CN-122021109-A - Core permeability prediction method, electronic equipment and storage medium

CN122021109ACN 122021109 ACN122021109 ACN 122021109ACN-122021109-A

Abstract

The embodiment of the application provides a method for predicting core permeability, electronic equipment and a storage medium. The method comprises the steps of constructing a digital core model of a sample core, and calculating pore structure parameters of the digital core model, wherein the pore structure parameters comprise porosity, the ratio of the minimum pore throat diameter to the maximum pore throat diameter, fractal dimension, specific surface value and tortuosity, and then inputting the porosity, pore throat diameter distribution parameter, fractal dimension, specific surface value and tortuosity of the digital core model into a permeability calculation model to obtain a permeability predicted value of the digital core. According to the method, when the core permeability is calculated, multi-scale parameters such as fractal dimension and the like are introduced, and the pore characteristics of a complex reservoir are fully described, so that the influence of the pore structure of a sample core on seepage is more accurately represented, and the prediction accuracy of the permeability is improved.

Inventors

YUE WENZHENG
ZHU YUMING
Dang Fuhao
Bi Feiyu
YANG FANGJUN

Assignees

中国石油大学(北京)

Dates

Publication Date: 20260512
Application Date: 20251218

Claims (10)

1. A method for predicting core permeability, comprising: Constructing a digital core model of a sample core; Determining pore structure parameters of the digital core model, wherein the pore structure parameters comprise porosity, a ratio of a minimum pore throat diameter to a maximum pore throat diameter, fractal dimension, a specific value and tortuosity; And inputting the porosity, the ratio, the fractal dimension, the specific value and the tortuosity of the digital core model into a permeability calculation model to obtain a permeability predicted value of the digital core.
2. The method of claim 1, wherein constructing a digital core model of a sample core comprises: scanning structural images of the sample core at a plurality of horizons, wherein the structural images comprise pore structures and skeleton structures of the sample core; Image segmentation is carried out on each structural image to obtain a core structure representation unit corresponding to each structural image, wherein the core structure representation unit is a minimum volume unit for representing the pore structure of the sample core; performing binarization processing on a plurality of core structure characterization units to obtain a corresponding binarization chart, wherein the binarization chart comprises pore pixel points and skeleton pixel points; and stacking a plurality of the binarization maps into a digital core model according to the horizons corresponding to the binarization maps.
3. The method of claim 2, wherein the determining pore structure parameters of the digital core model comprises: Calculating the porosity based on pore pixel points and skeleton pixel points in the digital core model; adopting a maximum sphere algorithm to construct a pore-throat distribution model of the digital core model, and acquiring pore-throat diameter distribution data based on the pore-throat distribution model; Determining a minimum pore throat diameter and a maximum pore throat diameter from the pore throat diameter distribution data, and determining a ratio of the minimum pore throat diameter to the maximum pore throat diameter; Determining the fractal dimension based on the porosity and the ratio; determining a specific surface value of the digital core based on the porosity, the minimum pore throat diameter, the maximum pore throat diameter, and the fractal dimension; Performing finite element seepage simulation on the digital core model to obtain a speed field and a pressure field generated by fluid flow, wherein the finite element seepage simulation performs numerical simulation on the flow of the fluid in the pores based on a finite element method; Determining an average velocity and an average velocity component of the seepage direction in the digital core model based on the velocity field; and obtaining the tortuosity based on the average speed and the average speed component.
4. The method of claim 1, wherein the permeability calculation model is determined based on the following equation: Wherein, the Is the permeability of the water, and the water, Is the fractal dimension of the pores of the digital core in a two-dimensional section, Is the ratio of the rock minimum pore throat diameter to the maximum pore throat diameter, Is the degree of porosity of the material, Is the tortuosity of the rock pore, Is a specific value.
5. The method according to claim 2, wherein the core structural characterization unit is a representative volume element, and the performing image segmentation on each structural image to obtain a core structural characterization unit corresponding to each structural image includes: intercepting an initial characterization unit aiming at any one of a plurality of structural images by taking the center of the structural image as a reference, wherein the initial characterization unit is of a preset unit size; Expanding the initial characterization unit according to a first preset proportion to obtain a first characterization unit; determining a first porosity of the first characterization unit, and judging whether an autocorrelation function of the first porosity reaches convergence; taking the first characterization unit as the core structure characterization unit under the condition that the autocorrelation function of the first porosity reaches convergence; and under the condition that the autocorrelation function of the first porosity does not reach convergence, taking the first characterization unit as an initial characterization unit to execute the step of expanding the initial characterization unit until a cut-off condition is reached.
6. The method of claim 3, wherein constructing the pore throat distribution model of the digital core model using a maximum sphere algorithm comprises: Traversing pore pixel points in the digital core model; Gradually increasing the radius corresponding to the sphere center according to a second preset proportion by taking the pore pixel point as the sphere center until the generated sphere is tangent to any skeleton pixel point, so as to obtain the maximum sphere corresponding to the pore pixel point; taking a plurality of maximum spheres with the radius larger than a first threshold value as pores, and taking a plurality of maximum spheres with the radius not larger than the first threshold value as throats; And (3) equivalent each pore to a sphere, and equivalent each throat to a rod body connected with two spheres, so as to obtain a pore-throat distribution model.
7. The method of claim 3, wherein performing a finite element percolation simulation on the digital core model results in a velocity field and a pressure field generated by fluid flow, comprising: Applying a constant pressure at a first end and a second end of the digital core model, the constant pressure at the first end being greater than the constant pressure at the second end; and executing finite element seepage simulation on the digital core model based on the constant pressure of the first end, the constant pressure of the second end and the flow rule of the fluid to obtain the velocity field and the pressure field.
8. A core permeability prediction apparatus, comprising: the construction module is used for constructing a digital core model of the sample core; The determining module is used for determining pore structure parameters of the digital core model, wherein the pore structure parameters comprise porosity, the ratio of the minimum pore throat diameter to the maximum pore throat diameter, fractal dimension, specific surface value and tortuosity; And the input module is used for inputting the porosity, the ratio, the fractal dimension, the specific value and the tortuosity of the digital core model into a permeability calculation model to obtain a permeability predicted value of the digital core.
9. An electronic device is characterized by comprising a memory and a processor; The memory stores computer-executable instructions; The processor executing computer-executable instructions stored in the memory, causing the processor to perform the method of any one of claims 1-7.
10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-7.

Description

Core permeability prediction method, electronic equipment and storage medium Technical Field The application relates to the technical field of oil and gas exploration, in particular to a core permeability prediction method, electronic equipment and a storage medium. Background In the development of hydrocarbon reservoirs, reservoir permeability is a core parameter that measures the ability of fluids to flow in the rock, directly affecting the recovery efficiency and recovery of the hydrocarbon. Traditional permeability prediction techniques rely primarily on empirical models and simplified physical models. The experimental model is fitted based on experimental data, an empirical relation between permeability and reservoir physical properties is established through macroscopic parameters such as porosity, pore throat radius and the like, and a permeability predicted value is calculated through the empirical relation. Simplified physical model the permeability expression is derived by assuming a regular geometry of pore structure (e.g., parallel capillaries, spherical pores, etc.), combined with hydrodynamic theory. However, the pore structure of the complex reservoir has the characteristics of high non-uniformity, wide pore-throat size distribution, tortuosity of communication paths and the like, and the influence of the reservoir microstructure on seepage is difficult to accurately reflect in the prior art, so that the prediction accuracy of permeability is reduced. Disclosure of Invention The embodiment of the application provides a core permeability prediction method, electronic equipment and a storage medium, which are used for more comprehensively reflecting the influence of a microstructure of a reservoir on seepage, so that the technical effect of improving the permeability prediction precision is achieved. In a first aspect, an embodiment of the present application provides a method for predicting core permeability, including: Constructing a digital core model of a sample core; Determining pore structure parameters of the digital core model, wherein the pore structure parameters comprise porosity, the ratio of the minimum pore throat diameter to the maximum pore throat diameter, fractal dimension, specific value and tortuosity; And inputting the porosity, the ratio, the fractal dimension, the specific surface value and the tortuosity of the digital core model into a permeability calculation model to obtain a permeability predicted value of the digital core. In one possible embodiment, constructing a digital core model of a sample core includes: Scanning structural images of the sample core at a plurality of layers, wherein the structural images comprise pore structures and skeleton structures of the sample core; image segmentation is carried out on each structural image, so that a core structure representation unit corresponding to each structural image is obtained, wherein the core structure representation unit is a minimum volume unit for representing the pore structure of a sample core; performing binarization processing on the plurality of core structure characterization units to obtain a corresponding binarization chart, wherein the binarization chart comprises pore pixel points and skeleton pixel points; and stacking a plurality of binarization graphs into a digital core model according to the horizons corresponding to the binarization graphs. In one possible embodiment, determining pore structure parameters of the digital core model includes: calculating porosity based on pore pixel points and skeleton pixel points in the digital core model; adopting a maximum sphere algorithm to construct a pore-throat distribution model of the digital core model, and acquiring pore-throat diameter distribution data based on the pore-throat distribution model; determining a minimum pore throat diameter and a maximum pore throat diameter from pore throat diameter distribution data, and determining a ratio of the minimum pore throat diameter to the maximum pore throat diameter; determining a fractal dimension based on the porosity and the ratio; determining a specific surface value of the digital core based on the porosity, the minimum pore throat diameter, the maximum pore throat diameter and the fractal dimension; Performing finite element seepage simulation on the digital core model to obtain a speed field and a pressure field generated by fluid flow, wherein the finite element seepage simulation performs numerical simulation on the flow of the fluid in the pores based on a finite element method; Determining an average velocity and an average velocity component of the seepage direction in the digital core model based on the velocity field; based on the average speed and the average speed component, tortuosity is obtained. In one possible implementation, the permeability calculation model is determined based on the following formula: Wherein, the Is the permeability of the water, and the water,Is the fractal