Shale reservoir full-scale microscopic pore throat structure characterization method

Abstract

The invention belongs to the technical field of oil-gas field development, and discloses a shale reservoir full-scale microscopic pore throat structure characterization method. The method comprises the following steps: sampling and scanning to obtain a millimeter-level subsample CT pore throat image and a micron-level subsample FIB-SEM pore throat image; the method comprises the following steps: calculating image characteristic values, carrying out clustering analysis to obtain distribution, a mean value and a variance of image gray levels, determining an aperture value D for splicing two kinds of image aperture distribution, determining an image intensity gray level judgment threshold, calculating CT porosity and FIB-SEM porosity, and constructing a micron-scale and nano-scale three-dimensional ball-and-stick model; gt in the micron-scale three-dimensional ball-and-stick model; d and pore size distribution smaller than or equal to D in the nanoscale three-dimensional ball-stick model are spliced, and a full-scale pore size distribution physical model is constructed; and constructing a mathematical representation model. According to the method, the full-scale pore size distribution characteristics of the shale sample can be obtained, the unit sample mass pore volume corresponding to pores of different scales and different pore size proportions are determined, operation and calculation are simple, and the method is efficient.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas field development, and in particular relates to a method for characterizing the full-scale microscopic pore throat structure of a shale reservoir. Background Art

[0002] The pore size distribution span of shale reservoir cores is very large, including micropores (pore diameter <2nm), mesopores (2nm≤pore diameter≤50nm) and macropores (pore diameter >50nm). Accurately obtaining the full-scale pore size distribution characteristics of shale is the basis for microscopic reservoir evaluation and has important guiding significance for studying the occurrence laws of shale oil and gas.

[0003] At present, the main testing methods for studying the pore size distribution of shale samples are low-temperature carbon dioxide adsorption, low-temperature nitrogen adsorption, high-pressure mercury injection and small-angle neutron scattering technology. Among them, the pore scale range of low-temperature carbon dioxide adsorption test is micropores, the pore scale range of low-temperature nitrogen adsorption test is mesopores and some macropores, the pore scale range of high-pressure mercury injection test is mesopores and macropores, and small-angle neutron scattering technology can measure cores with pore sizes less than 100nm.

[0004] In order to characterize the full-scale microscopic pore throat structure of shale reservoirs, the existing technical solutions are mainly based on low-temperature carbon dioxide adsorption test, low-temperature nitrogen adsorption test and high-pressure mercury injection test. The first pore size distribution data of the sample is measured by low-temperature gas adsorption method on a sample, and the corresponding high-pressure mercury injection method test is performed on the parallel samples to obtain the second pore size distribution data of the sample. The overlapping pore sizes are determined, and the first pore size distribution and the second pore size distribution are spliced. The obtained data is used to calculate the proportion of micropores, mesopores and macropores in the shale sample, and the full-scale pore size distribution data of the measured shale sample is obtained. However, this technical solution has the following defects:

[0005] (1) When combining multiple test methods, different test methods have different test principles, and the data differences are large when spliced, resulting in inaccurate measurement results;

[0006] (2) Due to the heterogeneity of the core, gas adsorption and mercury injection are not non-destructive experiments. Therefore, the existing testing methods require the setting of parallel samples (adjacent samples at the same depth drilled from the same full-diameter core) for gas testing and mercury injection testing, which results in poor data accuracy. Summary of the invention

[0007] The present invention aims to solve at least one technical problem existing in the prior art or related technology, and provides a method for characterizing the full-scale microscopic pore throat structure of a shale reservoir, which can obtain the full-scale pore size distribution characteristics of a shale sample, clarify the pore volume per unit sample mass corresponding to pores of different scales, and then calculate the proportion of different pore sizes. The operation and calculation are simple, and the method is efficient.

[0008] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0009] A method for characterizing the full-scale microscopic pore throat structure of a shale reservoir, the method specifically comprising the following steps:

[0010] Step S1: sample preparation and scanning;

[0011] Select shale samples to drill millimeter-scale sub-samples and obtain CT pore throat images through CT scanning;

[0012] Milling micrometer-scale sub-samples from millimeter-scale sub-samples, and obtaining FIB-SEM pore throat images through FIB-SEM scanning;

[0013] Step S2: digital three-dimensional model reconstruction;

[0014] Calculate the eigenvalue of each image, and obtain the distribution, mean and variance of the grayscale of each type of image through cluster analysis;

[0015] According to the image grayscale distribution frequency, the distribution relationship between the CT pore throat image and the FIB-SEM pore throat image is established, and the aperture value D used for splicing the pore size distribution of the CT pore throat image and the FIB-SEM pore throat image is determined;

[0016] The threshold for judging the image intensity is determined according to the image grayscale variance, and the pore throat structure of the image is judged according to the threshold, and the CT porosity and FIB-SEM porosity are calculated.

[0017] The images were imported into 3D visualization software, and micrometer-scale 3D ball-and-stick models and nanometer-scale 3D ball-and-stick models were constructed based on CT porosity and FIB-SEM porosity, respectively.

[0018] Step S3: splicing and constructing a full-scale pore size distribution physical model;

[0019] The pore size distribution of >D in the micrometer-scale three-dimensional ball-and-stick model and the pore size distribution of ≤D in the nanometer-scale three-dimensional ball-and-stick model are physically spliced ​​to construct a full-scale pore size distribution physical model;

[0020] Step S4: constructing a mathematical characterization model of the full-scale pore size distribution based on the characteristic parameters in the physical model of the full-scale pore size distribution.

[0021] Further, in step S1, the millimeter-scale sub-sample is a cylindrical sub-sample.

[0022] Furthermore, the step S2 specifically includes:

[0023] Step S201: normalizing the image data of the CT pore throat image and the FIB-SEM pore throat image;

[0024] Step S202: Calculate the feature value of each image in turn based on the machine learning algorithm, perform cluster analysis on the images using the K-means algorithm, and count and calculate the distribution, mean and variance of the grayscale of each type of image;

[0025] Step S203: establishing a distribution relationship between the CT pore throat image and the FIB-SEM pore throat image according to the image grayscale distribution frequency, and determining the aperture value D for pore size distribution splicing;

[0026] Determine the threshold value according to the image grayscale variance calculation result;

[0027] Step S204: Identify the pore throat structure according to the threshold value:

[0028] If the image intensity is ≤ the threshold, the pore throat structure of the corresponding image is pore;

[0029] If the image intensity > threshold, the pore throat structure of the corresponding image is matrix;

[0030] Step S205: Calculate the CT porosity and FIB-SEM porosity based on the pores extracted from the image;

[0031] Step S206: Compare the calculated CT porosity and FIB-SEM porosity with the experimentally measured helium porosity, and adjust the threshold value according to the comparison result;

[0032] According to the updated threshold value, steps S204-S205 are repeated to recalculate and update the CT porosity and the FIB-SEM porosity, until the comparison results of the finally calculated updated CT porosity, the FIB-SEM porosity and the experimentally measured helium porosity do not exceed the set error range;

[0033] Step S207: importing the CT pore throat image and the FIB-SEM pore throat image into a three-dimensional visualization software;

[0034] A micrometer-scale three-dimensional ball-and-stick model is constructed based on CT porosity to quantitatively characterize pore throat parameters.

[0035] A nanoscale three-dimensional ball-and-stick model that can quantitatively characterize pore throat parameters was constructed based on FIB-SEM porosity.

[0036] Furthermore, in step S203, the threshold is determined according to the image grayscale variance calculation result, specifically:

[0037] The image intensity corresponding to the minimum value of the image grayscale variance is the threshold.

[0038] Furthermore, in step S206, the calculated CT porosity and FIB-SEM porosity are compared with the experimentally measured helium porosity, and the threshold is adjusted according to the comparison result, specifically including:

[0039] The helium porosity of the shale sample to be tested is measured experimentally;

[0040] Compare the calculated CT porosity and FIB-SEM porosity with the experimentally measured helium porosity to determine whether the selected threshold is appropriate:

[0041] If the comparison result does not exceed the set error range, the selected threshold is appropriate;

[0042] If the comparison result exceeds the set error range, the update threshold is adjusted. The specific adjustment method is:

[0043] If the porosity calculated according to the threshold is greater than the helium porosity, the threshold is reduced, otherwise the threshold is increased.

[0044] Furthermore, in step S4, the characteristic parameters in the full-scale pore size distribution physical model include pore size and distribution frequency.

[0045] Furthermore, in step S4, the full-scale pore size distribution mathematical characterization model includes:

[0046] The calculation formulas for the proportion of different pore structure types in shale and the calculation formulas for the grayscale characteristics of different pore structure types in shale.

[0047] Furthermore, the formula for calculating the proportion of different typical pore structure types in shale is:

[0048]

[0049] In the above formula, φ i represents the proportion of pore structure type i; N i represents the number of pore structures of type i; Represents the total number of all types of pore structures.

[0050] Furthermore, the grayscale characteristics of different typical pore structure types in shale include: grayscale means of different types of pore structures and grayscale standard deviations of different types of pore structures.

[0051] Furthermore, the grayscale mean calculation formula of different types of pore structures is:

[0052]

[0053] The grayscale standard deviation calculation formula for different types of pore structures is:

[0054]

[0055] In the above formula, GA i Represents the grayscale mean of type i pore structure; represents the grayscale mean of the k-th i-type pore structure; N i represents the number of pore structures of type i; GS i Represents the grayscale standard deviation of type i pore structure.

[0056] Furthermore, in step S2, the pore size value D used for splicing the pore size distribution of the CT pore throat image and the FIB-SEM pore throat image is 500-700 nm.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] (1) The method for characterizing the full-scale microscopic pore throat structure of a shale reservoir provided by the present invention constructs a micrometer-scale three-dimensional ball-and-stick model and a nanometer-scale three-dimensional ball-and-stick model to obtain the pore size distribution above 600nm and below 600nm respectively, and then splices to establish a full-scale pore size distribution physical model, and constructs a full-scale pore size distribution mathematical characterization model based on the full-scale pore size distribution physical model; this method can obtain the full-scale pore size distribution characteristics of shale samples, clarify the pore volume per unit sample mass corresponding to pores of different scales, and then calculate the proportion of different pore sizes, and the operation and calculation are simple, and the method is efficient;

[0059] (2) The method for characterizing the full-scale microscopic pore throat structure of shale reservoirs provided by the present invention calculates the characteristic value of each image in turn based on a machine learning algorithm, performs cluster analysis on the images using a K-means algorithm, and establishes a distribution relationship between CT and FIB-SEM images according to the image grayscale distribution frequency by counting and calculating the distribution, mean and variance of each type of grayscale. The aperture value D for splicing the pore size distribution of the two images is determined. At the same time, a threshold for judging the image intensity is determined according to the image grayscale variance, and the pore throat structure of the image is discriminated according to the threshold, and the CT porosity and FIB-SEM porosity are calculated. Finally, the threshold is verified and adjusted with the help of helium porosity to obtain a more reasonable threshold (because the selection range of the threshold in the prior art is large and its rationality cannot be judged). According to the adjusted threshold, a more accurate pore throat structure and porosity that are closer to the actual situation are calculated, thereby ensuring the accuracy of the full-scale pore size distribution physical model obtained by the final splicing, and improving the accuracy of the prediction results;

[0060] (3) The method for characterizing the full-scale microscopic pore throat structure of shale reservoirs provided by the present invention can meet the demand for full-scale characterization of the pore size of the same shale sample. At the same time, the present invention can perform measurements at room temperature without the need to use parallel samples, and the measurement conditions and sample morphology are not restricted. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of a method for characterizing the full-scale microscopic pore throat structure of a shale reservoir in an embodiment of the present invention;

[0062] Figure 2 Schematic diagram of the structure of a micrometer-scale three-dimensional ball-and-stick model in an embodiment of the present invention;

[0063] Figure 3 Schematic diagram of the structure of a nanoscale three-dimensional ball-and-stick model in an embodiment of the present invention;

[0064] Figure 4 is a full-scale pore diameter distribution curve diagram of the shale sample tested in the embodiment of the present invention;

[0065] Figure 5 It is a full-scale throat diameter distribution curve diagram of the shale sample tested in the embodiment of the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Combination Figure 1 As shown, an embodiment of the present invention provides a method for characterizing the full-scale microscopic pore throat structure of a shale reservoir, which mainly obtains the pore size distribution above 600nm and below 600nm by constructing a micrometer-scale three-dimensional ball-and-stick model and a nanometer-scale three-dimensional ball-and-stick model, respectively, and then splices to establish a full-scale pore size distribution physical model, and constructs a full-scale pore size distribution mathematical characterization model according to the characteristic parameters (pore size, distribution frequency) of the full-scale pore size distribution physical model, thereby obtaining the full-scale pore size distribution characteristics of the shale sample, clarifying the pore volume per unit sample mass corresponding to pores of different scales, and then calculating the proportion of different pore sizes. The operation and calculation are simple, and the method is efficient.

[0068] The method specifically comprises the following steps:

[0069] Step S1: sample preparation and scanning;

[0070] A shale sample was selected, and first a cylindrical millimeter-scale sub-sample with a diameter and length of 3 mm was drilled, and a CT pore throat image was obtained by CT scanning;

[0071] Milling micrometer-scale sub-samples from millimeter-scale sub-samples, and obtaining FIB-SEM pore throat images through FIB-SEM scanning;

[0072] Step S2: digital three-dimensional model reconstruction;

[0073] Step S201: normalizing the image data of the CT pore throat image and the FIB-SEM pore throat image so that the grayscale value is between 0 and 255 to facilitate subsequent calculations;

[0074] Step S202: Based on the machine learning algorithm, the feature value of each image is calculated in turn, and the images are clustered using the K-means algorithm to count and calculate the distribution, mean and variance of the grayscale of each type of image;

[0075] Step S203: establishing a distribution relationship between the CT pore throat image and the FIB-SEM pore throat image according to the image grayscale distribution frequency, and determining the aperture value D for pore size distribution splicing for use in subsequent physical models;

[0076] Determine the threshold value according to the image grayscale variance calculation result;

[0077] Step S204: Identify the pore throat structure according to the threshold value:

[0078] If the image intensity is ≤ the threshold, the pore throat structure of the corresponding image is pore;

[0079] If the image intensity > threshold, the pore throat structure of the corresponding image is matrix;

[0080] Step S205: Calculate the CT porosity and FIB-SEM porosity based on the pores extracted from the image;

[0081] Step S206: Compare the calculated CT porosity and FIB-SEM porosity with the experimentally measured helium porosity, and adjust the threshold value according to the comparison result;

[0082] According to the updated threshold value, steps S204-S205 are repeated to recalculate and update the CT porosity and the FIB-SEM porosity, until the comparison results of the finally calculated updated CT porosity, the FIB-SEM porosity and the experimentally measured helium porosity do not exceed the set error range;

[0083] The adjustment of the threshold value according to the comparison result specifically includes:

[0084] The helium porosity of the shale sample to be tested is measured experimentally;

[0085] Compare the calculated CT porosity and FIB-SEM porosity with the experimentally measured helium porosity to determine whether the selected threshold is appropriate:

[0086] If the comparison result does not exceed the set error range, the selected threshold is appropriate;

[0087] If the comparison result exceeds the set error range, the update threshold is adjusted. The specific adjustment method is:

[0088] If the porosity calculated according to the threshold is greater than the helium porosity, the threshold is reduced, otherwise the threshold is increased;

[0089] Step S207: importing the CT pore throat image and the FIB-SEM pore throat image into a three-dimensional visualization software;

[0090] Based on the CT porosity, a micrometer-scale three-dimensional ball-and-stick model is constructed to quantitatively characterize the pore throat parameters, such as Figure 2 As shown;

[0091] Based on the FIB-SEM porosity, a nanoscale three-dimensional ball-and-stick model is constructed to quantitatively characterize the pore throat parameters, such as Figure 3 As shown;

[0092] Step S3: splicing and constructing a full-scale pore size distribution physical model;

[0093] In the micrometer-scale three-dimensional ball-and-stick model, the pore size distribution of >600nm in the millimeter-scale sub-samples was screened;

[0094] In the nanoscale three-dimensional ball-and-stick model, the pore size distribution of ≤600nm in the micron-scale sub-samples was screened;

[0095] The pore size distribution of >600nm and the pore size distribution of ≤600nm are physically spliced ​​to construct a full-scale pore size distribution physical model;

[0096] like Figure 4 The figure shows the full-scale pore diameter distribution curve of the tested shale sample, which includes three pore structure types: siltstone, dolomite and mudstone; Figure 5 Shown is the full-scale throat diameter distribution curve of the tested shale sample;

[0097] Step S4: constructing a mathematical characterization model of full-scale pore size distribution;

[0098] Based on the pore size and distribution frequency in the full-scale pore size distribution physical model, a mathematical characterization model of the full-scale pore size distribution is constructed, wherein the mathematical characterization model of the full-scale pore size distribution includes: a calculation formula for the proportion of different pore structure types in shale and a calculation formula for the grayscale characteristics of different pore structure types in shale;

[0099] Among them, the calculation formula for the proportion of different pore structure types in shale is:

[0100]

[0101] In the above formula (1), φi represents the proportion of pore structure type i; N i represents the number of pore structures of type i; Represents the total number of all types of pore structures;

[0102] The grayscale characteristics of different pore structure types in shale include: grayscale means of different types of pore structures and grayscale standard deviations of different types of pore structures.

[0103] Among them, the grayscale mean calculation formula for different types of pore structures is:

[0104]

[0105] The grayscale standard deviation calculation formula for different types of pore structures is:

[0106]

[0107] In the above formulas (2)-(3), GA i Represents the grayscale mean of type i pore structure; represents the grayscale mean of the k-th i-type pore structure; N i represents the number of pore structures of type i; GS i Represents the grayscale standard deviation of type i pore structure.

[0108] The proportion and grayscale characteristics of different typical pore structure types in shale can be calculated according to formulas (1)-(3). The calculation results are shown in Table 1.

[0109] Table 1 Calculation results of the proportion and grayscale characteristics of different typical pore structure types in shale

[0110] type Microstructural property description Proportion (%) Grayscale mean Grayscale variance 1 Dissolution type inorganic pores 18.63 183.61 12.27 2 Intergranular inorganic pores 42.05 141.57 17.05 3 Transport of organic matter and organic pores 29.13 114.79 13.41 4 Intragranular inorganic pores 6.41 132.47 16.23 5 Primary organic matter and organic pores 3.78 31.28 11.69

[0111] The single-scale and multi-scale analysis of multi-resolution and multi-dimensional shale microscopic images can objectively, comprehensively and accurately reveal the pore structure characteristics of shale. In the embodiment of the present invention, it can be seen from the analysis that the pore structure characteristics of the shale sample under test are as follows: pores with a pore radius less than 50nm are mainly organic pores, pores with a pore radius between 50-500nm are organic pores and inorganic pores, and pores with a pore radius greater than 500nm are mainly contributed by microcracks.

[0112] At the same time, based on the mean and variance of different types of grayscale after classification, the type pattern of the image is spatially identified, and combined with different types of ultra-high resolution CT pore throat images and FIB-SEM pore throat images, the fusion of multi-resolution and multi-dimensional shale samples can be achieved.

[0113] The above description is only an embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement and improvement made within the application scope of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for characterizing the full-scale microscopic pore throat structure of a shale reservoir, characterized in that: The method specifically comprises the following steps: Step S1: sample preparation and scanning; Select shale samples to drill millimeter-scale sub-samples and obtain CT pore throat images through CT scanning; Milling micrometer-scale sub-samples from millimeter-scale sub-samples, and obtaining FIB-SEM pore throat images through FIB-SEM scanning; Step S2: digital three-dimensional model reconstruction; Calculate the eigenvalue of each image, and obtain the distribution, mean and variance of the grayscale of each type of image through cluster analysis; According to the image grayscale distribution frequency, the distribution relationship between the CT pore throat image and the FIB-SEM pore throat image is established, and the aperture value D used for splicing the pore size distribution of the CT pore throat image and the FIB-SEM pore throat image is determined; The threshold for judging the image intensity is determined according to the image grayscale variance, and the pore throat structure of the image is judged according to the threshold, and the CT porosity and FIB-SEM porosity are calculated. The images were imported into 3D visualization software, and micrometer-scale 3D ball-and-stick models and nanometer-scale 3D ball-and-stick models were constructed based on CT porosity and FIB-SEM porosity, respectively. Step S3: splicing and constructing a full-scale pore size distribution physical model; The pore size distribution of >D in the micrometer-scale three-dimensional ball-and-stick model and the pore size distribution of ≤D in the nanometer-scale three-dimensional ball-and-stick model are physically spliced ​​to construct a full-scale pore size distribution physical model; Step S4: constructing a mathematical characterization model of the full-scale pore size distribution based on the characteristic parameters in the physical model of the full-scale pore size distribution.

2. The method according to claim 1, characterized in that In step S1, the millimeter-scale sub-sample is a cylindrical sub-sample.

3. The method according to claim 1, characterized in that: The step S2 specifically includes: Step S201: normalizing the image data of the CT pore throat image and the FIB-SEM pore throat image; Step S202: Calculate the feature value of each image in turn based on the machine learning algorithm, perform cluster analysis on the images using the K-means algorithm, and count and calculate the distribution, mean and variance of the grayscale of each type of image; Step S203: establishing a distribution relationship between the CT pore throat image and the FIB-SEM pore throat image according to the image grayscale distribution frequency, and determining the aperture value D for pore size distribution splicing; Determine the threshold value according to the image grayscale variance calculation result; Step S204: Identify the pore throat structure according to the threshold value: If the image intensity is ≤ the threshold, the pore throat structure of the corresponding image is pore; If the image intensity > threshold, the pore throat structure of the corresponding image is matrix; Step S205: Calculate the CT porosity and FIB-SEM porosity based on the pores extracted from the image; Step S206: Compare the calculated CT porosity and FIB-SEM porosity with the experimentally measured helium porosity, and adjust the threshold value according to the comparison result; According to the updated threshold value, steps S204-S205 are repeated to recalculate and update the CT porosity and the FIB-SEM porosity, until the comparison results of the finally calculated updated CT porosity, the FIB-SEM porosity and the experimentally measured helium porosity do not exceed the set error range; Step S207: importing the CT pore throat image and the FIB-SEM pore throat image into a three-dimensional visualization software; A micrometer-scale three-dimensional ball-and-stick model is constructed based on CT porosity to quantitatively characterize pore throat parameters. A nanoscale three-dimensional ball-and-stick model that can quantitatively characterize pore throat parameters was constructed based on FIB-SEM porosity.

4. The method according to claim 3, characterized in that In step S203, the threshold is determined according to the image grayscale variance calculation result, specifically: The image intensity corresponding to the minimum value of the image grayscale variance is the threshold.

5. The method according to claim 4, characterized in that In step S206, the calculated CT porosity and FIB-SEM porosity are compared with the experimentally measured helium porosity, and the threshold is adjusted according to the comparison result, specifically including: The helium porosity of the shale sample to be tested is measured experimentally; Compare the calculated CT porosity and FIB-SEM porosity with the experimentally measured helium porosity to determine whether the selected threshold is appropriate: If the comparison result does not exceed the set error range, the selected threshold is appropriate; If the comparison result exceeds the set error range, the update threshold is adjusted. The specific adjustment method is: If the porosity calculated according to the threshold is greater than the helium porosity, the threshold is reduced, otherwise the threshold is increased.

6. The method according to claim 1, characterized in that In step S4, the characteristic parameters in the full-scale pore size distribution physical model include pore size and distribution frequency.

7. The method according to claim 6, characterized in that In step S4, the full-scale pore size distribution mathematical characterization model includes: The calculation formulas for the proportion of different pore structure types in shale and the calculation formulas for the grayscale characteristics of different pore structure types in shale.

8. The method according to claim 7, characterized in that The calculation formula for the proportion of different typical pore structure types in shale is: In the above formula, φ i represents the proportion of pore structure type i; N i represents the number of pore structures of type i; Represents the total number of all types of pore structures.

9. The method according to claim 7, characterized in that: The grayscale characteristics of different typical pore structure types in shale include: grayscale means of different types of pore structures and grayscale standard deviations of different types of pore structures.

10. The method according to claim 9, characterized in that The grayscale mean calculation formula for different types of pore structures is: The grayscale standard deviation calculation formula for different types of pore structures is: In the above formula, GA i Represents the grayscale mean of type i pore structure; represents the grayscale mean of the k-th i-type pore structure; N i represents the number of pore structures of type i; GS i Represents the grayscale standard deviation of type i pore structure.

11. The method according to any one of claims 1 to 10, characterized in that: In the step S2, the pore size value D used for splicing the pore size distribution of the CT pore throat image and the FIB-SEM pore throat image is 500-700 nm.

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