Rock pore structure modeling method and device, storage medium and computer equipment

Abstract

This application relates to the field of structural modeling technology, specifically disclosing a method and apparatus, storage medium, and computer equipment for modeling rock pore structures. The method includes: acquiring a benchmark model of a real rock pore structure and extracting a first characteristic parameter from it to characterize the rock pore structure; randomly generating multiple geometric bodies in a three-dimensional modeling space according to preset geometric body generation parameters, determining the influence area of ​​each geometric body, and calculating the influence intensity value of each spatial point within the influence area; for each spatial point, superimposing the received influence intensity values ​​from each geometric body to obtain a cumulative influence value; constructing a three-dimensional influence field based on the cumulative influence value of each spatial point, and generating a three-dimensional pore structure model according to a preset intensity cutoff range; calculating a second characteristic parameter of the three-dimensional pore structure model, iteratively optimizing the three-dimensional pore structure model based on the deviation between the first and second characteristic parameters, and outputting the final rock pore structure model.

Description

Technical Field

[0001] This application relates to the field of structural modeling technology, and in particular to a method and apparatus for modeling rock pore structures, a storage medium, and a computer device. Background Technology

[0002] Rock pore structure is a key factor influencing macroscopic physical properties such as reservoir permeability, porosity, and fluid flow behavior. Its accurate characterization and reconstruction are of significant research importance for geotechnical engineering fields such as oil and gas exploration and development, groundwater resource assessment, and carbon dioxide geological sequestration. With the development of digital rock physics technology, pore structure modeling methods based on image processing or statistical features have gradually become a research hotspot. These methods extract statistical features from real rocks to construct representative three-dimensional pore structure models, providing a numerical basis for rock physical property analysis.

[0003] However, existing methods for modeling rock pore structures struggle to strike an effective balance between relying on real-world data and achieving batch generation. Physical imaging-based methods, such as microfocus X-ray CT scans, can acquire high-precision real-world rock pore structures, but are limited by high equipment costs, restricted rock sample size, and a limited number of samples. They can only achieve one-to-one replication of individual cases, failing to overcome the limitation of sample size. This leads to a degree of randomness in research based on rock pore structures, making it difficult to conduct statistically significant studies. While numerical reconstruction-based stochastic modeling methods can generate models in batches, their modeling process heavily relies on complex statistical constraints and the representativeness of the input data. The fidelity of the generated models is difficult to guarantee, often deviating from the statistical characteristics of real rock pore structures. Furthermore, they have poor adaptability to modeling pore structures of different lithologies and scales. Summary of the Invention

[0004] In view of this, this application provides a method, apparatus, storage medium, and computer equipment for modeling rock pore structures. Based on a benchmark model of real rock pore structures, it extracts first feature parameters to characterize the essential features of the rock pore structure, ensuring that the generated rock pore structure model is always calibrated based on real statistical characteristics. This avoids the insufficient fidelity problem caused by the reliance on complex statistical constraints in purely numerical reconstruction methods. By randomly generating geometry and constructing a continuous influence field, and then generating a binary three-dimensional pore structure model through a preset intensity cutoff range, it expands from limited real data to a large number of random models, overcoming the limitation of physical imaging methods that can only perform "one-to-one" case replication due to the limited number of samples. Simultaneously, through an iterative optimization mechanism, the statistical characteristics of the generated rock pore structure model are highly consistent with real rocks, ensuring the reliability and adaptability of the generated results.

[0005] According to one aspect of this application, a method for modeling the pore structure of rocks is provided, comprising:

[0006] A baseline model of the real rock pore structure is obtained, and a first characteristic parameter for characterizing the rock pore structure is extracted from the baseline model.

[0007] According to the preset geometry generation parameters, multiple geometries are randomly generated in the three-dimensional modeling space. For each geometry, the influence area corresponding to the geometry is determined, and the influence intensity value of the geometry on each spatial point in the influence area is calculated.

[0008] For each spatial point, the influence intensity values ​​received by the spatial point from each geometric body are superimposed to obtain the cumulative influence value of the spatial point;

[0009] Based on the cumulative influence value of each spatial point, a continuously changing three-dimensional influence field is constructed. Spatial points in the three-dimensional influence field whose cumulative influence value falls within the preset intensity cutoff range are marked as porous phases, and the remaining spatial points are marked as matrix phases, thereby generating a binary three-dimensional porous structure model.

[0010] The second characteristic parameter of the three-dimensional pore structure model is calculated. Based on the deviation between the first characteristic parameter and the second characteristic parameter, the three-dimensional pore structure model is iteratively optimized until the deviation between the second characteristic parameter and the first characteristic parameter of the three-dimensional pore structure model generated in the current iteration meets the preset convergence condition, and the final rock pore structure model is output.

[0011] According to another aspect of this application, a rock pore structure modeling apparatus is provided, comprising:

[0012] The benchmark model acquisition module is used to acquire a benchmark model of the real rock pore structure and extract a first feature parameter from the benchmark model to characterize the rock pore structure.

[0013] The calculation module is used to randomly generate multiple geometric bodies in the three-dimensional modeling space according to the preset geometric body generation parameters. For each geometric body, the influence area corresponding to the geometric body is determined, and the influence intensity value of the geometric body on each spatial point in the influence area is calculated.

[0014] The influence value superposition module is used to superimpose the influence intensity values ​​received by each spatial point from each geometric object to obtain the cumulative influence value of the spatial point.

[0015] The model generation module is used to construct a continuously changing three-dimensional influence field based on the cumulative influence value of each spatial point, and to mark the spatial points in the three-dimensional influence field whose cumulative influence value falls within the preset intensity cutoff range as the porous phase, and to mark the remaining spatial points as the matrix phase, thereby generating a binary three-dimensional porous structure model.

[0016] The iterative optimization module is used to calculate the second feature parameter of the three-dimensional pore structure model, and to perform iterative optimization on the three-dimensional pore structure model based on the deviation between the first feature parameter and the second feature parameter until the deviation between the second feature parameter and the first feature parameter of the three-dimensional pore structure model generated in the current iteration meets the preset convergence condition, and outputs the final rock pore structure model.

[0017] According to another aspect of this application, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described rock pore structure modeling method.

[0018] According to another aspect of this application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described rock pore structure modeling method.

[0019] By employing the aforementioned technical solutions, this application provides a method, apparatus, storage medium, and computer device for modeling rock pore structures. Based on a benchmark model of real rock pore structures, it extracts first feature parameters to characterize the essential features of the rock pore structure, ensuring that the generated rock pore structure model is always calibrated based on real statistical characteristics. This avoids the insufficient fidelity problem caused by the reliance on complex statistical constraints in purely numerical reconstruction methods. By randomly generating geometry and constructing a continuous influence field, and then generating a binary three-dimensional pore structure model through a preset intensity cutoff range, it expands from limited real data to a large number of random models, overcoming the limitation of physical imaging methods that can only perform "one-to-one" case replication due to the limited number of samples. Simultaneously, through an iterative optimization mechanism, the statistical characteristics of the generated rock pore structure model are highly consistent with real rocks, ensuring the reliability and adaptability of the generated results. The embodiments of this application can generate a large number of rock pore structure models with diverse structures while retaining the statistical characteristics of real rock pores, providing a reliable data foundation for subsequent engineering research such as statistically significant seepage simulation and mechanical analysis.

[0020] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 A flowchart illustrating a rock pore structure modeling method provided in an embodiment of this application is shown.

[0023] Figure 2 This illustration shows a comparison diagram of an original three-dimensional image and a pore cutout image provided in an embodiment of this application;

[0024] Figure 3 This illustration shows a schematic diagram of a three-dimensional influence field corresponding to 100 spheres provided in an embodiment of this application;

[0025] Figure 4 This illustration shows a three-dimensional influence field diagram corresponding to 1000 spheres provided in an embodiment of this application;

[0026] Figure 5 This illustration shows a three-dimensional influence field diagram corresponding to 10,000 spheres provided in an embodiment of this application;

[0027] Figure 6 A schematic diagram of a binary three-dimensional pore structure model provided in an embodiment of this application is shown;

[0028] Figure 7 The illustration shows a schematic diagram and a typical cross-sectional view of a binarized three-dimensional pore structure model under different preset intensity cut-off ranges provided in the embodiments of this application;

[0029] Figure 8 A schematic diagram and a typical cross-sectional view of a final rock pore structure model provided in an embodiment of this application are shown.

[0030] Figure 9 A schematic diagram of a rock pore structure modeling device provided in an embodiment of this application is shown.

[0031] Figure 10 A schematic diagram of the device structure of a computer device provided in an embodiment of this application is shown. Detailed Implementation

[0032] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0033] This embodiment provides a method for modeling the pore structure of rocks, such as Figure 1As shown, the method includes:

[0034] Step 101: Obtain a benchmark model of the real rock pore structure, and extract a first characteristic parameter from the benchmark model to characterize the rock pore structure.

[0035] Step 102: Based on the preset geometry generation parameters, randomly generate multiple geometries in the three-dimensional modeling space. For each geometry, determine the influence area corresponding to the geometry and calculate the influence intensity value of the geometry on each spatial point within the influence area.

[0036] Step 103: For each spatial point, the influence intensity values ​​received by the spatial point from each geometric body are superimposed to obtain the cumulative influence value of the spatial point.

[0037] Step 104: Based on the cumulative influence value of each spatial point, construct a continuously changing three-dimensional influence field, and mark the spatial points in the three-dimensional influence field whose cumulative influence value falls within the preset intensity cutoff range as the porous phase, and mark the remaining spatial points as the matrix phase, thereby generating a binary three-dimensional porous structure model.

[0038] Step 105: Calculate the second feature parameter of the three-dimensional pore structure model. Based on the deviation between the first feature parameter and the second feature parameter, iteratively optimize the three-dimensional pore structure model until the deviation between the second feature parameter and the first feature parameter of the currently generated three-dimensional pore structure model meets the preset convergence condition, and output the final rock pore structure model.

[0039] This application provides a method for modeling the pore structure of rocks. First, a baseline model of the pore structure of a real rock is obtained. Here, the baseline model can be a three-dimensional binary model that accurately reflects the spatial distribution of pores within a real rock core sample, generated through scanning and image processing. Then, first feature parameters used to quantitatively describe the essential characteristics of the pore structure can be extracted from the baseline model. These parameters serve as the calibration basis for all subsequent rock pore structure models, ensuring the consistency of the generated rock pore structure models with the statistical characteristics of real rocks.

[0040] Next, a continuous numerical field capable of generating porous structures is constructed in a virtual 3D modeling space. Specifically, based on preset geometry generation parameters, a large number of geometries are randomly generated in the 3D modeling space, and an influence region is defined for each geometries. The influence intensity value exerted by each geometries on each spatial point within that influence region is calculated. This process, by introducing multiple randomly distributed influence sources into the 3D modeling space, lays the foundation for the subsequent construction of porous structures with heterogeneous characteristics.

[0041] Subsequently, for each spatial point in the 3D modeling space, the intensity values ​​of the influence received from all geometric objects are summed to obtain a cumulative influence value that comprehensively reflects the degree of influence of the geometric objects on that point. The cumulative influence values ​​of different spatial points exhibit a continuously and gradually changing distribution characteristic in the 3D modeling space. The level of the cumulative influence value reflects the different strengths of the influence of the geometric objects on that spatial point, thus constructing a continuous 3D influence field containing the probability of porosity generation.

[0042] Based on this, the continuous three-dimensional influence field is transformed into a discrete three-dimensional pore structure model by using a preset intensity cutoff range. Specifically, according to the preset intensity cutoff range, spatial points in the three-dimensional influence field whose cumulative influence values ​​fall within this range are marked as pore phases, and the remaining points are marked as matrix phases, thereby generating a binary three-dimensional pore structure model composed of pores and matrix. By adjusting the preset intensity cutoff range, the porosity and pore morphology distribution characteristics of the generated three-dimensional pore structure model can be flexibly controlled.

[0043] Finally, iterative optimization ensures that the generated 3D pore structure model highly matches the statistical characteristics of the real rock pore structure. Specifically, the second characteristic parameter of the generated 3D pore structure model can be calculated and compared with the first characteristic parameter to calculate the deviation between the two. If the deviation does not meet the preset convergence condition, the preset geometry generation parameters or preset intensity cutoff range are adjusted according to the deviation, and the process returns to step 102 above until the statistical characteristics of the generated 3D pore structure model and the statistical characteristics of the real rock pore structure reach the preset matching accuracy, and the final rock pore structure model is output.

[0044] By applying the technical solution of this embodiment, firstly, a baseline model of the real rock pore structure is obtained. Then, a first feature parameter used to quantitatively describe the essential characteristics of the pore structure can be extracted from the baseline model. Next, according to preset geometry generation parameters, a large number of geometries are randomly generated in the three-dimensional modeling space, and an influence region is defined for each geometry, calculating the influence intensity value exerted by each geometry on each spatial point within that influence region. Subsequently, for each spatial point in the three-dimensional modeling space, the influence intensity values ​​received from all geometries are accumulated to obtain a cumulative influence value that comprehensively reflects the degree of influence of the geometries on that point. The cumulative influence values ​​of different spatial points exhibit a continuously varying distribution characteristic in the three-dimensional modeling space, and the level of the cumulative influence value reflects the different strengths of the influence of the geometries on that spatial point, thus constructing a continuous three-dimensional influence field containing the probability of pore generation. Based on this, the continuous three-dimensional influence field is transformed into a discrete three-dimensional pore structure model by using a preset intensity cutoff range. Finally, the second feature parameter of the generated three-dimensional pore structure model can be calculated and compared with the first feature parameter to calculate the deviation between the two. If the deviation does not meet the preset convergence condition, the preset geometry generation parameters or preset intensity cutoff range are adjusted according to the deviation, and a new three-dimensional pore structure model is regenerated until the statistical characteristics of the generated three-dimensional pore structure model and the statistical characteristics of the real rock pore structure reach the preset accuracy of agreement. The final rock pore structure model is then output. This embodiment uses a benchmark model of the real rock pore structure as a basis, extracting the first feature parameter to characterize the essential features of the rock pore structure. This ensures that the generated rock pore structure model is always calibrated based on real statistical characteristics, avoiding the insufficient fidelity problem caused by the reliance on complex statistical constraints in pure numerical reconstruction methods. By randomly generating geometry and constructing a continuous influence field, and then generating a binary three-dimensional pore structure model through a preset intensity cutoff range, the model expands from limited real data to a large number of random models, overcoming the limitation of physical imaging methods that can only perform "one-to-one" case replication due to the limited number of samples. Simultaneously, through an iterative optimization mechanism, the statistical characteristics of the generated rock pore structure model are highly consistent with real rocks, ensuring the reliability and adaptability of the generated results. The embodiments of this application can generate a large number of rock pore structure models with different structures in batches while preserving the statistical characteristics of real rock pores, providing a reliable data foundation for subsequent engineering research such as statistically significant seepage simulation and mechanical analysis.

[0045] In this embodiment of the application, optionally, the first feature parameter includes porosity, fractal dimension, and spatial variation coefficient; step 101 includes: scanning the target core sample to obtain three-dimensional image data; segmenting the pore space and matrix space from the three-dimensional image data according to a preset grayscale threshold; binarizing the segmentation result to generate a three-dimensional binary reference model composed of pore phase and matrix phase; calculating the ratio of the number of voxels occupied by the pore phase to the total number of voxels based on the three-dimensional binary reference model to obtain the porosity; using the box counting method, sequentially using multiple box sizes to mesh the space where the three-dimensional binary reference model is located, and counting the values ​​of each box size. The number of cubic boxes containing at least one porous voxel is taken as the effective number of boxes. In a double logarithmic coordinate system, data points are plotted with the natural logarithm of the box size as the abscissa and the natural logarithm of the corresponding effective number of boxes as the ordinate. Linear fitting is performed on the data points, and the negative value of the slope of the fitted line is taken as the fractal dimension. Using the sliding sub-block method, sub-blocks are moved voxel by voxel within the three-dimensional binary reference model. The local porosity corresponding to the sub-block after each move is calculated. Based on the local porosity corresponding to each move, the mean local porosity and the standard deviation of local porosity are calculated, and the ratio of the standard deviation of local porosity to the mean local porosity is taken as the spatial variation coefficient.

[0046] In this embodiment, a three-dimensional binary benchmark model is first established. Specifically, the target core sample is scanned to obtain three-dimensional image data that reflects its internal structure. A preset grayscale threshold is used to separate the pore space from the matrix space in the three-dimensional image. This step achieves region segmentation based on the difference in imaging grayscale between the pores and the matrix. The segmentation result is then binarized, assigning voxels corresponding to the pore space as the pore phase and voxels corresponding to the matrix space as the matrix phase, thereby generating a three-dimensional binary benchmark model composed of 0s and 1s. This provides a unified analytical object for the subsequent quantitative calculation of various feature parameters. Here, the target core sample refers to a real rock sample drilled or collected from the geological area under study. This application can generate a large number of rock pore structure models with the same statistical characteristics as the rock pore structure of these target core samples by selecting a limited number of target core samples.

[0047] In one specific embodiment, the pore space can be extracted based on the difference in X-ray attenuation characteristics between the pore space and the matrix space using a preset grayscale threshold segmentation method. The preset grayscale threshold can be determined based on grayscale histogram features, and its rationality can be verified by comparing it with the original 3D image, such as... Figure 2 As shown, the left image is the original 3D image, and the right image is a pore section with the pore space extracted. In the right image, the dark black areas represent the extracted pores. The image is then binarized so that the pore portion has a value of 1, and the matrix portion has a value of 0.

[0048] Next, porosity, a fundamental characteristic parameter, is calculated based on this benchmark model. Since each voxel in the three-dimensional binary benchmark model represents a spatial unit of equal volume, the ratio of the number of voxels occupied by the pore phase to the total number of voxels in the model directly reflects the proportion of pore volume in the entire rock volume. This ratio is porosity, one of the most basic and important parameters describing rock reservoir properties. All subsequent rock pore structure models require this parameter as one of the calibration bases.

[0049] Because real rock pore structures exhibit strong irregularity (irregular morphology, rough boundaries), significant scale correlation (pore sizes spanning multiple orders of magnitude from micrometers to millimeters), and statistical self-similarity (structural features remain consistent within a certain scale range), this application introduces fractal dimension to quantitatively describe the spatial occupancy and complexity of pore structures at multiple scales. Therefore, based on porosity, the fractal dimension is further calculated using box counting to quantify the complexity of the pore structure. First, a pre-defined sequence of proportionally decreasing box sizes is used to mesh the space of the baseline model in sequence, using each box size. It is important to note that only one box size is used for each mesh. For each box size, the number of cubic boxes containing at least one pore voxel within that size is counted; this is called the effective box count. As the box size decreases, the change in the effective box count reflects the self-similarity characteristics of the pore structure at different scales. Data points are plotted using the natural logarithm of the box size as the x-axis and the natural logarithm of the corresponding effective box count as the y-axis. A linear fit is performed on these data points, and the negative value of the slope of the resulting line is the fractal dimension. The larger the fractal dimension, the rougher the boundary of the pore structure, the more complex its morphology, and the stronger its space-filling ability.

[0050] In one specific embodiment, firstly, a series of decreasing box sizes are selected. = , ,in, Then, the 3D modeling space will be... Divide the three directions into equal intervals with side lengths of A cubic mesh, with in each direction , , There are several boxes, and a total of [number] boxes within the 3D modeling space. There are several cubic boxes. If a cubic box contains at least one porous voxel, it is considered a valid box, and the number of valid boxes is... .in, These represent the maximum and minimum values ​​of the 3D binary baseline model on each coordinate axis in the 3D modeling space.

[0051] Under multiple box sizes, there are Taking the natural logarithm yields a linear relationship. .

[0052] Data pairs Perform least squares linear fitting The slope of the fit is the fractal dimension. Negative values: .

[0053] in, ; .

[0054] It should be noted that in actual calculations, only... and Fitting is performed within a box size range that exhibits an approximately linear relationship to avoid the effects of limited resolution and boundary effects.

[0055] Furthermore, the spatial heterogeneity of porosity can be characterized, specifically through the sliding sub-block method to calculate the spatial variation coefficient. Within a three-dimensional binary baseline model, a cube with a fixed side length is used as a sub-block, and this sub-block is moved voxel by voxel to traverse the entire model space. Each time it moves, the proportion of the porous phase within the current sub-block is calculated as the local porosity at that location. After traversal, a large number of local porosity samples covering the entire model are obtained. Based on these samples, their mean and standard deviation are calculated. The standard deviation reflects the spatial fluctuation of local porosity, while the mean reflects the average level of overall porosity. The ratio of the standard deviation to the mean is used as the spatial variation coefficient. The larger this ratio, the more dispersed the spatial distribution of pores and the stronger the heterogeneity; the smaller the ratio, the more uniform the pore distribution. Here, the calculation method for local porosity can be the same as the aforementioned method for calculating porosity.

[0056] This application provides a comprehensive quantification of the pore structure of real rocks from three complementary dimensions. Porosity characterizes the abundance of pores, fractal dimension characterizes the complexity of pore morphology, and the coefficient of spatial variation characterizes the heterogeneity of pore distribution. These three parameters together constitute a multi-dimensional index system describing the essential characteristics of pore structure. This provides a precise quantitative basis for subsequently constructing a high-fidelity rock pore structure model through random generation and iterative calibration. This ensures that the generated model not only matches the overall pore ratio of real rocks but also highly matches the morphological complexity and spatial distribution characteristics of real rocks.

[0057] In one specific embodiment, the local porosity is calculated based on the following formula:

[0058] ;

[0059] in, Indicates local porosity. This represents the three-dimensional coordinates of the center point of the sub-block corresponding to the current calculation of local porosity. L This represents the side length of the sub-block. This represents the volume of the sub-block. This represents the spatial extent covered by a sub-block centered at r with side length L. Represents the porosity exponential function. This represents the inner integral variable of the sub-block.

[0060] In this embodiment of the application, optionally, step 102 includes: dividing the three-dimensional modeling space into multiple voxels according to a preset range and a preset resolution, wherein each voxel corresponds to a spatial point; randomly generating multiple geometries within the three-dimensional modeling space according to preset geometry generation parameters; for each geometry, determining the spatial range centered on the center of the geometry and with the size of the geometry as the influence radius as the influence area of ​​the geometry; traversing all target spatial points within the influence area; calculating the influence intensity value obtained by each target spatial point from the geometry; and obtaining the influence intensity value of the geometry on each target spatial point within the influence area. Correspondingly, step 103 includes: initializing a three-dimensional influence field array with the same dimension as the voxels; for each spatial point, accumulating the influence intensity value obtained by the spatial point from each geometry to the position of the corresponding spatial point in the three-dimensional influence field array; after completing the accumulation of all spatial points, using the cumulative influence value of each position in the three-dimensional influence field array as the cumulative influence value of the corresponding spatial point.

[0061] In this embodiment, firstly, a discretized spatial lattice is established within the 3D modeling space. Specifically, based on a preset spatial range and a preset resolution, the 3D modeling space can be divided into a regularly arranged voxel mesh, where each voxel represents an independent spatial point, serving as the basic unit for all subsequent calculations. This spatial discretization establishes a one-to-one mapping between randomly generated geometry and spatial positions, enabling precise recording and calculation of attribute changes at each spatial point.

[0062] Next, randomness is introduced into the predefined 3D modeling space. Specifically, based on preset geometry generation parameters, multiple geometric shapes are randomly generated within the 3D modeling space. The center positions of these geometric shapes are randomly distributed within the 3D modeling space, and their sizes are determined by random sampling based on a preset probability distribution in the preset geometry generation parameters. The number of geometric shapes can be determined according to a preset quantity in the preset geometry generation parameters. This random generation process provides diverse influence sources for the subsequent construction of a non-homogeneous 3D influence field. Different combinations of geometric shape distributions and sizes will directly affect the morphological characteristics of the final generated 3D influence field.

[0063] For each geometry, its physical range of influence is defined. Specifically, a spatial region is delineated as the influence area of ​​the geometry, centered on its center and with its dimensions as the radius of influence. Only spatial points falling within this region will be affected by the geometry. Subsequently, all target spatial points within this influence area are traversed, and the influence intensity value obtained by each target spatial point from the current geometry is calculated based on the geometry's dimensions and the distance from the target spatial point to the geometry's center. This influence intensity value can be negatively correlated with distance; the closer to the center, the greater the influence intensity value, and the farther away, the smaller the value, thus forming an influence distribution that decays outward from the center around each geometry.

[0064] While calculating the influence intensity of each geometry on each spatial point, a data structure can be set up to record the cumulative influence value of each spatial point. For this purpose, a three-dimensional influence field array with the same dimension as the voxel mesh is initialized, with each position in the array corresponding to a spatial point, and the initial value is set to zero. When traversing each geometry and its influence region, the calculated influence intensity value obtained by each spatial point from the current geometry is accumulated in real time at the corresponding position in the three-dimensional influence field array. After all geometries have been traversed, the value stored at each position in the three-dimensional influence field array is the sum of the influence intensity values ​​received by that spatial point from all geometries, which is the cumulative influence value of that spatial point. This cumulative influence value exhibits a continuously varying distribution in space, and its magnitude reflects the overall strength of the combined influence of multiple geometries at that location.

[0065] This application's embodiments, by randomly generating geometric shapes and superimposing their influence, can construct a continuous influence field with heterogeneous characteristics in a 3D modeling space, generating rich numerical distribution patterns without relying on complex statistical constraints. Preset geometric generation parameters, such as the number and size distribution of the geometric shapes, can directly control the roughness, undulation characteristics, and other macroscopic manifestations of the 3D influence field. Subsequently, different pore structures can be extracted from it by setting a preset intensity cutoff range. The entire process is computationally efficient and the physical meaning of the parameters is clear, providing a flexible and controllable implementation path for batch generating diverse rock pore structure models from limited real data.

[0066] In this embodiment of the application, optionally, the geometry is a sphere; the influence intensity value obtained by each target space point from the current geometry is calculated based on the following formula:

[0067] ;

[0068] in, This represents the influence intensity value obtained by the target spatial point from the current sphere. Represents the spatial coordinates of the target point. , indicating that positive or negative is randomly selected with equal probability. Indicates the current sphere i radius, Indicates the current sphere i The center coordinates of .

[0069] In this embodiment, the geometry can be a sphere; specifically, a non-homogeneous three-dimensional influence field can be generated using a "spherical series." A "spherical series" is a method for generating spatially non-uniform attribute fields based on the principle of random superposition. Its principle is to randomly generate... There are several spheres, their positions are random, and their radii follow a semi-normal distribution. The radius R of the i-th sphere i It can be The core of this method lies in expressing the complex 3D influence field distribution within the 3D modeling space as the linear superposition of the influence intensities within a large number of random spheres. Each sphere, as an independent primitive, possesses a simple internal influence intensity variation law and a finite influence range.

[0070] In the above formula, the max function ensures that the difference retains its original value when it is non-negative and takes zero when it is negative, guaranteeing that only points located inside or on the surface of the sphere will produce non-zero influence values. Then, the square root of the max function value is performed, and the resulting influence intensity value monotonically decreases with increasing distance, reaching its maximum value at the center of the sphere and decaying to zero at the surface, forming a continuous distribution that smoothly decays outward from the center of the sphere.

[0071] Based on this, a random positive or negative factor is introduced. The factor is randomly selected with equal probability as +1 or -1. This randomness allows each sphere to make either a positive or negative contribution to the spatial points within its influence area, breaking the monotonicity that might result from a single positive contribution. This allows multiple spheres to be superimposed to form more diverse numerical fluctuations in space, laying the foundation for the subsequent generation of a three-dimensional influence field with complex heterogeneous characteristics.

[0072] This application embodiment simultaneously achieves two key characteristics through the above formula: the influence intensity value decays with distance and the contribution direction varies randomly. The decay characteristic ensures that the influence of each sphere has locality and central enhancement, which is consistent with the natural manifestation of local characteristics in heterogeneous media; the introduction of random symbols makes positive and negative contributions interspersed in space, and after superposition, they can form a complex field distribution with ups and downs, providing a rich numerical basis for the subsequent generation of diverse pore structures by cutting off a preset intensity range.

[0073] Optionally, in this embodiment of the application, the step 104 of "constructing a continuously changing three-dimensional influence field based on the cumulative influence value of each spatial point" includes: reading the cumulative influence value of each spatial point in the three-dimensional influence field array; establishing a three-dimensional scalar field composed of spatial coordinates and cumulative influence values ​​according to the mapping relationship between the spatial coordinates of each spatial point in the three-dimensional modeling space and the cumulative influence value, wherein the cumulative influence value in the three-dimensional scalar field is continuously distributed in space; and using the three-dimensional scalar field as the continuously changing three-dimensional influence field.

[0074] In this embodiment, it is important to first clarify that the three-dimensional influence field array already stores the cumulative influence value corresponding to each spatial point. This value is obtained by summing the influence intensity values ​​exerted by each geometric object on that spatial point. The cumulative influence value refers to the degree to which a spatial point is affected by the combined influence of all geometric objects; numerically, it reflects the potential weight of that spatial point in the final porosity generation process. Reading these cumulative influence values ​​stored at each spatial point location and establishing a one-to-one mapping relationship between the three-dimensional coordinates of each spatial point and its corresponding cumulative influence value constitutes a correspondence in the data structure.

[0075] Based on this, the mapping relationship between spatial coordinates and cumulative influence values ​​essentially forms a three-dimensional scalar field. A scalar field is a mathematical object in space where each point is assigned a numerical value. Here, the cumulative influence value at each voxel location is the field value at that point. Since the cumulative influence value is formed by the superposition of multiple continuous functions, and the numerical changes between adjacent spatial points are smooth transitions, the values ​​in this scalar field exhibit a continuous distribution in space, without abrupt changes or discrete jumps. This lays the foundation for subsequently dividing different regions by pre-defined intensity cutoff ranges. It should be noted that since the influence intensity value of each geometric object on spatial points within its influence region is calculated based on a continuous function of the geometric object's size and the distance from the spatial point to the geometric object's center, and the linear superposition of multiple continuous functions maintains continuity, the cumulative influence values ​​of each spatial point exhibit a continuous distribution in space. The differences in cumulative influence values ​​between adjacent spatial points smoothly transition with distance, without numerical abrupt changes.

[0076] Finally, this three-dimensional scalar field, composed of spatial coordinates and cumulative influence values, is formally defined as a continuously varying three-dimensional influence field. This three-dimensional influence field is the direct basis for the subsequent generation of pore structures, and its numerical distribution characteristics directly determine the morphology and distribution of the final rock pore structure model. By applying different preset intensity cutoff ranges to this three-dimensional influence field, the selection area of ​​the pore phase can be flexibly controlled, thereby generating binary three-dimensional pore structure models with different porosities and pore morphologies.

[0077] like Figures 3-5As shown, schematic diagrams of three three-dimensional influence fields provided in embodiments of this application are illustrated, wherein, Figure 3 Corresponding to 100 spheres, Figure 4 Corresponding to 1000 spheres, Figure 5 Corresponding to 10,000 spheres.

[0078] This application's embodiments transform discrete cumulative calculation results into a continuous scalar field with a clear mathematical form, elevating the entire modeling process from the data structure level to the field level, providing a continuous and adjustable operational object for subsequent spatial segmentation. Simultaneously, because this three-dimensional influence field retains complete information about the superimposed influence of all geometric bodies, and its values ​​continuously change in space, it is possible to generate various pore structures with different characteristics from the same three-dimensional influence field by simply adjusting the preset intensity cutoff range, greatly improving the flexibility and controllability of the modeling.

[0079] In this embodiment of the application, optionally, step 104, "marking spatial points in the three-dimensional influence field whose cumulative influence values ​​fall within a preset intensity cutoff range as porous phases, and marking the remaining spatial points as matrix phases, to generate a binarized three-dimensional porous structure model," includes: establishing a binarized model array with the same dimension as the three-dimensional influence field, wherein the binarized model array is used to store the phase marking results of each spatial point; traversing all spatial points in the three-dimensional influence field, for each spatial point, determining whether the cumulative influence value of the spatial point falls within the preset intensity cutoff range: if the cumulative influence value of the spatial point falls within the preset intensity cutoff range, then assigning a first marker value for characterizing the porous phase to the corresponding position in the binarized model array; otherwise, assigning a second marker value for characterizing the matrix phase to the corresponding position in the binarized model array; after completing the traversal and assignment of all spatial points, outputting the binarized model array as a binarized three-dimensional porous structure model.

[0080] In this embodiment, firstly, a blank storage structure is established for the three-dimensional porous structure model to be generated. Since the three-dimensional influence field has been discretized in space using a voxel mesh, each spatial point corresponds to a cumulative influence value. Therefore, a binary model array with the same dimensionality as the three-dimensional influence field can be created. Each storage cell in this array corresponds one-to-one with a spatial point and is used to record the determination result of whether the point is ultimately labeled as a porous phase or a matrix phase, thus preparing for subsequent model output.

[0081] Next, the cumulative influence value corresponding to each spatial point in the three-dimensional influence field is obtained and compared with the preset intensity cutoff range. The preset intensity cutoff range is a numerical interval, and its upper and lower limits determine which cumulative influence values ​​are considered suitable to be porosity intervals.

[0082] Furthermore, each spatial point is assigned a corresponding value based on the judgment results. If the cumulative influence value of a spatial point falls within the preset intensity cutoff range, a first marker value representing the porous phase, such as the value 1, is assigned to the corresponding position in the binary model array; conversely, if the cumulative influence value is outside this range, a second marker value representing the matrix phase, such as the value 0, is assigned. This assignment process transforms continuous numerical information into explicit phase category information, giving spatial points that were originally just differences in numerical values ​​a physical category classification.

[0083] After traversing, judging, and assigning values ​​to all spatial points, each position in the binarized model array has a definite phase label. At this point, the array has completely recorded a three-dimensional spatial distribution composed of the porous phase and the matrix phase, i.e., a binarized three-dimensional porous structure model. Outputting this array as the final result completes the transformation from a continuous numerical field to a discrete porous model.

[0084] It should be noted that the cumulative influence value reflects the comprehensive degree of influence of multiple random geometric objects superimposed on each spatial point, and its numerical distribution characteristics have a statistically significant mapping relationship with the spatial heterogeneity of the actual rock pore structure. In this embodiment, by setting a preset intensity cutoff range to truncate the cumulative influence value, spatial points whose values ​​fall within the preset intensity cutoff range can be identified as pore phases, thereby realizing the transformation from a continuous numerical field to a discrete pore structure. This process simulates the random distribution law of pores in a heterogeneous medium.

[0085] In a specific embodiment, such as Figure 6 As shown, this application illustrates an embodiment of the present application. Figure 5 The three-dimensional influence field is binarized according to a specific preset intensity range to obtain a binarized three-dimensional pore structure model.

[0086] like Figure 7 As shown, the corresponding binarized three-dimensional pore structure models and their typical cross-sectional views are illustrated under different preset intensity cut-off ranges. It is evident that different preset intensity cut-off ranges can directly affect the generated three-dimensional pore structure model.

[0087] This application simplifies the complex problem of generating pore structures into a classification problem based on a preset intensity cutoff range. By adjusting the preset intensity cutoff range, the porosity and pore morphology of the generated model can be flexibly controlled. For example, cutting off high-value areas can generate large, concentrated pores, while cutting off intermediate-value areas can generate small, dispersed pores. The entire process is simple to operate, computationally efficient, and forms a good logical connection with the preceding cumulative influence value construction process. This makes the entire modeling process both physically interpretable and flexible and controllable in engineering applications.

[0088] In this embodiment of the application, step 105 may optionally include: using the three-dimensional pore structure model as the model to be optimized in the current iteration, calculating the second feature parameters of the model to be optimized; calculating the deviation between each parameter in the second feature parameters and the corresponding parameter in the first feature parameters to obtain multiple individual deviations; calculating a comprehensive deviation index based on the multiple individual deviations, and determining whether the comprehensive deviation index meets a preset convergence condition; if the comprehensive deviation index meets the preset convergence condition, then outputting the model to be optimized as the final rock pore structure model; if the comprehensive deviation index does not meet the preset convergence condition, then adjusting the preset geometry generation parameters and / or the preset intensity cutoff range based on the deviation direction and degree of the multiple individual deviations; based on the adjusted preset geometry generation parameters and / or preset intensity cutoff range, regenerating a new three-dimensional pore structure model in the three-dimensional modeling space as the model to be optimized in the next iteration, until the comprehensive deviation index of the generated three-dimensional pore structure model meets the preset convergence condition.

[0089] In this embodiment, the first step in the entire iterative optimization process is to perform feature quantization on the currently generated three-dimensional pore structure model. Specifically, this model can be used as the object to be optimized, and its second feature parameters, such as porosity, fractal dimension, and spatial variation coefficient, are calculated using the same method as the first feature parameter extraction method for the benchmark model of real rock pore structure. This step ensures that the generated three-dimensional pore structure model and the real rock pore structure are compared on the same quantization scale, providing a unified evaluation benchmark for subsequent deviation analysis.

[0090] Next, the differences between the generated 3D pore structure model and the real rock pore structure are quantified. Specifically, each parameter in the second feature parameter is compared with the corresponding parameter in the first feature parameter, and the difference or relative deviation between the two is calculated to obtain multiple individual deviations. For example, individual deviations may be the deviation between the porosity of the generated 3D pore structure model and the porosity of the reference model, the deviation between the fractal dimension of the generated 3D pore structure model and the fractal dimension of the reference model, and the deviation between the spatial coefficient of variation of the generated 3D pore structure model and the spatial coefficient of variation of the reference model. These three individual deviations reflect the degree of agreement between the generated 3D pore structure model and the real rock pore structure in the three dimensions of pore abundance, morphological complexity, and spatial heterogeneity, respectively.

[0091] After obtaining the three individual biases, they can be integrated into a single index that comprehensively evaluates the model's fit. Specifically, this can be achieved through mathematical operations, such as summing the squares of each individual bias and taking the square root, or by taking a weighted average of the individual biases, to obtain a comprehensive bias index. This index can reflect the overall difference between the generated 3D pore structure model and the real rock pore structure in terms of multidimensional statistical characteristics. Subsequently, this comprehensive bias index is compared with preset convergence conditions to determine whether the current 3D pore structure model's fit has reached an acceptable range.

[0092] If the comprehensive deviation index meets the preset convergence condition, it means that the currently generated three-dimensional pore structure model has a high degree of consistency with the real rock pore structure in terms of statistical characteristics, and has achieved the modeling goal. At this time, the model can be output as the final rock pore structure model, and the entire modeling process ends.

[0093] If the overall deviation index does not meet the preset convergence condition, the preset geometry generation parameters can be adjusted based on the specific information of the deviation. In a specific embodiment, the preset geometry generation parameters or preset intensity cutoff range can be adjusted in a targeted manner based on the deviation direction of each of the three individual deviations, such as whether it is too large or too small, and the magnitude of the deviation. For example, if the fractal dimension deviation is too large, the standard deviation of the geometry size distribution in the preset geometry generation parameters can be adjusted; if the spatial variation coefficient deviation is too large, the preset intensity cutoff range can be adjusted. This directional adjustment based on deviation information allows each iteration to optimize in the direction of reducing the difference.

[0094] Finally, based on the adjusted preset geometry generation parameters and preset intensity cutoff range, the entire three-dimensional pore structure model generation process is re-executed, starting with random geometry generation, constructing a three-dimensional influence field, and then cutting off the preset intensity cutoff range to generate a new binarized three-dimensional pore structure model. The newly generated three-dimensional pore structure model is used as the model to be optimized in the next iteration, and its second characteristic parameter, individual deviation, and comprehensive deviation index are recalculated, with the judgment and adjustment process repeated. This process is repeated until the comprehensive deviation index of the three-dimensional pore structure model generated in a certain iteration meets the preset convergence condition, at which point the final rock pore structure model is output.

[0095] This application embodiment integrates multidimensional statistical features into a single evaluation index and adjusts directional parameters based on the specific information of each individual deviation. This method can gradually improve the fidelity of the generated three-dimensional pore structure model while ensuring computational efficiency, and finally outputs a three-dimensional pore structure model that highly matches the statistical features of real rocks, providing a reliable data foundation for subsequent engineering applications.

[0096] Furthermore, as a refinement and extension of the specific implementation methods of the above embodiments, such as Figure 8 As shown, a final rock pore structure model and its typical cross-sectional view provided in this application embodiment are illustrated. The porosity, fractal dimension, and spatial variation coefficient of this rock pore structure model compared to the baseline model are shown in Table 1.

[0097] Table 1. Parameter statistics of simulated and real data

[0098]

[0099] Define a normalized composite deviation index:

[0100] ;

[0101] in, , , These are the three parameters of the generated rock pore structure model. , , These represent three parameters of the actual rock pore structure, and the final value E can be used to comprehensively evaluate the fit between the simulation and the actual values. Calculation Results It is 0.0726 A value of 0.1 indicates a good fit.

[0102] In summary, this application can simulate the real morphology of rock pores and can randomly generate a large number of non-repeating rock pore structure models by changing certain parameters.

[0103] This application innovatively introduces the field of stochastic modeling of three-dimensional rock pore structure, forming a novel method for constructing rock pores. Instead of directly simulating complex pore geometry, it generates a continuous heterogeneous influence field containing the probability and correlation characteristics of pore space by controlling finite and physically meaningful parameters in the three-dimensional modeling space. This field is then subjected to a preset intensity truncation range to form a discrete pore-matrix model.

[0104] The advantages of this application are mainly reflected in the following aspects:

[0105] 1. The innovative modeling approach and process enable flexible and controllable generation of complex pore structures. Unlike traditional methods that directly construct pore morphology or rely on complex statistical functions, this application treats pore generation as an explicit process of a potential stochastic influence field. This allows for the highly controllable generation of various complex structures, from isolated pores to highly connected networks, by adjusting a few intuitive parameters. This method naturally preserves the spatial correlation and statistical variability of pore clusters, effectively overcoming the inherent limitations of traditional stochastic modeling methods in characterizing long-range connectivity and the consistency of complex spatial structures.

[0106] 2. A closed-loop calibration mechanism using real rock core data as anchor points was constructed to ensure the scientific validity and high fidelity of the rock pore structure model. Using the real rock core pore structure obtained from high-precision CT scans as the standard, multi-dimensional feature parameters such as porosity, fractal dimension, and spatial variation coefficient were quantitatively extracted as modeling targets. This ensures that the statistical characteristics of the generated rock pore structure model achieve a preset level of accuracy in matching the real data. This not only improves the reliability and fidelity of the model but also gives it generalization ability—it can achieve sufficiently high-precision fitting for different lithologies (such as sandstone, shale, and carbonate rocks).

[0107] 3. It combines high efficiency, high flexibility, and strong interpretability. When the geometry is a sphere, the "spherical series" algorithm is employed, which is characterized by its intuitive principle and high computational efficiency. It can quickly generate complex 3D pore structure models, solving the problems of high computational cost or cumbersome processes associated with traditional methods. Furthermore, its control parameters (such as preset geometry generation parameters and preset intensity cutoff ranges) have clear physical or statistical significance, and exhibit a clear mapping relationship with the final model's pore size, complexity, and directionality, making the entire modeling process logically clear and highly interpretable.

[0108] 4. This method has broad application potential, providing a new tool for multidisciplinary research. It is not only suitable for modeling solid minerals but also demonstrates strong potential for modeling pore structures. By adjusting the preset intensity cutoff range and preset geometry generation parameters, a series of porous media models with specified porosity, pore size distribution, and connectivity can be easily generated. This provides an efficient, low-cost, and highly controllable digital core generation scheme for key areas such as oil and gas seepage simulation, carbon dioxide geological storage assessment, groundwater pollutant migration prediction, and multi-field coupling analysis of rock and soil masses, demonstrating significant engineering application value.

[0109] Furthermore, as Figure 1 To specifically implement the method, this application provides a rock pore structure modeling device, such as... Figure 9 As shown, the device includes:

[0110] The benchmark model acquisition module is used to acquire a benchmark model of the real rock pore structure and extract a first feature parameter from the benchmark model to characterize the rock pore structure.

[0111] The calculation module is used to randomly generate multiple geometric bodies in the three-dimensional modeling space according to the preset geometric body generation parameters. For each geometric body, the influence area corresponding to the geometric body is determined, and the influence intensity value of the geometric body on each spatial point in the influence area is calculated.

[0112] The influence value superposition module is used to superimpose the influence intensity values ​​received by each spatial point from each geometric object to obtain the cumulative influence value of the spatial point.

[0113] The model generation module is used to construct a continuously changing three-dimensional influence field based on the cumulative influence value of each spatial point, and to mark the spatial points in the three-dimensional influence field whose cumulative influence value falls within the preset intensity cutoff range as the porous phase, and to mark the remaining spatial points as the matrix phase, thereby generating a binary three-dimensional porous structure model.

[0114] The iterative optimization module is used to calculate the second feature parameter of the three-dimensional pore structure model, and to perform iterative optimization on the three-dimensional pore structure model based on the deviation between the first feature parameter and the second feature parameter until the deviation between the second feature parameter and the first feature parameter of the three-dimensional pore structure model generated in the current iteration meets the preset convergence condition, and outputs the final rock pore structure model.

[0115] Optionally, the first feature parameter includes porosity, fractal dimension, and spatial variation coefficient; the benchmark model acquisition module is used for:

[0116] The target core sample is scanned to obtain three-dimensional image data. Based on a preset grayscale threshold, the pore space and matrix space are segmented from the three-dimensional image data. The segmentation results are then binarized to generate a three-dimensional binarized reference model composed of the pore phase and the matrix phase.

[0117] Based on the aforementioned three-dimensional binary benchmark model, the ratio of the number of voxels occupied by the porous phase to the total number of voxels is calculated to obtain the porosity.

[0118] Using the box counting method, the space of the three-dimensional binary reference model is divided into meshes using various box sizes in turn. The number of cubic boxes containing at least one pore voxel under each box size is counted as the number of effective boxes. In a double logarithmic coordinate system, data points are plotted with the natural logarithm of the box size as the x-axis and the natural logarithm of the corresponding number of effective boxes as the y-axis. Linear fitting is performed on the data points, and the negative value of the slope of the fitted line is taken as the fractal dimension.

[0119] The sliding sub-block method is used to move sub-blocks one voxel at a time within the three-dimensional binary reference model. The local porosity of the sub-block after each move is calculated. Based on the local porosity corresponding to each move, the mean local porosity and the standard deviation of local porosity are calculated. The ratio of the standard deviation of local porosity to the mean local porosity is used as the spatial variation coefficient.

[0120] Optionally, the computing module is used for:

[0121] According to the preset range and preset resolution, the three-dimensional modeling space is divided into multiple voxels, where each voxel corresponds to a spatial point;

[0122] Based on preset geometry generation parameters, multiple geometric shapes are randomly generated within the three-dimensional modeling space;

[0123] For each geometry, the spatial range centered on the center of the geometry and with the size of the geometry as the radius of influence is defined as the influence area of ​​the geometry. All target spatial points within the influence area are traversed, and the influence intensity value obtained by each target spatial point from the geometry is calculated to obtain the influence intensity value of the geometry on each target spatial point within the influence area.

[0124] Accordingly, the influence value superposition module is used for:

[0125] Initialize a three-dimensional influence field array with the same voxel dimension;

[0126] For each spatial point, the influence intensity value obtained from each geometry is accumulated and added to the position of the corresponding spatial point in the three-dimensional influence field array. After the accumulation of all spatial points is completed, the cumulative influence value of each position in the three-dimensional influence field array is used as the cumulative influence value of the corresponding spatial point.

[0127] Optionally, the geometry is a sphere; the influence intensity value obtained by each target space point from the current geometry is calculated based on the following formula:

[0128] ;

[0129] in, This represents the influence intensity value obtained by the target spatial point from the current sphere. Represents the spatial coordinates of the target point. , indicating that positive or negative is randomly selected with equal probability. Indicates the current sphere i radius, Indicates the current sphere i The center coordinates of .

[0130] Optionally, the model generation module is used for:

[0131] Read the cumulative influence value of each spatial point in the three-dimensional influence field array, and establish a three-dimensional scalar field composed of spatial coordinates and cumulative influence value according to the mapping relationship between the spatial coordinates of each spatial point in the three-dimensional modeling space and the cumulative influence value. The cumulative influence value in the three-dimensional scalar field is continuously distributed in space.

[0132] The three-dimensional scalar field is used as the continuously changing three-dimensional influence field.

[0133] Optionally, the model generation module is further configured to:

[0134] Establish a binary model array with the same dimension as the three-dimensional influence field, wherein the binary model array is used to store the phase labeling results of each spatial point;

[0135] Traverse all spatial points in the three-dimensional influence field. For each spatial point, determine whether the cumulative influence value of the spatial point falls within the preset intensity cutoff range.

[0136] If the cumulative influence value of the spatial point falls within the preset intensity cutoff range, then a first marker value for characterizing the porous phase is assigned to the corresponding position in the binarized model array; otherwise, a second marker value for characterizing the matrix phase is assigned to the corresponding position in the binarized model array.

[0137] After completing the traversal and assignment of all spatial points, the binarized model array is output as a binarized three-dimensional pore structure model.

[0138] Optionally, the iterative optimization module includes:

[0139] Using the three-dimensional pore structure model as the model to be optimized in the current iteration, the second characteristic parameters of the model to be optimized are calculated;

[0140] Calculate the deviation between each parameter in the second feature parameter and the corresponding parameter in the first feature parameter to obtain multiple individual deviations;

[0141] Based on the multiple individual deviations, calculate the comprehensive deviation index, and determine whether the comprehensive deviation index meets the preset convergence condition:

[0142] If the comprehensive deviation index satisfies the preset convergence condition, the model to be optimized will be output as the final rock pore structure model.

[0143] If the comprehensive deviation index does not meet the preset convergence condition, then the preset geometry generation parameters and / or the preset intensity cutoff range are adjusted according to the deviation direction and degree of the multiple individual deviations.

[0144] Based on the adjusted preset geometry generation parameters and / or preset intensity cutoff range, a new three-dimensional pore structure model is regenerated in the three-dimensional modeling space as the model to be optimized in the next iteration, until the comprehensive deviation index of the generated three-dimensional pore structure model satisfies the preset convergence condition.

[0145] It should be noted that other corresponding descriptions of the functional units involved in the rock pore structure modeling device provided in this application embodiment can be found in [reference]. Figures 1 to 8 The corresponding descriptions in the method will not be repeated here.

[0146] This application also provides a computer device, which may specifically be a personal computer, a server, a network device, etc. Figure 10 As shown, the computer device includes a bus, a processor, memory, and a communication interface, and may also include an input / output interface and a display device. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores location information. The network interface allows communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the various method embodiments.

[0147] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0148] In one embodiment, a computer-readable storage medium is provided, which may be non-volatile or volatile, having stored thereon a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0149] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0150] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0151] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0152] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] The embodiments described above are merely examples of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

Claims

1. A method for modeling the pore structure of rocks, characterized in that, include: A baseline model of the real rock pore structure is obtained, and a first characteristic parameter for characterizing the rock pore structure is extracted from the baseline model. According to the preset geometry generation parameters, multiple geometries are randomly generated in the three-dimensional modeling space. For each geometry, the influence area corresponding to the geometry is determined, and the influence intensity value of the geometry on each spatial point in the influence area is calculated. For each spatial point, the influence intensity values ​​received by the spatial point from each geometric body are superimposed to obtain the cumulative influence value of the spatial point; Based on the cumulative influence value of each spatial point, a continuously changing three-dimensional influence field is constructed. Spatial points in the three-dimensional influence field whose cumulative influence value falls within the preset intensity cutoff range are marked as porous phases, and the remaining spatial points are marked as matrix phases, thereby generating a binary three-dimensional porous structure model. Calculate the second feature parameter of the three-dimensional pore structure model, and iteratively optimize the three-dimensional pore structure model based on the deviation between the first feature parameter and the second feature parameter until the deviation between the second feature parameter and the first feature parameter of the three-dimensional pore structure model generated in the current iteration meets the preset convergence condition, and output the final rock pore structure model. The first characteristic parameters include porosity, fractal dimension, and spatial variation coefficient; The process of obtaining a benchmark model for the real pore structure of rocks and extracting a first feature parameter from the benchmark model to characterize the pore structure of rocks includes: The target core sample is scanned to obtain three-dimensional image data. Based on a preset grayscale threshold, the pore space and matrix space are segmented from the three-dimensional image data. The segmentation results are then binarized to generate a three-dimensional binarized reference model composed of the pore phase and the matrix phase. Based on the aforementioned three-dimensional binary benchmark model, the ratio of the number of voxels occupied by the porous phase to the total number of voxels is calculated to obtain the porosity. Using the box counting method, the space of the three-dimensional binary reference model is divided into meshes using various box sizes in turn. The number of cubic boxes containing at least one pore voxel under each box size is counted as the number of effective boxes. In a double logarithmic coordinate system, data points are plotted with the natural logarithm of the box size as the x-axis and the natural logarithm of the corresponding number of effective boxes as the y-axis. Linear fitting is performed on the data points, and the negative value of the slope of the fitted line is taken as the fractal dimension. The sliding sub-block method is used to move sub-blocks one voxel at a time within the three-dimensional binary reference model. The local porosity of the sub-block after each move is calculated. Based on the local porosity corresponding to each move, the mean local porosity and the standard deviation of local porosity are calculated. The ratio of the standard deviation of local porosity to the mean local porosity is used as the spatial variation coefficient.

2. The method according to claim 1, characterized in that, The process involves randomly generating multiple geometric shapes in a 3D modeling space based on preset geometric shape generation parameters. For each geometric shape, the influence region corresponding to that geometric shape is determined, and the influence intensity value of the geometric shape on each spatial point within the influence region is calculated, including: According to the preset range and preset resolution, the three-dimensional modeling space is divided into multiple voxels, where each voxel corresponds to a spatial point; Based on preset geometry generation parameters, multiple geometric shapes are randomly generated within the three-dimensional modeling space; For each geometry, the spatial range centered on the center of the geometry and with the size of the geometry as the radius of influence is defined as the influence area of ​​the geometry. All target spatial points within the influence area are traversed, and the influence intensity value obtained by each target spatial point from the geometry is calculated to obtain the influence intensity value of the geometry on each target spatial point within the influence area. Accordingly, for each spatial point, the cumulative influence value of the spatial point is obtained by superimposing the influence intensity values ​​received from each geometric object, including: Initialize a three-dimensional influence field array with the same voxel dimension; For each spatial point, the influence intensity value obtained from each geometry is accumulated and added to the position of the corresponding spatial point in the three-dimensional influence field array. After the accumulation of all spatial points is completed, the cumulative influence value of each position in the three-dimensional influence field array is used as the cumulative influence value of the corresponding spatial point.

3. The method according to claim 2, characterized in that, The geometry is a sphere; the influence intensity value obtained by each target spatial point from the current geometry is calculated based on the following formula: ； in, This represents the influence intensity value obtained by the target spatial point from the current sphere. Represents the spatial coordinates of the target point. , indicating that positive or negative is randomly selected with equal probability. Indicates the current sphere i radius, Indicates the current sphere i The center coordinates of .

4. The method according to claim 2 or 3, characterized in that, The construction of a continuously changing three-dimensional influence field based on the cumulative influence values ​​of each spatial point includes: Read the cumulative influence value of each spatial point in the three-dimensional influence field array, and establish a three-dimensional scalar field composed of spatial coordinates and cumulative influence value according to the mapping relationship between the spatial coordinates of each spatial point in the three-dimensional modeling space and the cumulative influence value. The cumulative influence value in the three-dimensional scalar field is continuously distributed in space. The three-dimensional scalar field is used as the continuously changing three-dimensional influence field.

5. The method according to claim 1, characterized in that, The step of marking spatial points in the three-dimensional influence field whose cumulative influence values ​​fall within a preset intensity cutoff range as porous phases and marking the remaining spatial points as matrix phases to generate a binary three-dimensional porous structure model includes: Establish a binary model array with the same dimension as the three-dimensional influence field, wherein the binary model array is used to store the phase labeling results of each spatial point; Traverse all spatial points in the three-dimensional influence field. For each spatial point, determine whether the cumulative influence value of the spatial point falls within the preset intensity cutoff range. If the cumulative influence value of the spatial point falls within the preset intensity cutoff range, then a first marker value for characterizing the porous phase is assigned to the corresponding position in the binarized model array; otherwise, a second marker value for characterizing the matrix phase is assigned to the corresponding position in the binarized model array. After completing the traversal and assignment of all spatial points, the binarized model array is output as a binarized three-dimensional pore structure model.

6. The method according to claim 1, characterized in that, The process of calculating the second characteristic parameter of the three-dimensional pore structure model, iteratively optimizing the three-dimensional pore structure model based on the deviation between the first characteristic parameter and the second characteristic parameter, until the deviation between the second characteristic parameter and the first characteristic parameter of the currently generated three-dimensional pore structure model meets a preset convergence condition, and outputting the final rock pore structure model, includes: Using the three-dimensional pore structure model as the model to be optimized in the current iteration, the second characteristic parameters of the model to be optimized are calculated; Calculate the deviation between each parameter in the second feature parameter and the corresponding parameter in the first feature parameter to obtain multiple individual deviations; Based on the multiple individual deviations, calculate the comprehensive deviation index, and determine whether the comprehensive deviation index meets the preset convergence condition: If the comprehensive deviation index satisfies the preset convergence condition, the model to be optimized will be output as the final rock pore structure model. If the comprehensive deviation index does not meet the preset convergence condition, then the preset geometry generation parameters and / or the preset intensity cutoff range are adjusted according to the deviation direction and degree of the multiple individual deviations. Based on the adjusted preset geometry generation parameters and / or preset intensity cutoff range, a new three-dimensional pore structure model is regenerated in the three-dimensional modeling space as the model to be optimized in the next iteration, until the comprehensive deviation index of the generated three-dimensional pore structure model satisfies the preset convergence condition.

7. A rock pore structure modeling device, characterized in that, include: The benchmark model acquisition module is used to acquire a benchmark model of the real rock pore structure and extract a first feature parameter from the benchmark model to characterize the rock pore structure. The calculation module is used to randomly generate multiple geometric bodies in the three-dimensional modeling space according to the preset geometric body generation parameters. For each geometric body, the influence area corresponding to the geometric body is determined, and the influence intensity value of the geometric body on each spatial point in the influence area is calculated. The influence value superposition module is used to superimpose the influence intensity values ​​received by each spatial point from each geometric object to obtain the cumulative influence value of the spatial point. The model generation module is used to construct a continuously changing three-dimensional influence field based on the cumulative influence value of each spatial point, and to mark the spatial points in the three-dimensional influence field whose cumulative influence value falls within the preset intensity cutoff range as the porous phase, and to mark the remaining spatial points as the matrix phase, thereby generating a binary three-dimensional porous structure model. The iterative optimization module is used to calculate the second feature parameter of the three-dimensional pore structure model, and to perform iterative optimization on the three-dimensional pore structure model based on the deviation between the first feature parameter and the second feature parameter until the deviation between the second feature parameter and the first feature parameter of the three-dimensional pore structure model generated in the current iteration meets the preset convergence condition, and outputs the final rock pore structure model. The first feature parameters include porosity, fractal dimension, and spatial variation coefficient; the benchmark model acquisition module is used for: The target core sample is scanned to obtain three-dimensional image data. Based on a preset grayscale threshold, the pore space and matrix space are segmented from the three-dimensional image data. The segmentation results are then binarized to generate a three-dimensional binarized reference model composed of the pore phase and the matrix phase. Based on the aforementioned three-dimensional binary benchmark model, the ratio of the number of voxels occupied by the porous phase to the total number of voxels is calculated to obtain the porosity. Using the box counting method, the space of the three-dimensional binary reference model is divided into meshes using various box sizes in turn. The number of cubic boxes containing at least one pore voxel under each box size is counted as the number of effective boxes. In a double logarithmic coordinate system, data points are plotted with the natural logarithm of the box size as the x-axis and the natural logarithm of the corresponding number of effective boxes as the y-axis. Linear fitting is performed on the data points, and the negative value of the slope of the fitted line is taken as the fractal dimension. The sliding sub-block method is used to move sub-blocks one voxel at a time within the three-dimensional binary reference model. The local porosity of the sub-block after each move is calculated. Based on the local porosity corresponding to each move, the mean local porosity and the standard deviation of local porosity are calculated. The ratio of the standard deviation of local porosity to the mean local porosity is used as the spatial variation coefficient.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

9. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

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