Strain sensor based downhole fluid density measurement tool

Patent metadata

Field	Value
Publication number	US-11879905-B2
Application number	US-201916954828-A
Country	US
Kind code	B2
Filing date	Sep 17, 2019
Priority date	Sep 17, 2019
Publication date	Jan 23, 2024
Grant date	Jan 23, 2024

Abstract

Systems and methods for determining fluid density include receiving calibration data for a fluid density measurement tool. The fluid density measurement tool can include a cantilever beam and at least one strain sensor that is coupled to the cantilever beam. The cantilever beam can be housed in the fluid density measurement tool and is buoyed by a fluid that enters the fluid density measurement tool. The systems and methods measure strain values at the at least one strain sensor and determine a density of the fluid based on the calibration data, and the strain values measured at the at least one strain sensor.

First claim

What is claimed is:

1. A method for determining fluid density, the method comprising:
receiving calibration data for a fluid density measurement tool, the fluid density measurement tool comprising a cantilever beam and coupled with at least one strain sensor that is protected from a fluid, wherein the cantilever beam is housed in the fluid density measurement tool and is buoyed by the fluid that enters the fluid density measurement tool;
measuring, by the at least one strain sensor, strain values indicating a buoyancy force of the fluid on the cantilever beam; and
determining a density of the fluid based on the calibration data, and the strain values measured by the at least one strain sensor.

2. The method of claim 1 , wherein determining the density of the fluid based on the calibration data and the strain values comprises determining the density of the fluid based on one or more values derived from the strain values.

3. The method of claim 2 , wherein the one or more values derived from the strain values comprise Von Mises stress values.

4. The method of claim 1 , further comprising generating the calibration data utilizing a machine learning algorithm.

5. The method of claim 4 , wherein the calibration data comprises a machine learning model, and wherein generating the calibration data comprises:
exposing the fluid density measurement tool to fluids having varying densities, and at varying ambient property values and at varying orientations of the fluid density measurement tool;
storing strain sensor values, ambient property values, and known density values of the fluids as test data; and
creating the machine learning model based on the test data, the machine learning model correlating the strain sensor values, ambient property values, and known density values.

6. The method of claim 5 , further comprising determining Von Mises stress values from the strain sensor values and storing the Von Mises stress values in the test data.

7. The method of claim 5 , wherein the varying ambient properties comprise an ambient temperature.

8. The method of claim 1 , further comprising updating the calibration data after deployment of the fluid density measurement tool in a wellbore.

9. The method of claim 8 , wherein updating the calibration data comprises adjusting the calibration data based on comparing the density of the fluid as determined from the calibration data to a density of the fluid determined after the fluid sampled at a surface of the wellbore.

10. An apparatus comprising:
a fluid density measurement tool, the fluid density measurement tool comprising a cantilever beam and coupled with at least one strain sensor that is protected from a fluid, wherein the cantilever beam is located in the fluid density measurement tool and is buoyed by the fluid that enters the fluid density measurement tool;
a processor communicably coupled to the fluid density measurement tool; and
a machine-readable medium having program code executable by the processor to cause the apparatus to,
receive calibration data for the fluid density measurement tool,
receive, from the at least one strain sensor, measurements of strain values indicating a buoyancy force of the fluid exerted on the cantilever beam, and
determine a density of a fluid based on the calibration data, and the strain values measured at the at least one strain sensor.

11. The apparatus of claim 10 , wherein the cantilever beam is located in a chamber formed within a housing of the fluid density measurement tool, wherein the housing includes an opening to allow the fluid to enter the chamber of the fluid density measurement tool.

12. The apparatus of claim 10 , wherein the program code to determine the density of the fluid based on the calibration data and the strain values comprises program code to determine the density of the fluid based on one or more values derived from the strain values.

13. The apparatus of claim 12 , wherein the one or more values derived from the strain values comprise Von Mises stress values.

14. The apparatus of claim 10 , wherein the program code further comprises program code to update the calibration data after deployment of the fluid density measurement tool in a wellbore.

15. The apparatus of claim 14 , wherein the program code to update the calibration data comprises program code to adjust the calibration data based on a comparison of the density of the fluid as determined from the calibration data to a density determined from a sample of the fluid obtained at a surface of the wellbore.

16. One or more non-transitory machine-readable media comprising program code for determining a fluid density, the program code to:
receive calibration data for a fluid density measurement tool, the fluid density measurement tool comprising a cantilever beam and coupled with at least one strain sensor that is protected from a fluid, wherein the cantilever beam is housed in the fluid density measurement tool and is buoyed by the fluid that enters the fluid density measurement tool;
measure strain values indicating a buoyancy force of the fluid exerted on the cantilever beam; and
determine a density of a fluid based on the calibration data, and the strain values measured at the at least one strain sensor.

17. The one or more non-transitory machine-readable media of claim 16 , wherein the program code to determine the density of the fluid based on the calibration data and the strain values comprised program code to determine the density of the fluid based on one or more values derived from the strain values.

18. The one or more non-transitory machine-readable media of claim 17 , wherein the one or more values derived from the strain values comprise Von Mises stress values.

19. The one or more non-transitory machine-readable media of claim 16 , wherein the program code further comprises program code to generate the calibration data utilizing a machine learning algorithm.

20. The one or more non-transitory machine-readable media of claim 19 , wherein the calibration data comprises a machine learning model, and wherein program code to determine the calibration data comprises program code to:
store strain sensor values, ambient property values, and known density values of the fluids as test data obtained from the fluid density measurement tool; and create the machine learning model based on the test data, the machine learning model correlating the strain sensor values, ambient property values, and known density values.

Assignees

• Halliburton Energy Services Inc

Inventors

• Vick Jr James Dan
• Fripp Michael Linley

Classifications

• G01N9/10 Primary

  by observing bodies wholly or partially immersed in fluid materials · CPC title

• E21B47/01 Primary

  Devices for supporting measuring instruments on drill bits, pipes, rods or wirelines; Protecting measuring instruments in boreholes against heat, shock, pressure or the like · CPC title

• E21B47/10

  Locating fluid leaks, intrusions or movements · CPC title

• G01N9/18

  Special adaptations for indicating, recording, or control · CPC title

• G01N33/2823

  Raw oil, drilling fluid or polyphasic mixtures · CPC title