A method for generating a large-size digital core image based on particle partitioning and related apparatus

Abstract

The application belongs to the technical field of digital core image, and discloses a large-size digital core image generation method based on particle partition and a related device, which comprises the following steps: splicing a plurality of binary images of cores; performing pixel inversion processing on the spliced image; performing smoothing modification on the spliced boundary in the inverted image through an opening operation; extracting a pixel skeleton centerline image of the modified image; performing connected domain analysis on the pixel skeleton centerline image, dividing each particle partition according to connectivity, coloring each particle partition; extracting the particle partition boundary of the image splicing junction area according to the particle partition position on the particle partition map, and calculating the pore throat feature point and the partition centroid at the position; performing different proportional contraction on the particle partition boundary according to the partition centroid and the pore throat feature point; painting the reconstructed particle region black; replacing the image splicing junction area in the spliced image with the reconstructed particle image to obtain a large-size digital core image.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of digital core images, and particularly relates to a large-size digital core image generation method based on particle partition and a related device. BACKGROUND

[0002] A core can provide rich geological information, including formation structure, rock type, pore structure, etc. These information is crucial for determining the properties, distribution and development mode of an oil and gas reservoir. By analyzing the pore scale two-phase behavior of the core through microcosmic percolation mechanics, the variation characteristics of oil and water relative permeability can be obtained from the pore structure, so as to obtain the water content rule of the reservoir, which can be used for evaluating the reserve quality and recoverability of oil and gas resources and providing a scientific basis for exploration and development work.

[0003] A three-dimensional image of a digital core is obtained by stacking and reconstructing a plurality of two-dimensional core images, so that when a large-scale three-dimensional digital core model is established, a plurality of large-scale accurate two-dimensional digital core scanning images need to be obtained.

[0004] At present, the characterization image of a two-dimensional core is obtained through computer tomography or random numerical reconstruction based on the statistical characteristics of a porous medium.

[0005] However, when computer tomography is performed, the accurate large-format digital core image cannot be directly obtained through scanning due to the mutual restriction of scanning dimensions and resolution. When the digital reconstruction method is used to obtain the image, the initial boundary condition for reconstruction is difficult to determine. SUMMARY

[0006] To solve the problems in the prior art, the purpose of the present application is to provide a large-size digital core image generation method based on particle partition and a related device. The present application can realize continuous simulation from small-scale to large-scale digital core images, can solve the limitation of scanning dimensions and accuracy when generating images, and does not need to consider the initial boundary condition when constructing the numerical simulation, thereby providing a good foundation for large-size reservoir chip design.

[0007] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0008] A large-size digital core image generation method based on particle partition comprises the following processes:

[0009] A plurality of acquired binary images of cores are spliced to obtain a spliced image. In the acquired binary images of the cores, black represents a pore throat, and white represents a particle.

[0010] The spliced image is subjected to pixel inversion processing to obtain an inverted image.

[0011] Smooth the splicing boundary in the reversed image by an opening operation to obtain a modified image;

[0012] Extract a pixel skeleton centerline image of the modified image;

[0013] Perform connected domain analysis on the pixel skeleton centerline image, divide each particle partition according to connectivity, and color each particle partition to obtain a particle partition map;

[0014] On the particle partition map, extract the particle partition boundary of the image splicing junction area according to the particle partition position, and calculate the pore throat feature point and partition centroid of the particle partition of the image splicing junction area;

[0015] Shrink the particle partition boundary according to the partition centroid and the pore throat feature point at different proportions to obtain a reconstructed particle region;

[0016] Color the reconstructed particle region black to obtain a reconstructed particle image;

[0017] Replace the image splicing junction area in the spliced image with the reconstructed particle image to obtain a large-size digital core image.

[0018] Preferably, the plurality of binary images of cores are binary images of different cores, or binary images of different cores obtained by rotating the same core image;

[0019] When splicing the obtained plurality of binary images of cores, adjust all the binary images of cores to the same size and then splice them.

[0020] Preferably, when coloring each particle partition, adjacent particle partitions are colored with different colors.

[0021] Preferably, the pore throat feature point is a partition boundary point located at the intersection of three or more particle partitions.

[0022] Preferably, when shrinking the particle partition boundary according to the partition centroid and the pore throat feature point at different proportions, for any point (X i , Y i ) on each particle partition boundary, the coordinates (X , Y ) of the corresponding vertex of the control boundary after scaling are calculated by the following formula:

[0023]

[0024]

[0025] wherein X i is the horizontal coordinate of the arbitrary point, Yi is the longitudinal coordinate of any point, is the longitudinal coordinate of any point, is the longitudinal coordinate of any point, is the longitudinal coordinate of any point, is the longitudinal coordinate of any point, is the scaling factor of any point in the partition.

[0026] Preferably, the scaling factor of any point in the partition is calculated as follows:

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] wherein, is the scaling base 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 basic scaling of control, is the minimum distance from the partition point to the pore throat feature point in the partition, is the maximum value of the minimum distance from the partition point to the pore throat feature point, and n is the total number of all points in the connected domain, is the distance from the ith partition point to the jth pore throat feature point, and ||P i -T j || represents the Euclidean distance from the ith boundary point to the jth pore throat feature point , and m is the number of pore throat feature points.

[0033] Preferably, the scaling base away from the pore throat feature point and the basic scaling of control satisfy the following relationship:

[0034] .

[0035] The application also provides a large-size digital core image generation system based on particle partition, comprising:

[0036] An image splicing module is configured to splice a plurality of acquired binary images of cores to obtain a spliced image, wherein black represents a pore throat and white represents a particle in the acquired binary images of the cores.

[0037] A pixel inversion module is configured to perform pixel inversion processing on the spliced image to obtain an inverted image.

[0038] An image modification module is configured to perform smoothing modification on a spliced boundary in the inverted image by an opening operation to obtain a modified image.

[0039] A pixel skeleton centerline image extraction module is configured to extract a pixel skeleton centerline image of the modified image.

[0040] An image analysis module is configured to perform connected domain analysis on the pixel skeleton centerline image, divide each particle region according to connectivity, color each particle region, and obtain a particle region map.

[0041] A calculation module is configured to extract a particle region boundary of an image splicing joint area according to a particle region position on the particle region map, and calculate a pore throat feature point and a region centroid of the particle region of the image splicing joint area.

[0042] A scaling module is configured to perform different proportional contractions on the particle region boundary according to the pore throat feature point and the region centroid to obtain a reconstructed particle region.

[0043] A particle image reconstruction module is configured to color the reconstructed particle region into black to obtain a reconstructed particle image.

[0044] A large-size image generation module is configured to replace the image splicing joint area in the spliced image with the reconstructed particle image to obtain a large-size digital core image.

[0045] The application further provides an electronic device, which comprises:

[0046] one or more processors;

[0047] a storage device having one or more programs stored thereon;

[0048] 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 regions as described above.

[0049] The application further provides a storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the large-size digital core image generation method based on particle regions as described above.

[0050] The application has the following beneficial effects:

[0051] The application can perform image splicing operation on the basis of the acquired digital core structure characteristics, and then reconstruct and simulate the medium particle structure at the splicing boundary, guarantee the pore throat characteristics of the splicing boundary particles, and realize the generation of large-scale digital core image. The application realizes the continuous simulation from small-scale to large-scale digital core image, solves the limitation of scanning dimension and precision during image generation, and does not need to consider the boundary initial condition during numerical simulation construction, thereby providing a good foundation for large-size reservoir chip design. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0053] Figure 1 The flow chart of the large-size digital core image generation method based on particle partition provided by the embodiment of the application.

[0054] Figure 2 The small-size core scanning image for testing in the embodiment of the application.

[0055] Figure 3 The splicing schematic diagram of the core scanning image pixel inversion in the embodiment of the application.

[0056] Figure 4 The schematic diagram obtained after the opening operation of the spliced image in the embodiment of the application.

[0057] Figure 5 The skeleton centerline extraction schematic diagram of the spliced image in the embodiment of the application.

[0058] Figure 6 The particle partition schematic diagram of the spliced image in the embodiment of the application.

[0059] Figure 7 The particle partition boundary, feature point and center of mass schematic diagram of the splicing junction of the spliced image in the embodiment of the application.

[0060] Figure 8 The large-size core image result of the splicing junction of the spliced image under the first scaling ratio reconstruction in the embodiment of the application.

[0061] Figure 9 The large-size core image result of the splicing junction of the spliced image under the second scaling ratio reconstruction in the embodiment of the application.

[0062] Figure 10The large-size core image result of the splicing junction of the spliced image in the embodiment of the present application is reconstructed at a third scaling ratio. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical scheme and advantages of the present application more clear, the embodiment of the present application will be described in further detail below with reference to the drawings.

[0064] Reference Figure 1 The large-size digital core image generation method based on particle partition of the present application comprises the following steps:

[0065] Step 1: Obtain a plurality of small-size digital core binary images generated after CT scanning, and splice the images according to the arrangement mode of m*n (m≥1, n≥1) as required to obtain a spliced image; wherein the plurality of small-size digital core binary images generated after CT scanning can be a plurality of different binary images, in which case all the binary images should be made the same size before splicing; or can be a plurality of binary images obtained after rotating a same binary image at different angles; in the binary image of the core, black represents a pore throat, and white represents a particle;

[0066] Step 2: Preprocess the obtained spliced image, the process being as follows: perform an opening operation on the image pixels after inverting the image pixels to smooth the particle morphology of the spliced boundary in the image pixels after inverting the image (i.e. the inverted image), and obtain a modified image. The use of the opening operation in the present application can make the medium particles at the image splicing boundary not appear excessive longitudinal skeleton at the longitudinal splicing position and excessive transverse skeleton at the transverse splicing position when the pixel skeleton centerline of the image is extracted in step 3, and the shape of the generated skeleton is more natural.

[0067] Step 3: Extract the pixel skeleton centerline image of the spliced image (i.e. the modified image) after preprocessing in step 2, perform connected component analysis on the pixel skeleton centerline image, divide each particle partition according to the connectivity, and color each particle partition, wherein adjacent particle partitions are colored in different colors to obtain a particle partition map; in this step, the skeleton extraction is performed by using a skeleton thinning algorithm based on Euclidean distance transformation, and the connected region of the image is obtained by using a 4-neighbor seed filling algorithm.

[0068] Step 4: Extract the particle partition boundary of the image splicing junction region 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 of the image splicing junction region; wherein the pore throat feature points should be set as the partition boundary points at the intersection of three or more particle partitions to represent the pore throat characteristics of the porous medium.

[0069] Step 5: Shrink the particle partition boundary at different ratios according to the partition centroid and the pore throat feature points to obtain the reconstructed particle area, paint the reconstructed particle area in black, obtain the reconstructed particle image, and replace the image stitching intersection area in the stitched image with the reconstructed particle image. Thus, the generation of the large-scale digital core image is completed. In this step, when shrinking the particle partition boundary at different ratios according to the partition centroid and the pore throat feature points, for any point P on the boundary of each particle 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 i Also 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.

[0070] 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):

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] Where, ||P i -T j || represents the i-th boundary point P i To Pore ​​throat characteristic point T j The Euclidean distance of , for obtaining the i-th boundary point P i distance from the nearest feature point; with is a normalized processing of the distance of each boundary point to the nearest pore throat feature point, the boundary point closer to the pore throat feature point, the closer to 0. in is a scaling base away from the pore throat feature point, the greater the value, the greater the throat radius and the pore throat ratio, and the more obvious the pore throat feature of the reconstructed particle; is the basic scaling of control, the value determines the basic size of the reconstructed particle. It should be noted that .

[0077] Embodiment

[0078] The embodiment based on the large-size digital core image generation method of particle partition includes the following steps:

[0079] Step 101, select a 1000*1000 sandstone CT scanning two-dimensional slice image (which is a binary image) as a test splicing image, that is Figure 2 , Figure 2 In the figure, black pixels represent pore throats, and white pixels represent particles.

[0080] Step 102, splice the image shown in FIG. 1 according to a 3*3 arrangement mode to obtain a spliced image with a size of 3000*3000, and perform pixel inversion processing on the spliced image to exchange the pixel values of black and white pixel points, highlight the medium particles, and obtain an inverted image, that is Figure 2 . Figure 3

[0081] Step 201, perform an opening operation on the inverted image obtained in step 102 to modify the unnatural medium particles in the spliced image obtained after splicing the small-size image shown in FIG. 2 according to a 3*3 arrangement mode; specifically, perform a corrosion-then-dilation operation on the image using a convolution kernel to eliminate the noise points at the splicing boundary, set the convolution size to 5, and obtain a preprocessed spliced image shown in FIG. 3, denoted as a modified image. Figure 2 Figure 4

[0082] Step 301, extract the pixel skeleton centerline of the modified image obtained in step 201 using an image skeleton algorithm; specifically, perform Euclidean distance transformation to divide each pixel point in the modified image into white domain points, boundary points, and white domain points outside the domain, then take the white pixel points as target points for extraction distance and the black pixel points as background points, calculate the minimum distance of each target point to the background points, and obtain a scalar distance field from the white pore region to the black medium region. ​​​

[0083] Step 302, on the basis of the scalar distance field, the skeleton thinning algorithm is selected to extract the image skeleton, and each non-zero gray value pixel point in the image is traversed through iteration, the attributes of the 8-neighborhood pixel points around it are judged, the boundary points meeting the specific conditions are gradually removed (i.e. set to black pixels), until no additional boundary points can be removed, and the remaining pixel points will constitute the skeleton region of the image. The thinning algorithm starts to perform thinning operation on the outermost contour line of the generated distance field, and gradually shrinks inward along the direction in which the contour line decreases after distance transformation. Finally, the skeleton feature map of the modified image is extracted, denoted as the pixel skeleton centerline image, as shown in Figure 5 .

[0084] Step 303, taking the region constituted by the skeleton of the pixel skeleton centerline image as the control region of the particles, each particle is partitioned, and the 4-neighborhood seed filling algorithm is used to obtain the image connected region. A black pixel point in the image is selected as the seed, and the 4-neighborhood adjacent and black pixel points are regarded as the adjacent foreground pixels of the seed, added to the same pixel set, and marked as visited. The obtained pixel set is a connected region, which is the generated control region of a single particle medium. Then the next unvisited black pixel point is traversed until all sets are found. Each region is assigned a different color to obtain the particle partitioning map as shown in Figure 6 , which is used to distinguish each partition.

[0085] Step 401, according to the connected characteristics of the 4-neighborhood, the peripheral boundary of each control partition in the particle partitioning map is extracted.

[0086] Step 402, feature control points are found on the boundary of each control partition. Since the subsequent contraction and reconstruction of the region is to preserve the pore throat characteristics of the medium, the feature points are set to the partition boundary points located at the intersection of three and more particle partitions, i.e. the feature points are the intersection points of the three skeleton boundaries. The boundary of each region usually has at least 2-3 feature points.

[0087] Step 403, the centroid of the n points contained in each control region partition is obtained by using the formula .

[0088] The test results of the above steps are shown in Figure 7 .

[0089] Step 501, the distance of each partition boundary point to the pore throat feature point is calculated, and the maximum distance in the partition is normalized; any point on the control region boundary is denoted as Pi (i=1, 2, …, n), and the boundary has pore throat feature points Tj (j=1, 2, 3).

[0090]

[0091]

[0092]

[0093] Step 502: Normalize the points according to the maximum distance within 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).

[0094]

[0095]

[0096] In the above formula The value of is determined by actual needs, but it should be noted that .

[0097] Step 503, by formula The points on the partition boundary are 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-scale digital core image that conforms to nature after the reconstructed splicing boundary.

[0098] Step 504: Select α=0.2, β=0.75 to determine the first scaling ratio and obtain the test Figure 8 .

[0099] Step 505: Select α=0.15, β=0.7 to determine the second scaling ratio and obtain the test Figure 9 .

[0100] Step 506: Select α=0.2, β=0.6 to determine the third scaling ratio and obtain the test Figure 10 .

[0101] As can be seen, the present method for generating large-scale digital core images based on particle zoning can generate large-scale digital core images while preserving the pore-throat characteristics of the grains at the splicing boundary. This method overcomes the dimensional limitations of core images generated by computer scanning while eliminating the need to consider boundary issues and initial conditions when constructing core images using a single numerical simulation. This allows the generation of digital core images that better reflect natural conditions.

[0102] The present invention also provides a system for implementing the above-mentioned method for generating a large-scale digital core image based on particle partitioning, comprising:

[0103] Image stitching module: used to stitch together several acquired binary images of the core to obtain a stitched image;

[0104] Pixel inversion module: used for pixel inversion processing on the spliced image to obtain an inverted image;

[0105] Image modification module: used for smoothing modification on the splicing boundary in the inverted image through an opening operation to obtain a modified image;

[0106] Pixel skeleton centerline image extraction module: used for extracting a pixel skeleton centerline image of the modified image;

[0107] Image analysis module: used for connected domain analysis on the pixel skeleton centerline image, dividing each particle partition according to connectivity, coloring each particle partition to obtain a particle partition map;

[0108] Calculation module: used for extracting a particle partition boundary of the image splicing joint area according to the particle partition position on the particle partition map, and calculating a pore throat feature point and a partition centroid of the particle partition of the image splicing joint area;

[0109] Scaling module: used for different proportion contraction of the particle partition boundary according to the partition centroid and the pore throat feature point to obtain a reconstructed particle region;

[0110] Particle image reconstruction module: used for coloring the reconstructed particle region into black to obtain a reconstructed particle image;

[0111] Large-size image generation module: used for replacing the image splicing joint area in the spliced image with the reconstructed particle image to obtain a large-size digital core image.

[0112] The embodiment of the present application further provides a corresponding electronic device and a computer readable storage medium, which are used for implementing the scheme provided by the embodiment of the present application.

[0113] The device comprises a memory and a processor, the memory is used for storing instructions or codes, and the processor is used for executing the instructions or codes to enable the device to execute the large-size digital core image generation method based on particle partitioning described in any embodiment of the present application.

[0114] The storage medium stores a computer program, and when the processor executes the computer program, the large-size digital core image generation method based on particle partitioning described in any embodiment of the present application is implemented.

[0115] The present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt 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.

[0116] Obviously, the described embodiments are only part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor should belong to the protection scope of the present application.

Claims

1. A method for generating large-scale digital core images based on particle partitioning, characterized in that: The process includes the following: Splicing a plurality of 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 spliced ​​image to obtain an inverted image; Smoothing 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; 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; On the particle partition map, the particle partition boundaries of the image stitching intersection area are extracted according to the particle partition positions, and the pore throat feature points and the partition centroids of the particle partitions in the image stitching intersection area are calculated; The particle partition boundary is shrunk at different ratios according to the partition centroid and the pore throat feature points to obtain the reconstructed particle area. Specifically, when the particle partition boundary is shrunk at different ratios according to the partition centroid and the pore throat feature points, for any point (X i , Y i ), the coordinates of the vertex corresponding to the control boundary obtained 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 vertical coordinate 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; The scaling factor of each point in the partition The calculation formula is as follows: Where, 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, m is the number of pore throat feature points; The reconstructed particle area is painted black to obtain the 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 large-scale digital core images based on particle partitioning according to claim 1, characterized in that: The plurality of binary images of the rock cores are binary images of a plurality of different rock cores, or are binary images of different rock cores obtained by rotating the same binary image of the rock core; When stitching together a plurality of 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 large-scale digital core images based on particle partitioning according to claim 1, characterized in that: When coloring each particle partition, adjacent particle partitions are painted with different colors.

4. The method for generating large-scale digital core images 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 large-scale digital core images based on particle partitioning according to claim 1, characterized in that: Scaling base away from pore throat feature points and the base scaling of the control The following relationship is satisfied: 。 6. A large-scale digital core image generation system based on particle partitioning, characterized in that: include: Image stitching module: used to stitch together several acquired binary images of the core 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; Pixel skeleton medial axis image extraction module: used for extracting 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 boundaries of the image splicing intersection area according to the particle partition positions on the particle partition map, and calculate the pore throat feature points and partition centroids of the particle partitions in the image splicing intersection area; Scaling module: It is used to shrink the particle partition boundary according to the partition centroid according to the pore throat feature point at different ratios to obtain the reconstructed particle area; specifically, when shrinking the particle partition boundary according to the partition centroid according to the pore throat feature point at different ratios, for any point on the particle partition boundary (X i , Y i ), the coordinates of the vertex corresponding to the control boundary obtained 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 vertical coordinate 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; The scaling factor of each point in the partition The calculation formula is as follows: Where, 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, m is the number of pore throat feature points; 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 stitched image with the reconstructed particle image to obtain a large-size digital core image.

7. 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 according to any one of claims 1 to 6.

8. 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 according to any one of claims 1 to 6 is implemented.

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