Subsurface condition detection using tube waves in a multi-well system

Patent metadata

Field	Value
Publication number	US-2025369324-A1
Application number	US-202418920567-A
Country	US
Kind code	A1
Filing date	Oct 18, 2024
Priority date	May 30, 2024
Publication date	Dec 4, 2025
Grant date	—

Abstract

Techniques for determining subsurface conditions in a multi-well system may include detecting, at time t1, a tube wave at a first well system of the multi-well system. The techniques may further include detecting, at time t2, the tube wave at a second well system of the multi-well system. The techniques may further include determining a time differential td between t1 and t2. The techniques may further include determining, based at least in part on td, that the first well system and the second well system are in fluid communication via a formation.

First claim

1 . A method for determining subsurface conditions in a multi-well system, the method comprising:
detecting, at time t 1 , a tube wave at a first well system of the multi-well system; detecting, at time t 2 , the tube wave at a second well system of the multi-well system; determining a time differential t d between t 1 and t 2 ; and determining, based at least in part on t d , that the first well system and the second well system are in fluid communication via a formation.

2 . The method of claim 1 , further comprising:
determining a shortest distance length between a first wellbore of the first well system and a second wellbore of the second well system; determining a fracture length of a fracture between the first wellbore and the second wellbore; and determining, based at least in part on the shortest distance length and the fracture length, a complexity of the fracture.

3 . The method of claim 2 , wherein said determining the fracture length of the fracture between the first wellbore and the second wellbore comprises:
determining a first travel time of the tube wave in the first well system; determining a second travel time of the tube wave in the second well system; and determining, based at least in part on ta, the first travel time, and the second travel time, a third travel time of the tube wave in the formation.

4 . The method of claim 2 , wherein said determining the complexity of the fracture comprises determining a ratio of the fracture length to the shortest distance length.

5 . The method of claim 1 , wherein determining that the first well system and the second well system are in fluid communication via the formation comprises determining that t d is greater than a threshold.

6 . The method of claim 1 , further comprising in response to said determining that the first well system and the second well system are in fluid communication via the formation, determining, by a machine learning module, one or more downhole operations performable to meet a pre-determined objective.

7 . The method of claim 6 , further comprising performing the one or more downhole operations.

8 . A multi-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 the multi-well system, the instructions including:
instructions to detect a tube wave at a first well system of the multi-well system, wherein a time of detection is t 1 ;
instructions to detect the tube wave at a second well system of the multi-well system wherein a time of detection is t 2 ;
instructions to determine a time differential t d between t 1 and t 2 ; and
instructions to determine, based at least in part on t d , that the first well system and the second well system are in fluid communication via a formation.

9 . The multi-well system of claim 8 , the instructions further including:
instructions to determine a shortest distance length between a first wellbore of the first well system and a second wellbore of the second well system; instructions to determine a fracture length of a fracture between the first wellbore and the second wellbore; and instructions to determine, based at least in part on the shortest distance length and the fracture length, a complexity of the fracture.

10 . The multi-well system of claim 9 , wherein the instructions to determine the fracture length of the fracture between the first wellbore and the second wellbore includes:
instructions to determine a first travel time of the tube wave in the first well system; instructions to determine a second travel time of the tube wave in the second well system; and instructions to determine, based at least in part on t d , the first travel time, and the second travel time, a third travel time of the tube wave in the formation.

11 . The multi-well system of claim 9 , wherein said instructions to determine the complexity of the fracture includes instructions to determine a ratio of the fracture length to the shortest distance length.

12 . The multi-well system of claim 8 , wherein the instructions further include instructions to determine, in response to a determination that the first well system and the second well system are in fluid communication via the formation, one or more downhole operations performable to meet a pre-determined objective, wherein the determination of the one or more downhole operations is made by a machine learning module.

13 . The multi-well system of claim 12 , further comprising:
the first well system; the second well system; and wherein said instructions further include instructions to execute the one or more downhole operations on at least one of the first well system or the second well system.

14 . 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 multi-well system, the instructions comprising:
instructions to detect a first tube wave at a first well system of the multi-well system, wherein a time of detection is t 1 ; instructions to detect the first tube wave at a second well system of the multi-well system, wherein a time of detection is t 2 ; instructions to determine a first time differential t d1 between t 1 and t 2 ; and instructions to determine, based at least in part on t d1 , that the first well system and the second well system are in fluid communication via a formation.

15 . The one or more non-transitory computer-readable mediums of claim 14 , the instructions further including:
instructions to determine a shortest distance length between a first wellbore of the first well system and a second wellbore of the second well system; instructions to determine a fracture length of a fracture between the first wellbore and the second wellbore; and instructions to determine, based at least in part on the shortest distance length and the fracture length, a complexity of the fracture.

16 . The one or more non-transitory computer-readable mediums of claim 15 , wherein the instructions to determine the fracture length of the fracture between the first wellbore and the second wellbore includes:
instructions to determine a first travel time of the first tube wave in the first well system; instructions to determine a second travel time of the first tube wave in the second well system; and instructions to determine, based at least in part on t d1 , the first travel time, and the second travel time, a third travel time of the first tube wave in the formation.

17 . The one or more non-transitory computer-readable mediums of claim 15 , wherein the instructions to determine the complexity of the fracture includes instructions to determine a ratio of the fracture length to the shortest distance length.

18 . The one or more non-transitory computer-readable mediums of claim 15 , wherein the instructions further include instructions to determine, in response to a determination that the first well system and the second well system are in fluid communication via the formation, one or more downhole operations performable to meet a pre-determined objective, wherein the determination of the one or more downhole operations is made by a machine learning module.

19 . The one or more non-transitory computer-readable mediums of claim 18 , wherein the instructions further include:
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Assignees

• Halliburton Energy Services Inc

Inventors

• Shetty Dinesh Ananda
• Sun Zhijie
• Bale Derek Shelby
• Satti Rajani Prakash
• Lee Erik

Classifications

• E21B2200/22

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

• G01V1/42

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

• G01V1/282

  Application of seismic models, synthetic seismograms · CPC title

• G01V1/305

  Travel times · CPC title

• E21B49/00

  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