Method for adjusting pore throat channel of digital core image and related device

Abstract

The invention belongs to the technical field of digital core images, and discloses a digital core image pore throat channel adjustment method and a related device, and the method comprises the steps: carrying out the pixel color bitwise negation operation on an obtained digital core binary initial image, extracting a pore throat central axis to generate a particle skeleton diagram, and obtaining a connected domain partition diagram through connected domain extraction and labeling; selecting a pore throat channel reconstruction region through an interaction prompt box, marking corresponding particle pixels to generate an adjustment particle graph, and matching the control region to which the adjustment particle graph belongs to obtain a to-be-adjusted particle partition graph; and geometric transformation is carried out on the partition boundary according to a preset proportion based on the pore throat feature points, a pore throat channel is reconstructed, corresponding region pixels of the original image are replaced, and the pore throat channel adjustment of the digital core image is realized. By modifying the partial pore throat structure of the digital core, the pore channel structure of the selected area is adjusted on the basis of maintaining the pore throat characteristics of the digital core, and a result image applied to specific seepage simulation is generated.

Description

Technical Field

[0001] This invention belongs to the field of digital core imaging technology, specifically relating to a method and related apparatus for adjusting the pore throat channel of a digital core image. Background Technology

[0002] Digital core technology (DCT) involves digitizing core samples using high-resolution imaging techniques (such as CT scans and X-ray imaging) to generate two-dimensional or three-dimensional images of the core's internal structure, which can then be further analyzed and simulated on a computer. DCT not only accurately reproduces the microstructure of core samples but also provides a valuable tool for research in rock physics, rock mechanics, and oil and gas reservoir engineering.

[0003] In recent years, the application of digital cores in oil exploration and development has become increasingly widespread, especially in oil seepage simulation, reservoir numerical simulation, and engineering design, where it has demonstrated tremendous potential. Oil seepage simulation is one of the core components of reservoir engineering; simulating the flow of oil and natural gas within the reservoir is crucial for predicting reservoir development outcomes and optimizing extraction strategies.

[0004] Digital core technology allows researchers to precisely reconstruct the microscopic pore structure of core samples, encompassing key features such as porosity, pore throat distribution, and pore throat morphology. This provides more accurate fundamental data for seepage analysis. Using this pore structure data, researchers can deeply analyze important parameters such as the flow characteristics, pressure distribution, and velocity variations of petroleum fluids, thus providing a more realistic physical model of the seepage process. This precise core reconstruction significantly enhances the scientific rigor of oilfield development plans, providing theoretical support for optimizing development strategies and improving oil recovery rates. It also provides decision-making basis for the design and implementation of secondary and tertiary oil recovery technologies such as waterflooding and gasflooding.

[0005] However, in practical applications, the pore-throat structure of digital cores cannot always fully meet engineering requirements. Especially when researchers face special engineering needs, such as simulating specific types of reservoirs or optimizing oil production efficiency, existing core images often require certain adjustments and optimizations. Summary of the Invention

[0006] To address the problems existing in the prior art, the present invention aims to provide a method and related apparatus for adjusting the pore throat channel of a digital core image. The present invention modifies part of the pore throat structure of the digital core, thereby adjusting the pore channel structure of a selected area while maintaining the pore throat characteristics of the digital core, and generating a result image applicable to a specific seepage simulation.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for adjusting the pore throat channel in a digital core image includes the following steps: The pixel colors of the acquired initial digital core binarized image are inverted bitwise to generate a pixel-inverted core image. Extract the pore throat axis from the pixel-inverted core image to generate a particle skeleton map reflecting the topological features of the pore throat; Connectivity regions were extracted from the particle skeleton map, and each region was labeled to obtain a connected region partition map of the core image. By selecting an interactive prompt box to divide the specific area in the target image that needs to be reconstructed for the pore throat channel, using preset color pixels to label all pixels of the particulate matter that interacts with the prompt box, and generating an adjusted particle map; Based on the selected particles in the adjusted particle map, the control region to which the selected particles belong is matched in the connected domain partition map of the core image to obtain the particle partition map to be adjusted; Calculate the pore throat feature points and particle partition boundaries of the selected particle partition on the particle partition map to be adjusted; The particle partition boundary is geometrically transformed according to the pore throat feature points in a preset ratio to generate the reconstructed pore throat channel region. The pixel color of the reconstructed pore throat channel region is rendered to the same color as the pore throat channel pixel color in the core image, and the remaining black pixel region in the control partition is represented as the reconstructed grain to obtain the reconstructed image. The selected adjustment area in the initial binarized digital core image is replaced with the pixel content of the corresponding position in the reconstructed image to achieve pore throat channel adjustment in the digital core image.

[0008] Preferably, when extracting connected components from the particle skeleton map and labeling each region to obtain a connected component partitioning map of the core image: Each pixel in a connected component is assigned a uniform color identifier, and the pixel colors of different connected components are different. Each connected component corresponds to an independent particle control partition. All pixels in the same connected component belong to the same particle structure unit, and different connected components represent independent particle units.

[0009] Preferably, the interactive prompt box types include: dot selection, linear selection, elliptical selection, rectangular selection, and polygonal selection.

[0010] Preferably, pixels located at the intersection of three or more different particle partitions are selected as the pore throat feature points of the particle partition; Boundary points in a particle partitioning map are defined as: the set of pixels that constitute the outermost contour of a particle partition and whose pixel color values ​​match the identifier color of the particle partition. In the initial image of the digital core binarization, black pixels are used to mark the core pore throat channels, and white pixels are used to mark the granular material. When extracting the pore throat axis from the pixel-inverted core image and generating a particle skeleton map reflecting the topological features of the pore throat, black pixels are used to label the granular material and white pixels are used to label the pore throat channels. A skeleton extraction algorithm was used to extract the pore throat axis of the core image after pixel inversion, generating a particle skeleton map that reflects the topological features of the pore throat.

[0011] Preferably, the proportional index value involved in the independent generation of the adjustment orifice throat channel region for each particle partition should be determined by the following formula:

[0012]

[0013]

[0014]

[0015] Where m is the total number of pore throat feature points selected in the current particle partition; n is the total number of pixels located at the partition boundary in the current particle partition. This represents the Euclidean distance from the j-th pixel on the partition boundary to the i-th aperture throat feature point within that partition; This is a minimum value function used to obtain... The minimum value in; This is a function for obtaining the maximum value. The maximum value in; Indicates a point The minimum distance to each feature point; D represents the maximum value of the minimum distance to the throat feature point calculated from each point in the partition. Yes The distance-normalized proportional index value is used to quantify the relative spatial relationship between boundary points and pore throat feature points, thereby guiding the adjustment ratio of the pore throat channel region.

[0016] Preferably, when reconstructing and generating the adjusted aperture throat channel, for each pixel on the partition boundary, a circular adjustment area of ​​a specified length is generated inward along the normal direction of the pixel into the partition, and the color value of all pixels within the coverage of the circular area is set to white to represent the reconstructed aperture throat channel. The radius parameter of the circular adjustment area generated when reconstructing the aperture throat channel is the actual throat radius value of the aperture throat channel corresponding to that pixel. The geometric scale of the circular adjustment area is controlled by this radius to realize the reconstruction of the aperture throat channel structure.

[0017] Preferably, the adjusted orifice throat channel radius length generated after reconstruction is determined by the following formula:

[0018]

[0019]

[0020]

[0021] in, It is the reconstructed orifice throat channel radius coefficient, where L is the radius length of the reconstructed orifice throat channel. , These are the longest and minimum radii of the reconstructed pore throat channel, respectively. It is the approximate effective length of the current particle partition, expressed by the formula K. 1 / 2 The calculation determines that K is the area of ​​the current particle partition, i.e., the total number of pixels contained within the partition. The formula uses a linear interpolation method to adjust the radius coefficient. Mapping to the range between the longest and smallest radii determines the specific radius length of the pore throat channel in the current particle partition, thereby achieving directional control of the pore throat structure; scale is a global scale control parameter used to adjust the overall size of the generated pore throat channel; ptr is a pore throat ratio control parameter used to adjust the pore throat ratio of the pore throat channel; The opening control parameter is used to adjust the opening of the orifice throat channel.

[0022] The present invention also provides a system for adjusting the pore throat channel of a digital core image, used to implement the above-mentioned method for adjusting the pore throat channel of a digital core image, comprising: Pixel inversion operation unit: used to perform a bit-by-bit inversion operation on the acquired digital core binarized initial image to generate a core image with inverted pixels; Pore ​​throat central axis extraction unit: used to extract the pore throat central axis from the pixel-inverted core image and generate a particle skeleton map reflecting the topological features of the pore throat; Connected domain extraction unit: used to extract connected domains from the particle skeleton map and label each region to obtain a connected domain partition map of the core image; Adjusted particle map generation unit: used to divide the specific area in the target image that needs to be reconstructed for pore throat channels by selecting the interactive prompt box, use preset color pixels to mark all pixels of the particulate matter that interacts with the prompt box, and generate an adjusted particle map; The unit for obtaining the particle zoning map to be adjusted is used to obtain the particle zoning map to be adjusted by matching the control region to which the selected particle belongs in the connected domain zoning map of the core image based on the selected particle in the adjustment particle map. Calculation unit: used to calculate the pore throat feature points and particle partition boundaries of the selected particle partition on the particle partition map to be adjusted; Geometric transformation unit: used to geometrically transform the particle partition boundary according to the pore throat feature points in a preset ratio to generate the reconstructed pore throat channel region; Image reconstruction unit: used to render the pixel color of the reconstructed pore throat channel region to the same color as the pore throat channel pixel color in the core image, and the remaining black pixel area in the control partition is represented as the reconstructed grain to obtain the reconstructed image; Replacement unit: Used to replace the selected adjustment area in the initial image of the digital core binarization with the pixel content of the corresponding position in the reconstructed image, thereby realizing the adjustment of the pore throat channel of the digital core image.

[0023] The present invention also provides an electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for adjusting the pore throat channel of the digital core image as described above.

[0024] The present invention also provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the method for adjusting the pore throat channel of a digital core image as described above.

[0025] The present invention has the following beneficial effects: This invention presents a method for adjusting pore throat channels in digital core images, precisely addressing the issue of insufficient targeting in digital core pore throat adjustments. It achieves the goal of maintaining overall characteristics while optimizing the target area. Specifically, the method involves: first, inverting pixels in the binarized image to enhance pore throat region features, making the contrast between the pore throat and particle boundaries clearer and eliminating interference for subsequent pore throat axis extraction; then, generating a particle skeleton map through axis extraction to accurately capture topological features such as pore throat connectivity; finally, decomposing the core structure through connected component annotation to clarify the spatial assignment of pore throats and particles, providing a precise basis for directional adjustments. For specific engineering needs, this method uses an interactive prompt box to allow researchers to independently define the target area, and then annotates relevant particle pixels with preset colors, enabling on-demand definition of the adjustment area. This allows for both fine-tuning of micro-pore throats and overall adjustment of functional areas, adapting to diverse seepage simulation scenarios. In the adjustment phase, the target region is located by matching and adjusting the particle map and the connected domain partition map. After calculating the pore throat feature points and boundaries, a geometric transformation is performed proportionally based on the feature points to avoid distortion of the pore throat morphology. Subsequently, the reconstructed region is unified with the original particle color to achieve pixel-level seamless connection, optimizing the target pore throat without destroying the structure of the non-adjusted region. Finally, the output is completed by replacing the corresponding region in the initial image. It can be directly used for seepage simulation without remodeling, providing technical and support methods for digital core design under special engineering needs, and improving the scientific nature and recovery rate of oilfield development plans. Attached Figure Description

[0026] Figure 1 This is a flowchart of the method for adjusting the pore throat channel in digital core images according to the present invention; Figure 2 This is a binarized image of a two-dimensional CT scan slice of sandstone obtained in an embodiment of the present invention; Figure 3 These are the inverted core images obtained in the embodiments of the present invention; Figure 4 These are core skeleton images obtained in the embodiments of the present invention; Figure 5 This is a core connectivity image obtained in an embodiment of the present invention; Figure 6 This is the interactive interface of the reconstructed adjustment area selection device in this embodiment of the invention; Figure 7 These are the types of prompt boxes in the embodiments of the present invention; Figure 8 It is the pixel-marked image below the prompt box in the embodiment of the present invention; Figure 9 This refers to the instance-annotated particle image to be adjusted obtained in the embodiments of the present invention; Figure 10 The core images obtained in this embodiment of the invention are reconstructed at a scale of 0.05, ptr=6. The result image under =2); Figure 11 The core images obtained in this embodiment of the invention are reconstructed at a scale of 0.05, ptr=2. The result image under =6); Figure 12 The core images obtained in this embodiment of the invention are reconstructed at a scale of 0.1, ptr=6. The result image under =2); Figure 13 The core images obtained in this embodiment of the invention are reconstructed at a scale of 0.1, ptr=2. The resulting image under =6). Detailed Implementation

[0027] The present invention will be further described clearly and in detail below with reference to specific embodiments and the accompanying drawings. Those skilled in the art will be able to implement the present invention based on these descriptions. Furthermore, the embodiments of the present invention described below are generally only some, not all, of the embodiments of the present invention. Therefore, all other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0028] Reference Figure 1 The method for adjusting the pore throat channel in a digital core image according to the present invention includes the following steps: Step 1: Obtain the digital core binarized image to be adjusted, see [link to relevant documentation]. Figure 2 In the binarized core image, black represents pore throat channels and white represents granular material. To adapt to the input requirements of the subsequent skeleton extraction algorithm, the pixel values ​​of each point in the original binarized core image are bitwise inverted to reverse the pixel color semantics. That is, black pixels are labeled with granular material and white pixels are labeled with pore throat channels, thereby generating a pixel-inverted core image (referred to as the "inverted image"). See [link to relevant documentation]. Figure 3 .

[0029] Step 2: Based on the inverted core image, an image skeleton extraction algorithm based on Euclidean distance transform is used to extract the axial structure image of the pore throat channel skeleton, generating a particle skeleton map reflecting the topological features of the pore throat. (See [link]). Figure 4The particle skeleton image is also a binary image, where black pixels mark the internal regions of the particle skeleton, and white pixels mark the pore axis (i.e., the center line of the pore throat channel, which is also the boundary contour of the particle skeleton). Further, a connected component analysis using a 4-neighborhood seed filling algorithm is performed on the skeleton axis image to label all connected black pixel regions. Pixels within the same connected component are assigned a uniform label color, while pixels between different connected components are assigned different label colors, resulting in a particle partitioning map. See [link to relevant documentation]. Figure 5 In generating the connected component partitioning map, each connected component corresponds to an independent particle control partition. All pixels within the same connected component belong to the same particle structure unit, while different connected components represent independent particle units.

[0030] Step 3: Construct an interactive region annotation module (i.e., a reconstruction adjustment region selection device (graphical interactive interface)). This module uses interactive prompt boxes to delineate specific regions in the target image that require pore throat channel reconstruction. The user-specified red prompt box defines the area of ​​particles or regions to be adjusted within the image. In this step, all black pixels covered by the prompt box and their associated particles are marked as the target to be adjusted and highlighted with superimposed red pixels. The final image of the particles to be adjusted is generated. The reconstruction adjustment region selection device supports the following prompt box types: dotted selection, linear selection, elliptical selection, rectangular selection, and polygonal selection. Users can define the target adjustment region using any of these prompt box types.

[0031] Step 4: For the particle image to be adjusted, determine its corresponding particle partition. For each independent particle partition to be adjusted, perform the following operations: Obtain the partition boundary and pore throat feature points of the particle partition. The partition boundary is the outermost continuous set of pixels within the particle partition obtained through an edge detection algorithm, defined as the partition boundary. The boundary points of the particle partition are defined as the set of pixels that constitute the outermost contour of the partition and whose pixel color values ​​match the partition's identifier color. The pore throat feature points of the partition are selected from the boundary pixels located at the intersection nodes of three or more different particle partitions and simultaneously belonging to the current partition to be adjusted, used to characterize the pore throat features of the partition.

[0032] Step 5: Process each particle partition to be adjusted. For each pixel on the boundary of the current partition, determine the radius of the circular adjustment area along its normal direction into the partition according to the differential scaling parameter. Set the color value of all pixels within the coverage of the circular area to white, representing the reconstructed pore throat channel. The radius parameter of the circular adjustment area generated during the reconstruction of the pore throat channel is the actual throat radius value of the pore throat channel corresponding to that point. This radius controls the geometric scale of the circular adjustment area to achieve accurate reconstruction of the pore throat channel structure. The set of all generated white circular areas constitutes the reconstructed pore throat channel of the partition. The remaining black pixels inside the partition that are not covered by the white circular areas represent the reconstructed particle material. Finally, replace the user-specified adjustment area in the original digital core binarized image with the composite image of the reconstructed particles and pore throat channels generated in this step. Through this replacement operation, the final output is an adjusted digital core image that meets engineering requirements, realizing precise control and functional design of the digital core pore throat structure.

[0033] In step 5, for any boundary point Pi (i=1, 2, ..., n) on the boundary of the particle partition to be adjusted, it corresponds to an independent coefficient factor in the calculation of the pore throat channel radius. (i = 1, 2, ..., n). This coefficient factor is used to quantify the spatial correlation between boundary points and the pore throat feature structures within the partition, and is dynamically determined through its geometric relationship with the pore throat feature points and the pore throat feature points Tj (j = 1, 2, ..., m) in the partition boundary points:

[0034]

[0035]

[0036]

[0037] In the formula, ||Pi-Tj|| represents the i-th boundary point. To the Each throat feature point The Euclidean distance is given by m, where m is the total number of pore throat feature points selected in the current particle partition, and n is the total number of pixels located at the partition boundary in the current particle partition. This represents the Euclidean distance from the j-th pixel on the partition boundary to the i-th aperture throat feature point within that partition; It is the distance between the i-th boundary point Pi and its nearest feature point; This is a minimum value function used to obtain... The minimum value in; This is a function for obtaining the maximum value. The maximum value in; This is the coefficient factor (i.e., the scale index value) obtained after normalizing the distance from each boundary point to the nearest aperture throat feature point. It is used to quantify the relative spatial relationship between the boundary point and the aperture throat feature point, thereby guiding the adjustment scale of the aperture throat channel region. The closer a pixel is to the boundary point of the aperture throat feature point, the higher its... The closer it is to 0.

[0038] After obtaining the coefficient factor at this point, and combining it with the adjustable parameters of the engineering, the radius of the generated orifice throat channel is calculated using a linear interpolation model according to the following formula:

[0039]

[0040]

[0041]

[0042] in, It is the reconstructed orifice throat radius coefficient, where L is the orifice throat radius generated by the reconstructed orifice throat at the i-th boundary point. , These are the longest and minimum radii of the reconstructed orifice throat channel, respectively. It is the approximate effective length of the particle partition, given by formula K. 1 / 2 Determined, where K is the area of ​​the particle partition, i.e., the total number of pixels contained within the partition. The formula uses a linear interpolation method to convert the radius coefficient... Mapping to the range between the longest and smallest radii determines the specific radius length of the pore throat channels in the current grain partition, enabling directional control of the pore throat structure. When determining the reconstructed pore throat channel radius, the following three engineering-adjustable feature parameters also need to be introduced to adapt to the differentiated needs of actual application scenarios, generating reconstructed core images that meet practical requirements: scale: A global scale control parameter used to adjust the overall size of the generated pore throat channel; ptr: Used to adjust the pore-throat ratio of the pore-throat channel; Used to adjust the opening of the throat channel.

[0043] Example The method for adjusting the pore throat channel based on digital core images in this embodiment includes the following steps: Step 101: Select a 1284*1271 CT scan two-dimensional slice binarized image of sandstone (pre-processed with filtering and noise reduction) as the initial image for adjusting the pore throat channel, such as... Figure 2In this image, black pixels represent pore throats, and white pixels represent grains. This image serves as the base input for subsequent pore throat channel adjustment methods, used to verify the applicability and effectiveness of the methods in actual core structure data.

[0044] Step 102: To adapt to the input requirements of the subsequent skeleton extraction algorithm, the initial image is pixel-wise inverted to satisfy the pixel color value j'(x,y) = 255 - j(x,y); where j(x,y) is the color value of the current point. The inverted image is obtained, as shown below. Figure 3 Black pixels represent grains, and white pixels represent pores or throats.

[0045] Step 201: Extract the pixel skeleton central axis of the modified image obtained in Step 201 using the image skeleton algorithm, and perform pixel spatial classification based on Euclidean distance transformation. By calculating the Euclidean distance from each pixel in the image to the nearest background pixel (black pixel), the entire image pixels are divided into three functional regions: Points within the white area: Pixels located inside the set of white pixels (representing the aperture channel) and far from the background; Boundary point: The transition pixel point at the intersection of the white pixel set and the black pixel set; White out-of-field points: Non-target pixels located outside the set of white pixels or directly adjacent to the background.

[0046] Based on the above classification results, white pixels are used as the target objects for distance calculation, and black pixels are used as the distance reference benchmarks. Through iterative Euclidean distance calculation, the minimum straight-line geometric distance from each white target point to the nearest black background point is determined, and finally a scalar distance field reflecting the spatial distribution characteristics of the pore channels is generated.

[0047] Step 202 introduces an iterative skeleton thinning algorithm based on the scalar distance field to achieve accurate extraction of the image skeleton. This algorithm traverses the non-zero grayscale value region pixel by pixel, analyzes the attribute state of the pixels in its 8-neighborhood for each target pixel, and dynamically determines the redundant boundary pixels to be removed according to preset topological constraints.

[0048] In practice, the algorithm starts with the outermost contour line of the distance field as the initial thinning layer, iteratively shrinking the boundary pixels along the decreasing direction of the distance transformation contour lines: for boundary points that meet the removal criteria, their grayscale values ​​are reset to black, until there are no redundant boundary points in the image that can be further removed. The final set of retained pixels constitutes the core skeleton structure of the image, and this skeleton feature map is defined as the pixel skeleton midline image, such as... Figure 4 .

[0049] Step 203: Based on the extracted pixel skeleton central image, the area covered by the skeleton pixel set is defined as the control region of the granular medium. For the set of black pixels within this control region, a 4-neighborhood seed filling algorithm is used to perform connected component segmentation: Any unvisited black pixel in the image is selected as the initial seed point; the pixels to be processed are managed using a stack structure, and adjacent black pixels within the 4-neighborhood of the current seed point are marked as foreground pixels and added to the same pixel set; pixels already included in the set are marked as "visited" to avoid redundant calculations; the above expansion and marking process is repeated until all connected black pixels of the current seed point are included in the set, and the resulting pixel set is an independent connected region; all unvisited black pixels in the image are traversed, and the preceding steps are repeated until all independent connected regions are extracted; and a unique color identifier is assigned to each connected region to distinguish control regions of different granular media, ultimately generating a granular partition map, such as... Figure 5 .

[0050] Step 301: Accurately select the area to be reconstructed and adjusted using the graphical interactive module, such as... Figure 6 This module supports input of various geometric types, including five categories: point, line, ellipse, rectangle, and polygon. Figure 7 Users can select and adjust the target area multiple times according to actual needs, and mark all particulate matter within the selected area with red pixels, such as... Figure 8 Subsequently, the particles marked with red pixels and their respective particle partitions are locked as the target objects for subsequent aperture throat channel reconstruction operations, ensuring the accuracy and controllability of the adjustment range. Figure 9 The particle region that needs to be adjusted in the example is shown.

[0051] Step 401: Based on the connectivity characteristics of 4-neighborhood, extract the outer boundaries of each control partition in the particle partition map. The outer boundary is the set of partition pixels in the outermost layer. Its spatial position represents the physical distribution limit of the particle medium in the image plane, providing a basic geometric framework for subsequent pore throat feature point localization and boundary parameter calculation.

[0052] Step 402: Locate feature control points on the boundaries of each control zone. Based on the functional integrity constraints of the core pore-throat structure, feature points are set as the boundary points of three or more grain partitions, i.e., the feature points are the intersections of the three skeletal boundaries. Each region's boundary typically has at least 2-3 feature points, used for local geometric constraints in subsequent pore-throat channel adjustments.

[0053] Step 501: Calculate the Euclidean distance from the point on the boundary of each partition to the pore throat feature point, and normalize it according to the maximum distance in the partition to generate dimensionless relative distance parameters; any point on the boundary of the control area is denoted as Pi (i=1, 2, ..., n), and the boundary has a pore throat feature point Tj (j=1, 2, ..., m).

[0054] Step 502: Based on actual engineering needs, calculate the coefficient factors of each point on the partition boundary and the corresponding generated orifice throat channel radius through a multi-parameter coupling model.

[0055] Generally, the range of values ​​selected for the three engineering parameters involved—scale, orifice-throat ratio, and aperture—is as follows: 0 <scale<1,ptr> 2, >2. The above parameters are determined through experimental design or core physical property inversion. Their specific values ​​need to be set based on human experience or quantitative analysis results in actual engineering scenarios to ensure that the geometry of the reconstructed pore throat channel matches the actual application requirements.

[0056] Step 503: Perform a directional circular expansion operation on all pixels on the partition boundary, generating a radius of [missing information] along its normal direction. The white circular regions are superimposed to form a continuous reconstructed pore throat channel network in the image. This network is then overlaid onto the user-specified adjustment area in the original digital core binarized image to obtain a digital core reconstruction image that meets the requirements of engineering experiments.

[0057] Step 504, select scale=0.05, ptr=6. =2 Determine the pore throat features and obtain Figure 10 ; Step 505, select scale=0.05, ptr=2, =6 Determine the pore throat features and obtain Figure 11 ;; Step 506, select scale=0.1, ptr=6. =2 Determine the pore throat features and obtain Figure 12 ; Step 507, select scale=0.1, ptr=2, =6 Determine the pore throat features and obtain Figure 13 .

[0058] Furthermore, embodiments of the present invention also provide a system for implementing the above-described method for adjusting the pore throat channel of a digital core image, the system comprising: Pixel inversion operation unit: used to perform a bit-by-bit inversion operation on the acquired digital core binarized initial image to generate a core image with inverted pixels; Pore ​​throat central axis extraction unit: used to extract the pore throat central axis from the pixel-inverted core image and generate a particle skeleton map reflecting the topological features of the pore throat; Connected domain extraction unit: used to extract connected domains from the particle skeleton map and label each region to obtain a connected domain partition map of the core image; Adjusted particle map generation unit: used to divide the specific area in the target image that needs to be reconstructed for pore throat channels by selecting the interactive prompt box, use preset color pixels to mark all pixels of the particulate matter that interacts with the prompt box, and generate an adjusted particle map; The unit for obtaining the particle zoning map to be adjusted is used to obtain the particle zoning map to be adjusted by matching the control region to which the selected particle belongs in the connected domain zoning map of the core image based on the selected particle in the adjustment particle map. Calculation unit: used to calculate the pore throat feature points and particle partition boundaries of the selected particle partition on the particle partition map to be adjusted; Geometric transformation unit: used to geometrically transform the particle partition boundary according to the pore throat feature points in a preset ratio to generate the reconstructed pore throat channel region; Image reconstruction unit: used to render the pixel color of the reconstructed pore throat channel region to the same color as the pore throat channel pixel color in the core image, and the remaining black pixel area in the control partition is represented as the reconstructed grain to obtain the reconstructed image; Replacement unit: Used to replace the selected adjustment area in the initial image of the digital core binarization with the pixel content of the corresponding position in the reconstructed image, thereby realizing the adjustment of the pore throat channel of the digital core image.

[0059] The embodiments of the present invention also provide corresponding electronic devices and computer-readable storage media for implementing the solutions provided in the embodiments of the present invention.

[0060] The electronic device includes a storage device and one or more processors. The storage device stores instructions or code, and the processors execute the instructions or code to cause the device to perform the digital core image pore throat channel adjustment method according to any embodiment of this application.

[0061] The storage medium stores a computer program, which, when executed by a processor, implements the method for adjusting the pore throat channel of a digital core image according to any embodiment of this application.

[0062] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for adjusting the pore throat channel in a digital core image, characterized in that, The process includes the following: The pixel colors of the acquired initial digital core binarized image are inverted bitwise to generate a pixel-inverted core image. Extract the pore throat axis from the pixel-inverted core image to generate a particle skeleton map reflecting the topological features of the pore throat; Connectivity regions were extracted from the particle skeleton map, and each region was labeled to obtain a connected region partition map of the core image. By selecting an interactive prompt box to divide the specific area in the target image that needs to be reconstructed for the pore throat channel, using preset color pixels to label all pixels of the particulate matter that interacts with the prompt box, and generating an adjusted particle map; Based on the selected particles in the adjusted particle map, the control region to which the selected particles belong is matched in the connected domain partition map of the core image to obtain the particle partition map to be adjusted; Calculate the pore throat feature points and particle partition boundaries of the selected particle partition on the particle partition map to be adjusted; The particle partition boundary is geometrically transformed according to the pore throat feature points in a preset ratio to generate the reconstructed pore throat channel region. The pixel color of the reconstructed pore throat channel region is rendered to the same color as the pore throat channel pixel color in the core image, and the remaining black pixel region in the control partition is represented as the reconstructed grain to obtain the reconstructed image. The selected adjustment area in the initial binarized digital core image is replaced with the pixel content of the corresponding position in the reconstructed image to achieve pore throat channel adjustment in the digital core image.

2. The method for adjusting the pore throat channel in a digital core image according to claim 1, characterized in that, When extracting connected components from the particle skeleton map and labeling each region to obtain a connected component partitioning map of the core image: Each pixel in a connected component is assigned a uniform color identifier, and the pixel colors of different connected components are different. Each connected component corresponds to an independent particle control partition. All pixels in the same connected component belong to the same particle structure unit, and different connected components represent independent particle units.

3. The method for adjusting the pore throat channel in a digital core image according to claim 1, characterized in that, The interactive prompt box types include: dot selection, linear selection, elliptical selection, rectangular selection, and polygonal selection.

4. The method for adjusting the pore throat channel in a digital core image according to claim 1, characterized in that, Select the pixels located at the intersection of three or more different particle partitions as the pore throat feature points of the particle partition; Boundary points in a particle partitioning map are defined as: the set of pixels that constitute the outermost contour of a particle partition and whose pixel color values ​​match the identifier color of the particle partition. In the initial image of the digital core binarization, black pixels are used to mark the core pore throat channels, and white pixels are used to mark the granular material. When extracting the pore throat axis from the pixel-inverted core image and generating a particle skeleton map reflecting the topological features of the pore throat, black pixels are used to label the granular material and white pixels are used to label the pore throat channels. A skeleton extraction algorithm was used to extract the pore throat axis of the core image after pixel inversion, generating a particle skeleton map that reflects the topological features of the pore throat.

5. The method for adjusting the pore throat channel in a digital core image according to claim 1, characterized in that, The proportional index value involved in the independently generated adjustment orifice throat channel region for each particle partition should be determined by the following formula: Where m is the total number of pore throat feature points selected in the current particle partition; n is the total number of pixels located at the partition boundary in the current particle partition. This represents the Euclidean distance from the j-th pixel on the partition boundary to the i-th aperture throat feature point within that partition; This is a minimum value function used to obtain... The minimum value in; This is a function for obtaining the maximum value. The maximum value in; Indicates a point The minimum distance to each feature point; D represents the maximum value of the minimum distance to the throat feature point calculated from each point in the partition. Yes The distance-normalized proportional index value is used to quantify the relative spatial relationship between boundary points and pore throat feature points, thereby guiding the adjustment ratio of the pore throat channel region.

6. The method for adjusting the pore throat channel in a digital core image according to claim 5, characterized in that, When reconstructing and generating the adjusted aperture throat channel, for each pixel on the partition boundary, a circular adjustment area of ​​a specified length is generated inward along the normal direction of the pixel into the partition, and the color value of all pixels within the coverage of the circular area is set to white to represent the reconstructed aperture throat channel. The radius parameter of the circular adjustment area generated when reconstructing the aperture throat channel is the actual throat radius value of the aperture throat channel corresponding to that pixel. The geometric scale of the circular adjustment area is controlled by this radius to realize the reconstruction of the aperture throat channel structure.

7. The method for adjusting the pore throat channel in a digital core image according to claim 6, characterized in that, The reconstructed and adjusted orifice throat channel radius is determined by the following formula: in, It is the reconstructed orifice throat channel radius coefficient, where L is the radius length of the reconstructed orifice throat channel. , These are the longest and minimum radii of the reconstructed pore throat channel, respectively. It is the approximate effective length of the current particle partition, expressed by the formula K. 1 / 2 The calculation determines that K is the area of ​​the current particle partition, i.e., the total number of pixels contained within the partition. The formula uses a linear interpolation method to adjust the radius coefficient. Mapping to the range between the longest and smallest radii determines the specific radius length of the pore throat channel in the current particle partition, thereby achieving directional control of the pore throat structure; scale is a global scale control parameter used to adjust the overall size of the generated pore throat channel; ptr is a pore throat ratio control parameter used to adjust the pore throat ratio of the pore throat channel; The opening degree control parameter is used to adjust the opening degree of the orifice throat channel.

8. A system for adjusting the pore throat channel in a digital core image, characterized in that, include: Pixel inversion operation unit: used to perform a bit-by-bit inversion operation on the acquired digital core binarized initial image to generate a core image with inverted pixels; Pore ​​throat central axis extraction unit: used to extract the pore throat central axis from the pixel-inverted core image and generate a particle skeleton map reflecting the topological features of the pore throat; Connected domain extraction unit: used to extract connected domains from the particle skeleton map and label each region to obtain a connected domain partition map of the core image; Adjusted particle map generation unit: used to divide the specific area in the target image that needs to be reconstructed for the pore throat channel by selecting the interactive prompt box, use preset color pixels to mark all pixels of the particulate matter that interacts with the prompt box, and generate an adjusted particle map; The unit for obtaining the particle zoning map to be adjusted is used to obtain the particle zoning map to be adjusted by matching the control region to which the selected particle belongs in the connected domain zoning map of the core image based on the selected particle in the adjustment particle map. Calculation unit: used to calculate the pore throat feature points and particle partition boundaries of the selected particle partition on the particle partition map to be adjusted; Geometric transformation unit: used to geometrically transform the particle partition boundary according to the pore throat feature points in a preset ratio to generate the reconstructed pore throat channel region; Image reconstruction unit: used to render the pixel color of the reconstructed pore throat channel region to the same color as the pore throat channel pixel color in the core image, and the remaining black pixel area in the control partition is represented as the reconstructed grain to obtain the reconstructed image; Replacement unit: Used to replace the selected adjustment area in the initial image of the digital core binarization with the pixel content of the corresponding position in the reconstructed image, thereby realizing the adjustment of the pore throat channel of the digital core image.

9. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for adjusting the pore throat channel of a digital core image as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, It stores a computer program, wherein the computer program, when executed by a processor, implements the method for adjusting the pore throat channel of a digital core image as described in any one of claims 1-7.

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