ChemDraw-based coal micro-pore structure characterization method

Abstract

The invention discloses a ChemDraw-based coal micro-pore structure characterization method, and relates to the technical field of coal mine gas geology, and the method comprises the steps: obtaining coal micro-pore structure data through a plurality of technologies, and carrying out the preprocessing; analyzing the preprocessed coal micro-pore structure data, and extracting key information of coal micro-pores; generating a coal micro-pore three-dimensional structure model based on the key information of the coal micro-pores; simulating the coal micro-pore three-dimensional structure model through a physical simulation tool to obtain a coal micro-pore structure physical simulation result; comparing the physical simulation result of the microcosmic pore structure with the coal microcosmic pore structure data, and evaluating and optimizing a coal microcosmic pore three-dimensional structure model; and importing the evaluated and optimized coal micro-pore three-dimensional structure model by utilizing an API (Application Program Interface) of ChemDraw, and automatically displaying and exporting two-dimensional and three-dimensional structures. The invention provides a ChemDraw-based coal micro-pore structure characterization method, which realizes comprehensive, accurate and efficient pore structure analysis and display.

Description

Technical Field

[0001] The invention relates to the technical field of coal mine gas geology, in particular to a coal microscopic pore structure characterization method based on ChemDraw. Background Art

[0002] The microscopic pore structure of coal is highly complex and multi-scale. The pore morphology, size distribution, and connectivity between pores have a significant impact on the permeability and adsorption performance of coalbed methane. The pore size ranges from nanometers to micrometers, presenting a variety of geometric forms. These fine structures directly determine the flow path and permeability of gas in coal seams. Because these microscopic characteristics are highly complex and interrelated, accurately characterizing the pore network of coal samples has become the key to predicting the permeability and adsorption performance of coalbed methane.

[0003] Traditional experimental methods can usually provide data such as pore distribution and average permeability of coal samples at a macroscopic level, but these methods can only provide overall statistical information. This limitation makes it very difficult to accurately predict the flow behavior of gas in microscopic pore structures based on experimental data. Summary of the invention

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

[0005] Therefore, the present invention provides a coal microscopic pore structure characterization method based on ChemDraw to solve the problem that the complexity of coal microscopic pore structure is difficult to accurately characterize and simulate.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for characterizing coal microscopic pore structure based on ChemDraw, which comprises obtaining coal microscopic pore structure data and performing preprocessing;

[0008] Analyze the pre-processed coal microscopic pore structure data to extract key information of coal microscopic pores;

[0009] Generate a three-dimensional structure model of coal micropores based on the key information of coal micropores;

[0010] The three-dimensional structure model of coal micro-pores is simulated by physical simulation tools to obtain the physical simulation results of coal micro-pore structure;

[0011] Compare the physical simulation results of microscopic pore structure with the data of coal microscopic pore structure to evaluate and optimize the three-dimensional structure model of coal microscopic pore;

[0012] Using the API interface of ChemDraw, the three-dimensional structure model of coal micropores after evaluation and optimization is imported to automatically display and export the two-dimensional and three-dimensional structures.

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

[0014] As a preferred solution of the coal microscopic pore structure characterization method based on ChemDraw described in the present invention, the scanning electron microscope image and the nuclear magnetic resonance imaging data are subjected to image denoising, image enhancement, data fusion and registration. The specific steps are as follows:

[0015] The scanning electron microscope images were processed using wavelet denoising algorithm;

[0016] For MRI data, the non-local means denoising algorithm was used to remove imaging noise;

[0017] Using histogram equalization to enhance the contrast of scanning electron microscope images;

[0018] For images with lower resolution, super-resolution reconstruction algorithms are used to further enhance the image resolution;

[0019] In 3D MRI images, Sobel edge enhancement technique is used to highlight the structural features of pores;

[0020] Extract the pore features of the same area in the SEM image and MRI data, and align them in space using the cross-correlation registration method;

[0021] The aligned scanning electron microscope image and the magnetic resonance imaging data are fused through voxel fusion technology, and a local non-rigid registration algorithm is used to perform fine registration to generate high-resolution pore three-dimensional point cloud data.

[0022] As a preferred solution of the coal microscopic pore structure characterization method based on ChemDraw described in the present invention, the pre-processed coal microscopic pore structure data is analyzed to extract key information of the coal microscopic pores. The specific steps are as follows:

[0023] The high-resolution pore 3D point cloud data is combined with 3D edge detection algorithm and morphological opening and closing operation to obtain the pore morphology;

[0024] The high-resolution pore 3D point cloud data is segmented into independent pore groups through the point cloud segmentation and clustering algorithm;

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

[0026] The neighborhood distances of all pore group centroids are calculated based on the centroid positions of the pore groups, and the distribution pattern of pores in three-dimensional space is analyzed.

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

[0028] As a preferred solution of the coal microscopic pore structure characterization method based on ChemDraw described in the present invention, wherein: based on the key information of the coal microscopic pores, a three-dimensional structure model of the coal microscopic pores is generated, and the specific steps are as follows:

[0029] Based on the key information of coal microscopic pores, combined with high-resolution pore 3D point cloud data, the Delaunay triangulation surface reconstruction algorithm is used to generate the 3D surface model and 3D network model of coal microscopic pores;

[0030] The shortest path graph algorithm is used to analyze the three-dimensional network model of coal microscopic pores, identify the mainstream channels and secondary channels of the pores, and generate a three-dimensional pore network model with flow characteristics;

[0031] The Laplace smoothing technique is used to retain the details of the three-dimensional pore network model, and the volume preservation algorithm is used to generate a three-dimensional structural model of coal micropores.

[0032] As a preferred solution of the coal microscopic pore structure characterization method based on ChemDraw described in the present invention, wherein: the coal microscopic pore three-dimensional structure model is simulated by a physical simulation tool to obtain the physical simulation result of the coal microscopic pore structure, and the specific steps are as follows:

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

[0034] Perform global meshing on the three-dimensional structure model of coal microscopic pores;

[0035] The porous media model is used to simulate the penetration and diffusion process of fluids in solid materials with pore structures, and the Langmuir adsorption model is added to simulate the adsorption and desorption behavior of gases in coal samples.

[0036] The fluid permeability, pore size distribution, specific surface area and pore connectivity obtained from the simulation together constitute the physical simulation results of the microscopic pore structure.

[0037] As a preferred solution of the coal microscopic pore structure characterization method based on ChemDraw described in the present invention, wherein: the physical simulation results of the microscopic pore structure are compared with the coal microscopic pore structure data, and the coal microscopic pore three-dimensional structure model is evaluated and optimized. The specific steps are as follows:

[0038] Compare the fluid permeability in physical simulations with the experimentally measured fluid permeability to evaluate the effect of the pore network on fluid flow;

[0039] The pore size distribution generated by physical simulation was compared with the pore size distribution obtained by mercury intrusion and pore size distribution obtained by nuclear magnetic resonance imaging to analyze the accuracy of the pore structure;

[0040] Compare the specific surface area in the network simulation with the test data measured by the specific surface area method to evaluate the simulation accuracy of the pore surface characteristics;

[0041] The connectivity in the simulated pore network was compared with that in the actual coal sample to analyze the effect of connected pores on fluid behavior;

[0042] Compare the difference between the simulated value and the experimental measurement value, and calculate the absolute error value and the relative percentage error of the experimental data;

[0043] Combined with error analysis, the causes of errors are identified. Based on the error analysis results, the geometry, connectivity, physical parameters and boundary conditions of the pore network are adjusted through a global optimization algorithm, and the local grid is refined.

[0044] The optimized coal micropore three-dimensional structure model was physically simulated again, and the new simulation results were compared with the experimental data to evaluate the optimization effect.

[0045] As a preferred solution of the coal microscopic pore structure characterization method based on ChemDraw described in the present invention, wherein: using the API interface of ChemDraw, the three-dimensional structure model of coal microscopic pores after evaluation and optimization is imported, and the two-dimensional and three-dimensional structures are automatically displayed and exported. The specific steps are as follows:

[0046] Use ChemDraw API interface to import the three-dimensional structure model of coal micro-pores, read the three-dimensional structure model of coal micro-pores through API calls, and parse its geometric information;

[0047] Through the coordinate system of ChemDraw, the structural information in the three-dimensional structure model of coal microscopic pores is mapped to the two-dimensional workspace and three-dimensional workspace of ChemDraw;

[0048] Using the rendering function of the API interface, a visual representation of the pore structure of the coal microscopic pore three-dimensional structure model is automatically generated;

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

[0050] Generate 2D schematic diagrams of plane projection and contour extraction through API interface;

[0051] Automatically export 2D and 3D structure images in image format, vector graphics and 3D model format, and provide interactive functions to allow users to manipulate 3D models in real time;

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

[0053] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the coal microscopic pore structure characterization method based on ChemDraw as described in the first aspect of the present invention is implemented.

[0054] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the coal microscopic pore structure characterization method based on ChemDraw as described in the first aspect of the present invention is implemented.

[0055] The beneficial effects of the present invention are as follows: the present invention effectively removes interference signals in 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 technologies, thereby improving image quality. The cross-correlation registration and voxel fusion methods are adopted to achieve accurate alignment and integration between data, and construct a high-resolution pore three-dimensional point cloud database. With the help of three-dimensional edge detection, morphological processing and regional growth algorithm, the geometric characteristics of the pores are revealed, and the spatial distribution of the pore group is clarified by using point cloud segmentation and centroid positioning technology. The three-dimensional pore network model is created and optimized by using methods such as Delaunay triangulation and Laplace smoothing. The present invention uses COMSOL software for detailed grid division, combines porous media with Langmuir model, simulates the motion characteristics of fluid in the pores, and obtains a series of important physical parameters. By comparing and analyzing the simulation results with the actual measured values, the key attributes of the model are adjusted, and the visualization and interactive display of the optimized model are realized with the help of ChemDraw API, which comprehensively improves the research accuracy and efficiency of the microscopic pore structure of coal. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0057] Figure 1 This is a flow chart of the coal microscopic pore structure characterization method based on ChemDraw in Example 1.

[0058] Figure 2 This is a flow chart of extracting key information about coal micropores in Example 1. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0060] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0061] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0062] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for characterizing the microscopic pore structure of coal based on ChemDraw, comprising the following steps:

[0063] S1. Obtaining coal microscopic pore structure data and preprocessing it, including the following steps:

[0064] A variety of techniques include scanning electron microscope scanning, specific surface area test method, mercury intrusion method and nuclear magnetic resonance imaging; coal microscopic pore structure data include scanning electron microscope images, specific surface area data of coal sample micropores and mesopores, pore size distribution and porosity of coal samples, 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, adsorption and desorption behavior of coalbed methane and the connectivity of the pore network, providing a reliable scientific basis for the efficient exploitation of coalbed methane. Preprocessing includes image denoising, image enhancement, data fusion and registration; the physical simulation tool is COMSOL Multiphysics (Comosol simulation software), which is a powerful multi-physics simulation software widely used in scientific research, engineering design and technology development. The software can simulate and analyze the coupling behavior between multiple physical phenomena, and solve complex multi-physics problems with high-precision numerical methods.

[0065] The SEM images are processed using the wavelet denoising algorithm. Specifically, the SEM images are cropped, grayed and normalized, and then an appropriate wavelet basis (such as Daubechies wavelet) is selected for multi-layer wavelet decomposition to decompose the SEM images into low-frequency and high-frequency coefficients of different scales. A hard threshold or soft threshold is applied to the high-frequency coefficients for denoising, suppressing noise and retaining details, while keeping the low-frequency coefficients unchanged. After denoising, the SEM images are reconstructed by inverse wavelet transform, and smoothing filtering or contrast enhancement is performed if necessary.

[0066] The non-local means denoising algorithm suppresses noise in MRI while preserving the details of the pore structure. Specifically, the MRI is grayscaled and normalized, and then similar pixels are found within the search window of each pixel. The similarity weights are calculated based on the weighted Euclidean distance, and the pixels are denoised by weighted average based on these weights.

[0067] The contrast of the scanning electron microscope image is enhanced by using histogram equalization technology. Specifically, the scanning electron microscope image is grayed and normalized, and then the grayscale histogram of the image is calculated, and the pixel intensity is redistributed through the cumulative distribution function to smooth the grayscale value of the image, so that its contrast is enhanced.

[0068] For scanning electron microscope images and magnetic resonance imaging with lower resolution, 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 networks, 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 magnetic resonance imaging, Sobel edge enhancement technology (Sobel edge detection technology) is used to highlight the structural features of pores. Specifically, the Sobel operator is used to calculate the gradient of the image in the horizontal and vertical directions to generate an edge response map. Then, the gradient amplitudes in the two directions are synthesized to obtain the edge intensity map of the image, thereby highlighting the edges and structural features of the pores.

[0070] The Canny edge detection algorithm was used to extract the pore features of the same area in the SEM image and the MRI data, and the cross-correlation registration method was used to align the spatial positions.

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

[0072] S2. Analyze the pre-processed coal microscopic pore structure data to extract key information of coal microscopic pores, including the following steps:

[0073] The high-resolution pore 3D point cloud data is combined with the 3D edge detection algorithm and morphological opening and closing operations to obtain the pore morphology. Specifically, the high-resolution pore 3D point cloud data is denoised and smoothed, and then the edge features of the pores are extracted by the 3D edge detection algorithm. The opening operation (corrosion followed by expansion) in the 3D morphological operation is applied to remove small noise and artifacts, and then the small gaps in the pore edge are filled by the closing operation (expansion followed by corrosion) to further smooth the boundary.

[0074] The high-resolution pore 3D point cloud data is segmented into independent pore groups by using the point cloud segmentation and clustering algorithm. Specifically, the high-resolution pore 3D point cloud data is downsampled to reduce the computational complexity. Then, the 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 the distance threshold, each independent pore can be effectively identified and separated.

[0075] It should be noted that independent pore groups include x, y and z axis coordinates.

[0076] The centroid position of the pore group is determined by the average coordinate centroid calculation method for independent pore groups. Specifically, the three-dimensional point cloud data of each pore group is traversed, and 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 distances of the centroids of all pore groups are calculated according to the centroid positions of the pore groups, and the distribution pattern of the pores in three-dimensional space is obtained by analysis. Specifically, the Euclidean neighborhood distance between each pair of centroids is calculated, and the neighboring relationship of the centroids is judged according to the Euclidean neighborhood distance to obtain the distribution pattern of the pore group in three-dimensional space, which includes uniform distribution, random distribution and clustered distribution.

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

[0079] S3. Based on the key information of coal microscopic pores, a three-dimensional structure model of coal microscopic pores is generated, including the following steps:

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

[0081] It should be noted that the Delaunay triangulation surface reconstruction algorithm is a geometric algorithm used to construct a triangular mesh on a given set of points to meet specific optimization conditions. It is a very important concept in computational geometry, especially in the fields of 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 micropores and identify the mainstream channels and secondary channels of the pores. It is necessary to first construct the adjacency matrix of the pore network and record the connection relationship between the pore nodes. Define weights for the connected edges between pore nodes, such as pore size or channel length. Use the Dijkstra algorithm to calculate the shortest path from each pore node to other nodes. Identify the main path that runs through the pore network, that is, the mainstream channel. Identify the secondary channels by analyzing the secondary paths. Calculate the fluid resistance coefficient of each channel and allocate the fluid flow. Calculate the fluid velocity distribution in each channel. Finally, based on the results of the shortest path algorithm, a three-dimensional network model of coal micropores with flow characteristics is generated. This process can accurately locate the main and secondary paths of gas flow, thereby revealing the dominant flow direction and secondary influx areas in the pore network. Through this analysis, the flow path of coalbed methane can be clarified, which provides an important reference for permeability prediction and mining plan optimization.

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

[0084] The details of the three-dimensional network model of coal micropores with flow characteristics are retained through Laplace smoothing technology, and the volume preservation algorithm is used to generate a three-dimensional structural model of coal micropores. Specifically, the positions of the vertices of the three-dimensional network model of coal micropores with flow characteristics are adjusted iteratively through Laplace smoothing technology to eliminate surface noise and sharp edges, and keep the flow channels and key structures of the pores unchanged. In order to avoid volume shrinkage during the smoothing process, the volume preservation algorithm is used to perform local and global volume correction on the three-dimensional network model of coal micropores with flow characteristics to ensure that the geometric morphology and volume characteristics of the pores are consistent with the original three-dimensional network model of coal micropores with flow characteristics.

[0085] S4, simulating the three-dimensional structure model of coal microscopic pores by a physical simulation tool to obtain the physical simulation results of the coal microscopic 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 microscopic pores. Specifically, the porous media flow module is selected to assign physical parameters such as density, elastic modulus, porosity and permeability to the coal body, and its density, viscosity and diffusion coefficient are set based on the selected fluid (such as methane or water). By defining boundary conditions such as inlet and outlet, it is ensured that the flow of fluid in the pores meets the physical reality requirements.

[0087] The three-dimensional structure model of the coal microscopic pores is meshed globally, and the simulation accuracy of the narrow and connected parts of the pore network is improved by using refined meshing. Specifically, the entire pore network is meshed globally, the narrow pore areas and connected parts are identified, and the mesh is locally refined in these key areas to ensure the simulation accuracy of fluid flow and diffusion behavior. By setting a smaller mesh size based on geometric feature refinement rules and combining adaptive meshing technology, the mesh in high gradient areas is automatically optimized.

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

[0089] The porous media model is used to simulate the penetration and diffusion process of fluid in solid materials with pore structures, and the Langmuir adsorption model is added to simulate the adsorption and desorption behaviors 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 microscopic pore structure.

[0091] It should be noted that the porous media model is a mathematical model used to describe the interaction between the solid skeleton and the fluid (such as gas or liquid) in porous materials (such as rocks, soils, coal, ceramics, etc.). Porous media materials are composed of solid structures and pores throughout them. The pores can be filled with fluids. The model simulates real physical processes by describing the flow, diffusion, and penetration of fluids in these pores. The Langmuir adsorption model assumes that adsorption is uniform and occurs at specific locations on the solid surface. Each adsorption site can adsorb one gas molecule, and there is no multi-layer adsorption or interaction between adsorption sites.

[0092] S5. Compare the physical simulation results of microscopic pore structure with the data of coal microscopic pore structure to evaluate and optimize the three-dimensional structure model of coal microscopic pore, including the following steps:

[0093] The fluid permeability in the physical simulation is compared with the experimentally measured fluid permeability to evaluate the effect of the pore network on the fluid flow. The expression is as follows,

[0094]

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

[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 verify the reliability and accuracy of pore network model in predicting CBM permeability behavior.

[0097] The pore size distribution data obtained by simulation and experiment are sorted according to the same pore size interval, and the pore size is divided into several intervals, and each interval has a corresponding pore volume ratio or pore number. For each pore size interval, the relative error between the physical simulation data and the experimental data is calculated. 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 nuclear magnetic resonance imaging through relative error calculation, and the accuracy of the pore structure is analyzed, which is expressed as follows:

[0098]

[0099] Among them, ε i is the relative error value of the ith aperture interval distribution, V sim,i is the pore volume proportion of the pore size distribution in the pore size interval i in the physical simulation, D 实验 The pore volume fraction for the pore size distribution obtained experimentally.

[0100] It should be noted that the relative error value of pore size distribution is used to determine whether the simulation model can truly reflect the microscopic pore characteristics of the coal sample and ensure that the geometric shape of the model is consistent with the experimental observation, thereby improving 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, as shown below:

[0102]

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

[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, thereby optimizing the prediction of gas adsorption and desorption behaviors.

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

[0106]

[0107] Among them, E is the relative error value of connectivity, E模拟 is the connectivity in physical simulation, E 实验 is the connectivity measured experimentally.

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

[0109] The threshold of relative error is obtained by machine learning analysis of historical data. Combined with error analysis, the cause of the error is identified through threshold identification, which relative error value is too large. According to the error analysis results, the geometric shape, connectivity, physical parameters and boundary conditions of the pore network are adjusted through the global optimization algorithm, and the local grid is refined. Adjusting these parameters through the global optimization algorithm and refining the grid division of the local area can effectively reduce the difference between the simulation and experimental results and gradually improve the simulation accuracy.

[0110] The optimized coal microscopic pore three-dimensional structure model was physically simulated again, and the new simulation results were compared with the experimental data. The optimization effect was evaluated based on the relationship between the relative error value and the threshold.

[0111] It should be noted that through this iterative process, the accuracy of the simulation can be gradually improved, and the model can be ensured to better reflect the actual physical properties of the coal sample, and finally high-precision simulation results such as permeability, pore size distribution, specific surface area and connectivity can be obtained.

[0112] S6. Using the API interface of ChemDraw, the three-dimensional structure model of coal micropores after evaluation and optimization is imported to automatically display and export the two-dimensional and three-dimensional structures, including the following steps:

[0113] The optimized coal microscopic pore three-dimensional structure model is exported into a common three-dimensional model file format (such as STL (stereoscopic plane graphics), OBJ (three-dimensional object file), etc.).

[0114] Use ChemDraw (chemical structure drawing) API (application programming interface) interface to import 3D models, read 3D model files through API calls, and parse their geometric information, including atomic coordinates, bond lengths, bond angles, etc.

[0115] Through the coordinate system of ChemDraw, the structural information in the 3D model is mapped to the 2D and 3D workspaces of ChemDraw. Specifically, since the default working mode of ChemDraw is 2D, the geometric structure of the 3D molecule needs to be projected onto the 2D plane. This process involves mathematical coordinate transformation, using orthogonal projection or perspective projection to convert the atomic coordinates in the 3D space into the coordinates in the 2D workspace of ChemDraw.

[0116] The rendering function of the API interface is used to automatically generate a visualization of the 3D pore structure. Specifically, the user can specify rendering parameters (such as resolution, color, lighting, etc.), and finally download or embed the generated visualization results for display or sharing.

[0117] The model perspective is set through the API interface to generate high-quality surface rendering effects, and the color and texture of different areas are set according to the physical characteristics of the pores. Specifically, the camera perspective and zoom ratio are set, and then the color and texture of different areas are set according to the physical characteristics of the pores (such as pore size or surface roughness). Finally, a high-quality surface rendering effect is generated and the 3D visualization result is embedded and displayed.

[0118] Generate a 2D schematic diagram of plane projection and contour extraction through the API interface. Specifically, specify the contour extraction algorithm to generate a 2D contour map, and generate a high-quality 2D schematic diagram by adjusting parameters such as resolution, line thickness and color.

[0119] Automatically export 2D and 3D structure images in image format, vector graphics and 3D model format, and provide interactive functions to allow users to operate 3D models in real time. Automatically export 3D structure images, vector graphics (such as SVG (Scalable Vector Graphics) format) and 3D model formats (such as STL, OBJ format), and combine interactive functions to allow users to operate 3D models in real time in the application.

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

[0121] This embodiment also provides a computer device, which is suitable for the coal microscopic pore structure characterization method based on ChemDraw, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the coal microscopic pore structure characterization method based on ChemDraw as proposed in the above embodiment.

[0122] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used 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 the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0123] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for characterizing the coal microscopic pore structure based on ChemDraw as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0124] In summary, the present invention effectively removes interference signals in 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 technologies, thereby improving image quality. By adopting cross-correlation registration and voxel fusion methods, accurate alignment and integration between data are achieved, and a high-resolution pore three-dimensional point cloud database is constructed. With the help of three-dimensional edge detection, morphological processing and regional growth algorithm, the geometric characteristics of the pores are revealed, and the spatial distribution of the pore group is clarified by point cloud segmentation and centroid positioning technology. The three-dimensional pore network model is created and optimized by using methods such as Delaunay triangulation and Laplace smoothing. The present invention uses COMSOL software for detailed grid division, combines porous media with Langmuir model, simulates the motion characteristics of fluid in the pores, and obtains a series of important physical parameters. By comparing and analyzing the simulation results with the actual measured values, the key attributes of the model are adjusted, and the visualization and interactive display of the optimized model are realized with the help of ChemDraw API, which comprehensively improves the research accuracy and efficiency of coal microscopic pore structure.

[0125] The present invention provides a method for characterizing the microscopic pore structure of coal based on ChemDraw. The method obtains the microscopic pore structure data of coal through scanning electron microscope scanning, specific surface area test method, mercury injection method and nuclear magnetic resonance imaging technology, and performs preprocessing, analysis, three-dimensional modeling, physical simulation and optimization display on the data, thereby realizing a comprehensive, accurate and efficient characterization of the microscopic pore structure of coal.

[0126] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of a method for characterizing the coal microscopic pore structure based on ChemDraw are provided.

[0127] In order to verify the innovation and effectiveness of the coal microscopic pore structure characterization method based on ChemDraw, the preprocessed image was analyzed by three-dimensional edge detection algorithm, morphological opening and closing operation and region growing algorithm to extract the morphological characteristics of the microscopic pores in the coal sample. Based on the extracted pore information, the Delaunay triangulation algorithm was used to generate a three-dimensional pore model, and the three-dimensional pore structure was physically simulated by COMSOL Multiphysics software, focusing on the simulation of 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 was simulated.

[0128] In order to verify the accuracy of the simulation data, the physical simulation results were compared with the experimental data. The fluid permeability was tested by laboratory equipment and compared with the simulation value; the pore size distribution and porosity data were measured by mercury intrusion and nuclear magnetic resonance technology, respectively, for comparison with the simulation value. In order to further optimize the three-dimensional model, the geometric morphology, boundary conditions and physical parameters in the model were adjusted using a global optimization algorithm, and finally an optimized model was generated. The model was imported through the ChemDraw API interface to achieve the visualization of the two-dimensional and three-dimensional structures. The API interface automatically generated a three-dimensional display image, and set the color and texture for different areas in the structure according to different physical properties, showing the integrity and complexity of the pore distribution.

[0129] The details are shown in Table 1 below:

[0130] Table 1 Coal sample experimental and simulation data

[0131]

[0132]

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

[0134] In the comparison of pore size distribution, the difference between the simulated value and the experimental value is less than 0.05μm, indicating that the image processing and data fusion technology in the invention can effectively extract the key geometric features of the pores. In particular, the wavelet denoising and super-resolution reconstruction technology solves the problem of large pore size distribution errors caused by insufficient image resolution in the prior art. At the same time, the fusion processing combined with multiple data sources makes the simulated pore size distribution closer to the experimental measurement results.

[0135] In the comparison of specific surface area, the deviation between the simulation value and the experimental value is controlled within 1m 2 / g or less, indicating that the physical model and optimization algorithm used can accurately simulate the surface characteristics of the coal sample. By refining and optimizing 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 part and the connected part. Compared with the shortcomings of the prior art in calculating the specific surface area, the present 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 embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for characterizing coal microscopic pore structure based on ChemDraw, characterized by: include, Obtain coal microscopic pore structure data and perform preprocessing; Analyze the pre-processed coal microscopic pore structure data to extract key information of coal microscopic pores; Generate a three-dimensional structure model of coal micropores based on the key information of coal micropores; The three-dimensional structure model of coal micro-pores is simulated by physical simulation tools to obtain the physical simulation results of coal micro-pore structure; Compare the physical simulation results of microscopic pore structure with the data of coal microscopic pore structure to evaluate and optimize the three-dimensional structure model of coal microscopic pore; Using the API interface of ChemDraw, the three-dimensional structure model of coal micropores after evaluation and optimization is imported to automatically display and export the two-dimensional and three-dimensional structures.

2. The method for characterizing coal microscopic pore structure based on ChemDraw according to claim 1, characterized in that: The coal microscopic pore structure data include scanning electron microscope images, specific surface area data of coal sample micropores and mesopores, pore size distribution and porosity of coal samples, and nuclear magnetic resonance imaging data; the preprocessing includes image denoising, image enhancement, data fusion and registration.

3. The method for characterizing coal microscopic pore structure based on ChemDraw according to claim 2, characterized in that: Image denoising, image enhancement, data fusion and registration are performed on scanning electron microscope images and nuclear magnetic resonance imaging data. The specific steps are as follows: The scanning electron microscope images were processed using wavelet denoising algorithm; For MRI data, the non-local means denoising algorithm was used to remove imaging noise; Using histogram equalization to enhance the contrast of scanning electron microscope images; For images with lower resolution, super-resolution reconstruction algorithms are used to further enhance the image resolution; In 3D MRI images, Sobel edge enhancement technique is used to highlight the structural features of pores; Extract the pore features of the same area in the SEM image and MRI data, and align them in space using the cross-correlation registration method; The aligned scanning electron microscope image and the magnetic resonance imaging data are fused through voxel fusion technology, and a local non-rigid registration algorithm is used to perform fine registration to generate high-resolution pore three-dimensional point cloud data.

4. The method for characterizing coal microscopic pore structure based on ChemDraw according to claim 3, characterized in that: The pre-processed coal microscopic pore structure data is analyzed to extract the key information of coal microscopic pores. The specific steps are as follows: High-resolution pore 3D point cloud data is combined with 3D edge detection algorithm and morphological opening and closing operation to obtain pore morphology; The high-resolution pore 3D point cloud data is segmented into independent pore groups through the point cloud segmentation and clustering algorithm; The independent pore groups are determined by the average coordinate centroid calculation method to determine the centroid position of the pore group; The neighborhood distances of all pore group centroids are calculated based on the centroid positions of the pore groups, and the distribution pattern of pores in three-dimensional space is analyzed. The pore morphology, the centroid position of the pore group and the distribution pattern of the pores in three-dimensional space together constitute the key information of coal microscopic pores.

5. The method for characterizing coal microscopic pore structure based on ChemDraw according to claim 4, characterized in that: Based on the key information of coal microscopic pores, a three-dimensional structure model of coal microscopic pores is generated. The specific steps are as follows: Based on the key information of coal microscopic pores, combined with high-resolution pore 3D point cloud data, the Delaunay triangulation surface reconstruction algorithm is used to generate the 3D surface model and 3D network model of coal microscopic pores; The shortest path graph algorithm is used to analyze the three-dimensional network model of coal microscopic pores, identify the mainstream channels and secondary channels of the pores, and generate a three-dimensional pore network model with flow characteristics; The Laplace smoothing technique is used to retain the details of the three-dimensional pore network model, and the volume preservation algorithm is used to generate a three-dimensional structural model of coal micropores.

6. The method for characterizing coal microscopic pore structure based on ChemDraw according to claim 5, characterized in that: The three-dimensional structure model of coal microscopic pores is simulated by physical simulation tools to obtain the physical simulation results of coal microscopic pore structure. The specific steps are as follows: Based on COMSOL Multiphysics, physical parameters are assigned to the solid and fluid of the three-dimensional structure model of coal microscopic pores; Perform global meshing on the three-dimensional structure model of coal microscopic pores; The porous media model is used to simulate the penetration and diffusion process of fluids in solid materials with pore structures, and the Langmuir adsorption model is added to simulate the adsorption and desorption behavior of gases in coal samples. The fluid permeability, pore size distribution, specific surface area and pore connectivity obtained from the simulation together constitute the physical simulation results of the microscopic pore structure.

7. The method for characterizing coal microscopic pore structure based on ChemDraw according to claim 6, characterized in that: The physical simulation results of the microscopic pore structure are compared with the coal microscopic pore structure data to evaluate and optimize the coal microscopic pore three-dimensional structure model. The specific steps are as follows: Compare fluid permeabilities in physical simulations with experimentally measured permeabilities to assess the impact of pore networks on fluid flow; The pore size distribution generated by physical simulation was compared with the pore size distribution obtained by mercury intrusion and pore size distribution obtained by nuclear magnetic resonance imaging to analyze the accuracy of the pore structure; Compare the specific surface area in the network simulation with the test data measured by the specific surface area method to evaluate the simulation accuracy of the pore surface characteristics; The connectivity in the simulated pore network was compared with that in the actual coal sample to analyze the effect of connected pores on fluid behavior; Compare the difference between the simulated value and the experimental measurement value, and calculate the absolute error value and the relative percentage error of the experimental data; Combined with error analysis, the causes of errors are identified. Based on the error analysis results, the geometry, connectivity, physical parameters and boundary conditions of the pore network are adjusted through a global optimization algorithm, and the local grid is refined. The optimized coal micropore three-dimensional structure model was physically simulated again, and the new simulation results were compared with the experimental data to evaluate the optimization effect.

8. The method for characterizing coal microscopic pore structure based on ChemDraw according to claim 7, characterized in that: Using the API interface of ChemDraw, the three-dimensional structure model of coal micropores after evaluation and optimization is imported to automatically display and export the two-dimensional and three-dimensional structures. The specific steps are as follows: Use ChemDraw API interface to import coal microscopic pore 3D structure model, read coal microscopic pore 3D structure model file through API call, and parse its geometric information; Through the coordinate system of ChemDraw, the structural information in the three-dimensional structure model of coal microscopic pores is mapped to the two-dimensional workspace and three-dimensional workspace of ChemDraw; Using the rendering function of the API interface, a visual representation of the pore structure of the coal microscopic pore three-dimensional structure model is automatically generated; Set the model perspective through the API interface, generate high-quality surface rendering effects, and set colors and textures for different areas based on the physical properties of the pores; Generate 2D schematic diagrams of plane projection and contour extraction through API interface; Automatically export 2D and 3D structure images in image format, vector graphics and 3D model format, and provide interactive functions to allow users to manipulate 3D models in real time; Generate pore model display images, geometric parameters, simulation results and error analysis reports through API.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the coal microscopic pore structure characterization method based on ChemDraw according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the coal microscopic pore structure characterization method based on ChemDraw as described in any one of claims 1 to 8 are implemented.

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