A method, apparatus, equipment and medium for constructing digital cores of shale rocks.

Abstract

This application discloses a method, apparatus, equipment, and medium for constructing digital cores of shale, relating to the field of oil and gas exploration and development technology. The method includes: acquiring FIB-SEM and MAPS images of a target shale sample; segmenting different components in the FIB-SEM image based on the grayscale gradient and grayscale values ​​using a maximum entropy algorithm, and generating a target 3D image corresponding to the target shale sample; optimizing the pore size of the target 3D image based on the MAPS image to obtain an optimized 3D image; performing morphological erosion and dilation operations on the pores in the optimized 3D image using preset structuring elements and determining the corresponding pore boundaries using a Boolean superposition algorithm; and then determining the corresponding pore type based on the distribution of different components at the pore boundaries to complete the construction of a digital core for the target shale sample. This method enables automatic segmentation of multiple components in shale samples and identification of pore types in three-dimensional space.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and in particular to a method, apparatus, equipment and medium for constructing digital cores of shale. Background Technology

[0002] The heterogeneity and anisotropy of mineral and organic matter distribution in shale reservoirs lead to complex internal components and pore structures. Both nanoscale organic pores and nano / microscale inorganic pores exist simultaneously. The fluid occurrence states within these two types of pores are drastically different, complicating nuclear magnetic resonance (NMR) responses and seepage characteristics. This makes effective NMR logging evaluation difficult. Therefore, it is urgent to clarify the NMR response characteristics and transport mechanisms of shale. Multi-component digital cores of shale offer advantages such as economy and reusability, forming the basis for NMR response simulation and micro-nanoscale fluid flow simulation studies. Therefore, the construction of multi-component digital cores for shale is of great significance.

[0003] Currently, the construction of multi-component digital cores for shale mainly involves image segmentation and pore type identification. Regarding image segmentation methods, several issues exist: manual thresholding is time-consuming and susceptible to subjective operator judgment. The OTSU algorithm, which determines the grayscale threshold by selecting the maximum inter-class variance and minimum intra-class variance of the grayscale distribution, is a common image segmentation method, but its computation speed is slow; knowing prior information about the threshold can accelerate the calculation. The maximum entropy algorithm uses the maximum information content of each component to determine the optimal segmentation threshold. This method focuses on the overall information of the image and is stable, but it ignores the boundary information of the image. The watershed algorithm is an image region segmentation algorithm based on mathematical morphology. It connects pixels with similar spatial locations and grayscale values ​​to form a closed contour, achieving image region segmentation. However, this method is very sensitive to noise, often over-segmenting the image, and ignores the heterogeneous characteristics of multi-component shale images. For pore type identification methods, SEM (scanning electron microscope) images combined with energy dispersive spectroscopy (EDS) are typically used to determine organic and inorganic pores by identifying the elemental content of minerals or organic matter at pore boundaries. This method can analyze pore types in two-dimensional images, but it is time-consuming and does not consider the influence of the heterogeneity of organic matter and minerals in three-dimensional pore space, thus failing to identify organic and inorganic pores in three-dimensional space. In summary, when constructing multi-component digital cores of shale, considering the heterogeneous distribution of reservoir minerals and organic matter, achieving automatic multi-component segmentation and accurate three-dimensional pore type identification of shale digital cores is a pressing problem that needs to be solved. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method, apparatus, equipment, and medium for constructing shale digital cores, which can take into account the heterogeneity of mineral and organic matter distribution in shale reservoirs when constructing multi-component digital cores, and achieve automatic multi-component segmentation and accurate identification of three-dimensional spatial pore types in shale digital cores. The specific solution is as follows:

[0005] Firstly, this application discloses a method for constructing digital cores of shale, including:

[0006] Acquire FIB-SEM and MAPS images of the target shale sample;

[0007] The maximum entropy algorithm is used to segment different components in the FIB-SEM image based on the gray-level gradient and gray level, and to generate a target three-dimensional image corresponding to the target shale sample.

[0008] The aperture of the target 3D image is optimized based on the MAPS image to obtain an optimized 3D image;

[0009] Morphological erosion and dilation operations are performed on the pores in the optimized 3D image using preset structuring elements, and the corresponding pore boundaries are determined using a Boolean superposition algorithm. Then, the corresponding pore type is determined based on the distribution of different components at the pore boundaries, so as to complete the construction of the digital core for the target shale sample.

[0010] Optionally, the maximum entropy-based algorithm segments different components in the FIB-SEM image according to the gray-level gradient and gray level of the FIB-SEM image, including:

[0011] The gray level gradient and gray level distribution histogram are determined based on the gray level gradient and gray level at each pixel in the focused ion beam scanning electron microscope image.

[0012] The optimal gray-level segmentation threshold is determined based on the maximum entropy algorithm according to the gray-level gradient distribution histogram and the gray-level distribution histogram, and different components in the FIB-SEM image are segmented according to the optimal gray-level segmentation threshold.

[0013] Optionally, determining the gray-level gradient distribution histogram and gray-level distribution histogram based on the gray-level gradient and gray level at each pixel in the FIB-SEM image includes:

[0014] Based on the digital difference method, the gray-level gradient at each pixel of the FIB-SEM image is determined using the Sobel operator representing four directions;

[0015] The gray-level gradients corresponding to different gray-levels are accumulated and weighted averaged to obtain the average gradients corresponding to different gray-levels. The gray-level gradient distribution histogram is determined based on the average gradients corresponding to different gray-levels. The gray-level distribution histogram is determined using the imhist function in Matlab.

[0016] Optionally, the maximum entropy-based algorithm determines the optimal gray-level segmentation threshold based on the gray-level gradient distribution histogram and the gray-level distribution histogram, and segments different components in the FIB-SEM image according to the optimal gray-level segmentation threshold, including:

[0017] The optimal gray-level segmentation threshold in the gray-level gradient distribution histogram is determined based on the maximum sum of entropy values ​​among different components, between the maximum and minimum gray-level gradient values.

[0018] The grayscale distribution corresponding to different components is determined based on the optimal grayscale segmentation threshold, and the different components in the FIB-SEM image are segmented based on the grayscale distribution corresponding to different components.

[0019] Optionally, optimizing the aperture of the target 3D image based on the MAPS image to obtain an optimized 3D image includes:

[0020] The pore clusters in the target 3D image are marked using the 26-point connected component labeling method, and the pixels marked with the same pore cluster are determined as pores in the target 3D image. Then, the first equivalent pore radius is determined based on the pores in the target 3D image.

[0021] The pore distribution features in the MAPS image were extracted using the binarization function in Matlab, and the second equivalent pore radius was determined based on the pore distribution features.

[0022] Morphological dilation calculations were performed on the pores using structural elements of different sizes, with the root mean square error between the second and first equivalent pore radii used as a constraint. The dilated 3D image corresponding to the minimum root mean square error was then used as the optimized 3D image.

[0023] Optionally, before performing morphological erosion and dilation operations on the pores in the optimized 3D image using preset structuring elements and determining the corresponding pore boundaries using a Boolean superposition algorithm, the method further includes:

[0024] The pore clusters in the optimized 3D image are labeled using the 26-point connected component labeling method, and the pixels labeled with the same pore cluster are determined as pores in the optimized 3D image.

[0025] Optionally, determining the corresponding pore type based on the distribution of different components at the pore boundary to complete the construction of a digital core for the target shale sample includes:

[0026] Based on the distribution probability of organic matter and framework minerals at the pore boundaries, the pores in the optimized three-dimensional image are determined to be organic or inorganic pores, so as to complete the construction of the digital core for the target shale sample.

[0027] Secondly, this application discloses a shale digital core construction device, comprising:

[0028] The image acquisition module is used to acquire FIB-SEM and MAPS images of the target shale sample;

[0029] The image segmentation module is used to segment different components in the FIB-SEM image based on the maximum entropy algorithm according to the gray-level gradient and gray level of the FIB-SEM image, and generate a target three-dimensional image corresponding to the target shale sample.

[0030] An aperture optimization module is used to optimize the aperture of the target three-dimensional image based on the MAPS image to obtain an optimized three-dimensional image.

[0031] The pore type identification module is used to perform morphological erosion and dilation operations on the pores in the optimized three-dimensional image using preset structural elements and to determine the corresponding pore boundaries using a Boolean superposition algorithm. Then, based on the distribution of different components at the pore boundaries, the corresponding pore type is determined to complete the construction of the digital core for the target shale sample.

[0032] Thirdly, this application discloses an electronic device, comprising:

[0033] Memory, used to store computer programs;

[0034] A processor is used to execute the computer program to implement the steps of the aforementioned shale digital core construction method.

[0035] Fourthly, this application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned shale digital core construction method.

[0036] In constructing a digital core for shale, this application first acquires FIB-SEM and MAPS images of the target shale sample. Based on the maximum entropy algorithm, it segments different components in the FIB-SEM image according to the grayscale gradient and grayscale, generating a target 3D image corresponding to the target shale sample. The pore size of the target 3D image is then optimized based on the MAPS image to obtain an optimized 3D image. Finally, morphological erosion and dilation operations are performed on the pores in the optimized 3D image using preset structuring elements, and the corresponding pore boundaries are determined using a Boolean superposition algorithm. Then, the corresponding pore type is determined based on the distribution of different components at the pore boundaries, thus completing the construction of the digital core for the target shale sample. As can be seen, in constructing a digital core of shale, this application first acquires FIB-SEM and MAPS images of the target shale sample. Then, based on the maximum entropy algorithm, it segments different components in the FIB-SEM image according to the grayscale gradient and grayscale values. Based on the segmented FIB-SEM image, it generates a target 3D image of the target shale sample. The pore size of the target 3D image is optimized based on the MAPS image. Finally, the pores in the optimized 3D image undergo 3D morphological erosion and 3D morphological dilation operations. The Boolean superposition algorithm is used to determine the corresponding pore boundaries. Finally, the pore type is determined based on the distribution of different components at the pore boundaries, thus completing the construction of the digital core for the target shale sample. Therefore, this application, when constructing shale digital cores, uses the maximum entropy algorithm to segment different components in the FIB-SEM image based on the grayscale gradient and grayscale, automatically segmenting the different components in the FIB-SEM image considering the heterogeneity of shale reservoir mineral and organic matter distribution. On the other hand, morphological operations and Boolean superposition algorithms are used to determine the corresponding pore boundaries. Finally, based on the distribution of different components at the pore boundaries, organic and inorganic pores are determined in three-dimensional space. In summary, this application can achieve automatic multi-component segmentation of shale digital cores and accurate identification of pore types in three-dimensional space, taking into account the heterogeneity of shale reservoir mineral and organic matter distribution when constructing multi-component shale digital cores. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0038] Figure 1 A flowchart of a shale digital core construction method provided in this application;

[0039] Figure 2 A flowchart illustrating a specific shale digital core construction method provided in this application;

[0040] Figure 3 An example image of the target 3D image corresponding to the segmented FIB-SEM image provided in this application;

[0041] Figure 4 A schematic diagram illustrating the aperture distribution optimization of the target 3D image provided in this application;

[0042] Figure 5 Examples of identification of organic and inorganic pores provided in this application;

[0043] Figure 6 A schematic diagram of a shale digital core construction device provided in this application;

[0044] Figure 7 This application provides a structural diagram of an electronic device. Detailed Implementation

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] Currently, the construction of multi-component digital cores for shale mainly involves image segmentation and pore type identification. For image segmentation methods, several issues exist: manual thresholding is time-consuming and susceptible to subjective operator judgment. The OTSU algorithm, which determines the grayscale threshold by selecting the maximum inter-class variance and minimum intra-class variance of the grayscale distribution, is a common image segmentation method, but its computation speed is slow; knowing prior information about the threshold can accelerate the calculation. The maximum entropy algorithm uses the maximum information content of each component to determine the optimal segmentation threshold. This method focuses on the overall information of the image and is stable, but it ignores the boundary information. The watershed algorithm is a mathematical morphology-based image region segmentation algorithm that connects spatially close pixels with similar grayscale values ​​to form a closed contour, achieving image region segmentation. However, this method is highly sensitive to noise, often oversegmenting the image, and ignores the heterogeneous characteristics of multi-component shale images. For pore type identification methods, SEM images combined with energy dispersive spectroscopy (EDS) are typically used to determine organic and inorganic pores by identifying the elemental content of minerals or organic matter at the pore boundaries. This method can analyze pore types in two-dimensional images, but it is time-consuming and does not consider the influence of organic matter and mineral heterogeneity in three-dimensional pore space, making it unable to identify organic and inorganic pores in three-dimensional space. Therefore, this application provides a shale digital core construction method that considers the heterogeneity of shale reservoir mineral and organic matter distribution during multi-component shale digital core construction, enabling automatic multi-component segmentation of shale digital cores and accurate identification of pore types in three-dimensional space.

[0047] This invention discloses a method for constructing digital cores of shale rocks. See [link to relevant documentation]. Figure 1 As shown, the method includes:

[0048] Step S11: Obtain FIB-SEM and MAPS images of the target shale sample.

[0049] In this embodiment, FIB-SEM images and MAPS images of the target shale sample are acquired. It is understood that the FIB-SEM images (i.e., focused ion beam scanning electron microscope images) are a set of several images of the target shale sample. Acquiring the FIB-SEM images facilitates subsequent segmentation of different components within the FIB-SEM images, and generates a three-dimensional image of the target shale sample based on the several FIB-SEM images. Further, the MAPS images (i.e., automatically acquired images) are images of the target shale sample at different resolutions. The low-resolution sub-images of the MAPS images are used to determine the fractures in the target shale sample, and the high-resolution sub-images of the MAPS images are used to determine the porosity of the target shale sample. Through the above technical solution, FIB-SEM images and MAPS images of the target shale sample are acquired, facilitating subsequent segmentation of different components within the FIB-SEM images and generation of a target three-dimensional image of the target shale sample. Furthermore, the pore size of the target three-dimensional image is optimized based on the MAPS images.

[0050] Step S12: Based on the maximum entropy algorithm, the different components in the FIB-SEM image are segmented according to the gray-level gradient and gray level of the FIB-SEM image, and a target three-dimensional image corresponding to the target shale sample is generated.

[0051] In this embodiment, the maximum entropy algorithm is used to segment different components in the FIB-SEM image based on its gray-level gradient and grayscale value, generating a target 3D image corresponding to the target shale sample. It is understood that the target shale sample contains four different components: organic matter, framework minerals, pyrite, and pores. These components have different gray-level gradients. The maximum entropy algorithm is then used to segment these components based on their gray-level gradient and grayscale value, and the target 3D image corresponding to the target shale sample is constructed from the segmented FIB-SEM image. Through this technical solution, when constructing a digital core of shale, the maximum entropy algorithm is used to segment different components in the FIB-SEM image based on its gray-level gradient and grayscale value, automatically segmenting the different components in the FIB-SEM image while considering the heterogeneous distribution of minerals and organic matter in shale reservoirs.

[0052] Step S13: Optimize the aperture of the target 3D image based on the MAPS image to obtain an optimized 3D image.

[0053] In this embodiment, the pore size of the target 3D image is optimized based on the MAPS image to obtain an optimized 3D image. It is understood that the high-resolution and low-resolution sub-images of the MAPS image are used together to determine the porosity of the target shale sample, and the pore size of the target 3D image is further optimized based on the MAPS image. Through the above technical solution, an optimized 3D image is determined, facilitating the subsequent determination of the corresponding pore type based on the distribution of different components at the pore boundaries.

[0054] Step S14: Perform morphological erosion and dilation operations on the pores in the optimized 3D image using preset structuring elements and determine the corresponding pore boundaries using a Boolean superposition algorithm. Then, determine the corresponding pore type based on the distribution of different components at the pore boundaries to complete the construction of the digital core for the target shale sample.

[0055] In this embodiment, morphological erosion and dilation operations are performed on the pores in the optimized 3D image using preset structuring elements, and the corresponding pore boundaries are determined using a Boolean superposition algorithm. Then, the corresponding pore type is determined based on the distribution of different components at the pore boundaries, thereby completing the construction of a digital core for the target shale sample. Specifically, suitable preset structuring elements are selected to perform 3D morphological erosion operations on the pores in the optimized 3D image, followed by 3D morphological dilation operations, and the corresponding pore boundaries are determined using a Boolean superposition algorithm. Finally, the corresponding pore type is determined based on the distribution of different components at the pore boundaries, thereby completing the construction of a digital core for the target shale sample. Through the above technical solution, morphological operations and a Boolean superposition algorithm are used to determine the corresponding pore boundaries, and finally, organic and inorganic pores are determined in three-dimensional space based on the distribution of different components at the pore boundaries.

[0056] As can be seen, in this embodiment, when constructing a digital core of shale, the FIB-SEM image and MAPS image of the target shale sample are first acquired. Then, based on the maximum entropy algorithm, different components in the FIB-SEM image are segmented according to the grayscale gradient and grayscale values. A target 3D image corresponding to the target shale sample is generated based on the segmented FIB-SEM image. The pore size of the target 3D image is optimized based on the MAPS image. Finally, 3D morphological erosion and 3D morphological dilation operations are performed on the pores of the optimized 3D image. The Boolean superposition algorithm is used to determine the corresponding pore boundaries. Finally, the corresponding pore type is determined based on the distribution of different components at the pore boundaries, thus completing the construction of the digital core for the target shale sample. Therefore, this application, when constructing shale digital cores, uses the maximum entropy algorithm to segment different components in the FIB-SEM image based on the grayscale gradient and grayscale, automatically segmenting the different components in the FIB-SEM image considering the heterogeneity of shale reservoir mineral and organic matter distribution. On the other hand, morphological operations and Boolean superposition algorithms are used to determine the corresponding pore boundaries. Finally, organic and inorganic pores are determined in three-dimensional space based on the distribution of different components at the pore boundaries. In summary, this application can automatically segment and accurately identify the three-dimensional pore types of shale digital cores, taking into account the heterogeneity of shale reservoir mineral and organic matter distribution when constructing multi-component shale digital cores.

[0057] See Figure 2 As shown, this embodiment of the invention discloses a specific method for constructing digital cores of shale. Compared with the previous embodiment, this embodiment further explains and optimizes the technical solution.

[0058] Step S21: Obtain FIB-SEM and MAPS images of the target shale sample.

[0059] Step S22: Determine the gray-level gradient distribution histogram and gray-level distribution histogram based on the gray-level gradient and gray level of each pixel in the FIB-SEM image.

[0060] In this embodiment, determining the gray-level gradient distribution histogram and gray-level distribution histogram based on the gray-level gradient and gray level of each pixel in the FIB-SEM image includes: determining the gray-level gradient of each pixel in the FIB-SEM image using the Sobel operator representing four directions based on the digital difference method; accumulating the gray-level gradients corresponding to different gray-levels and performing weighted averaging to obtain the average gradient corresponding to different gray-levels, and determining the gray-level gradient distribution histogram based on the average gradient corresponding to different gray-levels; and determining the gray-level distribution histogram using the imhist function in Matlab. Specifically, the gray-level gradient of each pixel in the FIB-SEM image is calculated using the digital difference method, and the Sobel gradient operator is selected to process the focused ion beam scanning electron microscope image. The sum of the absolute values ​​of the gray-level gradients of the Sobel operator in the horizontal, vertical, diagonal, and anti-diagonal directions is calculated, which is the magnitude of the image gray-level gradient. By accumulating the gray-level gradients corresponding to each gray-level and then performing weighted averaging, the average gradient at each gray-level scale can be obtained.

[0061]

[0062] Where, q j x represents the number of pixels at the j-th gray level; j represents the pixel level in the image; x ij ,y ij These represent the position coordinates of the i-th pixel at the j-th gray level.

[0063] Step S23: Based on the maximum entropy algorithm, determine the optimal gray-level segmentation threshold according to the gray-level gradient distribution histogram and the gray-level distribution histogram, and segment different components in the FIB-SEM image according to the optimal gray-level segmentation threshold.

[0064] In this embodiment, the maximum entropy algorithm determines the optimal gray-level segmentation threshold based on the gray-level gradient and gray-level distribution histogram, and segments different components in the FIB-SEM image according to the optimal gray-level segmentation threshold. This includes: determining the optimal gray-level segmentation threshold in the gray-level distribution histogram based on the maximum sum of entropy values ​​between different components being between the maximum and minimum gray-level gradient values ​​in the gray-level gradient distribution histogram; determining the gray-level distribution corresponding to different components based on the optimal gray-level segmentation threshold; and segmenting different components in the FIB-SEM image according to the gray-level distribution corresponding to different components. Specifically, in the gray-level gradient distribution histogram, the minimum gray-level gradient region represents the same component, and the maximum gray-level gradient represents different components; therefore, the gray-level threshold segmentation point between the two components lies between these two regions. Simultaneously, three pairs of LVs (LV1, LV2, LV3) and LPs (LP1, LP2, LP3) are selected. Based on the maximum entropy algorithm, three optimal gray-level segmentation thresholds corresponding to the gray-level distribution histogram are found among (LV1, LP1), (LV2, LP2), and (LV3, LP3), respectively, to maximize the sum of the entropy values ​​of each component. The corresponding objective function can be expressed as:

[0065]

[0066] Where LV1, LV2, LV3 and LP1, LP2, LP3 represent the three minimum gray-level gradient values ​​and the three maximum gray-level gradient values ​​in the gray-level gradient histogram, respectively; E0(k) is the entropy value; p(i) is the gray-level distribution of the entire gray-level image; p0(K), p1(K), p2(K), p3(K) are the gray-level distributions corresponding to the distribution intervals of the four components, respectively. The gray-level value corresponding to the maximum value in the above formula is the optimal segmentation threshold selected by this method. An example image of the target 3D image corresponding to the segmented gray-level distribution histogram image is shown below. Figure 3 As shown, three optimal grayscale segmentation thresholds are used to divide the four components in the focused ion beam scanning electron microscope image. Figures (a) and (d) represent the segmented pores; Figures (b) and (e) represent the segmented pyrite; and Figures (c) and (f) represent the segmented organic matter. The transparent areas in the figures are the framework minerals. Figures (a), (b), and (c) represent the three-dimensional spatial distribution of pores, pyrite, and organic matter in the shale sample; and Figures (d), (e), and (f) represent the planar distribution of pores, pyrite, and organic matter in a typical profile of the shale sample.

[0067] Step S24: Optimize the aperture of the target 3D image based on the MAPS image to obtain an optimized 3D image.

[0068] In this embodiment, optimizing the aperture of the target 3D image based on the MAPS image to obtain an optimized 3D image includes: marking pore clusters in the target 3D image using a 26-point connected component labeling method and determining pixels marked with the same pore cluster as pores in the target 3D image; determining a first equivalent pore radius based on the pores in the target 3D image; extracting pore distribution features in the MAPS image using a binarization function in Matlab and determining a second equivalent pore radius based on the pore distribution features; performing morphological dilation operations on the pores using structuring elements of different sizes, using the root mean square error between the second and first equivalent pore radii as a constraint condition. The 3D image dilation result corresponding to the minimum root mean square error is used as the optimized 3D image. Specifically, based on the concept of pore clusters, pore clusters are marked using a 26-point connected component labeling method, and all pixels marked with the same pore cluster are considered as one pore. The volume of each pore is calculated and equivalent to a sphere, and the first equivalent pore radius is:

[0069]

[0070] Where k represents the k-th pore cluster, V k This represents the volume of the k-th pore cluster. Based on the MAPS sub-images at high and low resolutions, the pore size distribution features of each sub-image in the MAPS image are extracted using the binarization function built into Matlab software. The second equivalent pore radius is:

[0071]

[0072] Where g represents the g-th pore cluster, S g Let represent the area of ​​the g-th pore cluster. To resolve the discrepancy in size and resolution between the MAPS image and the target 3D image, morphological dilation operations are performed on the pores using structuring elements of different sizes. The mean square root error between the second and first equivalent pore radii is used as a constraint. The objective function is:

[0073] min{||R MAPS -R FIB-SEM ||2};

[0074] Among them, R MAPS Represents the aperture distribution acquired from the MAPS image and its high-resolution sub-images; R FIB-SEM This represents the aperture distribution obtained from the target 3D image. In one specific embodiment, a schematic diagram of the aperture distribution optimization of the target 3D image is shown below. Figure 4As shown in Figure (a), the cumulative aperture distribution curve of the target 3D image and the cumulative aperture distribution curve determined by the MAPS image are displayed respectively. Figure (b) represents the root mean square error of the MAPS aperture distribution and the target 3D image aperture distribution as a function of the size of the 3D morphological structural element during the 3D morphological corrosion process. The black asterisks in the figure represent the size of the selected structural element. Figure (c) represents the aperture distribution result after MAPS constraint.

[0075] Step S25: Perform morphological erosion and dilation operations on the pores in the optimized 3D image using preset structuring elements and determine the corresponding pore boundaries using a Boolean superposition algorithm. Then, determine the corresponding pore type based on the distribution of different components at the pore boundaries to complete the construction of the digital core for the target shale sample.

[0076] In this embodiment, the process of performing morphological erosion and dilation operations on the pores in the optimized 3D image using preset structural elements and determining the corresponding pore boundaries using a Boolean superposition algorithm, and then determining the corresponding pore type based on the distribution of different components at the pore boundaries, to complete the construction of a digital core for the target shale sample, includes: marking pore clusters in the optimized 3D image using a 26-point connected component labeling method and determining pixels marked with the same pore cluster as pores in the optimized 3D image; determining whether the pores in the optimized 3D image are organic or inorganic pores based on the distribution probability of organic matter and framework minerals at the pore boundaries, to complete the construction of a digital core for the target shale sample. Specifically, based on the concept of pore clusters, all pore clusters are marked using the 26-point connected component labeling method, and a 2×2×2 cube structural element is selected. For each marked pore cluster in the digital core, a 3D morphological erosion operation is first performed, followed by a 3D morphological dilation operation, and the boundary corresponding to each pore cluster is calculated using a Boolean superposition algorithm. The pore type of the three-dimensional pore space is determined based on the relative distribution probabilities of organic matter and framework minerals at the boundaries of each pore cluster. In one specific embodiment, an example diagram illustrating the identification of organic and inorganic pores is shown below. Figure 5 As shown, Figure (a) represents the three-dimensional distribution characteristics of organic pores identified by three-dimensional morphology; Figure (b) represents the typical cross-sectional distribution characteristics of organic pores identified by three-dimensional morphology; Figure (c) represents the three-dimensional distribution characteristics of inorganic pores identified by three-dimensional morphology; and Figure (d) represents the typical cross-sectional distribution characteristics of inorganic pores identified by three-dimensional morphology. Figures (e) and (g) are the original grayscale images of focused ion beam scanning electron microscopy. Figures (f) and (h) show the results of segmenting typical images. In the figures, Py represents pyrite; IP represents inorganic pores; OP represents organic pores; OM represents organic matter; and SF represents organic matter shrinkage cracks.

[0077] As can be seen, in this embodiment, the maximum entropy algorithm is used to segment different components in the FIB-SEM image based on the gray-level gradient and gray level, taking into account the heterogeneity of shale reservoir mineral and organic matter distribution to automatically segment different components in the FIB-SEM image. On the other hand, morphological operations and Boolean superposition algorithms are used to determine the corresponding pore boundaries. Finally, based on the distribution of different components at the pore boundaries, organic and inorganic pores are determined in three-dimensional space. In summary, this application can achieve automatic multi-component segmentation of shale digital cores and accurate identification of pore types in three-dimensional space when constructing multi-component shale digital cores, taking into account the heterogeneity of shale reservoir mineral and organic matter distribution.

[0078] See Figure 6 As shown in the figure, this application discloses a shale digital core construction device, comprising:

[0079] Image acquisition module 11 is used to acquire FIB-SEM images and MAPS images of the target shale sample;

[0080] The image segmentation module 12 is used to segment different components in the FIB-SEM image based on the maximum entropy algorithm according to the gray-level gradient and gray level of the FIB-SEM image, and generate a target three-dimensional image corresponding to the target shale sample.

[0081] Aperture optimization module 13 is used to optimize the aperture of the target three-dimensional image based on the MAPS image to obtain an optimized three-dimensional image;

[0082] The pore type identification module 14 is used to perform morphological erosion and expansion operations on the pores in the optimized three-dimensional image using preset structural elements and to determine the corresponding pore boundaries using a Boolean superposition algorithm. Then, based on the distribution of different components at the pore boundaries, the corresponding pore type is determined to complete the construction of the digital core for the target shale sample.

[0083] As can be seen, in this embodiment, when constructing a digital core of shale, the FIB-SEM image and MAPS image of the target shale sample are first acquired. Then, based on the maximum entropy algorithm, different components in the FIB-SEM image are segmented according to the grayscale gradient and grayscale values. A target 3D image corresponding to the target shale sample is generated based on the segmented FIB-SEM image. The pore size of the target 3D image is optimized based on the MAPS image. Finally, 3D morphological erosion and 3D morphological dilation operations are performed on the pores of the optimized 3D image. The Boolean superposition algorithm is used to determine the corresponding pore boundaries. Finally, the corresponding pore type is determined based on the distribution of different components at the pore boundaries, thus completing the construction of the digital core for the target shale sample. Therefore, this application, when constructing shale digital cores, uses the maximum entropy algorithm to segment different components in the FIB-SEM image based on the grayscale gradient and grayscale, automatically segmenting the different components in the FIB-SEM image considering the heterogeneity of shale reservoir mineral and organic matter distribution. On the other hand, morphological operations and Boolean superposition algorithms are used to determine the corresponding pore boundaries. Finally, based on the distribution of different components at the pore boundaries, organic and inorganic pores are determined in three-dimensional space. In summary, this application can achieve automatic multi-component segmentation of shale digital cores and accurate identification of pore types in three-dimensional space, taking into account the heterogeneity of shale reservoir mineral and organic matter distribution when constructing multi-component shale digital cores.

[0084] In some specific embodiments, the image segmentation module 12 specifically includes:

[0085] The gray-level gradient and gray-level determination unit is used to determine the gray-level gradient distribution histogram and the gray-level distribution histogram based on the gray-level gradient and gray-level at each pixel in the FIB-SEM image.

[0086] The component segmentation unit is used to determine the optimal gray-level segmentation threshold based on the gray-level gradient distribution histogram and the gray-level distribution histogram using the maximum entropy algorithm, and to segment different components in the FIB-SEM image according to the optimal gray-level segmentation threshold.

[0087] In some specific embodiments, the gray-level gradient and gray-level determination unit is specifically used for: determining the gray-level gradient at each pixel of the FIB-SEM image based on the digital difference method using the Sobel operator representing four directions; accumulating the gray-level gradients corresponding to different gray-levels and performing weighted averaging to obtain the average gradient corresponding to different gray-levels, and determining the gray-level gradient distribution histogram based on the average gradient corresponding to different gray-levels; and determining the gray-level distribution histogram using the imhist function in Matlab.

[0088] In some specific embodiments, the component segmentation unit is specifically used to: determine the optimal gray-level segmentation threshold in the gray-level distribution histogram based on the maximum sum of entropy values ​​between different components between the maximum and minimum gray-level gradient values ​​in the gray-level gradient distribution histogram; determine the gray-level distribution corresponding to different components according to the optimal gray-level segmentation threshold; and segment different components in the FIB-SEM image according to the gray-level distribution corresponding to different components.

[0089] In some specific embodiments, the aperture optimization module 13 specifically includes:

[0090] The first pore radius determination unit is used to mark pore clusters in the target three-dimensional image using the 26-point connected component marking method and determine the pixels marked with the same pore cluster as pores in the target three-dimensional image, and then determine the first equivalent pore radius based on the pores in the target three-dimensional image.

[0091] The second pore radius determination unit is used to extract the pore distribution features in the MAPS image using the binarization function in Matlab and determine the second equivalent pore radius based on the pore distribution features.

[0092] The optimization unit performs morphological dilation calculations on the pores using structural elements of different sizes, using the root mean square error between the second and first equivalent pore radii as a constraint. The dilation result of the 3D image corresponding to the minimum root mean square error is used as the optimized 3D image.

[0093] In some specific embodiments, the shale digital core construction device further includes:

[0094] A pore determination module is used to mark pore clusters in the optimized 3D image using a 26-point connected component labeling method and to determine pixels marked with the same pore cluster as pores in the optimized 3D image.

[0095] In some specific embodiments, the pore type identification module 14 is specifically used to: determine whether the pores in the optimized three-dimensional image are organic pores or inorganic pores based on the distribution probability of organic matter and framework minerals at the pore boundary, so as to complete the construction of a digital core for the target shale sample.

[0096] Figure 7The illustration shows an electronic device 20 provided in an embodiment of this application. This electronic device 20 may further include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the shale digital core construction method disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0097] In this embodiment, the power supply 23 is used to provide voltage to the various hardware devices on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to the specific application needs, and is not specifically limited here.

[0098] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0099] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20, as well as the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the shale digital core construction method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0100] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned shale digital core construction method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0101] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0102] The present invention provides a detailed description of a method, apparatus, equipment, and medium for constructing digital cores of shale. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for constructing digital cores of shale rocks, characterized in that, include: Acquire focused ion beam scanning electron microscope (FIB-SEM) images and automatically acquired MAPS images of the target shale sample; The maximum entropy algorithm is used to segment different components in the FIB-SEM image based on the gray-level gradient and gray level, and to generate a target three-dimensional image corresponding to the target shale sample. The aperture of the target 3D image is optimized based on the MAPS image to obtain an optimized 3D image; Morphological erosion and dilation operations are performed on the pores in the optimized 3D image using preset structuring elements, and the corresponding pore boundaries are determined using a Boolean superposition algorithm. Then, the corresponding pore type is determined according to the distribution of different components at the pore boundaries, so as to complete the construction of the digital core for the target shale sample. The step of optimizing the aperture of the target 3D image based on the MAPS image to obtain an optimized 3D image includes: The pore clusters in the target 3D image are marked using the 26-point connected component labeling method, and the pixels marked with the same pore cluster are determined as pores in the target 3D image. Then, the first equivalent pore radius is determined based on the pores in the target 3D image. The pore distribution features in the MAPS image were extracted using the binarization function in Matlab, and the second equivalent pore radius was determined based on the pore distribution features. Morphological expansion calculations are performed on pores using structural elements of different sizes. The root mean square error between the second and first equivalent pore radii is used as a constraint condition, and the expansion result of the three-dimensional image corresponding to the minimum root mean square error is used as the optimized three-dimensional image.

2. The shale digital core construction method according to claim 1, characterized in that, The maximum entropy-based algorithm segments different components in the FIB-SEM image according to the gray-level gradient and gray level, including: The gray-level gradient distribution histogram and gray-level distribution histogram are determined based on the gray-level gradient and gray-level of each pixel in the FIB-SEM image. The optimal gray-level segmentation threshold is determined based on the maximum entropy algorithm according to the gray-level gradient distribution histogram and the gray-level distribution histogram, and different components in the FIB-SEM image are segmented according to the optimal gray-level segmentation threshold.

3. The shale digital core construction method according to claim 2, characterized in that, The step of determining the gray-level gradient distribution histogram and gray-level distribution histogram based on the gray-level gradient and gray level at each pixel in the FIB-SEM image includes: Based on the digital difference method, the gray-level gradient at each pixel of the FIB-SEM image is determined using the Sobel operator representing four directions; The gray-level gradients corresponding to different gray-levels are accumulated and weighted averaged to obtain the average gradients corresponding to different gray-levels. The gray-level gradient distribution histogram is determined based on the average gradients corresponding to different gray-levels. The gray-level distribution histogram is determined using the imhist function in Matlab.

4. The shale digital core construction method according to claim 2, characterized in that, The maximum entropy-based algorithm determines the optimal gray-level segmentation threshold based on the gray-level gradient distribution histogram and the gray-level distribution histogram, and segments different components in the FIB-SEM image according to the optimal gray-level segmentation threshold, including: The optimal gray-level segmentation threshold in the gray-level gradient distribution histogram is determined based on the maximum sum of entropy values ​​among different components, between the maximum and minimum gray-level gradient values. The grayscale distribution corresponding to different components is determined based on the optimal grayscale segmentation threshold, and the different components in the FIB-SEM image are segmented based on the grayscale distribution corresponding to different components.

5. The method for constructing digital cores of shale rocks according to claim 1, characterized in that, Before performing morphological erosion and dilation operations on the pores in the optimized 3D image using preset structuring elements, and determining the corresponding pore boundaries using a Boolean superposition algorithm, the method further includes: The pore clusters in the optimized 3D image are labeled using the 26-point connected component labeling method, and the pixels labeled with the same pore cluster are determined as pores in the optimized 3D image.

6. The method for constructing shale digital cores according to any one of claims 1 to 5, characterized in that, The step of determining the corresponding pore type based on the distribution of different components at the pore boundary to complete the construction of a digital core for the target shale sample includes: Based on the distribution probability of organic matter and framework minerals at the pore boundaries, the pores in the optimized three-dimensional image are determined to be organic or inorganic pores, so as to complete the construction of the digital core for the target shale sample.

7. A shale digital core construction device, characterized in that, include: The image acquisition module is used to acquire focused ion beam scanning electron microscope (FIB-SEM) images and automatically acquired MAPS images of the target shale sample. The image segmentation module is used to segment different components in the FIB-SEM image based on the maximum entropy algorithm according to the gray-level gradient and gray level of the FIB-SEM image, and generate a target three-dimensional image corresponding to the target shale sample. An aperture optimization module is used to optimize the aperture of the target three-dimensional image based on the MAPS image to obtain an optimized three-dimensional image. The pore type identification module is used to perform morphological erosion and dilation operations on the pores in the optimized three-dimensional image using preset structural elements and to determine the corresponding pore boundaries using a Boolean superposition algorithm. Then, the corresponding pore type is determined according to the distribution of different components of the pore boundaries, so as to complete the construction of the digital core for the target shale sample. The aperture optimization module includes: The first pore radius determination unit is used to mark pore clusters in the target three-dimensional image using the 26-point connected component marking method and determine the pixels marked with the same pore cluster as pores in the target three-dimensional image, and then determine the first equivalent pore radius based on the pores in the target three-dimensional image. The second pore radius determination unit is used to extract the pore distribution features in the MAPS image using the binarization function in Matlab and determine the second equivalent pore radius based on the pore distribution features. The optimization unit is used to perform morphological expansion calculations on the pores using structural elements of different sizes. The root mean square error between the second equivalent pore radius and the first equivalent pore radius is used as a constraint condition, and the expansion result of the three-dimensional image corresponding to the minimum root mean square error is used as the optimized three-dimensional image.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the shale digital core construction method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Used to store computer programs; wherein, when executed by a processor, the computer programs implement the steps of the shale digital core construction method as described in any one of claims 1 to 6.

Images