Digital imaging technology-based method for calculating relative permeability of tight core

Patent metadata

Field	Value
Publication number	US-11747260-B2
Application number	US-202017087647-A
Country	US
Kind code	B2
Filing date	Nov 3, 2020
Priority date	Nov 14, 2019
Publication date	Sep 5, 2023
Grant date	Sep 5, 2023

Abstract

The invention discloses a digital imaging technology-based method for calculating relative permeability of tight core, comprising the following steps: step S 1 : preparing a small column sample of tight core satisfying resolution requirements; step S 2 : scanning the sample by MicroCT-400 and establish a digital core; step S 3 : performing statistical analysis on parameters reflecting the characteristics of rock pore structure and shape according to the digital core; step S 4 : calculating tortuosity fractal dimension DT and porosity fractal dimension Df by a 3D image fractal box dimension algorithm; step S 5 : performing statistical analysis on maximum pore equivalent diameter λmax and minimum pore equivalent diameter λmin by a label. The present invention solves the problems of time consumption of experiment, instrument accuracy, incapability of repeated calculation simulations and resource waste by repeated physical experiment.

First claim

What is claimed is:

1. A digital imaging technology-based method for calculating relative permeability of tight core, comprising the following steps:
Step S 1 : preparing a small column sample of tight core satisfying resolution requirements;
Step S 2 : scanning the sample by MicroCT-400 and establishing a digital core;
Step S 3 : performing statistical analysis on parameters reflecting rock pore structures and shape characteristics according to the digital core;
Step S 4 : calculating a tortuosity fractal dimension D T and a porosity fractal dimension D f by a 3D image fractal box dimension algorithm;
Step S 5 : performing statistical analysis on maximum pore equivalent diameter λ max and minimum pore equivalent diameter λ min by a label;
Step S 6 : simulating a water-oil displacement in single fractal capillary, calculating a critical capillary diameter λ cr , and obtaining the critical capillary diameter at a displacement time t;
Step S 7 : determining a fluid type at an outflow end with the critical capillary diameter, calculating a water-phase fluid volume V w and a pore volume V p in the tight core at the time t, and then calculating a water saturation S w of the core;
Step S 8 : calculating a flow rate Q s of a single phase flow;
Step S 9 : simulating the water-oil displacement in the low-permeability tight core, and calculating the oil phase flow rate Q o and water phase flow rate Q w at the outflow end at the time t;
Step S 10 : calculating the relative permeability k rw of water phase and the relative permeability k ro of oil phase at the displacement time t;
Step S 11 : changing the time t and judging whether the water saturation S w remains unchanged; if so, go to Step S 12 ; if not, return to Step S 6 ; and
Step S 12 : plotting a relative permeability curve.

2. The digital imaging technology-based method for calculating relative permeability of tight core according to claim 1 , wherein the tight core in Step S 1 is 5 to 10 mm in diameter, and greater than 10 mm in length.

3. The digital imaging technology-based method for calculating relative permeability of tight core according to claim 1 , wherein Step S 2 comprises the following sub-steps:
Step S 21 : performing CT scanning with appropriate lens and reconstruct 3D image data according to a size of the core;
Step S 22 : defining and selecting Region of Interest (ROI) in 3D image data; and
Step S 23 : performing filtering and threshold segmentation on ROI to obtain 3D digital core of pore structure distribution.

4. The digital imaging technology-based method for calculating relative permeability of tight core according to claim 1 , wherein the 3D image fractal box dimension algorithm in Step S 4 comprises the following sub-steps:
Step S 41 : based on the graph statistics parameters obtained in Step S 3 , drawing N(r)˜r diagram in log-log coordinates and a negative slope of the line is the value of fractal dimension D f ,
lgN ( r )= lga−D f lgr;

where, D f is porosity fractal dimension, r is pore equivalent diameter, N(r) is the number of pores with radius greater than r, a is a constant coefficient;
Step S 42 : the porosity p of the core is obtained by image statistics and the average tortuosity is calculated by the following formula:

τ
=

1
2

[

1
+

1
2

⁢

1
-
φ

+

1
-
φ

⁢

(

1

1
-
φ

-
1

)

2

+

1
4

/

(

1
-

1
-
φ

)

]

;

where, τ is tortuosity, φ is porosity; and
Step S 43 : calculating the tortuosity fractal dimension D T by the following formula:

D
T

=

1
+

ln
⁢

τ

ln
⁡
(

L
m

2
⁢

r
av

)

;

where, D T is the tortuosity fractal dimension, r av is a mean pore radius, L m is a characteristic length obtained by:

L
m

=

1
-
φ

φ

⁢

π
⁢

D
f

⁢

r

Assignees

• Univ Southwest Petroleum

Inventors

• Duan Yonggang
• Li Zhenglan
• Fang Quantang
• Wei Mingqiang
• Peng Yu
• Wei Shihao
• Wu Zijian
• Sheng Shuyao

Classifications

• Y02A10/40

  Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping · CPC title

• G01N15/088 Primary

  Investigating volume, surface area, size or distribution of pores; Porosimetry · CPC title

• E21B49/02

  by mechanically taking samples of the soil · CPC title

• E21B49/0875

  determining specific fluid parameters · CPC title

• G01N15/0826 Primary

  and measuring fluid flow rate, i.e. permeation rate or pressure change · CPC title