Methods and systems for predicting interfacial tension of reservoir fluids using downhole fluid measurements

Patent metadata

Field	Value
Publication number	US-11550975-B2
Application number	US-202016940436-A
Country	US
Kind code	B2
Filing date	Jul 28, 2020
Priority date	Jul 28, 2020
Publication date	Jan 10, 2023
Grant date	Jan 10, 2023

Abstract

Methods and systems are provided for characterizing interfacial tension (IFT) of reservoir fluids, which involves obtaining fluid property data that represents fluid properties of a reservoir fluid sample measured downhole at reservoir conditions, and inputting the fluid property data to a computational model that determines a value of oil-water IFT of the reservoir fluid sample based on the fluid property data. In embodiments, the fluid property data represents single-phase fluid properties of the reservoir fluid sample, such as fluid density and viscosity of an oil phase of the reservoir fluid sample and fluid density of a water phase of the reservoir fluid sample. In embodiments, the computation model can be based on machine learning or analytics combined with a thermodynamics-based physics model.

First claim

What is claimed is:

1. A method for characterizing interfacial tension (IFT) of reservoir fluids, comprising:
a) obtaining fluid property data that represents fluid properties of a reservoir fluid sample measured downhole at reservoir conditions by a downhole tool;
b) performing, using the downhole tool, downhole measurements of the fluid properties of the reservoir fluid sample by inputting the fluid property data to a computational model that determines a value of oil-water IFT of the reservoir fluid sample based on the fluid property data; and
c) predicting, using the downhole tool, emulsions in reservoir fluids and developing treatments that mitigate production issues associated therewith based on the fluid property data measured downhole at the reservoir conditions.

2. The method according to claim 1 , wherein:
the operations of a) through c) are performed by a processor.

3. The method according to claim 2 , wherein:
the processor is part of a cloud-based computing environment.

4. The method according to claim 1 , further comprising:
storing the value of the oil-water IFT of the reservoir fluid sample as part of data accessible by an advisory tool for reservoir analysis and optimization.

5. The method according to claim 4 , wherein:
the advisory tool is provided by a cloud-based computing environment for reservoir analysis and optimization.

6. The method according to claim 1 , wherein:
the fluid property data represents single-phase fluid properties of the reservoir fluid sample.

7. The method according to claim 6 , wherein:
the single-phase fluid properties of the reservoir fluid sample comprise fluid density and viscosity of an oil phase of the reservoir fluid sample.

8. The method according to claim 6 , wherein:
the single-phase fluid properties of the reservoir fluid sample further comprises a fluid density of a water phase of the reservoir fluid sample.

9. The method according to claim 1 , wherein:
the computational model relates critical temperature of an oil phase of the reservoir fluid sample to the value of the oil-water IFT of the reservoir fluid sample.

10. The method according to claim 9 , wherein:
the computation model relates single-phase fluid properties of the oil phase of the reservoir fluid sample to the critical temperature of the oil phase of the reservoir fluid sample.

11. The method according to claim 10 , wherein:
the single-phase fluid properties of the oil phase of the reservoir fluid sample comprise a fluid density and a viscosity of the oil phase of the reservoir fluid sample.

12. The method according to claim 1 , wherein:
the computational model has the form

γ

O
⁢
W

=

[

1.58

(

ρ
w

-

ρ
o

)

+
1.76

(

T

G
(

μ
,

ρ
0

)

0.3125

]

4

,

where γ OW , is the value of the oil-water IFT of the reservoir fluid sample, ρ w and ρ o are density of a water phase and oil phase, respectively, T is the reservoir fluid temperature as part of the reservoir conditions, and G(μ, ρ 0 ) represents a critical temperature of the oil phase given the fluid density ρ 0 and viscosity μ of the oil phase.

13. The method according to claim 1 , further comprising:
storing the value of the oil-water IFT of the reservoir fluid sample in a cloud-based database or a local database.

14. The method according to claim 1 , wherein:
the downhole measurements are configured to measure single-phase fluid properties of the reservoir fluid sample.

15. The method according to claim 14 , wherein:
the single-phase fluid properties of the reservoir fluid sample comprise a fluid density and a viscosity of an oil phase of the reservoir fluid sample.

16. The method according to claim 14 , wherein:
the single-phase fluid properties of the reservoir fluid sample further comprise a fluid density of a water phase of the reservoir fluid sample.

17. The method according to claim 1 , wherein:
the operations are performed for multiple zones in a well or multiple wells in a field or multiple fields in a given formation reservoir.

18. A system for reservoir analysis, comprising:
at least one processor configured to:
a) obtain fluid property data that represents fluid properties of a reservoir fluid sample measured downhole at reservoir conditions by a downhole tool;
b) perform, using the downhole tool, downhole measurements of the fluid properties of the reservoir fluid sample by inputting the fluid property data to a computational model that determines a value of oil-water IFT of the reservoir fluid sample based on the fluid property data; and
c) predict, using the downhole tool, emulsions in reservoir fluids and developing treatments that mitigate production issues associated therewith based on the fluid property data measured downhole at the reservoir conditions.

19. The system according to claim 18 , wherein:
the system interfaces to or is part of an advisory tool.

20. The system according to claim 19 , wherein:
the advisory tool is configured to use the value of the oil-water IFT of the reservoir fluid sample to determine a capillary number for a reservoir formation and an estimate of oil reserves for the reservoir formation.

21. The system according to claim 19 , wherein:
the advisory tool is configured to use the value of the oil-water IFT of the reservoir fluid sample to simulate or model or optimize oil production or recovery strategies.

22. The system according to claim 19 , wherein:
the advisory tool is configured to use the fluid property data measured downhole at reservoir conditions to predict corrosive properties of reservoir fluid and to develop treatments that mitigate production issues associated therewith.

23. The system according

Assignees

• Schlumberger Technology Corp
• Saudi Arabian Oil Co

Inventors

• Mahavadi Sharath Chandra
• Singh Robin
• Abdallah Wael
• Al-Hamad Mohammed
• Sauerer Bastian
• Ma Shouxiang
• Zhang Leilei

Classifications

• E21B47/07

  Temperature · CPC title

• G06F30/27 Primary

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title

• E21B49/10

  using side-wall fluid samplers or testers · CPC title

• G01N2203/0212

  Theories, calculations · CPC title

• E21B49/081

  with down-hole means for trapping a fluid sample (E21B49/10 takes precedence) · CPC title