Large-size digital core image generation method based on particle partition and related device

Abstract

The invention belongs to the technical field of digital rock core images, and discloses a large-size digital rock core image generation method based on particle partitioning and a related device, and the method comprises the steps: splicing a plurality of binary images of a rock core; pixel inversion processing is carried out on the spliced image; performing smooth modification on a splicing boundary in the inverted image through open operation; extracting a pixel skeleton axis image of the modified image; performing connected domain analysis on the axis image of the pixel skeleton, dividing each particle partition according to connectivity, and painting each particle partition; on the particle partition map, extracting a particle partition boundary of an image splicing and connecting region according to the particle partition position, and calculating a pore throat feature point and a partition mass center at the position; carrying out different-proportion shrinkage on the particle partition boundary according to the partition mass center and the pore throat feature point; coating the reconstructed particle area to be black; and replacing the image splicing connection area in the spliced image with the reconstructed particle image to obtain a large-size digital core image.

Description

Technical Field

[0001] The invention belongs to the technical field of digital core images, and in particular relates to a large-size digital core image generation method based on particle partitioning and a related device. Background Art

[0002] Cores can provide a wealth of geological information, including stratigraphic structure, rock type, pore structure, etc. This information is crucial to determining the nature, distribution and development of oil and gas reservoirs. By analyzing the pore-scale two-phase behavior of cores through microscopic seepage mechanics, we can obtain the variation characteristics of the relative permeability of oil and water from the pore structure to obtain the water content law of the reservoir, which can be used to evaluate the reserve quality and recoverability of oil and gas resources and provide a scientific basis for exploration and development.

[0003] The three-dimensional image of the digital core is obtained by stacking and reconstructing multiple two-dimensional core images. Therefore, when building a large-scale three-dimensional digital core model, it is necessary to obtain multiple large-scale accurate two-dimensional digital core scanning images.

[0004] Currently, there are two ways to obtain characterization images of two-dimensional cores: through computer tomography or through random numerical reconstruction based on the statistical characteristics of porous media to obtain reconstructed images.

[0005] However, due to the mutual limitations of scanning dimension and resolution during computed tomography scanning, accurate large-format digital core images cannot be directly obtained through scanning. And it is difficult to determine the initial boundary conditions for reconstruction when obtaining images using digital reconstruction methods. Summary of the invention

[0006] In order to solve the problems existing in the prior art, the purpose of the present invention is to provide a large-scale digital core image generation method based on particle partitioning and related devices. The present invention can realize continuous simulation from small-scale to large-scale digital core images, and can solve the limitations of scanning dimension and accuracy when generating images, and there is no need to consider the boundary initial conditions when constructing numerical simulations, providing a good foundation for the design of large-scale reservoir chips.

[0007] To achieve the above purpose, the technical solution adopted by the present invention is as follows: A method for generating a large-size digital core image based on particle partitioning includes the following steps: Splicing several acquired binary images of the core to obtain a spliced ​​image; in the acquired binary images of the core, black represents pore throats and white represents particles; Performing pixel inversion processing on the stitched image to obtain an inverted image; Smoothly modify the splicing boundary in the inverted image by an opening operation to obtain a modified image; Extracting a pixel skeleton medial axis image of the modified image; Conducting connected domain analysis on the pixel skeleton medial axis image, dividing each particle partition according to connectivity, coloring each particle partition, and obtaining a particle partition map; On the particle partition map, the particle partition boundary of the image stitching intersection area is extracted according to the particle partition position, and the pore throat characteristic points and the partition centroid of the particle partition of the image stitching intersection area are calculated; The particle partition boundary is shrunk at different proportions according to the centroid of the partition and the pore throat characteristic points to obtain the reconstructed particle area; The reconstructed particle area is painted black to obtain a reconstructed particle image; The image stitching intersection area in the stitched image is replaced with the reconstructed particle image to obtain a large-size digital core image.

[0008] Preferably, the plurality of binary images of the cores are binary images of a plurality of different cores, or are binary images of different cores obtained by rotating the same binary image of the core; When stitching together several acquired binary images of the cores, all the binary images of the cores are adjusted to images of the same size and then stitched together.

[0009] Preferably, when each particle partition is painted, adjacent particle partitions are painted with different colors.

[0010] Preferably, the pore throat characteristic point is a partition boundary point located at the intersection of three or more particle partitions.

[0011] Preferably, when the particle partition boundary is shrunk at different proportions according to the partition centroid and the pore throat characteristic point, for any point (X i , Y i ), the coordinates of the vertices corresponding to the control boundary after scaling ( , ) is calculated by the following formula:

[0012]

[0013] Among them, X i is the horizontal coordinate of any point, Y i is the ordinate of any point, is the horizontal coordinate of the vertex corresponding to the control boundary, is the ordinate of the vertex corresponding to the control boundary, is the horizontal coordinate of the partition centroid, is the ordinate of the partition centroid, is the scaling factor for each point in the partition.

[0014] Preferably, the scaling factor of each point in the partition is The calculation formula is as follows:

[0015]

[0016]

[0017]

[0018]

[0019] In the formula, is the scaling factor away from the pore throat feature point, is the ratio of the distance from the partition point to the pore throat feature point to the maximum distance, is the base scaling of the control, is the minimum distance from each point in the partition to each pore throat feature point in the partition, is the maximum value of the minimum distance from each point in the partition to the pore throat feature point, n is the total number of all points in the connected domain, is the distance from the i-th partition point to the j-th pore throat feature point, ||P i -T j || means that the i-th boundary point is being solved To the jth pore throat feature point The Euclidean distance is , and m is the number of pore throat feature points.

[0020] Preferably, the scaling factor away from the pore throat feature point and the base scaling of the control Satisfies the following relationship: .

[0021] The present invention also provides a large-size digital core image generation system based on particle partitioning, comprising: Image stitching module: used to stitch several acquired binary images of the core to obtain a stitched image; in the acquired binary images of the core, black represents pore throats and white represents particles; Pixel inversion module: used for performing pixel inversion processing on the spliced ​​image to obtain an inverted image; Image modification module: used for performing smooth modification on the splicing boundary in the inverted image through an opening operation to obtain a modified image; A pixel skeleton medial axis image extraction module is used to extract the pixel skeleton medial axis image of the modified image; Image analysis module: used to perform connected domain analysis on the pixel skeleton medial axis image, divide each particle partition according to connectivity, color each particle partition, and obtain a particle partition map; Calculation module: used to extract the particle partition boundary of the image splicing intersection area according to the particle partition position on the particle partition map, and calculate the pore throat feature points and the partition centroid of the particle partition in the image splicing intersection area; Scaling module: used to shrink the particle partition boundary according to the partition centroid and pore throat feature points at different proportions to obtain the reconstructed particle area; Particle image reconstruction module: used to paint the reconstructed particle area black to obtain a reconstructed particle image; Large-size image generation module: used to replace the image stitching intersection area in the stitching image with the reconstructed particle image to obtain a large-size digital core image.

[0022] The present invention also provides an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the large-size digital core image generation method based on particle partitioning as described above in the present invention.

[0023] The present invention also provides a storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, the method for generating a large-size digital core image based on particle partitioning as described above in the present invention is implemented.

[0024] The present invention has the following beneficial effects: The present invention can perform image stitching operations based on the acquired digital core structure characteristics, and then reconstruct and simulate the medium particle structure at the stitching boundary to ensure the pore throat characteristics of the stitching boundary particles and realize the generation of large-scale digital core images. The present invention realizes the continuous simulation of digital core images from small scale to large scale, and solves the limitations of scanning dimension and accuracy when generating images, and there is no need to consider the boundary initial conditions when constructing numerical simulations, providing a good foundation for the design of large-scale reservoir chips. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1A flow chart of a method for generating a large-size digital core image based on particle partitioning provided in an embodiment of the present invention.

[0027] Figure 2 This is a small-sized core scanning image used for testing in an embodiment of the present invention.

[0028] Figure 3 It is a schematic diagram of splicing the core scanning image after pixel inversion in an embodiment of the present invention.

[0029] Figure 4 Schematic diagram of the image after the opening operation of the spliced ​​image in the embodiment of the present invention.

[0030] Figure 5 Schematic diagram of skeleton medial axis extraction of spliced ​​images in an embodiment of the present invention.

[0031] Figure 6 Schematic diagram of particle partitioning of a stitched image in an embodiment of the present invention.

[0032] Figure 7 Schematic diagram of particle partition boundaries, feature points, and centroids at the splicing intersection of spliced ​​images in an embodiment of the present invention.

[0033] Figure 8 It is a large-size core image result at the stitching boundary of the stitching image in the embodiment of the present invention reconstructed at the first scaling ratio.

[0034] Fig. 9 It is a large-size core image result at the stitching boundary of the stitching image in the embodiment of the present invention reconstructed at the second scaling ratio.

[0035] Fig.10 It is a large-size core image result of the stitching junction of the stitching image under the third scaling ratio reconstruction in the embodiment of the present invention. DETAILED DESCRIPTION

[0036] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0037] Reference Figure 1 The method for generating a large-size digital core image based on particle partitioning of the present invention comprises the following steps: Step 1: Obtain multiple binary images of small-sized digital cores generated by CT scanning, and splice the images in an m*n (m≥1, n≥1) arrangement as required to obtain a spliced ​​image; wherein the binary images of the multiple small-sized digital cores generated by CT scanning can be multiple different binary images, in which case all binary images should be of the same size before splicing; or multiple binary images obtained by rotating the same binary image at different angles; in the binary image of the core, black represents pore throats and white represents particles; Step 2: Preprocess the obtained spliced ​​image, the process is as follows: after inverting the image pixels, perform an opening operation to make the splicing boundary particle morphology in the image after the image pixels are inverted (i.e., the inverted image) smooth, and obtain a modified image. The present invention uses an opening operation to ensure that when the medium particles at the image splicing boundary extract the pixel skeleton center axis in step 3, there will not be too many longitudinal skeletons at the longitudinal splicing, more transverse skeletons at the transverse splicing, and the shape generated by the skeleton is more natural.

[0038] Step 3: Extract the pixel skeleton medial axis image of the spliced ​​image (i.e., the modified image) preprocessed in step 2, perform connected domain analysis on the pixel skeleton medial axis image, divide each particle partition according to connectivity, and color each particle partition. When coloring, paint adjacent particle partitions with different colors to obtain a particle partition map; in this step, when performing skeleton extraction, a skeleton refinement algorithm based on Euclidean distance transform is adopted, and a 4-neighborhood seed filling algorithm is used to obtain the image connected area.

[0039] Step 4: On the particle partition map, extract the particle partition boundaries of the image stitching intersection area according to the particle partition positions, and calculate the pore throat feature points and partition centroids of the particle partitions in the image stitching intersection area; among them, the selection of pore throat feature points should be set to the partition boundary points located at the intersection of three or more particle partitions, which are used to characterize the pore throat characteristics of the porous medium.

[0040] Step 5: Shrink the grain partition boundary at different ratios according to the centroid of the partition and the pore throat feature points to obtain the reconstructed grain area, paint the reconstructed grain area black, obtain the reconstructed grain image, replace the image stitching intersection area in the stitched image with the reconstructed grain image, and thus complete the generation of the large-scale digital core image. In this step, when shrinking the grain partition boundary at different ratios according to the centroid of the partition and the pore throat feature points, for any point P on the boundary of each grain partition i (i=1, 2, ..., n), there must be a corresponding scaling ratio (i.e. scaling factor) Ψ i (i = 1, 2, ..., n), and each particle partition boundary has a pore throat characteristic point T j (j=1, 2, 3), where any point P iAlso called any boundary point P i . Any point P i The corresponding scaling ratio should be determined by any point P i To the pore throat characteristic point T j The scaled coordinate function of each point in the particle partition is: .in,( , ) are the coordinates of the centroid of the control partition, ( , ) is any point P i The coordinates of , ) are the coordinates of the control boundary vertices after scaling. n is the total number of points in the connected domain.

[0041] In order to meet the pore throat characteristics of porous media, a larger scaling ratio is required in the pore throat region. i The value of is determined by the following formula (here it is assumed that the number of pore throat characteristic points is 3):

[0042]

[0043]

[0044]

[0045]

[0046] In the formula, ||P i -T j || represents the i-th boundary point P i To Pore ​​throat characteristic point T j The Euclidean distance of , used to obtain the i-th boundary point P i The distance to the nearest feature point; and It is the normalization of the distance from each boundary point to the nearest pore throat feature point. The closer the boundary point is to the pore throat feature point, the closer its The closer to 0. In It is the scaling base away from the pore-throat feature point. The larger its value is, the larger the throat radius and pore-throat ratio is, and the more obvious the pore-throat feature of the reconstructed particle is. is the basic scaling factor of the control, and its value determines the basic size of the reconstructed particles. .

[0047] Example The method for generating a large-size digital core image based on particle partitioning in this embodiment includes the following steps: Step 101, select a 1000*1000 sandstone CT scan two-dimensional slice image (which is a binary image) as a test stitching image, that is, Figure 2 , Figure 2 In the figure, black pixels represent pore throats and white pixels represent particles.

[0048] Step 102: Figure 2 The images shown are spliced ​​in a 3*3 arrangement to obtain a spliced ​​image of 3000*3000 in size. The spliced ​​image is pixel-inverted to swap the black and white pixel values ​​to highlight the particles of the expression medium and obtain an inverted image, namely Figure 3 .

[0049] Step 201, performing an opening operation on the inverted image obtained in step 102 to modify Figure 2 After the small-size images shown in the figure are spliced ​​in a 3*3 arrangement, the resulting spliced ​​image has unnatural medium particles at the splicing boundary. Specifically, the image is eroded and then expanded using a convolution kernel to eliminate the noise at the splicing boundary. The convolution size is set to 5 to obtain Figure 4 The preprocessed stitched image shown is denoted as a modified image.

[0050] Step 301, using the image skeleton algorithm to extract the pixel skeleton medial axis of the modified image obtained in step 201, specifically, using the Euclidean distance transform to divide each pixel point in the modified image into a point inside the white domain, a boundary point, and a point outside the white domain, and then using the white pixel point as the target point for extracting the distance, and the black pixel point as the background point, for each target point, the minimum distance to the background point is calculated, and a scalar distance field from the white pore area to the black medium area is obtained.

[0051] Step 302, based on the scalar distance field, a skeleton thinning algorithm is selected to extract the image skeleton. Each pixel point with a non-zero gray value in the image is iterated, and the attributes of the eight neighboring pixels around it are determined. The boundary points that meet specific conditions are gradually eliminated (i.e., they are set to black pixels) until no additional boundary points can be eliminated. The remaining pixels will constitute the skeleton area of ​​the image. The thinning algorithm starts the thinning operation on the outermost contour line of the generated distance field, and gradually shrinks inward along the direction of the decreasing contour line after the distance transformation. Finally, the skeleton feature map of the modified image is extracted, which is recorded as the pixel skeleton medial axis image, such as Figure 5 shown.

[0052] Step 303, using the area formed by the skeleton of the axis image in the pixel skeleton as the control area of ​​the particle, partition each particle, and use the 4-neighborhood seed filling algorithm to obtain the image connected area. Select a black pixel in the image as a seed, and use the stack to realize the 4-neighborhood adjacent and black pixels as foreground pixels adjacent to the seed, add them to the same pixel set, and mark the pixel as visited. The resulting set of pixels is a connected area, that is, the generation control area of ​​a single particle medium. Then traverse the next unvisited black pixel until all sets are found. And assign different colors to each area to obtain Figure 6 The particle partition diagram shown is used to distinguish the various partitions.

[0053] Step 401, extracting the outer boundaries of each control partition in the particle partition map according to the connectivity characteristics of the 4-neighborhood.

[0054] Step 402, find characteristic control points on the boundaries of each control partition. Since the subsequent shrinkage reconstruction of the region is performed by retaining the pore throat characteristics of the medium, the characteristic points are set as the partition boundary points located at the intersection of three or more particle partitions, that is, the characteristic points are the intersection points of the three skeleton boundaries. The boundary of each region usually has at least 2-3 characteristic points. Step 403, using the formula Get the centroid of the n points contained in each control area partition.

[0055] The test results of the above steps are as follows Figure 7 shown.

[0056] Step 501, calculate the distance from the point on the boundary of each partition to the pore throat feature point, and perform normalization according to the maximum distance in the partition; any point on the boundary of the control area is recorded as Pi (i=1, 2, ..., n), and the boundary has a pore throat feature point Tj (j=1, 2, 3).

[0057]

[0058]

[0059]

[0060] Step 502: normalize according to the maximum distance in the partition, and calculate the scaling ratio of each point on the boundary of the control partition. Each point has a corresponding scaling ratio Ψi (i=1, 2, ..., n).

[0061]

[0062]

[0063] In the above formula The value of is determined by actual needs, but it should be noted that .

[0064] Step 503, by formula Each point on the partition boundary is scaled relative to the centroid, and the resulting point set is used as the scaled particle reconstruction area. The image is redrawn to obtain a large-size digital core image that conforms to nature after the reconstruction and splicing boundaries.

[0065] Step 504, select α=0.2, β=0.75 to determine the first scaling ratio, and obtain the test Figure 8 .

[0066] Step 505, select α=0.15, β=0.7 to determine the second scaling ratio, and obtain the test Fig. 9 .

[0067] Step 506, select α=0.2, β=0.6 to determine the third scaling ratio, and obtain the test Fig.10 .

[0068] It can be seen that the large-size digital core image generation method based on particle partitioning of the present invention can generate a large-size digital core image on the basis of ensuring the pore throat characteristics of the splicing boundary particles. It not only solves the dimensional limitation of computer scanning to generate core images, but also does not need to consider the boundary problem and initial conditions when constructing core images by single numerical simulation, and generates a digital core image that is more suitable for natural conditions.

[0069] The present invention also provides a system for implementing the large-size digital core image generation method based on particle partitioning as described above, comprising: Image stitching module: used to stitch several acquired binary images of cores to obtain a stitched image; Pixel inversion module: used for performing pixel inversion processing on the spliced ​​image to obtain an inverted image; Image modification module: used for performing smooth modification on the splicing boundary in the inverted image through an opening operation to obtain a modified image; A pixel skeleton medial axis image extraction module is used to extract the pixel skeleton medial axis image of the modified image; Image analysis module: used to perform connected domain analysis on the pixel skeleton medial axis image, divide each particle partition according to connectivity, color each particle partition, and obtain a particle partition map; Calculation module: used to extract the particle partition boundary of the image splicing intersection area according to the particle partition position on the particle partition map, and calculate the pore throat feature points and the partition centroid of the particle partition in the image splicing intersection area; Scaling module: used to shrink the particle partition boundary according to the partition centroid and pore throat feature points at different proportions to obtain the reconstructed particle area; Particle image reconstruction module: used to paint the reconstructed particle area black to obtain a reconstructed particle image; Large-size image generation module: used to replace the image stitching intersection area in the stitching image with the reconstructed particle image to obtain a large-size digital core image.

[0070] The embodiments of the present invention also provide corresponding electronic devices and computer-readable storage media for implementing the solutions provided by the embodiments of the present invention.

[0071] The device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes the large-size digital core image generation method based on particle partitioning described in any embodiment of the present application.

[0072] The storage medium stores a computer program, wherein the computer program, when executed by the processor, implements the large-size digital core image generation method based on particle partitioning described in any embodiment of the present application.

[0073] The present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, readable storage media, optical storage, etc.) containing computer-usable program codes.

[0074] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

Claims

1. A method for generating large-size digital core images based on particle partitioning, characterized in that: The process includes the following: Splicing several acquired binary images of the core to obtain a spliced ​​image; in the acquired binary images of the core, black represents pore throats and white represents particles; Performing pixel inversion processing on the stitched image to obtain an inverted image; Smoothly modify the splicing boundary in the inverted image by an opening operation to obtain a modified image; Extracting a pixel skeleton medial axis image of the modified image; Conducting connected domain analysis on the pixel skeleton medial axis image, dividing each particle partition according to connectivity, coloring each particle partition, and obtaining a particle partition map; On the particle partition map, the particle partition boundary of the image stitching intersection area is extracted according to the particle partition position, and the pore throat characteristic points and the partition centroid of the particle partition of the image stitching intersection area are calculated; The particle partition boundary is shrunk at different proportions according to the centroid of the partition and the pore throat characteristic points to obtain the reconstructed particle area; The reconstructed particle area is painted black to obtain a reconstructed particle image; The image stitching intersection area in the stitched image is replaced with the reconstructed particle image to obtain a large-size digital core image.

2. The method for generating a large-size digital core image based on particle partitioning according to claim 1, characterized in that: The plurality of binary images of the cores are binary images of a plurality of different cores, or are binary images of different cores obtained by rotating the same binary image of the core; When stitching together several acquired binary images of the cores, all the binary images of the cores are adjusted to images of the same size and then stitched together.

3. The method for generating a large-size digital core image based on particle partitioning according to claim 1, characterized in that: When each particle partition is colored, adjacent particle partitions are colored differently.

4. The method for generating a large-size digital core image based on particle partitioning according to claim 1, characterized in that: The pore throat characteristic point is a partition boundary point located at the intersection of three or more particle partitions.

5. The method for generating a large-size digital core image based on particle partitioning according to claim 1 is characterized in that: When the particle partition boundary is shrunk at different proportions according to the centroid of the partition and the pore throat characteristic points, for any point (X i , Y i ), the coordinates of the vertices corresponding to the control boundary after scaling ( , ) is calculated by the following formula: Among them, X i is the horizontal coordinate of any point, Y i is the ordinate of any point, is the horizontal coordinate of the vertex corresponding to the control boundary, is the ordinate of the vertex corresponding to the control boundary, is the horizontal coordinate of the partition centroid, is the ordinate of the partition centroid, is the scaling factor for each point in the partition.

6. The method for generating a large-size digital core image based on particle partitioning according to claim 5, characterized in that: The scaling factor of each point in the partition The calculation formula is as follows: In the formula, is the scaling factor away from the pore throat feature point, is the ratio of the distance from the partition point to the pore throat feature point to the maximum distance, is the base scaling of the control, is the minimum distance from each point in the partition to each pore throat feature point in the partition, is the maximum value of the minimum distance from each point in the partition to the pore throat feature point, n is the total number of all points in the connected domain, is the distance from the i-th partition point to the j-th pore throat feature point, ||P i -T j || means that the i-th boundary point is being solved To the jth pore throat feature point The Euclidean distance is , and m is the number of pore throat feature points.

7. The method for generating a large-size digital core image based on particle partitioning according to claim 6, characterized in that: Scaling base away from pore throat feature points and the base scaling of the control Satisfies the following relationship: 。 8. A large-size digital core image generation system based on particle partitioning, characterized in that: include: Image stitching module: used to stitch several acquired binary images of cores to obtain a stitched image; In the binary image of the obtained core, black represents pore throats and white represents particles; Pixel inversion module: used for performing pixel inversion processing on the spliced ​​image to obtain an inverted image; Image modification module: used for performing smooth modification on the splicing boundary in the inverted image through an opening operation to obtain a modified image; A pixel skeleton medial axis image extraction module is used to extract the pixel skeleton medial axis image of the modified image; Image analysis module: used to perform connected domain analysis on the pixel skeleton medial axis image, divide each particle partition according to connectivity, color each particle partition, and obtain a particle partition map; Calculation module: used to extract the particle partition boundary of the image splicing intersection area according to the particle partition position on the particle partition map, and calculate the pore throat feature points and the partition centroid of the particle partition in the image splicing intersection area; Scaling module: used to shrink the particle partition boundary according to the partition centroid and pore throat feature points at different proportions to obtain the reconstructed particle area; Particle image reconstruction module: used to paint the reconstructed particle area black to obtain a reconstructed particle image; Large-size image generation module: used to replace the image stitching intersection area in the stitching image with the reconstructed particle image to obtain a large-size digital core image.

9. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the large-size digital core image generation method based on particle partitioning as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: A computer program is stored thereon, wherein when the computer program is executed by a processor, the method for generating a large-size digital core image based on particle partitioning as described in any one of claims 1 to 7 is implemented.

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