Coal digital core modeling method based on histogram matching

Abstract

The invention relates to the technical field of rock physics, discloses a histogram matching-based coal digital core modeling method, and is suitable for digital core reconstruction of experimental rock physics research. Comprising the following steps: S10, collecting a rock core and preparing a sample; s20, sample microstructure CT scanning and physical property parameter testing; s30, preprocessing the CT image; s40, performing rock component CT image segmentation based on a histogram matching algorithm; and S50, digital rock core reconstruction based on CT image stacking. On the basis of digital image processing, a rock physical experiment and a digital rock core technology are combined, and the histogram matching-based coal digital rock core modeling method is provided. The method is low in cost and high in operability, the reconstructed rock core and the original rock core have high matching degree, and a high-precision model can be accurately provided for physical property characteristic finite element simulation of the coal digital rock core.

Description

Technical Field

[0001] This application relates to the technical field of petrophysics, and particularly relates to a method for modeling digital cores of coal based on histogram matching. Background Art

[0002] With the gradual increase in the demand for underground minerals, the exploration and development of minerals have gradually shifted from conventional reservoirs to complex reservoirs, and the structural characteristics of complex reservoir rocks are of great significance to the physical properties of reservoir rocks. Traditional petrophysical experiments are difficult to quantitatively obtain the quantitative relationship between the structural characteristics of complex reservoirs and physical properties, while digital petrophysical experiment technology can better make up for the deficiencies of traditional petrophysical experiments. Digital petrophysical experiments mainly include the establishment of three-dimensional digital cores and the simulation of petrophysical properties.

[0003] At present, digital core modeling methods can be divided into two categories: physical experiment methods and numerical reconstruction methods. The former can obtain fine digital core spatial structure characteristics through experimental testing means, with a high cost; the latter indirectly constructs three-dimensional digital cores by means of stochastic simulation and geological process simulation methods. The spatial structure characteristics of rocks are established through topological approximation, and the reconstructed digital cores have the same statistical characteristics as real cores. Physical experiment methods mainly include X-ray CT scanning method, confocal laser scanning method, and serial section imaging method. Among them, the X-ray CT scanning method has a resolution of the micron level and has good application effects in sandstone and carbonate rocks.

[0004] The X-ray CT scanning method determines the density size relationship of each part of the core by determining the absorption coefficient of the core to X-rays. There are high density differences between pores, matrix minerals and filling minerals in sandstone and carbonate rocks. Therefore, after preprocessing the X-ray CT scanning images of sandstone and carbonate rocks, a simple histogram threshold segmentation method can be used to complete the rock components. However, coal samples are brittle and soft, and the mineral components are mainly organic matter and clay minerals. The small density difference between rock components makes it difficult to depict the internal spatial structure characteristics of coal samples. Summary of the Invention

[0005] The purpose of this application is to provide a method for modeling digital cores of coal based on histogram matching to solve the problem of difficult characterization of the internal spatial structure characteristics of coal samples.

[0006] In order to achieve the above purpose, the following technical solutions are adopted:

[0007] This application provides a method for modeling digital cores of coal based on histogram matching, and the method includes:

[0008] Obtain the core of the coal seam section and prepare core samples;

[0009] Perform physical property parameter tests on the core sample to obtain the mineral component analysis results and the sample porosity, and calculate the core component volume fraction based on the mineral component analysis results and the sample porosity;

[0010] Use X-rays to perform CT scans on the core sample to obtain CT images, and preprocess the CT images to obtain rock component CT images;

[0011] Determine the histogram distribution function of the rock component CT image, construct a target transformation function based on the Gaussian function and the core component volume fraction, construct a matching mapping function based on the principle of minimizing the gray level error between the histogram distribution function and the target transformation function, and convert the histogram distribution function to the function h eq (x), and after that, perform threshold segmentation on the function h eq (x) to complete the segmentation of the rock component CT image;

[0012] Based on the segmented rock component CT image, extract the pore structure of the core sample and the spatial structure characteristics of the rock minerals, determine the spatial distribution characteristics between the rock components based on the connectivity between the pore structure and the rock minerals, use the spatial distribution characteristics between the rock components as the mineral morphology characteristics, verify the mineral morphology characteristics through the target morphology characteristics, and when the verification is passed, output the constructed coal digital core model; perform electron microscope scanning on the core sample, and use the obtained mineral morphology characteristics as the target morphology characteristics.

[0013] Preferably, obtain the core of the coal seam section and prepare the core sample, including:

[0014] Obtain the core of the coal seam section as a sample by means of drilling coring, determine the sampling position, record the sample length and the sampling depth of the sample, wrap the sample with plastic wrap for sealing and send it to the core sample preparation site;

[0015] At the core sample preparation site, process the sample into a plunger-shaped core sample by wire cutting.

[0016] Preferably, perform physical property parameter tests on the core sample to obtain the mineral component analysis results and the sample porosity, including:

[0017] Crush the remaining part of the core after cutting the plunger sample to obtain a pulverized coal sample with a particle size less than 200 mesh and grind it. Use an XRD instrument to perform X-ray diffraction on the ground pulverized coal sample and analyze its diffraction pattern to obtain the mineral component analysis results; among them, the mineral component analysis results include the mineral mass ratio;

[0018] Use the QKX-II gas porosity to test the sample porosity of the core sample by the transient method to obtain the sample porosity.

[0019] Preferably, calculating the volume fraction of core components based on the mineral component analysis results and the sample porosity includes:

[0020] Converting the mineral mass ratio to a volume ratio through the following formula:

[0021] V mi = ρ all W mi / ρ mi

[0022] where ρ mi , V mi , W mi are respectively the density, volume proportion, and mass proportion of the i-th mineral, and ρ all is the sum of the densities of all minerals;

[0023] Based on the volume ratio and the sample porosity, calculate the volume fraction V of core components through the following formula:

[0024]

[0025] where is the sample porosity.

[0026] Preferably, preprocessing the CT image to obtain a rock component CT image includes:

[0027] Performing an image excision operation on the CT image to eliminate the influence of the core image boundary and the low imaging resolution at the top and bottom of the plunger sample, and obtaining a first CT image;

[0028] Performing gray-scale transformation enhancement on the first CT image to expand the gray-scale range of the first CT image to the 0-255 range, and obtaining a second CT image;

[0029] Performing an anisotropic diffusion filtering operation on the second CT image to filter out the noise signal and retain the detailed features, and obtaining a rock component CT image; wherein, the detailed features include fractures and mineral boundaries.

[0030] Preferably, the histogram distribution function of the rock component CT image is expressed as:

[0031] h(x) = {h(x)|h(x) = sum(I = x), x = 0, 1, …, L - 1}

[0032] where h(x) is the histogram distribution function, L is the number of gray levels of the image, x is the gray value in the image, sum is the accumulation symbol, and I is the gray level.

[0033] Preferably, the target transformation function constructed based on the Gaussian function and the volume fraction of core components is expressed as:

[0034] ht(x) = V * f(x)

[0035] Where ht(x) is the target transformation function, V is the volume fraction of core components, and f(x) is the Gaussian function, expressed as:

[0036]

[0037] Where b i is the gray scale of the i-th mineral in the image, C is the standard deviation, a is the peak height of the Gaussian function curve, and e is the natural constant.

[0038] Preferably, the matching mapping function constructed based on the principle of the minimum gray scale error between the histogram distribution function and the target transformation function is expressed as:

[0039] M(x i ) = arg min|h(x i ) - h t (x j )|

[0040] Where x i = 0, 1, …, L - 1, x j = 0, 1, …, L - 1, M(x i ) is the target conversion function, arg min is the average minimum symbol, x i is the standard image gray value, and x j is the gray value of the image to be transformed.

[0041] Preferably, the method for verifying the mineral morphological characteristics through the target morphological characteristics includes:

[0042] Calculating the similarity between the target morphological characteristics and the mineral morphological characteristics;

[0043] Based on the set similarity threshold, if the similarity is less than the set similarity threshold, the verification passes, otherwise the verification fails.

[0044] The beneficial effects of this application are:

[0045] Based on digital image processing of this application, combining rock physics experiments and digital core technology, a coal digital core modeling method based on histogram matching is proposed. Based on histogram matching, the histogram distribution of the original core image and the theoretical sample mineral content distribution are mapped according to the principle of the minimum difference in gray scale cumulative values. Histogram matching analysis of the preprocessed CT image can better improve the accuracy of coal digital core modeling. This method has low cost, strong operability, and a high matching degree between the reconstructed core and the original core, and can accurately provide a high-precision model for the finite element simulation of the physical properties of coal digital cores. Brief Description of the Drawings

[0046] Figure 1 It is a flowchart of a coal digital core modeling method based on histogram matching provided by an embodiment of the present application;

[0047] Figure 2 It is a schematic diagram of microscopic structure CT scanning and CT image processing provided by an embodiment of the present application; wherein, (a), the sample testing process; (b), the first CT image; (c), the second CT image; (d), the rock component image;

[0048] Figure 3 It is a schematic diagram of the segmentation of the rock component CT image based on the histogram matching algorithm provided by an embodiment of the present application; wherein, (a), the gray probability density function of the original image; (b), the XRD mineral diffraction peak spectrum; (c), the volume histogram of mineral components; (d), the probability density distribution of the Gaussian function; (e), the gray probability density function of the transformed image; (f), the comparison of the SEM image and the rock component division result after histogram matching transformation;

[0049] Figure 4 It is a schematic diagram of the reconstruction and verification of the coal three-dimensional digital core provided by an embodiment of the present application; wherein, (a), the reconstruction result of the HR-6#-10 coal digital core; (b), the reconstruction result of the HR-6#-19 coal digital core; (c), the reconstruction result of the HR-6#-25 coal digital core; (d), the reconstruction result of the HR-6#-34 coal digital core; (e), the reconstruction result of the HR-6#-40 coal digital core; Detailed Embodiment

[0050] The following illustrates the implementation manners of the present application through specific specific examples. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0051] The following further describes in detail the specific implementation manners of the present application in conjunction with the drawings and embodiments.

[0052] Please refer to Figure 1 , which is a flowchart of a coal digital core modeling method based on histogram matching provided by an embodiment of the present application. The embodiment of the present application provides a coal digital core modeling method based on histogram matching, including the following steps S10-S50.

[0053] S10: Obtain the core of the coal seam section and prepare the core sample.

[0054] In this embodiment, the purpose of step S10 is to achieve core collection and sample preparation. Exemplarily, according to the "Core Analysis Method" (GB / T 29172-2012), the sampling location, sample length, and sample number of the samples obtained from the drilling are determined, and the samples are sealed and stored. Since coal samples are brittle and soft, the samples are processed into standard plunger-shaped samples with a diameter of 25 mm and a length of 50 mm by wire cutting.

[0055] Specifically: The core of the coal seam section is obtained by drilling coring, and a GPS instrument is used to determine the sampling location, record the sample length (such as 20 cm), record the sampling depth of the sample (such as 150 m), wrap the sample with plastic wrap for sealing and storage, and send it to the sample preparation site. Since coal samples are brittle and soft, the samples are processed into standard plunger-shaped samples with a diameter of 25 mm and a length of 50 mm by wire cutting.

[0056] S20: Conduct physical property parameter tests on the core samples to obtain the mineral component analysis results and sample porosity, and calculate the core component volume fraction based on the mineral component analysis results and sample porosity.

[0057] In this embodiment, step S20 is the step to achieve physical property parameter tests. Exemplarily, referring to the standard of "X-ray Diffractometer" (JB / T 11144-2011), the X-ray diffraction method is used to determine the mineral components in the samples; referring to the standard of "Determination of Porosity, Permeability and Saturation of Shale" (GB / T 34533-2023) to measure the porosity of the core samples; calculate the core component volume fraction V based on the core porosity test and sample mineral component analysis results.

[0058] Specifically: The remaining 50 g of the sample after the core is cut into plunger samples is crushed into a coal powder sample with a particle size less than 200 mesh. The milled 3- coal powder sample is added to the middle of the groove of the sample holder by the positive pressure method. Then, an XRD instrument is used to perform X-ray diffraction on the sample, and Jade software is used to analyze its diffraction pattern, as Figure 3 shown in (b) in, to obtain the mineral component V of the coal sample m . The transient method of sample porosity test is performed on the plunger sample by QKX-II gas porosity, and the sample porosity is recorded Since the XRD test analysis result is the mass ratio, while the parameters required in the digital core construction process are volume ratios, it is necessary to convert the mineral mass ratio to a volume ratio before performing rock component analysis. The conversion formula is shown as follows.

[0059] V mi =ρ all W mi / ρ mi (1)

[0060] where ρ mi , V mi , W mi are the density, volume fraction, and mass fraction of the i-th mineral respectively, and ρ all is the sum of the densities of all minerals.

[0061] Furthermore, the volume fraction V of the core components is

[0062]

[0063] where is the porosity of the sample.

[0064] S30: Use X-rays to perform CT scanning on the core sample to obtain a CT image, and preprocess the CT image to obtain a rock component CT image.

[0065] In this embodiment, the purpose of step S30 is to obtain a rock component CT image through microscopic structure CT scanning and CT image preprocessing. Exemplarily, referring to the standard of "Microbeam analysis - Computerized tomography (CT) analysis method for micro-nano scale pore structure of tight rocks" (GB / T 38531 - 2020), a high-resolution 3D scanning X-ray microscope system is used to perform CT scanning on the core sample; image resection is performed to eliminate the influence of the core image boundary and the low imaging resolution at the top and bottom of the plug sample; micron CT scan image compression; contrast enhancement of the micron CT scan image of the plug sample based on the gray transformation method; filtering of the CT scan image based on the anisotropic diffusion filtering method.

[0066] Specifically: Referring to the standard of "Microbeam analysis - Computerized tomography (CT) analysis method for micro-nano scale pore structure of tight rocks" (GB / T 38531 - 2020), a high-resolution 3D scanning X-ray microscope system is used to perform CT scanning on the core sample. The plug-shaped sample is placed in a high-precision three-dimensional X-ray microscopic imaging system, and CT imaging is performed on the plug-shaped sample layer by layer, and the resolutions in the x, y, and z directions during the CT imaging process are recorded, such as 23.976*23.976*23.976um. The sample testing process is as Figure 2 shown in (a) therein, and a CT image can be obtained. Since there are virtual images at the boundary of the CT scanned sample, and the exposure rate is low at the top and bottom of the sample due to the X-ray angle problem, the CT image needs to be resected before preprocessing to obtain the first CT image. Then, since the gray range of the first CT image is narrow and there is system noise interference, the first CT image needs to be preprocessed before constructing a digital core based on histogram matching. Taking the slice in the XZ direction of the sample as an example, as Figure 2As shown in (b), gray-scale transformation enhancement is performed based on the first CT image, aiming to expand the gray-scale range of the original image to the range of 0-255 to obtain the second CT image. The second CT image after contrast enhancement is as shown in Figure 2 (c). The resolution between rock minerals in different density ranges is significantly enhanced. To eliminate the system noise interference in the second CT image, anisotropic diffusion filtering is performed on the second CT image after contrast enhancement to filter out the noise signal and retain details such as fractures and mineral boundaries, obtaining the final CT image of rock components, as shown in Figure 2 (d).

[0067] S40: Determine the histogram distribution function of the CT image of rock components, construct a target transformation function based on the Gaussian function and the volume fraction of core components, construct a matching mapping function based on the principle of minimizing the gray-level error between the histogram distribution function and the target transformation function, and convert the histogram distribution function into the function h eq (x) based on the matching mapping function, and then perform threshold segmentation on the function h eq (x) to complete the segmentation of the CT image of rock components.

[0068] In this embodiment, the purpose of step S40 is to realize the segmentation of the CT image of rock components based on the histogram matching algorithm. Specifically, the histogram distribution function h(x) = {h(x)|h(x) = sum(I = x), x = 0, 1, …, L-1} of all images (i.e., the CT image of rock components) after preprocessing is statistically calculated, where L is the number of gray levels of the image, x is the gray value in the image, sum is the summation symbol, and I is the gray level.

[0069] Construct a target transformation function ht(x) = V*f(x) based on the Gaussian function and the volume fraction of core components, where the Gaussian function b i is the gray level of the i-th mineral in the image, C is the standard deviation, and a is the peak height of the Gaussian function curve. Construct a matching mapping function M(x i ) = arg min|h(x i ) - h t (x j )| based on the principle of minimizing the gray-level error between h(x) and ht(x), where x i = 0, 1, …, L-1, x j = 0, 1, …, L-1, M(x i ) is the target transformation function, arg min is the average minimum symbol, x i is the gray value of the standard image, and x j is the gray value of the image to be transformed. Convert the histogram distribution function h(x) into the function h eqAfter (x), perform threshold segmentation on the function h eq (x) to complete the segmentation of the CT image of rock components.

[0070] More specifically, statistically calculate the histogram distribution function h(x) = {h(x)|h(x) = sum(I = x), x = 0, 1, …, L - 1} of all images after preprocessing, where L is the number of gray levels of the image, as shown in Figure 3 (a). Based on the Gaussian function and the volume fraction of core components, construct the target transformation function ht(x) = V * f(x), where the Gaussian function V is the volume fraction of core components, obtained from step S20. b i is the gray level of the i-th mineral on the image. For example, the gray level corresponding to pores is 10; the gray level corresponding to organic matter is 50; the gray level corresponding to clay minerals is 100; the gray level corresponding to boehmite is 150; the gray level corresponding to calcite is 200. C is the standard deviation, such as C = 0.5; a is the peak height of the Gaussian function curve, such as a = 1, and construct the target transformation function as shown in Figure 3 (c). Based on the principle of minimizing the gray level error between h(x) and ht(x), construct the matching mapping function M(x i ) = arg min|h(x i ) - h t (x j )|. Further, match the histogram of h(x) to the target transformation function ht(x) within the gray level range of 0 - 255. During the matching process, ensure that the gray level range is the closest and the gray level error is the smallest, that is, distribute the single-peak histogram distribution in the original image to the multi-peak histogram distribution, and different peak ranges correspond to different minerals. Statistically calculate the probability density function h eq (x) of the image after histogram matching transformation. In h eq (x), count different peaks, and perform threshold segmentation based on the boundaries between different peaks. Different peaks correspond to different minerals to complete the segmentation of the CT image of rock components. The image segmentation result is shown in the lower part of Figure 3 (f).

[0071] S50: Based on the segmented CT image of rock components, extract the pore structure of the core sample and the spatial structure characteristics of rock minerals. Determine the spatial distribution characteristics between rock components based on the connectivity between the pore structure and rock minerals. Use the spatial distribution characteristics between rock components as the mineral morphological characteristics, and verify the mineral morphological characteristics through the target morphological characteristics. When the verification passes, output the constructed digital core model of coal; perform electron microscopy scanning on the core sample, and use the obtained mineral morphological characteristics as the target morphological characteristics.

[0072] In this embodiment, the purpose of step S50 is to finally output the constructed coal digital core model through coal digital core reconstruction and verification. After preprocessing the grayscale image and segmenting the CT image of rock components using the histogram matching algorithm, the spatial structure features of the pore structure of the plunger sample and the rock minerals are extracted respectively, and the spatial distribution features among the rock components are analyzed based on the connectivity between the pores and the rock minerals. Referring to the standard of "Microbeam Analysis - Identification of Authigenic Clay Minerals in Sedimentary Rocks - Scanning Electron Microscopy and Energy Dispersive Spectrometer Method" (GB / T 17361-2013), the remaining sample after wire cutting is scanned by electron microscopy, and the morphological differences between the minerals of the reconstructed digital core and the minerals identified by electron microscopy are compared and analyzed to verify the rationality of the grayscale image segmentation.

[0073] Specifically: As Figure 4 shown, after preprocessing the grayscale image and segmenting the CT image of rock components using the histogram matching algorithm, based on 3D image processing software such as Avizo, the CT image of coal is reconstructed by the method of image stacking. The spatial structure of each rock component is reconstructed according to the color level range to which the pores and minerals belong. Based on the connectivity between the pores and the rock minerals, the spatial distribution features among the rock components are analyzed. Further, considering the connectivity between the pores or minerals, such as point connection / line connection / surface connection, the pores are divided into isolated pores, connected pores, and fractures, and the minerals are divided into dot-shaped, block-shaped, and layered minerals, and their spatial distribution features and the proportion of different types of rock components are analyzed. A 1*1 cm sized block sample is taken from the remaining sample after wire cutting, polished with polishing liquid, and after being polished smoothly, electroplated with gold and scanned by electron microscopy. The morphological differences between the minerals of the reconstructed digital core and the minerals identified by electron microscopy are compared and analyzed, such as Figure 3 (f) in. The rationality of the grayscale image segmentation is verified. Taking the morphological features of the minerals obtained by electron microscopy as the target morphological features and the spatial distribution features among the rock components as the mineral morphological features, the similarity between the target morphological features and the mineral morphological features reconstructed by CT scanning is compared. After verification, different minerals with different morphologies are defined, and different minerals with different morphologies are coupled based on the 3D image software, so as to construct a complex coal digital core model. This model can be used for 3D simulation of rock mechanics and fluid mechanics to facilitate understanding the mechanical and fluid characteristics of complex coal matrix and coal structure.

[0074] The methods for comparing the similarity between the target morphological features and the mineral morphological features reconstructed by CT scanning include direct comparison, eigenvector comparison, and topological and structural comparison, either one of them or a combination thereof, where multiple combinations mean that at least two combinations of the above three can meet the requirement of consistent similarity. The method of direct comparison includes: calculating the mean square error (MSE) or structural similarity index (SSIM) as the similarity at the pixel level, and calculating the Hausdorff distance (maximum surface deviation) or Chamfer distance (average nearest point distance) as the similarity in terms of spatial distance. The method of eigenvector comparison includes: calculating the Euclidean distance, cosine similarity, Mahalanobis distance, or cosine similarity of eigenvectors as the similarity. After calculating the similarity, based on the set similarity threshold, different types of similarity correspond to different similarity thresholds. When the similarity is less than the set similarity threshold, it indicates that the verification passes; otherwise, the verification fails. The method of topological and structural comparison includes: inputting the target morphological features and the mineral morphological features reconstructed by CT scanning into a graph neural network (GNN), comparing the porosity or pore size distribution curve (KS test or histogram intersection), so as to judge whether the similarity meets the requirements.

[0075] The above embodiments are only used to illustrate the present application, rather than to limit the present application. Those of ordinary skill in the relevant technical field can also make various changes and modifications without departing from the spirit and scope of the present application. Therefore, all equivalent technical solutions also belong to the scope of the present application. The patent protection scope of the present application shall be defined by the claims.

Claims

1. A method for modeling digital cores of coal based on histogram matching, characterized in that, The method includes: Obtaining a core sample from a coal seam section and preparing a core sample; Performing physical property parameter tests on the core sample to obtain a mineral component analysis result and the sample porosity, and calculating the core component volume fraction based on the mineral component analysis result and the sample porosity; Performing CT scanning on the core sample using X-rays to obtain a CT image, and preprocessing the CT image to obtain a rock component CT image; Determine the histogram distribution function of the CT image of rock components, construct the target transformation function based on the Gaussian function and the volume fraction of core components, construct the matching mapping function based on the principle of minimizing the gray level error between the histogram distribution function and the target transformation function, and convert the histogram distribution function into the function h eq (x). After that, perform threshold segmentation on the function h eq (x) to complete the segmentation of the CT image of rock components; Based on the segmented rock component CT image, extracting the pore structure of the core sample and the spatial structure characteristics of the rock minerals, determining the spatial distribution characteristics between the rock components based on the connectivity between the pore structure and the rock minerals, using the spatial distribution characteristics between the rock components as the mineral morphology characteristics, verifying the mineral morphology characteristics through the target morphology characteristics, and when the verification is passed, outputting the constructed coal digital core model; performing electron microscopy scanning on the core sample, and using the obtained mineral morphology characteristics as the target morphology characteristics.

2. The method for modeling digital cores of coal based on histogram matching according to claim 1, wherein, Obtaining a core sample from a coal seam section and preparing a core sample includes: Obtaining a core sample from the coal seam section by means of core drilling as a sample, determining the sampling location, recording the sample length and the sampling depth of the sample, wrapping the sample with plastic wrap for sealing and sending it to the core sample preparation site; At the core sample preparation site, processing the sample into a plunger-shaped core sample by wire cutting.

3. The method for modeling digital cores of coal based on histogram matching according to claim 1, wherein Performing physical property parameter tests on the core sample to obtain a mineral component analysis result and the sample porosity includes: Crushing the remaining part of the core after cutting the plunger sample to obtain a pulverized coal sample with a particle size less than 200 mesh and grinding it. Performing X-ray diffraction on the ground pulverized coal sample using an XRD instrument and analyzing its diffraction pattern to obtain a mineral component analysis result; wherein, the mineral component analysis result includes the mineral mass ratio; Performing transient method sample porosity testing on the core sample using a QKX-II gas porosity meter to obtain the sample porosity.

4. The method for modeling digital cores of coal based on histogram matching according to claim 3, wherein Calculating the core component volume fraction based on the mineral component analysis result and the sample porosity includes: Converting the mineral mass ratio to a volume ratio through the following formula: V mi = ρ all W mi / ρ mi where ρ mi , V mi , W mi are the density, volume fraction, and mass fraction of the i-th mineral respectively, and ρ all is the sum of the densities of all minerals; Based on the volume ratio and the sample porosity, calculating the core component volume fraction V through the following formula: wherein is the porosity of the sample.

5. The method for modeling coal digital cores based on histogram matching according to claim 1, wherein Preprocessing the CT image to obtain a rock component CT image includes: Performing an image excision operation on the CT image to eliminate the influence of the core image boundary and the lower imaging resolution at the top and bottom of the plunger sample, obtaining a first CT image; Performing gray level transformation enhancement on the first CT image to expand the gray level range of the first CT image to the 0-255 range, obtaining a second CT image; Performing an anisotropic diffusion filtering operation on the second CT image to filter out the noise signal and retain the detail features, obtaining a rock component CT image; wherein, the detail features include fractures and mineral boundaries.

6. The method for modeling digital cores of coal based on histogram matching according to claim 1, wherein The histogram distribution function of the rock component CT image is expressed as: h(x) = {h(x)|h(x) = sum(I = x), x = 0, 1, …, L - 1} Where h(x) is the histogram distribution function, L is the number of gray levels of the image, x is the gray value in the image, sum is the summation symbol, and I is the gray level.

7. The method for building a digital core of coal based on histogram matching according to claim 6, characterized in that, The target transformation function constructed based on the Gaussian function and the volume fraction of core components is expressed as: ht(x) = V * f(x) where ht(x) is the target transformation function, V is the volume fraction of core components, and f(x) is the Gaussian function, which is expressed as: where b i is the gray scale of the i-th mineral on the image, C is the standard deviation, a is the peak height of the Gaussian function curve, and e is the natural constant.

8. The method for modeling digital cores of coal based on histogram matching according to claim 7, wherein, The matching mapping function constructed based on the principle of the minimum gray level error between the histogram distribution function and the target transformation function is expressed as: M(x i ) = arg min |h(x i ) - h t (x j )| where x i = 0, 1, …, L - 1, x j = 0, 1, …, L - 1, M(x i ) is the target conversion function, arg min is the average minimum symbol, x i is the standard image gray value, x j is the gray value of the image to be transformed.

9. The method for modeling digital cores of coal based on histogram matching according to claim 1, wherein The methods for verifying the mineral morphological features through the target morphological features include: Calculating the similarity between the target morphological features and the mineral morphological features; Based on the set similarity threshold, if the similarity is less than the set similarity threshold, the verification passes; otherwise, the verification fails.

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