Method and equipment for constructing high-precision research area of medium pores

Abstract

The invention discloses a construction method of a medium pore high-precision research area, which comprises the following steps: S1, selecting a specified unit body as a test medium according to a preset porosity, and acquiring high-precision medium pore imaging data by using a CT (Computed Tomography) scanning technology, obtaining micron-level high-precision pore imaging data in the medium by combining an image post-processing method and a selection process of a binarization segmentation threshold value; s2, medium pore imaging data are input into an Avi zo system platform for connectivity analysis of internal pores, isolated pores are omitted, and communicated pores are extracted; s3, analyzing and obtaining the unit medium with the highest pore precision, scanning the unit medium through CT, and establishing a module task tree process to calculate the porosity of the medium body, so as to obtain a grid file for really representing the internal pores of the medium; and S4, importing grids of the grid file into simulation software, and generating a medium pore high-precision research area. According to the method, the construction requirements of the micron-level medium research area in different physical scenes can be met.

Description

Technical Field

[0001] The present invention relates to the technical field of groundwater medium pore characterization, and in particular to a method and equipment for constructing a medium pore high-precision research area. Background Art

[0002] In groundwater scientific research, the medium is an important research area and the only area where groundwater seepage occurs. How to construct this research area is very important for groundwater-related research. Common research area construction methods include physical test scenarios in the laboratory and field test scenarios of groundwater projects. However, the economic and time costs of organizing each test are high, and errors in medium filling, variable setting, data reading, etc. are prone to occur during the control variable test. Therefore, the physical test scenario or field test scenario is used as a prototype to construct the corresponding numerical model and use the test results for correction and verification, in order to increase research variables in the numerical model, expand the test scenario, and realize research under more influencing factor combination scenarios, so as to expand the research scenario at a lower cost.

[0003] Most existing conceptual models for numerical simulation of blockage treat the study area as a whole and set medium parameters to fit the numerical simulation results with physical test results. Even if the anisotropy of the medium and parameters such as porosity and pore roughness are set, the pores inside the medium and the solid skeleton of the medium are analyzed as a whole, and the true structure and distribution of the pores inside the medium cannot be reproduced in the numerical model.

[0004] In addition, although the establishment of relevant numerical models includes a calibration and verification process, the changes in the relatively macroscopic parameters obtained from laboratory physical tests or engineering field tests during the test process are the apparent phenomena of mutual feedback and mutual constraints between different mechanism processes at the relatively microscopic pore scale at the scale of the medium research area. Directly constructing a numerical model of the entire research area cannot determine whether the overall relevant parameters verified through calibration are interfered with by the "emergence" phenomenon.

[0005] The above-mentioned pore characterization methods all involve a certain degree of generalization and simplification, and ultimately construct an ideal model research area that ignores certain pore microscopic characteristics. Finding pore characteristic parameters that are more representative than the generalized ideal model and quantitatively expressing the pore characteristics are technical challenges that need to be solved urgently.

[0006] To this end, this application specifically proposes a method for constructing a high-precision research area of medium pores to solve the above technical problems. Summary of the Invention

[0007] Compared with the current research methods in the field of groundwater that are generally based on the overall seepage characteristics of the medium, the present invention provides a method for constructing a research area of high-precision medium pores for groundwater scientific research. The main purpose of the present invention is to provide a method for constructing a research area of high-precision medium pores by combining CT scanning technology and the Avizo platform, which can reproduce the spatial details of medium pores in the groundwater research field with high precision at the μm level, has a certain universality, and can meet the construction requirements of μm-level medium research areas under different physical scenarios to solve the technical problems proposed in the background technology.

[0008] The present invention adopts the following technical solutions to solve the above technical problems:

[0009] A method for constructing a research area of high-precision medium pores, comprising:

[0010] S1. Select a specified unit body as the test medium according to the preset porosity, and use CT scanning technology to obtain high-precision imaging data of the medium pores. Combine the image post-processing method and the selection process of the binary segmentation threshold to obtain μm-level high-precision pore imaging data inside the medium;

[0011] S2. Input the imaging data of the medium pores into the Avizo system platform, and perform connectivity analysis of the internal pores of the imaging data through the Axis-Connectivity module of the Avizo system platform. Omit the isolated pores and extract the connected pores;

[0012] S3. Based on the imaging data processed in step S2, further analyze through the Avizo system platform to obtain the unit body medium with the highest pore precision. Scan the unit body medium by CT, and calculate the porosity of the medium body by forming a module task tree process to obtain a grid file for truly representing the internal pores of the medium;

[0013] S4. Import the grid of the grid file into a specified numerical simulation software to generate a research area of high-precision medium pores.

[0014] Preferably, the specific operation process for obtaining high-precision imaging data of the medium pores in step S1 includes:

[0015] S11. Select a specified unit body as the test medium according to the preset porosity, and initially select the size of the unit body, with the scale set at the mm level to the m level;

[0016] S12. Based on the three-dimensional imaging after CT scan image processing, perform image cropping in the Avizo system platform starting from the smallest size and gradually increasing the size, and calculate the porosity respectively;

[0017] S13. Obtain the upper and lower limits of the porosity closest to the measured porosity in the physical pre-experiment, and repeat the interception and calculation of the porosity within the upper and lower limits with a smaller size change step. After repeatedly intercepting and calculating the porosity for multiple times, the most representative unit imaging data at the scanning imaging resolution accuracy can be obtained;

[0018] S14. According to the preset scenario conditions, obtain the largest medium body, place it in the CT scanning device for scanning imaging, and import the scanned image into the Avizo platform for analysis and processing.

[0019] Preferably, the specific operation process of the Axis-Connectivity module for analyzing the connectivity of internal pores in the S2 step includes:

[0020] S21. Perform geometric morphological operation operations including dilation, erosion, opening operation, and closing operation on the image to extract the connectivity of pores and remove noise;

[0021] S22. After the morphological operation is completed, identify all connected pore regions through a connected component labeling algorithm based on depth-first search to obtain a pore structure diagram.

[0022] Preferably, the geometric morphological operation operations in the S21 step specifically include:

[0023] (1) Dilation operation: Perform a dilation operation on the binary image to fill the holes in the pores and expand the boundaries of the pores, so that the original isolated pores are connected;

[0024] (2) Erosion operation: Shrink the boundaries of the pores to remove small isolated noise points;

[0025] (3) Opening operation: First perform an erosion operation and then a dilation operation to remove small objects or noise and smooth the pore contour;

[0026] (4) Closing operation: First perform a dilation operation and then an erosion operation to fill the gaps in the pores, eliminate the gaps or cracks at the edges of the pores in the image, and make the connected pores complete.

[0027] Preferably, the specific operation process of obtaining the pore structure diagram in the S22 step includes:

[0028] S221. For each voxel position (x, y, z) whose neighborhood is connected to other voxels, recursively traverse and label all connected voxels along the depth direction of the graph. The neighborhood of a voxel is defined as the 26 voxels directly connected to it, including the adjacent voxels in the up, down, left, right, front, back, and diagonal directions;

[0029] S222. Start from an unvisited voxel and assign a new label;

[0030] S223. Add the voxels connected to the current pixel in the neighborhood of the pixel to the stack;

[0031] S224. Continue to traverse the voxels in the stack until all connected voxels are marked as the same component;

[0032] S225. After the marking is completed, continue to process other unmarked voxels in the image until all voxels are marked;

[0033] S226. According to the marking results, extract all connected pore regions, and filter out the preset pore regions by setting specified conditions including area and shape, remove pores that are too small or too sparse, and finally obtain a pore structure diagram.

[0034] Preferably, during the connected region marking process in step S224, the connected component algorithm is used to mark the connected pores in the image, and each connected region represents a connected body composed of interconnected pores.

[0035] Preferably, the process for obtaining the unit cell medium with the highest porosity in step S3 includes:

[0036] Continuously shrink the cut medium body, and calculate the porosity through the task tree process until the porosity data changes stably within a 5% interval of the measured porosity data of the medium body, then the taken medium body can be regarded as a representative unit cell;

[0037] Physically re-dig the representative unit cell and place it in a CT scanning device for scanning. Utilize the CT imaging principle that the smaller the scanning object, the greater the imaging accuracy to obtain the scanning imaging with the highest possible accuracy.

[0038] Preferably, the specific calculation process of the porosity of the medium body includes:

[0039] Cut the representative unit cell in the unit cell medium, and perform binary segmentation on the representative unit cell to distinguish the solid phase and liquid phase of the CT scan image, so as to convert the grayscale image into a binary image that can represent the pore distribution and pore structure. The relationship between the segmentation threshold C and the porosity n is as follows:

[0040]

[0041] In the formula, C is the segmentation threshold, n is the porosity, I max 、I min are the maximum and minimum grayscale values of the image, V(i) is the number of pixels with grayscale value i, and K is the candidate segmentation threshold of the grayscale value;

[0042] Obtain the value range of the segmentation threshold C through scanning, substitute it into the above relational expression, and finally obtain the medium porosity closest to the actual measurement in the physical pre-experiment.

[0043] On the other hand, the present invention also discloses a computer-readable storage medium storing a computer program, which when executed by a processor causes the processor to execute the steps of the above method.

[0044] On yet another aspect, the present invention also discloses a computer device including a memory and a processor, the memory storing a computer program, which when executed by the processor causes the processor to execute the steps of the above method.

[0045] As can be seen from the above technical solutions, the present invention provides a method for constructing a high-precision research area of medium porosity. Compared with the prior art, the present invention has the following advantages:

[0046] 1. By selecting representative medium unit cells and using CT scanning technology to obtain accurate medium pore imaging, and extracting and analyzing the connected pore network on the Avizo platform, the present invention can efficiently and accurately reproduce the pore characteristics inside the medium, thereby accurately capturing the microscopic structure and connectivity characteristics of the pores inside the medium, effectively avoiding errors caused by insufficient image resolution or interference from pore isolated regions in traditional methods, and finally realizing a high-fidelity reproduction of the true distribution of medium pores.

[0047] 2. The present invention can accurately reproduce the true structure and distribution of pores inside the medium in the numerical model, avoiding errors caused by pore feature simplification and generalization.

[0048] 3. By setting a porosity stability verification process for dynamically reducing the size of the unit cell, the present invention can ensure that the porosity of the selected representative unit cell is highly consistent with the measured value, thereby significantly reducing the systematic error introduced by sample selection deviation, and finally improving the accuracy and repeatability of pore structure modeling.

[0049] 4. By setting a double optimization of morphological operations and connected component labeling algorithms, the present invention can effectively remove image noise and accurately extract the connected pore network, thereby constructing a pore structure model that conforms to physical laws, and finally providing a more realistic description of the fluid transmission path for seepage simulation.

[0050] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Of course, any product implementing the present invention does not necessarily need to achieve all the above advantages simultaneously. Description of the Drawings

[0051] The accompanying drawings of the specification, which form a part of this application, are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0052] Figure 1 is a schematic diagram of the overall process of the present invention;

[0053] Figure 2 is a schematic diagram of the task tree for calculating the porosity of the scanned object in Avizo of the present invention;

[0054] Figure 3 is a schematic longitudinal cross-sectional view of the CT scan imaging of a small sample of a quartz sand medium sand column of the present invention;

[0055] Figure 4 is a schematic cross-sectional view of the CT scan imaging of a small sample of a quartz sand medium sand column of the present invention;

[0056] Figure 5 is a schematic longitudinal cross-sectional view of the CT scan imaging of a small sample of a glass bead medium sand column of the present invention;

[0057] Figure 6 is a schematic cross-sectional view of the CT scan imaging of a small sample of a glass bead medium sand column of the present invention;

[0058] Figure 7 is a schematic diagram of the medium pore wall grid generated from the CT scan image with a resolution of 100 μm of the present invention;

[0059] Figure 8 is a schematic diagram of the medium pore wall grid generated from the CT scan image with a resolution of 10 μm of the present invention;

[0060] Figure 9 is a schematic comparison diagram of the representative unit volume of the quartz sand medium of the present invention, where the left figure is a grid diagram and the right figure is a schematic diagram of the research area;

[0061] Figure 10 is a schematic comparison diagram of the representative unit volume of the glass bead medium of the present invention, where the left figure is a grid diagram and the right figure is a schematic diagram of the research area;

[0062] Figure 11 is a schematic diagram of the operation task interface of the Volume Edit module of the present invention;

[0063] Figure 12 is a schematic diagram of the task tree of the interactive threshold segmentation module of the present invention;

[0064] Figure 13 is the CT scan image after binary segmentation of the present invention;

[0065] Figure 14Schematic diagram of CT scan for the present invention. The left figure is the original CT scan image, and the right figure is the CT scan after contrast adjustment and filtering for noise reduction. Detailed implementation manners

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0067] In the embodiment, refer in detail to Figures 1 to 14 .

[0068] As Figure 1 shown, a method for obtaining and constructing a high-precision research area of internal pores in a medium in the field of groundwater scientific research based on CT scanning and post-imaging processing technology. This method can obtain a research area of medium pores with a precision of μm level, and proposes a series of method systems such as Avizo processing task tree, taking representative unit cells, and extracting connected pores. Specifically, it includes:

[0069] S1. Select representative unit cells for the medium in the test scene according to the porosity, and re-dig the medium of the representative unit cells. Use CT scanning technology, combined with the image post-processing method and the selection process of its binary segmentation threshold, to obtain a μm-level high-precision pore imaging inside the medium.

[0070] Process the CT scan image to obtain a grid file that truly represents the internal pores of the medium. The accuracy is extremely high, and the size, structure, and distribution of the pores can be expressed through the grid. The pore walls can be described at the μm scale through the connection of a large number of grid surfaces in space. It includes the topological structure, spatial distribution, plane orientation, etc. of the pore walls at the μm-level resolution, and can directly express the microscopic structural characteristics of the pore walls of saturated porous media. These information can be used to analyze the size, shape, connectivity of the pores, and the mutual relationship between the pore walls, and further provide important basic data for studying the fluid flow, permeability, effective porosity, etc. of porous media.

[0071] S11. According to the action range and action scale of each influencing factor in the research scene, initially select the size of the representative unit cells to be taken, and the scale is generally in the range of mm level to m level.

[0072] S12. Determine whether the selected unit cell has sufficient representativeness through the calculation and comparison of effective porosity. Based on the three-dimensional imaging after CT scan image processing, gradually increase the size of the image starting from the minimum size in Avizo through the corresponding module, calculate the porosity respectively, obtain the upper and lower limits closest to the measured porosity in the physical pre-experiment, and repeat the steps of intercepting and calculating the porosity at a smaller size change step within the upper and lower limits. Repeating this process multiple times can obtain the most representative unit cell at the scanning imaging resolution accuracy.

[0073] S13. According to the on-site conditions of the research scenario, obtain the largest possible medium body, put it into the CT scanning device for scanning imaging, and import the scanned image into the Avizo platform for analysis and processing.

[0074] At this time, it should be supplemented that the representative unit cell of the medium in the test scenario is selected according to the porosity. The representative elementary volume (REV) refers to the medium within a partial spatial range that can represent the overall physical properties of the medium and the engineering scenario. First, according to the action range and scale of each influencing factor in the research scenario, preliminarily select the size of the representative unit cell taken, and the scale is generally in the range of mm to m. Since the effective pores of the medium are the most direct and important influencing factors for groundwater seepage and particle movement, it is possible to determine whether the selected unit cell has sufficient representativeness through the calculation and comparison of effective porosity.

[0075] The Avizo platform can be applied to high-precision analysis in the field of physics, three-dimensional reconstruction and reproduction of scanned objects in the fields of archaeology and paleontology, visualization processing in life science and biomedicine, material characterization and quality control in materials science, digital inspection and material analysis in the industrial field, and three-dimensional visualization and analysis simulation in the field of earth science, etc.

[0076] For the processing of CT scan images, generally, Image J is used to obtain and slice the original images, and then Avizo is used for professional processing such as image threshold segmentation, three-dimensional reconstruction, and pore parameter acquisition.

[0077] The characteristic of Avizo is that it can call each module with different functions at any time and organize and adjust the logical relationship and sequence between modules through the module task tree in the software, which is convenient for connecting the pre-integration processing and post-reading analysis of CT scan images. In Avizo, the program packages for processing images and exporting the research area can be directly called, which greatly improves the processing efficiency of CT scan images and also extends the processable spatial range of scanned images.

[0078] Call modules such as "Volume Edit", "Interactive Thresholding", and "Volume Fraction" in Avizo to form a module task tree process to calculate the porosity of the medium body through CT scan images.

[0079] In a specific embodiment, two media, quartz sand and glass beads, with the same average particle size and similar particle size distributions are selected and filled into an acrylic container respectively. Then, the drainage method is used to measure the porosity of the two media.

[0080] The acrylic containers filled with different media are respectively placed into an industrial CT scanning device for scanning and imaging.

[0081] As Figure 2 shown, import the CT scan imaging into Avizo for image processing, and use the "Volume Edit" module to intercept media bodies of different sizes for porosity calculation to obtain the Representative Elementary Volume (REV).

[0082] As Figures 3 to 6 shown, the two media that meet the size of the representative elementary volume are separately dug out and put back into the CT scanning device for scanning and imaging.

[0083] The numerical model establishment platform of this embodiment is COMSOL Multiphysics. Therefore, first determine the file types of the research area files supported by the COMSOL platform, all the importable research area file formats and mesh file formats.

[0084] Take the intersection of the research area mesh file types that can be output in the software Avizo for processing CT scan images and the above-mentioned COMSOL inputtable area mesh file types, and it is found that Avizo can generate area files in the mphbin. format and mesh files in the stl. format that can be imported into COMSOL. Since the imported area mesh directly describes the pores actually existing inside the medium, and a certain number of misaligned surfaces appear due to errors and ghosting during the CT scan experiment, resulting in the area files and mesh files generated in Avizo not being closed. It is necessary to export the stl. format mesh file and manually repair all the misaligned surfaces separately to obtain a closed mesh file, and then import it into COMSOL to generate a research area that can be used for numerical modeling. Since the area file in the mphbin. format is an integrated area directly generated in Avizo and cannot be manually repaired at the unclosed places, this format of area file is abandoned.

[0085] Obtain the stl format pore grid file for the scanned image of the medium before taking the representative elementary volume. Since the resolution of the CT scanned image of this size medium is 100 μm, due to the accuracy limitation of the pores obtained by reading the CT scanned image, most of the throats at the pore connections in the generated study area after importing the stl format grid processed are closed in COMSOL, and the interconnected pore network in the physical actual scenario is divided into non - interconnected "islands", as Figure 7 shown. This reflects the necessity of taking the representative elementary volume.

[0086] Because of the reduction in the size of the scanned object for the representative elementary volume of the medium, the resolution of the CT scanned image reaches 10 μm, and an stl format grid file that can be imported into COMSOL is obtained through the processing in Avizo. Since the grid file directly describes the pores that actually exist inside the medium, each pore contains tens of thousands of faces, as Figure 8 shown. These interconnected grid faces can directly describe the roughness of the pore walls.

[0087] such as Figure 9 and Figure 10 shown, the μm - level high - precision stl format grids of quartz sand medium and glass bead medium can both generate closed study areas in COMSOL. The method for constructing the study area proposed in the present invention is feasible, and it is not limited to the two media mentioned in the embodiments and the numerical simulation platform of COMSOL Multiphysics only. The method for constructing the study area proposed in the present invention has extremely high versatility.

[0088] S2. In the Avizo platform, perform the connectivity analysis of the internal pores on the image of the representative elementary volume through the "Axis - Connectivity" module, omit the isolated pores and extract the connected pores to save the image processing cost, and at the same time, can accurately capture the microscopic structure and connectivity characteristics of the pores inside the medium, so as to effectively avoid the errors caused by insufficient image resolution or interference of isolated pore areas in the traditional method, and finally achieve the high - fidelity reproduction of the true distribution of the pores in the medium.

[0089] In addition, since the interconnected pore network inside the medium is the existence space of the pore flow field inside the medium, and the isolated pores are not completely connected to this pore network, the water movement state in them is static. Therefore, the connected pores have the representativeness of the medium in the groundwater science research scenario and also have the research value of constructing a high - precision study area through CT scanning and post - processing.

[0090] Perform a series of geometric morphological operations (such as dilation, erosion, opening operation, and closing operation) on the image to extract the connectivity of the pores and remove the noise.

[0091] Dilation: Performing a dilation operation on a binary image can fill small holes in the pores and expand the boundaries of the pores. This helps to connect those scattered and adjacent pore regions, making the originally isolated pores become connected.

[0092] Erosion: The erosion operation will shrink the boundaries of the pores and remove small isolated noise points. After dilation, performing the erosion operation can effectively remove some irregular isolated pore regions.

[0093] Opening: First perform erosion and then dilation, which can effectively remove small objects or noise and smooth the contour of the pores. This is very effective for removing isolated pores that do not meet the size requirements.

[0094] Closing: First perform dilation and then erosion, which can fill small gaps in the pores, eliminate small gaps or cracks at the edges of the pores in the image, and make the connected pores more complete.

[0095] Through the above morphological operations, a relatively complete pore structure diagram is obtained. Next, through a connected component labeling algorithm based on depth-first search, all connected pore regions are identified:

[0096] Depth-first search traverses and labels all connected voxels along the depth direction of the graph in a recursive manner. For each voxel position (□,□,□), it can be connected to other voxels through its neighborhood. The neighborhood of a voxel is defined as the 26 voxels directly connected to it, including the adjacent voxels in the up, down, left, right, front, back, and diagonal directions (i.e., the cubic neighborhood in three-dimensional space).

[0097] Starting from an unvisited voxel, assign a new label. Add the voxels connected to the current pixel in the neighborhood of this pixel to the stack. Continue to traverse the voxels in the stack until all connected voxels are labeled as the same component. After the labeling is completed, continue to process other unlabeled voxels in the image until all voxels are labeled:

[0098] Connected region labeling: Use the connected component algorithm to label the connected pores in the image. Each connected region represents a connected body composed of interconnected pores.

[0099] Extraction of connected pores: According to the labeling results, extract all connected pore regions. By setting conditions such as area and shape, the pore regions of actual interest can be screened out, and the too small or too sparse pores can be removed.

[0100] Therefore, by setting double optimizations of morphological operations and connected component labeling algorithms in the Avizo platform, image noise can be effectively removed and the connected pore network can be accurately extracted, thereby constructing a pore structure model that conforms to physical laws, and ultimately providing a more realistic description of the fluid transmission path for seepage simulation.

[0101] At this time, it should be supplemented that a grid file that can truly represent the internal pores of the medium can be obtained by processing CT scan images. Because of its extremely high precision, the size, structure, and distribution of the pores can be expressed through the grid, and even the pore walls can be described at the μm scale through the connection of a large number of grid surfaces in space. This high-precision representation of the real space and real details of the medium pores can reproduce the medium pores with higher physical authenticity compared to the existing overall relevant parameters of the medium, and at the same time avoid the occurrence of the "phenomenon of different parameters with the same effect".

[0102] S21. Call modules such as "Volume Edit", "Interactive Thresholding", and "Volume Fraction" to form a module task tree process to calculate the porosity of the medium body through CT scan images;

[0103] The denoised CT scan images need to be binarized to distinguish the solid phase and the liquid phase, and the grayscale image is converted into a binary image that can characterize the pore distribution and pore structure. Since binarization requires selecting a specific grayscale value as the segmentation threshold, pixels with grayscale greater than or equal to the selected value are defined as liquid, and pixels with grayscale smaller than the selected value are defined as solid. Therefore, the "Volume Edit" module is used to cut out the representative elementary volume before binarization. The porosity of the medium obtained through physical pre-experiments can be used to select the binarization segmentation threshold for CT scan images. The relationship between the segmentation threshold C and the porosity n is as follows:

[0104]

[0105] In the formula, C is the segmentation threshold, n is the porosity, I max 、I min are the maximum and minimum grayscale values of the image, V(i) is the number of pixels with grayscale value i, and K is the candidate segmentation threshold of the grayscale value.

[0106] By calculating the approximate value range of the segmentation threshold C, and then continuously substituting it into the "Interactive Thresholding" interactive threshold segmentation module in Avizo for segmentation and porosity calculation, the porosity of the medium closest to the actual measurement of the physical pre-experiment is finally obtained. The binarized image is reconstructed into a three-dimensional image through the "Volume Rendering" module, such as Figure 11 、 Figure 12and Figure 13 as shown

[0107] At this time, by setting up a porosity stability verification process for dynamically reducing the size of the unit cell, it can be ensured that the porosity of the selected representative unit cell is highly consistent with the measured value (error ≤ 5%), thereby significantly reducing the systematic error introduced by sample selection bias and ultimately improving the accuracy and repeatability of pore structure modeling.

[0108] After a series of processes such as the above-mentioned contrast adjustment, filtering and noise reduction, image cutting, binary segmentation, and three-dimensional reconstruction, the original image obtained by CT scanning is processed into an image that can reflect the true pore distribution and pore structure inside the medium at the corresponding time point during the physical experiment. Since CT scanning cannot obtain the pore distribution smaller than the resolution, there is a certain difference between the obtained pore distribution after segmentation and the actual pore distribution. However, this is already the pore information extraction closest to the actual pore distribution under the existing technical conditions, and the influence of smaller nanoscale pores on the flow field is also extremely small.

[0109] S22. Continuously reduce the cut medium body through the "Volume Edit" module, and calculate the porosity through the task tree process until the porosity data changes stably within the 5% range of the measured porosity data of the medium body. Then, the selected medium body can be regarded as the representative elementary volume (REV);

[0110] S23. Physically re-dig this representative elementary volume and place it in the CT scanning device for scanning. Utilize the CT imaging principle that the smaller the scanning object, the greater the imaging accuracy to obtain the scanning imaging with the highest possible accuracy; according to the on-site conditions of the research scenario, physically re-dig this representative elementary volume and place it in the CT scanning device for scanning. Utilize the CT imaging principle that the smaller the scanning object, the greater the imaging accuracy to obtain the scanning imaging with the highest possible accuracy. According to the technical parameters of existing industrial CT scanning equipment, for scanning objects within the m-level scale range, scanning images with μm-level accuracy can be generated. Therefore, it is necessary to obtain the largest possible medium body and place it in the CT scanning equipment for scanning imaging, and then import the scanning image into the Avizo platform for analysis and processing.

[0111] Therefore, this method can efficiently and accurately reproduce the pore characteristics inside the medium by selecting representative medium unit cells, obtaining accurate medium pore imaging using CT scanning technology, and combining with the Avizo platform for the extraction and analysis of connected pore networks. Compared with the current research methods in the conventional groundwater field that are generally based on the overall seepage characteristics of the medium, it can provide a method for constructing a high-precision medium pore research area for groundwater scientific research.

[0112] S25. Processing the CT scanning image can obtain a grid file that can truly represent the pores inside the medium

[0113] Based on the above specific embodiments, CT scanning tests were carried out on quartz sand media and glass bead medium cylinders with the size of the sand column being a quartz sand medium body with a height of 50 cm and a diameter of 9 cm. The image resolution obtained by CT scanning of such a sand column size is about 100 μm. Due to accuracy limitations, most of the throats at the pore connections of the stl. format mesh obtained by processing the pores obtained by reading the CT scan images are closed, and the physically interconnected pore network is divided into non-connected "islands". According to the principle of CT scanning imaging technology, the smaller the size of the scanned object, the higher the resolution of the obtained scan image. Considering the technical parameters of the industrial CT scanning equipment used, cylindrical sand column samples with a height of 3 cm and a diameter of 1 cm were respectively dug from the top center of the saturated quartz sand medium sand column and the glass bead medium sand column for higher-precision CT scanning. The scanned object and the scanning process are shown in Figures 3 to 6 . During the whole process from digging and encapsulating the sand column samples to CT scanning, the sand column sample medium as the scanned object has always been in a saturated state, and there are only two solid-liquid phases of the medium and water inside the encapsulation container.

[0114] Due to the reduction of the size of the scanned object, the resolution reached 10 μm. This improvement in resolution is very obvious. Observe and compare Figure 5 (10 μm resolution), Figure 6 (10 μm resolution) and Figure 13 (100 μm resolution). For the former, the boundary between the solid-liquid two phases can be clearly seen, while for the latter, even after filtering, the boundary between the solid-liquid two phases can only be seen after binary segmentation processing by the relevant modules of Avizo, as shown in Figure 14 .

[0115] In summary, different from the traditional research methods based on the overall seepage characteristic parameters of the medium, this method realizes the precise connection from the microscopic pore structure to the macroscopic seepage behavior through the systematic integration of the above technical means. Therefore, by setting up a grid-based and numerical simulation connection mechanism based on the task tree process, high-precision pore imaging data can be directly converted into grid files suitable for numerical simulation, accurately reproducing the true structure and distribution of the pores inside the medium in the numerical model, avoiding the model distortion caused by the simplification of pore characteristics in traditional parametric modeling, further avoiding the errors brought by the simplification and generalization of pore characteristics, and finally significantly improving the reliability and prediction accuracy of the seepage simulation results. It provides a method for constructing a research area with a high precision of μm level for the numerical simulation of groundwater seepage and other groundwater-related research, and at the same time provides a high-precision research method that breaks through the limitations of traditional parametric modeling for fields such as groundwater science and oil and gas development. It can significantly improve the accuracy and reliability of pore structure reproduction during the construction of the research area of the medium pores, and finally provide a high-fidelity experimental basis for research such as the numerical simulation of groundwater seepage, with broad application prospects.

[0116] On the other hand, the present invention also discloses a computer-readable storage medium storing a computer program, which when executed by a processor causes the processor to execute the steps of the above method.

[0117] On yet another aspect, the present invention also discloses a computer device including a memory and a processor, the memory storing a computer program, which when executed by the processor causes the processor to execute the steps of the above method.

[0118] In yet another embodiment provided by the present application, there is also provided a computer program product containing instructions, which when run on a computer causes the computer to execute the method for constructing a high-precision research area of any medium pore in the above embodiments.

[0119] It can be understood that the system provided by the embodiments of the present invention corresponds to the method provided by the embodiments of the present invention, and the explanations, examples and beneficial effects of the relevant content can refer to the corresponding parts in the above method.

[0120] The embodiments of the present application also provide an electronic device including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus.

[0121] The memory is used for storing a computer program.

[0122] The processor is used for implementing the method for constructing a high-precision research area of the medium pore when executing the program stored on the memory.

[0123] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus can be divided into an address bus, a data bus, a control bus, and the like.

[0124] The communication interface is used for communication between the above electronic device and other devices.

[0125] The memory can include a random access memory and can also include a non-volatile memory, such as at least one disk memory. Optionally, the memory can also be at least one storage device located away from the aforementioned processor.

[0126] The above-mentioned processor can be a general-purpose processor, including a central processing unit, a network processor, etc.; it can also be a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0127] It should also be noted that the electronic device further includes a terminal device, which can also be referred to as a terminal, a user equipment, a mobile station, a mobile terminal, etc. The terminal device can be a mobile phone, a smart TV, a wearable device, a tablet computer, a computer with wireless transceiver function, a virtual reality terminal device, an augmented reality terminal device, a wireless terminal in industrial control, a wireless terminal in unmanned driving, a wireless terminal in remote surgery, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, and so on. The embodiments of the present application do not limit the specific technologies and specific device forms adopted by the terminal device.

[0128] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium, or a semiconductor medium (such as a solid-state drive), etc.

[0129] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

[0130] In addition, it should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present invention, the directional indications are only used to explain the relative position relationship and movement conditions between components in a specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0131] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel scenarios. Taking "A and / or B" as an example, it includes scenario A, scenario B, or the scenario where A and B are satisfied simultaneously. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

Claims

1. A method for constructing a high-precision research area of medium pores, characterized in that, Including: S1. Select a specified unit cell as the test medium according to the preset porosity, and use CT scanning technology to obtain high-precision pore imaging data of the medium. Combine image post-processing methods and the selection process of binary segmentation thresholds to obtain high-precision pore imaging data at the μm level inside the medium; S2. Input the pore imaging data of the medium into the Avizo system platform, and perform connectivity analysis of the internal pores on the imaging data through the Axis-Connectivity module of the Avizo system platform. Omit the isolated pores and extract the connected pores; S3. Based on the imaging data processed in step S2, further analyze through the Avizo system platform to obtain the unit cell medium with the highest pore accuracy. Scan the unit cell medium by CT, and establish a module task tree process to calculate the porosity of the medium body to obtain a grid file for realistically representing the internal pores of the medium; S4. Import the grid of the grid file into a specified numerical simulation software to generate a high-precision research area for the pores of the medium.

2. The construction method of the high-precision research area of the medium pores, characterized in that The specific operation process for obtaining high-precision pore imaging data of the medium in step S1 includes: S11. Select a specified unit cell as the test medium according to the preset porosity, and initially select the size of the unit cell, with the scale set between mm and m; S12. Based on the three-dimensional imaging after CT scan image processing, perform image cropping in the Avizo system platform starting from the smallest size and gradually increasing the size, and calculate the porosity respectively; S13. Obtain the upper and lower limits closest to the measured porosity in the physical pre-experiment, and repeat cropping and calculating the porosity within the upper and lower limits with a smaller size change step. After repeating cropping and calculating the porosity multiple times, the most representative unit cell imaging data at the scanning imaging resolution accuracy can be obtained; S14. According to the preset scenario conditions, obtain the largest medium body, place it in the CT scanning device for scanning imaging, and import the scanned image into the Avizo platform for analysis and processing.

3. The construction method of the high-precision research area for medium pores as described in claim 1, characterized in that The specific operation process for the Axis-Connectivity module to perform connectivity analysis of the internal pores on the imaging data in step S2 includes: S21. Perform geometric morphological operation operations including dilation, erosion, opening operation, and closing operation on the image to extract the connectivity of the pores and remove noise; S22. After the morphological operation is completed, identify all connected pore regions through a connectivity component labeling algorithm based on depth-first search to obtain a pore structure diagram.

4. The method for constructing a high-precision research area of medium pores according to claim 3, characterized in that The geometric morphological operation operations in step S21 specifically include: (1) Dilation operation: Perform a dilation operation on the binary image to fill the holes in the pores and expand the boundaries of the pores, so that the original isolated pores are connected; (2) Erosion operation: Shrink the boundaries of the pores to remove small isolated noise points; (3) Opening operation: First perform an erosion operation and then a dilation operation to remove small objects or noise and smooth the pore contour; (4) Closing operation: First perform a dilation operation and then an erosion operation to fill the gaps in the pores, eliminate the gaps or cracks at the edges of the pores in the image, and make the connected pores complete.

5. The method for constructing a high-precision research area of medium pores as claimed in claim 3, characterized in that The specific operation process for obtaining the pore structure diagram in step S22 includes: S221. For each voxel position (x, y, z) where the neighborhood is connected to other voxels, recursively traverse and label all connected voxels along the depth direction of the graph. The neighborhood of a voxel is defined as the 26 voxels directly connected to it, including the adjacent voxels in the up-down, left-right, front-back, and diagonal directions. S222. Start from an unvisited voxel and assign a new label. S223. Add the voxels in the neighborhood of the pixel that are connected to the current pixel to the stack. S224. Continue to traverse the voxels in the stack until all connected voxels are labeled as the same component. S225. After the labeling is completed, continue to process the other unlabeled voxels in the image until all voxels are labeled. S226. According to the labeling results, extract all connected pore regions, and filter out the preset pore regions by setting specified conditions including area and shape, removing pores that are too small or too sparse, and finally obtain the pore structure diagram.

6. The method for constructing a high-precision research area of medium pores as described in claim 5, characterized in that During the connected region labeling process in step S224, use the connected component algorithm to label the connected pores in the image, and each connected region represents a connected body composed of interconnected pores.

7. The construction method of the high-precision research area of the medium pores, characterized in that, The process for obtaining the unit body medium with the highest porosity in step S3 includes: Continuously shrink the cut medium body and calculate the porosity through the task tree process until the porosity data changes stably within a 5% range of the measured porosity data of the medium body, and then the taken medium body can be regarded as a representative unit body. Physically re-dig the representative unit body and place it in the CT scanning device for scanning. Utilize the CT imaging principle that the smaller the scanning object, the greater the imaging accuracy to obtain the scanning imaging with the highest possible accuracy.

8. The method for constructing a high-precision research area of medium pores as claimed in claim 7, wherein The specific calculation process of the porosity of the medium body includes: Cut the representative unit body in the unit body medium and perform binary segmentation on the representative unit body to distinguish the solid phase and liquid phase of the CT scan image, so as to convert the grayscale image into a binary image that can characterize the pore distribution and pore structure. The relationship between the segmentation threshold C and the porosity n is as follows: where C is the segmentation threshold, n is the porosity, I max , I min are the maximum and minimum gray values of the image, V(i) is the number of pixels with gray value i, and K is the candidate segmentation threshold of the gray value; Obtain the value range of the segmentation threshold C through scanning, substitute it into the above relationship, and finally obtain the medium porosity closest to the physical pre-experiment measurement.

9. A computer-readable storage medium, characterized in that, A computer program is stored, and when the computer program is executed by a processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 8.

10. A computer device, characterized in that, It includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 8.

Images