A coal micro-pore structure characterization method based on ChemDraw

Abstract

The application discloses a coal micro-pore structure characterization method based on ChemDraw and relates to the technical field of coal mine gas geology, which comprises the following steps: obtaining coal micro-pore structure data through various technologies and performing pretreatment; analyzing the pretreated coal micro-pore structure data and extracting key information of the coal micro-pore; generating a three-dimensional structure model of the coal micro-pore based on the key information of the coal micro-pore; simulating the three-dimensional structure model of the coal micro-pore through a physical simulation tool to obtain a physical simulation result of the coal micro-pore structure; comparing the physical simulation result of the coal micro-pore structure with the coal micro-pore structure data to evaluate and optimize the three-dimensional structure model of the coal micro-pore; and importing the three-dimensional structure model of the coal micro-pore which is evaluated and optimized into the API interface of ChemDraw to automatically display and export the two-dimensional and three-dimensional structures. The application provides a coal micro-pore structure characterization method based on ChemDraw, and realizes comprehensive, accurate and efficient pore structure analysis and display.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal mine gas geology, and particularly relates to a coal micro-pore structure characterization method based on ChemDraw. BACKGROUND

[0002] The micro-pore structure of coal has high complexity and multi-scale characteristics. The morphology, size distribution and connectivity of the pores have a significant impact on the permeability and adsorption performance of coalbed methane. The size of the pores ranges from nanometers to micrometers, and presents diverse geometric shapes. These subtle structures directly determine the flow path and permeability of gas in the coal seam. Due to the high complexity and interrelation of these micro-features, accurate characterization of the pore network of the coal sample is crucial for predicting the permeability and adsorption performance of coalbed methane.

[0003] Traditional experimental methods can generally provide data such as the pore distribution and average permeability of the coal sample on a macroscopic level. However, these methods can only provide overall statistical information. This limitation makes it very difficult to accurately predict the flow behavior of gas in the micro-pore structure based on experimental data. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a coal micro-pore structure characterization method based on ChemDraw to solve the problem of the complexity of the coal micro-pore structure being difficult to accurately characterize and simulate.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a coal micro-pore structure characterization method based on ChemDraw, which includes obtaining coal micro-pore structure data and pre-processing the same.

[0008] The pre-processed coal micro-pore structure data is analyzed to extract key information of the coal micro-pores.

[0009] Based on the key information of the coal micro-pores, a three-dimensional structure model of the coal micro-pores is generated.

[0010] The three-dimensional structure model of the coal micro-pores is simulated by a physical simulation tool to obtain a physical simulation result of the coal micro-pore structure.

[0011] The physical simulation result of the micro-pore structure is compared with the coal micro-pore structure data to evaluate and optimize the three-dimensional structure model of the coal micro-pores.

[0012] Using ChemDraw's API, the optimized three-dimensional structure model of coal micropores is imported for automated display and export of two-dimensional and three-dimensional structures.

[0013] As a preferred embodiment of the coal micropore structure characterization method based on ChemDraw described in this invention, the coal micropore structure data includes scanning electron microscope images, specific surface area data of coal sample micropores and mesopores, pore size distribution and porosity of coal sample, and nuclear magnetic resonance imaging data; the preprocessing includes image denoising, image enhancement, data fusion and registration.

[0014] As a preferred embodiment of the ChemDraw-based coal micropore structure characterization method of the present invention, the following steps are performed on scanning electron microscope images and nuclear magnetic resonance imaging data: image denoising, image enhancement, data fusion, and registration.

[0015] Wavelet denoising algorithm is used to process scanning electron microscope images;

[0016] For magnetic resonance imaging data, a nonlocal mean denoising algorithm is used to remove imaging noise.

[0017] Histogram equalization technique is used to enhance the contrast of scanning electron microscope images;

[0018] For images with low resolution, super-resolution reconstruction algorithms can be used to further enhance the image resolution;

[0019] In three-dimensional nuclear magnetic resonance imaging images, the Sobel edge enhancement technique is used to highlight the structural features of pores;

[0020] Pore ​​features of the same regions in scanning electron microscope images and nuclear magnetic resonance imaging data were extracted and spatially aligned using the cross-correlation registration method.

[0021] By using voxel fusion technology, aligned scanning electron microscope images and nuclear magnetic resonance imaging data are fused together, and a local non-rigid registration algorithm is used to perform fine registration, generating high-resolution pore 3D point cloud data.

[0022] As a preferred embodiment of the ChemDraw-based coal micropore structure characterization method of the present invention, the following steps are taken to analyze the preprocessed coal micropore structure data and extract key information about the coal micropores:

[0023] High-resolution pore 3D point cloud data is combined with 3D edge detection algorithms and morphological opening and closing operations to obtain pore morphology;

[0024] The high-resolution pore three-dimensional point cloud data is segmented into independent pore groups by a point cloud segmentation clustering algorithm;

[0025] The independent pore groups are determined by an average coordinate centroid calculation method to determine the pore group centroid position;

[0026] The neighborhood distance of all pore group centroids is calculated according to the pore group centroid position, and the distribution mode of the pores in the three-dimensional space is analyzed;

[0027] The pore morphology, pore group centroid position and distribution mode of the pores in the three-dimensional space jointly constitute the key information of the coal micro-pore.

[0028] As a preferred scheme of the coal micro-pore structure characterization method based on ChemDraw, the coal micro-pore three-dimensional structure model is generated based on the key information of the coal micro-pore, and the specific steps are as follows,

[0029] Based on the key information of the coal micro-pore, combined with high-resolution pore three-dimensional point cloud data, a Delaunay triangular surface reconstruction algorithm is used to generate a three-dimensional surface model and a three-dimensional network model of the coal micro-pore;

[0030] The three-dimensional network model of the coal micro-pore is analyzed using a shortest path graph algorithm to identify the main flow channel and the secondary channel of the pores, and a three-dimensional pore network model with flow characteristics is generated;

[0031] Through Laplace smoothing technology, the details of the three-dimensional pore network model are retained, and a volume-preserving algorithm is used to generate a coal micro-pore three-dimensional structure model.

[0032] As a preferred scheme of the coal micro-pore structure characterization method based on ChemDraw, the coal micro-pore three-dimensional structure model is simulated by a physical simulation tool to obtain the physical simulation results of the coal micro-pore structure, and the specific steps are as follows,

[0033] Based on COMSOL Multiphysics, physical parameters are given to the solid and fluid of the coal micro-pore three-dimensional structure model;

[0034] The coal micro-pore three-dimensional structure model is globally meshed;

[0035] Based on the porous medium model, the permeation and diffusion process of the fluid in the solid material with pore structure is simulated, and the Langmuir adsorption model is added to simulate the adsorption and desorption behavior of the gas in the coal sample;

[0036] The fluid permeability, pore size distribution, specific surface area and pore connectivity obtained by simulation jointly constitute the physical simulation results of the micro-pore structure.

[0037] As a preferred scheme of the coal micro-pore structure characterization method based on ChemDraw according to the present application, wherein: the physical simulation results of the micro-pore structure are compared with the coal micro-pore structure data, and the optimized coal micro-pore three-dimensional structure model is evaluated, and the specific steps are as follows,

[0038] The fluid permeability in the physical simulation is compared with the fluid permeability measured by the experiment, and the influence of the pore network on the fluid flow is evaluated;

[0039] The pore size distribution generated by the physical simulation is compared with the pore size distribution obtained by the mercury injection method and the pore size distribution obtained by the nuclear magnetic resonance imaging, and the accuracy of the pore structure is analyzed;

[0040] The specific surface area in the network simulation is compared with the test data measured by the specific surface area method, and the simulation accuracy of the pore surface characteristics is evaluated;

[0041] The connectivity in the simulated pore network is compared with the connectivity in the actual coal sample, and the influence of the connected pores on the fluid behavior is analyzed;

[0042] The difference between the simulation value and the experimental measurement value is compared, and the absolute error value and the relative percentage error of the experimental data are calculated;

[0043] Combined with the error analysis, the causes of the error are identified, the geometric shape, connectivity, physical parameters and boundary conditions of the pore network are adjusted by the global optimization algorithm according to the error analysis results, and the local grid is refined;

[0044] The optimized coal micro-pore three-dimensional structure model is subjected to physical simulation again, and the new simulation results are compared with the experimental data to evaluate the optimization effect.

[0045] As a preferred scheme of the coal micro-pore structure characterization method based on ChemDraw according to the present application, wherein: the API interface of ChemDraw is used to import the coal micro-pore three-dimensional structure model evaluated and optimized, and the automatic display and export of the two-dimensional and three-dimensional structures are performed, and the specific steps are as follows,

[0046] The coal micro-pore three-dimensional structure model is imported using the ChemDraw API interface, the coal micro-pore three-dimensional structure model is read by API calling, and the geometric information thereof is parsed;

[0047] The structure information in the coal micro-pore three-dimensional structure model is mapped to the two-dimensional workspace and the three-dimensional workspace of ChemDraw through the coordinate system of ChemDraw;

[0048] The rendering function of the API interface is used to automatically generate the visual representation of the pore structure of the coal micro-pore three-dimensional structure model;

[0049] Set the model view through the API interface, generate high-quality surface rendering effects, and set colors and textures for different areas according to the physical properties of the pores;

[0050] Generate a two-dimensional sketch of the plane projection and contour extraction through the API interface;

[0051] Automatically export two-dimensional and three-dimensional structure images in image formats, vector graphics and three-dimensional model formats, and provide interactive functions to allow users to operate the three-dimensional model in real time;

[0052] Generate display images, geometric parameters, simulation results and error analysis reports of the pore model through the API.

[0053] In a second aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program, when executed by the processor, implements any step of the coal micro-pore structure characterization method based on ChemDraw according to the first aspect of the present application.

[0054] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the coal micro-pore structure characterization method based on ChemDraw according to the first aspect of the present application.

[0055] The present application has the following advantages: The present application effectively eliminates the interference signals in the scanning electron microscope images and nuclear magnetic resonance imaging data by comprehensively applying wavelet denoising, non-local mean denoising, histogram equalization and super-resolution reconstruction, and improves the image quality. The present application realizes accurate alignment and integration of data by using cross-correlation registration and voxel fusion, and constructs a high-resolution pore three-dimensional point cloud database. The present application reveals the geometric characteristics of the pores by using three-dimensional edge detection, morphological processing and region growing algorithm, and determines the spatial distribution of the pore groups by using point cloud segmentation and centroid positioning technology. The present application creates and optimizes the three-dimensional pore network model by using Delaunay triangulation and Laplace smoothing. The present application simulates the motion characteristics of fluid in the pores by using COMSOL software for meticulous meshing, combining porous media and the Langmuir model, and obtains a series of important physical parameters. The present application adjusts the key properties of the model by comparing and analyzing the simulation results and the actual measured values, and realizes the visual presentation and interactive display of the optimized model by using ChemDraw API, thereby comprehensively improving the research accuracy and efficiency of the coal micro-pore structure. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0057] Figure 1 Flow chart for the method of characterizing the micro-pore structure of coal based on ChemDraw in Example 1.

[0058] Figure 2 Flow chart for extracting the key information of the micro-pore of coal in Example 1. DETAILED DESCRIPTION

[0059] In order to make the above objectives, features and advantages of the present application more apparent, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0060] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the concept of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0061] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0062] Example 1, refer to Figure 1 and Figure 2 , the first embodiment of the present application, the embodiment provides a method for characterizing the micro-pore structure of coal based on ChemDraw, comprising the following steps:

[0063] S1, obtaining the micro-pore structure data of coal and pre-processing, including the following steps,

[0064] The various techniques include scanning electron microscope scanning, specific surface area testing method, mercury injection method, and nuclear magnetic resonance imaging; the coal microscopic pore structure data includes scanning electron microscope images, specific surface area data of coal sample micropores and mesopores, pore size distribution and porosity of the coal sample, and nuclear magnetic resonance imaging data; these data not only reveal the subtle mechanism of coalbed methane permeation and adsorption behavior, but also provide high-precision initial conditions for numerical simulation. The acquisition of these data can help accurately predict the permeability of coalbed methane, adsorption and desorption behavior, and the connectivity of the pore network, providing a reliable scientific basis for the efficient exploitation of coalbed methane. The pretreatment includes image denoising, image enhancement, data fusion and registration; the physical simulation tool is COMSOL Multiphysics, which is a powerful multi-physical field simulation software widely used in scientific research, engineering design and technical development. The software can simulate and analyze the coupling behavior between various physical phenomena, and solve complex multi-physical field problems with high-precision numerical methods.

[0065] The scanning electron microscope image is processed using the wavelet denoising algorithm. Specifically, the scanning electron microscope image is cropped, grayed, and normalized, and then an appropriate wavelet basis (such as Daubechies wavelet) is selected for multi-layer wavelet decomposition, which decomposes the scanning electron microscope image into low-frequency and high-frequency coefficients of different scales. The high-frequency coefficients are denoised by applying hard thresholding or soft thresholding to suppress noise and preserve details, and the low-frequency coefficients are kept unchanged. After denoising, the scanning electron microscope image is reconstructed by inverse wavelet transform, and if necessary, smoothing filtering or contrast enhancement is performed.

[0066] The non-local mean denoising algorithm suppresses noise in nuclear magnetic resonance imaging while preserving the details of the pore structure. Specifically, the nuclear magnetic resonance image is grayed and normalized, and then similar pixels are found within a search window for each pixel, and the similarity weight is calculated based on the weighted Euclidean distance, and the pixels are denoised by weighted average based on these weights.

[0067] The histogram equalization technique is used to enhance the contrast of the scanning electron microscope image. Specifically, the scanning electron microscope image is grayed and normalized, and then the gray histogram of the image is calculated, and the pixel intensity is redistributed through the cumulative distribution function, and the gray value of the image is smoothed to enhance the contrast.

[0068] For low-resolution scanning electron microscope images and nuclear magnetic resonance imaging, super-resolution reconstruction algorithms are used to further enhance the image resolution. Specifically, a suitable reconstruction method (such as interpolation or deep learning-based convolutional neural network, such as SRCNN (super-resolution convolutional neural network) or SRGAN (super-resolution generative adversarial network)) is selected to generate high-resolution images by searching for similar areas or learning the mapping relationship between low-resolution and high-resolution images.

[0069] In nuclear magnetic resonance imaging, the Sobel edge enhancement technique is used to highlight the structural features of the pores. Specifically, the Sobel operator is used to calculate the gradients of the image in the horizontal and vertical directions respectively, generating an edge response map. Then by combining the gradient amplitudes of the two directions, the edge intensity map of the image is obtained, highlighting the edges and structural features of the pores.

[0070] The Canny edge detection algorithm is used to extract the pore features of the same region in the scanning electron microscope image and the nuclear magnetic resonance imaging data, and the cross-correlation registration method is used for spatial alignment.

[0071] Through voxel fusion technology, the aligned scanning electron microscope image and nuclear magnetic resonance imaging data are fused, and a local non-rigid registration algorithm is used for fine registration to generate high-resolution pore three-dimensional point cloud data.

[0072] S2, analyze the preprocessed coal micro-pore structure data and extract key information of coal micro-pores, including the following steps,

[0073] The high-resolution pore three-dimensional point cloud data is combined with a three-dimensional edge detection algorithm and a morphological opening and closing operation to obtain the pore morphology. Specifically, the high-resolution pore three-dimensional point cloud data is denoised and smoothed, and then the edge features of the pores are extracted by a three-dimensional edge detection algorithm. The opening operation (first erosion and then dilation) in three-dimensional morphological operation is applied to remove small noise and artifacts, and then the closing operation (first dilation and then erosion) is applied to fill small gaps in the pore edges and further smooth the boundaries.

[0074] The high-resolution pore three-dimensional point cloud data is segmented into independent pore groups by a point cloud segmentation and clustering algorithm. Specifically, the high-resolution pore three-dimensional point cloud data is down-sampled to reduce computational complexity. Then, a point cloud segmentation and clustering algorithm (such as the region growing algorithm based on Euclidean distance) is applied to segment the point cloud into multiple independent pore groups according to the density or distance of the point cloud in space. By setting the minimum number of neighborhood points and distance threshold, each independent pore is effectively identified and separated.

[0075] It should be noted that the independent pore group contains x, y and z axis coordinates.

[0076] The independent pore group determines the pore group centroid position by the average coordinate centroid calculation method. Specifically, the three-dimensional point cloud data of each pore group is traversed, the sum of the x, y and z axis coordinates of all points of each pore group is calculated respectively, and then divided by the total number of points to obtain the centroid coordinates of the pore group.

[0077] The neighborhood distance of all pore group centroids is calculated according to the pore group centroid position, and the distribution mode of the pores in the three-dimensional space is analyzed. Specifically, the Euclidean neighborhood distance between each pair of centroids is calculated, the neighborhood relationship of the centroids is judged according to the Euclidean neighborhood distance, and the distribution mode of the pores in the three-dimensional space is obtained, including uniform distribution, random distribution and aggregation distribution.

[0078] The pore morphology, pore group centroid position and distribution mode of the pores in the three-dimensional space jointly constitute the key information of the coal micro-pore.

[0079] S3, based on the key information of the coal micro-pore, a three-dimensional structure model of the coal micro-pore is generated, including the following steps,

[0080] Based on the key information of the coal micro-pore, combined with high-resolution three-dimensional point cloud data of the pores, a Delaunay triangulation surface reconstruction algorithm is used to generate a three-dimensional surface model and a three-dimensional network model of the coal micro-pore. Specifically, the high-resolution three-dimensional point cloud data of the pores is triangulated to generate a three-dimensional surface model of the pores, and then the pore group centroid position is connected according to the pore morphology and the distribution mode of the pores in the three-dimensional space to construct a three-dimensional network model of the coal micro-pore. The Delaunay triangulation surface reconstruction algorithm ensures the smoothness of the surface reconstruction and the rationality of the geometric structure, effectively capturing the complex morphology of the pores.

[0081] It should be noted that the Delaunay triangulation surface reconstruction algorithm is a geometric algorithm for constructing a triangular mesh on a given point set, satisfying certain optimization conditions. It is a very important concept in computational geometry, and has wide applications in fields such as surface reconstruction, computer graphics, geographic information systems and finite element analysis.

[0082] The shortest path graph algorithm is used to analyze the three-dimensional network model of coal micro-pores, identify the main flow channels and secondary channels of the pores, and build an adjacency matrix of the pore network to record the connection between the nodes. The weight of the connection between the nodes is defined, such as the size of the pore diameter or the length of the channel. The Dijkstra algorithm is used to calculate the shortest path from each node to other nodes. The main path through the pore network, i.e. the main flow channel, is identified. By analyzing the secondary path, the secondary channel is identified. The fluid resistance coefficient of each channel is calculated, and the fluid flow is distributed. The fluid velocity distribution in each channel is calculated. Finally, based on the results of the shortest path algorithm, a three-dimensional network model of coal micro-pores with flow characteristics is generated. This process can accurately locate the main path and secondary path of gas flow, revealing the dominant flow direction and secondary flow area in the pore network. Through this analysis, the flow path of coalbed methane can be determined, providing important reference for permeability prediction and optimization of mining scheme.

[0083] It should be noted that Dijkstra algorithm is widely used in graph theory to find the shortest path from a single source to all other vertices in a weighted graph. Dijkstra algorithm assumes that there is no negative weight edge in the graph (i.e. the weight of the edge cannot be negative), but can handle non-negative weight of the edge. In computer networks, it is used to find the optimal data transmission path from the source to the destination node.

[0084] Through the Laplace smoothing technique, the details of the three-dimensional network model of coal micro-pores with flow characteristics are preserved, and the volume-preserving algorithm is used to generate the three-dimensional structure model of coal micro-pores. Specifically, the position of the vertices of the three-dimensional network model of coal micro-pores with flow characteristics is adjusted through the iteration of the Laplace smoothing technique, to eliminate surface noise and sharp edges, and to keep the flow channels and key structures of the pores unchanged. In order to avoid volume shrinkage in the smoothing process, the volume-preserving algorithm is applied to the three-dimensional network model of coal micro-pores with flow characteristics for local and global volume correction, to ensure that the geometric shape and volume characteristics of the pores are consistent with the original three-dimensional network model of coal micro-pores with flow characteristics.

[0085] S4, simulate the three-dimensional structure model of coal micro-pores through a physical simulation tool to obtain the physical simulation results of the coal micro-pore structure, including the following steps,

[0086] Based on COMSOL Multiphysics, physical parameters are assigned to the solid and fluid of the three-dimensional structure model of coal micro-pores. Specifically, the porous medium flow module is selected to assign physical parameters such as density, elastic modulus, porosity and permeability to the coal body, and the density, viscosity and diffusion coefficient of the selected fluid (such as methane or water) are set. By defining boundary conditions such as inlet and outlet, the flow of fluid in the pores is ensured to meet the physical reality requirements.

[0087] A global mesh generation process is performed on the 3D structure model of coal micropores. This refined mesh generation improves the simulation accuracy of narrow and connected regions within the pore network. Specifically, a global mesh generation is performed on the entire pore network to identify narrow pore regions and connected parts. Local mesh refinement is then applied to these critical areas to ensure the accuracy of fluid flow and diffusion simulations. By using geometrically based refinement rules to set smaller mesh sizes and incorporating adaptive mesh generation techniques, the mesh in high-gradient regions is automatically optimized.

[0088] It should be noted that global meshing of the three-dimensional coal micropore structure model can significantly improve the simulation accuracy of these key regions. The refined mesh can better capture the flow characteristics of fluids in narrow channels and the connectivity between pores, effectively avoiding errors that may occur in traditional coarse mesh simulations and ensuring the accuracy of permeability and connectivity calculations.

[0089] The permeation and diffusion process of fluids in solid materials with porous structures is simulated based on a porous media model, and the Langmuir adsorption model is added to simulate the adsorption and desorption behavior of gases in coal samples.

[0090] The fluid permeability, pore size distribution, specific surface area, and pore connectivity obtained from the simulation together constitute the physical simulation results of the micropore structure.

[0091] It should be noted that the porous media model is a mathematical model used to describe the interaction between the solid framework and fluids (such as gases or liquids) in porous materials (e.g., rocks, soils, coal, ceramics). Porous media materials consist of a solid structure and pores distributed throughout it. These pores can be filled with fluids, and the model simulates real physical processes by describing the flow, diffusion, and permeation of fluids within these pores. The Langmuir adsorption model assumes that adsorption is uniform, occurs at specific locations on the solid surface, and each adsorption site can adsorb one gas molecule, with no multilayer adsorption or interactions between adsorption sites.

[0092] S5. Compare the physical simulation results of the micropore structure with the data on the micropore structure of coal, and evaluate and optimize the three-dimensional structure model of the coal micropores. This includes the following steps.

[0093] The influence of the porous network on fluid flow is evaluated by comparing the fluid permeability in the physical simulation with that measured experimentally. The expression is as follows.

[0094]

[0095] Where A is the relative error value of permeability, A 模拟For the fluid permeability in physical simulation, A 实验 is the experimentally measured fluid permeability.

[0096] It should be noted that the relative error value of permeability helps to reveal the influence of pore network structure on flow resistance in simulation, and verifies the reliability and accuracy of the pore network model in predicting the permeation behavior of coalbed methane.

[0097] The pore size distribution data obtained by simulation and experiment are arranged in the same pore size interval, the pore size is divided into several intervals, and there is a corresponding pore volume ratio or pore number in each interval. For each pore size interval, the relative error of the physical simulation data and the experimental data is calculated. The pore size distribution generated by physical simulation and the pore size distribution obtained by mercury injection method and the pore size distribution obtained by nuclear magnetic resonance imaging are compared by relative error calculation to analyze the accuracy of the pore structure, which is represented as follows,

[0098]

[0099] wherein ε i is the relative error value of the i-th pore size interval distribution, V sim,i is the pore volume ratio of the i-th pore size interval pore size distribution in physical simulation, D 实验 is the pore volume ratio of the pore size distribution obtained by experiment.

[0100] It should be noted that the relative error value of the pore size distribution is used to determine whether the simulation model can truly reflect the micro-pore characteristics of the coal sample, ensure that the geometric shape of the model is consistent with the experimental observation, and thus improve the credibility of the simulation.

[0101] The specific surface area in the network simulation is compared with the test data measured by the specific surface area method to evaluate the simulation accuracy of the pore surface characteristics, which is represented as follows,

[0102]

[0103] wherein C is the relative error value of the specific surface area, C 模拟 is the specific surface area in physical simulation, C 实验 is the experimentally measured specific surface area.

[0104] It should be noted that the relative error value of the specific surface area can verify whether the simulation of the pore surface characteristics is accurate, and thus optimize the prediction of gas adsorption and desorption behavior.

[0105] The connectivity in the simulated pore network is compared with the connectivity in the actual coal sample to analyze the influence of the connected pores on the fluid behavior, which is represented as follows,

[0106]

[0107] wherein E is the relative error value of the connectivity, E模拟 E is the connectivity in physical simulation 实验 E is the measured connectivity in experiment.

[0108] It should be noted that the relative error value of connectivity can verify whether the simulation accurately reflects the true connectivity structure of the pore network, and help optimize the permeability and fluid flow prediction.

[0109] The threshold value of the relative error is obtained by machine learning analysis of historical data, combined with error analysis, and through threshold identification, the cause of the error is identified as which relative error value is too large. According to the error analysis results, the geometry, connectivity, physical parameters and boundary conditions of the pore network are adjusted by a global optimization algorithm, and the local grid is refined. By adjusting these parameters through a global optimization algorithm and refining the grid division in the local area, the difference between the simulation and the experimental results can be effectively reduced, and the simulation accuracy can be gradually improved.

[0110] The optimized coal micro-pore three-dimensional structure model is subjected to physical simulation again, and the new simulation results are compared with the experimental data. According to the relationship between the relative error value and the threshold value, the optimization effect is evaluated.

[0111] It should be noted that through this iterative process, the accuracy of the simulation can be gradually improved, and the model can better reflect the actual physical characteristics of the coal sample, and ultimately obtain high-precision simulation results of permeability, pore size distribution, specific surface area and connectivity, etc.

[0112] S6, using the API interface of ChemDraw, importing the coal micro-pore three-dimensional structure model after evaluation and optimization, automatically displaying and exporting two-dimensional and three-dimensional structures, including the following steps,

[0113] The optimized coal micro-pore three-dimensional structure model is exported as a common three-dimensional model file format (such as STL (stereographic planar figure), OBJ (three-dimensional object file), etc.).

[0114] The three-dimensional model is imported using the ChemDraw (chemical structure drawing) API (application programming interface) interface, the three-dimensional model file is read through API calling, and its geometric information is parsed, including atomic coordinates, bond lengths, bond angles, etc.

[0115] Through the coordinate system of ChemDraw, the structural information in the three-dimensional model is mapped into the two-dimensional workspace and three-dimensional workspace of ChemDraw. Specifically, since the default working mode of ChemDraw is two-dimensional, the geometric structure of the three-dimensional molecule needs to be projected onto a two-dimensional plane. This process involves mathematical coordinate transformation, using orthogonal projection or perspective projection to convert atomic coordinates in three-dimensional space to coordinates in ChemDraw's two-dimensional workspace.

[0116] The rendering function of the API interface is used to automatically generate a visual representation of the three-dimensional pore structure. Specifically, the user can specify rendering parameters (such as resolution, color, lighting, etc.), and finally download or embed the generated visualization result for display or sharing.

[0117] The model view is set through the API interface, a high-quality surface rendering effect is generated, and colors and textures are set for different regions according to the physical properties of the pores. Specifically, the camera view and zoom ratio are set, then colors and textures are set for different regions according to the physical properties of the pores (such as pore size or surface roughness), and finally a high-quality surface rendering effect is generated and the three-dimensional visualization result is embedded and displayed.

[0118] The API interface is used to generate a two-dimensional sketch of the plane projection and contour extraction. Specifically, the contour extraction algorithm is specified, a two-dimensional contour map is generated, and high-quality two-dimensional sketches are generated by adjusting parameters such as resolution, line thickness, and color.

[0119] Automatic export of two-dimensional and three-dimensional structure images in image format, vector graphics, and three-dimensional model format, and provide interactive functions to allow users to manipulate three-dimensional models in real time. The images, vector graphics (such as SVG (Scalable Vector Graphics) format), and three-dimensional model format (such as STL, OBJ format) of the three-dimensional structure are automatically exported, and the interactive functions are combined to allow users to manipulate the three-dimensional model in real time in the application.

[0120] It should be noted that the API is used to generate display images, geometric parameters, simulation results, and error analysis reports of the pore model. It can provide a comprehensive model summary for the user. By integrating the simulation results and error analysis into the report, the user can quickly understand the performance, accuracy, and matching degree with experimental data of the model, providing strong support for subsequent model optimization and decision-making.

[0121] The embodiment also provides a computer device suitable for the coal micro-pore structure characterization method based on ChemDraw, which comprises a memory and a processor. The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the coal micro-pore structure characterization method based on ChemDraw proposed in the above embodiment.

[0122] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved by WIFI, a carrier network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, a trackball or a touchpad arranged on the shell of the computer device, or an external keyboard, a touchpad or a mouse, etc.

[0123] The embodiment also provides a storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the method for representing micro-pore structure of coal based on ChemDraw. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk.

[0124] In summary, the present application effectively eliminates the interference signals in the scanning electron microscope image and the nuclear magnetic resonance imaging data by comprehensively applying wavelet denoising, non-local mean denoising, histogram equalization and super-resolution reconstruction, and improves the image quality. The precise alignment and integration between the data are realized by using cross-correlation registration and voxel fusion, and a high-resolution three-dimensional point cloud database of pores is constructed. The geometric characteristics of the pores are revealed by means of three-dimensional edge detection, morphological processing and region growing algorithm, and the spatial distribution of the pore groups is clarified by using point cloud segmentation and centroid positioning technology. The three-dimensional pore network model is created and optimized by using Delaunay triangulation and Laplace smoothing method. The COMSOL software is used for meticulous meshing, the motion characteristics of the fluid in the pores are simulated by combining the porous medium and the Langmuir model, and a series of important physical parameters are obtained. By comparing and analyzing the simulation results with the actual measured values, the key properties of the model are adjusted, and the visual presentation and interactive display of the optimized model are realized by means of ChemDraw API, which comprehensively improves the research accuracy and efficiency of the coal micro-pore structure.

[0125] The present application provides a coal micro-pore structure characterization method based on ChemDraw, which obtains coal micro-pore structure data by scanning electron microscope scanning, specific surface area test method, mercury intrusion method and nuclear magnetic resonance imaging technology method, and pre-processes, analyzes, three-dimensionally models, physically simulates and optimally displays the data, realizing comprehensive, accurate and efficient characterization of the coal micro-pore structure.

[0126] Example 2, referring to Table 1, is a second embodiment of the present application, which gives experimental simulation data of the coal micro-pore structure characterization method based on ChemDraw to further verify the technical scheme of the present application.

[0127] In order to verify the innovation and effectiveness of the coal micro-pore structure characterization method based on ChemDraw, the pre-processed image is analyzed by three-dimensional edge detection algorithm, morphological opening and closing operation and region growing algorithm, and the morphological characteristics of the micro-pores in the coal sample are extracted. Based on the extracted pore information, a three-dimensional pore model is generated by using Delaunay triangulation algorithm, and the three-dimensional pore structure is physically simulated by using COMSOL Multiphysics software, focusing on simulating the permeation process, pore connectivity, specific surface area and pore size distribution of the coal sample in the porous medium. In addition, combined with the Langmuir adsorption model, the adsorption and desorption behavior of coalbed methane on the pore wall is simulated.

[0128] To verify the accuracy of the simulation data, the physical simulation results were compared with experimental data. Fluid permeability was tested by laboratory equipment and compared with simulation values; mercury intrusion method and nuclear magnetic resonance technology were used to measure pore size distribution and porosity data, respectively, for comparison with simulation values. To further optimize the three-dimensional model, a global optimization algorithm was used to adjust the geometric shape, boundary conditions, and physical parameters in the model, and an optimized model was finally generated. The model was imported through the ChemDraw API interface to realize the visualization of two-dimensional and three-dimensional structures. The API interface automatically generated a three-dimensional display image and set colors and textures for different regions in the structure according to different physical properties, showing the integrity and complexity of the pore distribution.

[0129] The details are shown in the following Table 1:

[0130] Table 1: Experimental and simulation data of coal samples

[0131]

[0132]

[0133] According to the table data, by comparing the fluid permeability, pore size distribution, and specific surface area of the experiment and simulation, it can be seen that the invention embodies significant innovation and advantages in multiple aspects. From the comparison of fluid permeability, the maximum difference between the simulation value and the experimental value is less than 5%, which indicates that the physical simulation method in the invention can accurately capture the influence of the micro-pore structure of the coal sample on fluid flow. Compared with the prior art, the three-dimensional structure model generated by the Delaunay triangulation algorithm can better reflect the connectivity and flow path of the pores, thereby improving the accuracy of permeability simulation.

[0134] In the comparison of pore size distribution, the difference between the simulation value and the experimental value is less than 0.05 μm, indicating that the image processing and data fusion technology in the invention can effectively extract the key geometric features of the pores. Especially through wavelet denoising and super-resolution reconstruction technology, the problem of large pore size distribution error caused by insufficient image resolution in the prior art is solved. At the same time, combining multiple data sources for fusion processing makes the simulated pore size distribution closer to the experimental determination result.

[0135] In the comparison of specific surface area, the deviation between the simulation value and the experimental value is controlled within 1 m 2 / g, indicating that the physical model and optimization algorithm used can accurately simulate the surface properties of the coal sample. Through the refinement and optimization of the pore network, the model in the invention can better reflect the complex morphology of the pores, especially improving the simulation accuracy of the narrow and connected regions. Compared with the deficiencies in the calculation of specific surface area in the prior art, the invention improves the reliability of the simulation results by refining the grid division and optimizing the physical parameters.

[0136] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not to limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A coal micro-pore structure characterization method based on ChemDraw, characterized by: The coal micro-pore structure data is obtained and pre-processed, and the specific steps are as follows: The scanning electron microscope image is processed by using the wavelet denoising algorithm; For the magnetic resonance imaging data, a non-local mean denoising algorithm is used to remove imaging noise; The contrast of the scanning electron microscope image is enhanced by using the histogram equalization technique; For the image with low resolution, the super-resolution reconstruction algorithm is used to further enhance the image resolution; In the three-dimensional magnetic resonance imaging image, the Sobel edge enhancement technique is used to highlight the structural features of the pores; The pore features in the same area of the scanning electron microscope image and the magnetic resonance imaging data are extracted, and the cross-correlation registration method is used for spatial position alignment; Through the voxel fusion technology, the aligned scanning electron microscope image and the magnetic resonance imaging data are fused, and the local non-rigid registration algorithm is used for fine registration to generate high-resolution pore three-dimensional point cloud data; The pre-processed coal micro-pore structure data is analyzed, and the key information of the coal micro-pore is extracted; Based on the key information of the coal micro-pore, a three-dimensional structure model of the coal micro-pore is generated; The three-dimensional structure model of the coal micro-pore is simulated by a physical simulation tool, and the specific steps are as follows: Physical parameters are assigned to the solid and fluid of the three-dimensional structure model of the coal micro-pore based on COMSOL Multiphysics; The three-dimensional structure model of the coal micro-pore is globally meshed; The percolation and diffusion process of fluid in the solid material with pore structure is simulated based on the porous medium model, and the Langmuir adsorption model is added to simulate the adsorption and desorption behavior of gas in the coal sample; The fluid permeability, pore size distribution, specific surface area, and pore connectivity obtained by simulation together constitute the physical simulation results of the micro-pore structure; The physical simulation results of the micro-pore structure are compared with the coal micro-pore structure data to evaluate and optimize the three-dimensional structure model of the coal micro-pore; The API interface of ChemDraw is used to import the evaluated and optimized three-dimensional structure model of the coal micro-pore, and the two-dimensional and three-dimensional structures are automatically displayed and exported. The coal micro-pore structure data includes scanning electron microscope images, specific surface area data of coal sample micropores and mesopores, pore size distribution and porosity of the coal sample, and magnetic resonance imaging data; the pre-processing includes image denoising, image enhancement, data fusion and registration.

2. The ChemDraw-based coal micro-pore structure characterization method of claim 1, wherein: The pre-processed coal micro-pore structure data is analyzed, and the key information of the coal micro-pore is extracted, and the specific steps are as follows, 3. The ChemDraw-based coal micro-pore structure characterization method of claim 2, wherein: The high-resolution pore three-dimensional point cloud data is combined with the three-dimensional edge detection algorithm and the morphological opening and closing operation to obtain the pore morphology; The high-resolution pore three-dimensional point cloud data is segmented into independent pore groups by the point cloud segmentation clustering algorithm; The pore group centroid position is determined by the average coordinate centroid calculation method; The neighborhood distance of all pore group centroids is calculated based on the pore group centroid position, and the distribution mode of the pores in the three-dimensional space is analyzed; The pore morphology, pore group centroid position, and distribution mode of the pores in the three-dimensional space together constitute the key information of the coal micro-pore. ​ 4. The ChemDraw-based coal micro-pore structure characterization method of claim 3, wherein: Based on the key information of coal micro-pore, a three-dimensional structure model of coal micro-pore is generated, the specific steps are as follows, Based on the key information of coal micro-pore, combined with high-resolution three-dimensional point cloud data of pore, Delaunay triangular surface reconstruction algorithm is used to generate three-dimensional surface model and three-dimensional network model of coal micro-pore; The three-dimensional network model of coal micro-pore is analyzed using the shortest path graph algorithm, and the main flow channel and secondary channel of the pore are identified, and a three-dimensional pore network model with flow characteristics is generated; Through Laplace smoothing technology, the details of the three-dimensional pore network model are retained, and the volume preservation algorithm is used to generate the three-dimensional structure model of coal micro-pore.

5. The ChemDraw-based coal micro-pore structure characterization method of claim 4, wherein: The physical simulation results of micro-pore structure are compared with the coal micro-pore structure data to evaluate the optimized three-dimensional structure model of coal micro-pore, the specific steps are as follows, The fluid permeability in physical simulation is compared with the permeability measured by experiment to evaluate the influence of pore network on fluid flow; The pore size distribution generated by physical simulation is compared with the pore size distribution obtained by mercury injection method and the pore size distribution obtained by nuclear magnetic resonance imaging to analyze the accuracy of the pore structure; The specific surface area in network simulation is compared with the test data measured by specific surface area method to evaluate the simulation accuracy of pore surface characteristics; The connectivity in the simulated pore network is compared with the connectivity in the actual coal sample to analyze the influence of connected pores on fluid behavior; The difference between the simulation value and the experimental measurement value is compared, and the absolute error value and the relative percentage error of the experimental data are calculated; Combined with error analysis, the causes of error are identified, and the geometry, connectivity, physical parameters and boundary conditions of the pore network are adjusted through global optimization algorithm according to the error analysis results, and the local grid is refined; The optimized three-dimensional structure model of coal micro-pore is used for physical simulation again, and the new simulation results are compared with the experimental data to evaluate the optimization effect.

6. The ChemDraw-based coal micro-pore structure characterization method of claim 5, wherein: Using the API interface of ChemDraw, the coal micro-pore three-dimensional structure model after optimization is imported, and the two-dimensional and three-dimensional structure is automatically displayed and exported, the specific steps are as follows, The coal micro-pore three-dimensional structure model is imported using ChemDraw API interface, the three-dimensional structure model file is read by API calling, and its geometric information is parsed; Through the coordinate system of ChemDraw, the structure information in the coal micro-pore three-dimensional structure model is mapped to the two-dimensional workspace and three-dimensional workspace of ChemDraw; Using the rendering function of API interface, the visualization of coal micro-pore three-dimensional structure model is automatically generated; Through API interface, the model view angle is set to generate high-quality surface rendering effect, and different regions are set with color and texture according to the physical characteristics of the pore; Through API interface, two-dimensional schematic diagram of plane projection and contour extraction is generated; Automatic export of two-dimensional and three-dimensional structure images in image format, vector graphics and three-dimensional model format, and provide interactive function, allow users to operate three-dimensional model in real time; Through API, the display image, geometric parameters, simulation results and error analysis report of the pore model are generated.

7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the coal micro-pore structure characterization method based on ChemDraw in any one of claims 1-6.

8. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the coal micro-pore structure characterization method based on ChemDraw in any one of claims 1-6.

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