Subsurface condition detection using tube waves

Patent metadata

Field	Value
Publication number	US-2025370154-A1
Application number	US-202418921650-A
Country	US
Kind code	A1
Filing date	Oct 21, 2024
Priority date	May 30, 2024
Publication date	Dec 4, 2025
Grant date	—

Abstract

A technique for detecting subsurface conditions using tube waves includes receiving a tube wave signal that corresponds to a tube wave within the wellbore. The technique also includes determining one or more categories associated with the tube wave signal. The technique also includes determining an inversion algorithm of a plurality of inversion algorithms based, at least in part, on the one or more categories. The technique also includes using the inversion algorithm of the plurality of algorithms to determine one or more estimates of subsurface conditions.

First claim

1 . A method for determining subsurface conditions in a wellbore, the method comprising:
receiving a tube wave signal corresponding to a tube wave within the wellbore; determining one or more categories associated with the tube wave signal; determining, based at least in part on the one or more categories, an inversion algorithm of a plurality of inversion algorithms; and determining, using the inversion algorithm of the plurality of inversion algorithms, one or more estimates of subsurface conditions.

2 . The method of claim 1 , further comprising training a machine learning module on a set of training data, wherein the training data comprises at least a set of previously categorized tube wave signals, wherein said determining the one or more categories associated with the tube wave signal is performed by the machine learning module.

3 . The method of claim 2 , wherein the training data further comprises at least one of operational parameters, wellbore design parameters, or completion parameters.

4 . The method of claim 2 , further comprising generating the set of previously categorized tube wave signals using a classifier module.

5 . The method of claim 1 , further comprising training a machine learning module on a set of training data, wherein the training data comprises a set of combinations, the combinations comprising one or more categories associated with a categorized tube wave signal, an indication of an inversion algorithm of the plurality of inversion algorithms, and an indication of a performance of the inversion algorithm on the categorized tube wave signal, wherein said determining the inversion algorithm of the plurality of inversion algorithms is performed by the machine learning module.

6 . The method of claim 5 , wherein the training data further comprises at least one of operational parameters, wellbore design parameters, or completion parameters.

7 . The method of claim 1 , further comprising:
determining that at least one category of the one or more categories is in a predefined set of categories; and in response to said determining that the at least one category of the one or more categories is in the predefined set of categories, generate an indication of a subsurface condition.

8 . The method of claim 1 , further comprising:
generating the tube wave in the wellbore; and transforming the tube wave into the tube wave signal.

9 . A well system comprising:
a computing system comprising:
one or more processors; and
one or more non-transitory computer-readable mediums including instructions which, when executed by the one or more processors, cause the one or more processors to determine subsurface conditions in a wellbore, the instructions including:
instructions to receive a tube wave signal corresponding to a tube wave within the wellbore;
instructions to determine one or more categories associated with the tube wave signal;
instructions to determine, based at least in part on the one or more categories, an inversion algorithm of a plurality of inversion algorithms; and
instructions to determine, using the inversion algorithm of the plurality of inversion algorithms, one or more estimates of subsurface conditions.

10 . The well system of claim 9 , the instructions further including instructions to train a machine learning module on a set of training data, wherein the training data comprises at least a set of previously categorized tube wave signals, wherein said instructions to determine one or more categories associated with the tube wave signal are included in the machine learning module.

11 . The well system of claim 10 , wherein the training data further comprises at least one of operational parameters, wellbore design parameters, or completion parameters.

12 . The well system of claim 9 , the instructions further including instructions to train a machine learning module on a set of training data, wherein the training data comprises a set of combinations, the combinations comprising one or more categories associated with a categorized tube wave signal, an indication of an inversion algorithm of the plurality of inversion algorithms, and an indication of a performance of the inversion algorithm on the categorized tube wave signal, wherein the instructions to determine the inversion algorithm of the plurality of inversion algorithms are included in the machine learning module.

13 . The well system of claim 12 , wherein the training data further comprises at least one of operational parameters, wellbore design parameters, or completion parameters.

14 . The well system of claim 9 , further comprising:
a pressure excitation device configured to generate the tube wave in the well system; and a device communicatively coupled with the computing system, the device configured to transform the tube wave into the tube wave signal and transmit the tube wave signal to the computing system.

15 . One or more non-transitory computer-readable mediums including instructions which, when executed by a processor, cause the processor to determine subsurface conditions in a wellbore, the instructions comprising:
instructions to receive a tube wave signal corresponding to a tube wave within the wellbore; instructions to determine one or more categories associated with the tube wave signal; instructions to determine, based at least in part on the one or more categories, an inversion algorithm of a plurality of inversion algorithms; and instructions to determine, using the inversion algorithm of the plurality of inversion algorithms, one or more estimates of subsurface conditions.

16 . The one or more non-transitory computer-readable mediums of claim 15 , the instructions further including instructions to train a machine learning module on a set of training data, wherein the training data comprises at least a set of previously categorized tube wave signals, wherein said instructions to determine one or more categories associated with the tube wave signal are included in the machine learning module.

17 . The one or more non-transitory computer-readable mediums of claim 16 , wherein the training data further comprises at least one of operational parameters, wellbore design parameters, or completion parameters.

18 . The one or more non-transitory computer-readable mediums of claim 15 , the instructions further including instructions to train a machine learning module on a set of training data, wherein the training data comprises a set of combinations, the combinations comprising one or more categories associated with a categorized tube wave signal, an indication of an inversion algorithm of the plurality of inversion algorithms, and an indication of a performance of the inversion algorithm on the categorized tube wave signal, wherein the instructions to determine the inversion algorithm of the plurality of inversion algorithms are included in the machine learning module.

19 . The one or more non-transitory computer-readable mediums of claim 18 , wherein the training data further comprises at least one of operational parameters, wellbore design parameters, or completion parameters.

20 . The one or more non-transitory computer-readable mediums of claim 15 , the instructions further comprising:
instructions to determine that at least one category of the one or more categories is in a predefined set of categories; and instructions to generate, in response to a determination that at least one category of the one or more categories is in the predefined set of categories, an indicat

Assignees

• Halliburton Energy Services Inc

Inventors

• Bale Derek Shelby
• Shetty Dinesh Ananda
• Yu Yang

Classifications

• E21B2200/22

  Fuzzy logic, artificial intelligence, neural networks or the like · CPC title

• G01V2210/1299

  Subsurface, e.g. in borehole or below weathering layer or mud line · CPC title

• G01V1/42

  using generators in one well and receivers elsewhere or vice versa (G01V1/52 takes precedence) · CPC title

• G01V1/50 Primary

  Analysing data · CPC title

• E21B49/00 Primary

  Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells · CPC title