for the sequence of digital acoustic segments and the stored acoustic models of flow regime data in the flow conduit.

17. The computer implemented method of claim 13, wherein the step of processing the feature vectors to determine a model of flow parameters comprises the step of:

determining a most probable model of flow parameters of the multiphase flow based on the determined flow vectors for the sequence of digital acoustic segments and the stored acoustic models of flow regime data in the flow conduit.

18. The computer implemented method of claim 13, wherein the step of processing the feature vectors to determine a model of flow parameters comprises Artificial Neural Network modeling.

19. The computer implemented method of claim 13, wherein the step of processing the feature vectors to determine a model of flow parameters comprises the steps of:

(a) receiving the feature vectors as input states for Artificial Neural Network processing;

(b) performing the Artificial Neural Network processing based on the input states to determine flow parameters of the model of multiphase flow; and

(c) providing as output states the determined flow parameters of the model of multiphase flow.

20. The computer implemented method of claim 13, wherein the step of processing the feature vectors to determine a model of flow parameters comprises Hidden Markov modeling and Artificial Neural Network modeling.

21. The computer implemented method of claim 13, further including the step of forming a training model for performing the step of processing the feature vectors.

22. The computer implemented method of claim 21, further including the step of storing the formed training model in the memory of the computer.

23. The computer implemented method of claim 13, further including the step of providing the determined model of flow parameters of the multiphase flow for display.

24. A data processing system for determining flow parameters of multiphase flow of fluids in a flow conduit based on acoustic emissions from the multiphase flow, the data processing comprising:

(a) a data memory storing a database of acoustic models of flow regime data in the flow conduit; and

(b) a processor performing the steps of;

(1) segmenting the acoustic emission signals into a sequence of digital acoustic emission segments;

(2) determining a feature vector for each of the sequence of digital acoustic emission segments; and

(3) processing the feature vectors to determine a model of flow parameters of the multiphase flow.

25. The data processing system of claim 24, further including the data memory storing a database of actual multiphase flow conditions.

26. The data processing system of claim 24, further including the processor in processing the feature vectors to determine a model of flow parameters receiving as inputs actual multiphase flow conditions data from the database.

27. The data processing system of claim 24, wherein the processor in processing the feature vectors to determine a model of flow parameters performs Hidden Markov modeling.

28. The data processing system of claim 24, wherein the processor in processing the feature vectors to determine a model of flow parameters performs the step of:

determining a model of flow parameters of the multiphase flow based on the determined flow vectors for the sequence of digital acoustic segments and the stored acoustic models of flow regime data in the flow conduit.

29. The data processing system of claim 24, wherein the processor in determining a model of flow parameters determines a most probable model of flow parameters of the multiphase flow based on the determined flow vectors for the sequence of digital acoustic segments and the stored acoustic models of flow regime data in the flow conduit.

30. The data processing system of claim 24, wherein the processor in processing the feature vectors to determine a model of flow parameters performs Artificial Neural Network modeling.

31. The data processing system of claim 24, wherein the processor in processing the feature vectors to determine a model of flow parameters performs the steps of:

(a) receiving the feature vectors as input states for Artificial Neural Network processing;

(b) performing the Artificial Neural Network processing based on the input states to determine flow parameters of the model of multiphase flow; and

(c) providing as output states the determined flow parameters of the model of multiphase flow.

32. The data processing system of claim 24, wherein the processor in processing the feature vectors to determine a model of flow parameters performs Hidden Markov modeling and Artificial Neural Network modeling.

33. The data processing system of claim 24, wherein the processor further forms a training model for performing the step of processing the feature vectors.

34. The data processing system of claim 24, wherein the processor further stores the formed training model in the memory.

35. The data processing system of claim 24, wherein the processor further provides the determined model of flow parameters of the multiphase flow for display.

36. A data storage device having stored in a non-transitory computer readable medium computer operable instructions for causing a data processing system to determine in a processor of the data processing system flow parameters of multiphase flow of fluids in a flow conduit based on acoustic emissions from the multiphase flow and acoustic models of flow regime data in the conduit stored in a database of the computer, the instructions stored in the data storage device causing a processor in the data processing system to perform the following steps:

(a) segmenting the acoustic emission signals into a sequence of digital acoustic emission segments;

(b) determining a feature vector for each of the sequence of digital acoustic emission segments;

(c) processing the feature vectors to determine a model of flow parameters of the multiphase flow.

37. The data storage device of claim 36, wherein the data memory stores a database of actual multiphase flow conditions, and wherein the instructions further comprise instructions causing the processor to perform the step of processing the feature vectors to determine a model of flow parameters based on actual multiphase flow conditions data from the database.

38. The data storage device of claim 36, wherein the instructions for processing the feature vectors to determine a model of flow parameters comprise instructions to perform Hidden Markov modeling.

39. The data storage device of claim 36, wherein the instructions for processing the feature vectors to determine a model of flow parameters comprise instructions to perform the step of:

determining a model of flow parameters of the multiphase flow based on the determined flow vectors for the sequence of digital acoustic segments and the stored acoustic models of flow regime data in the flow conduit.

40. The data storage device of claim 36, wherein the instructions for processing the feature vectors to determine a model of flow parameters comprise instructions to perform the step of:

determining a most probable model of flow parameters of the multiphase flow based on the determined flow vectors for the sequence of digital acoustic segments and the stored acoustic models of flow regime data in the flow conduit.

41. The data storage device of claim 36, wherein the instructions for processing the feature vectors to determine a model of flow parameters comprise instructions to perform Artificial Neural Network modeling.

42. The data storage device of claim 36, wherein the instructions for processing the feature vectors to determine a model of flow parameters comprise instructions to perform the steps of:

(a) receiving the feature vectors as input states for Artificial Neural Network processing;

(b) performing the Artificial Neural Network processing based on the input states to determine flow parameters of the model of multiphase flow; and

(c) providing as output states the determined flow parameters of the model of multiphase flow.

43. The data storage device of claim 36, wherein the instructions for processing the feature vectors to determine a model of flow parameters comprise instructions to perform Hidden Markov modeling and Artificial Neural Network modeling.

44. The data storage device of claim 36, wherein the instructions further comprise instructions to perform the step of forming a training model for performing the step of processing the feature vectors.

45. The data storage device of claim 36, wherein the instructions further comprise instructions to perform the step of storing the formed training model in the memory of the computer.

46. The data storage device of claim 36, wherein the instructions further comprise instructions to perform the step of providing the determined model of flow parameters of the multiphase flow for display.