CN115393370A - Shale digital core construction method, device, equipment and medium - Google Patents
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Abstract
The application discloses a shale digital core construction method, device, equipment and medium, and relates to the technical field of oil and gas exploration and development. The method comprises the following steps: obtaining an FIB-SEM image and an MAPS image of a target shale sample; based on a maximum entropy algorithm, segmenting different components in the FIB-SEM image according to the gray gradient and the gray scale of the FIB-SEM image, and generating a target three-dimensional image corresponding to the target shale sample; optimizing the aperture of the target three-dimensional image based on the MAPS image to obtain an optimized three-dimensional image; performing morphological corrosion and expansion operation on the pores in the optimized three-dimensional image by using preset structural elements, determining a corresponding pore boundary by using a Boolean superposition algorithm, and determining a corresponding pore type according to the distribution condition of different components of the pore boundary so as to complete the construction of the digital core for the target shale sample. By the scheme, multi-component automatic segmentation and three-dimensional pore type identification of the shale sample can be realized.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration and development, in particular to a shale digital core construction method, device, equipment and medium.
Background
The heterogeneity and anisotropy of the distribution of minerals and organic matters in the shale reservoir makes the components and pore structures in the shale complex. Both nano-scale organic pores and nano/micro-scale inorganic pores exist. The fluid presence in the two pores is quite different, which complicates the Nuclear Magnetic Resonance (NMR) response and the seepage characteristics. It is difficult to perform effective nmr well log evaluation. Therefore, there is an urgent need to define the nuclear magnetic resonance response characteristics and migration mechanisms of shales. The shale multi-component digital core has the advantages of economy and reusability, and is the basis of nuclear magnetic resonance response simulation and micro-nano scale fluid flow simulation research. Therefore, the construction of the shale multi-component digital core is of great significance.
At present, the construction of a shale multi-component digital core mainly comprises image segmentation and pore type identification. For the image segmentation method, the related art has some problems: manual threshold segmentation methods are time consuming and subject to subjective judgment by the operator. The OTSU algorithm determines the gray threshold of an image by selecting the maximum inter-class variance and the minimum intra-class variance of gray distribution, which is a common method in image segmentation, but the calculation speed is slow, and if the prior information of the threshold is known, the calculation speed is accelerated. The maximum entropy algorithm determines an optimal segmentation threshold value by utilizing the maximum information quantity of each component, the method focuses on the whole information of the image when processing the image, the model is stable, but the model ignores the boundary information of the image. A watershed algorithm is an image region segmentation algorithm based on mathematical morphology, pixel points with similar spatial positions and gray values are connected with one another to form a closed contour, and therefore region segmentation of an image is achieved. For the pore type identification method, organic and inorganic pores are generally determined by means of SEM (scanning electron microscope) images in combination with Energy spectrum analysis (EDS) by determining the content of elements in minerals or organic matter at the pore boundaries. The method can analyze the pore type in the two-dimensional image, but the method is time-consuming, does not consider the influence of organic matters and mineral heterogeneity in the three-dimensional pore space, and cannot identify organic pores and inorganic pores in the three-dimensional space. In conclusion, when the shale multi-component digital core is constructed, the characteristics of heterogeneity of the distribution of reservoir minerals and organic matters are considered, and the automatic multi-component segmentation and the accurate identification of the three-dimensional space pore type of the shale digital core are difficult problems to solve urgently.
Disclosure of Invention
In view of the above, the present invention aims to provide a method, an apparatus, a device and a medium for constructing a shale digital core, which can consider the characteristics of heterogeneity of shale reservoir minerals and organic matter distribution when constructing a shale multi-component digital core, and realize multi-component automatic segmentation and accurate identification of three-dimensional space pore types of the shale digital core. The specific scheme is as follows:
in a first aspect, the application discloses a shale digital core construction method, which includes:
obtaining an FIB-SEM image and an MAPS image of a target shale sample;
based on a maximum entropy algorithm, segmenting different components in the FIB-SEM image according to the gray scale gradient and the gray scale of the FIB-SEM image, and generating a target three-dimensional image corresponding to the target shale sample;
optimizing the aperture of the target three-dimensional image based on the MAPS image to obtain an optimized three-dimensional image;
performing morphological corrosion and expansion operation on the pores in the optimized three-dimensional image by using a preset structural element, determining a corresponding pore boundary by using a Boolean superposition algorithm, and determining a corresponding pore type according to the distribution condition of different components of the pore boundary so as to complete the construction of the digital core for the target shale sample.
Optionally, the segmenting different components in the FIB-SEM image according to the gray scale gradient and the gray scale of the FIB-SEM image based on the maximum entropy algorithm includes:
determining a gray gradient and a gray distribution histogram according to the gray gradient and the gray level of each pixel in the focused ion beam scanning electron microscope image;
and determining an optimal gray segmentation threshold value according to the gray gradient distribution histogram and the gray distribution histogram based on a maximum entropy algorithm, and segmenting different components in the FIB-SEM image according to the optimal gray segmentation threshold value.
Optionally, the determining a gray scale gradient distribution histogram and a gray scale distribution histogram according to the gray scale gradient and the gray scale at each pixel in the FIB-SEM image includes:
determining the gray gradient of each pixel of the FIB-SEM image by utilizing Sobel operators representing four directions based on a digital difference method;
accumulating the gray gradients corresponding to different gray levels, carrying out weighted average processing to obtain average gradients corresponding to different gray levels, and determining a gray gradient distribution histogram according to the average gradients corresponding to different gray levels; the gray distribution histogram is determined using the imhist function in Matlab.
Optionally, the determining an optimal grayscale segmentation threshold according to the grayscale gradient distribution histogram and the grayscale distribution histogram based on a maximum entropy algorithm, and segmenting different components in the FIB-SEM image according to the optimal grayscale segmentation threshold includes:
determining an optimal gray scale segmentation threshold value in the gray scale distribution histogram between the maximum gray scale gradient value and the minimum gray scale gradient value of the gray scale gradient distribution histogram based on the maximum sum of entropy values among different components;
and determining the gray distribution corresponding to different components according to the optimal gray segmentation threshold, and segmenting different components in the FIB-SEM image according to the gray distribution corresponding to different components.
Optionally, the optimizing the aperture of the target three-dimensional image based on the MAPS image to obtain an optimized three-dimensional image includes:
marking pore clusters in the target three-dimensional image by using a 26-point connected domain marking method, determining pixels marked by the same pore clusters as pores of the target three-dimensional image, and then determining a first equivalent pore radius according to the pores of the target three-dimensional image;
extracting pore distribution characteristics in the MAPS image by using a binarization function in Matlab, and determining a second equivalent pore radius according to the pore distribution characteristics;
and performing morphological expansion operation on the pores by using structural elements with different sizes, and taking the root mean square error of the radius of the second equivalent pore and the radius of the first equivalent pore as a constraint condition. And taking the three-dimensional image expansion result corresponding to the minimum root-mean-square error as the optimized three-dimensional image.
Optionally, before performing morphological erosion and expansion operation on the pores in the optimized three-dimensional image by using a preset structural element and determining a corresponding pore boundary by using a boolean addition algorithm, the method further includes:
and marking pore clusters in the optimized three-dimensional image by using a 26-point connected domain marking method, and determining pixels marked by the same pore clusters as pores of the optimized three-dimensional image.
Optionally, the determining, according to the distribution of different components of the pore boundary, a corresponding pore type to complete the construction of the digital core for the target shale sample includes:
and judging that the pores of the optimized three-dimensional image are organic pores or inorganic pores according to the distribution probability of the organic matters and the framework minerals at the pore boundary so as to complete the construction of the digital core aiming at the target shale sample.
In a second aspect, the application discloses a shale digital core construction device, including:
the image acquisition module is used for acquiring an FIB-SEM image and an MAPS image of the target shale sample;
the image segmentation module is used for segmenting different components in the FIB-SEM image according to the gray scale gradient and the gray scale of the FIB-SEM image based on a maximum entropy algorithm and generating a target three-dimensional image corresponding to the target shale sample;
the aperture optimization module is used for optimizing the aperture of the target three-dimensional image based on the MAPS image so as to obtain an optimized three-dimensional image;
and the pore type identification module is used for performing morphological corrosion and expansion operation on pores in the optimized three-dimensional image by using preset structural elements, determining a corresponding pore boundary by using a Boolean superposition algorithm, and then determining a corresponding pore type according to the distribution condition of different components of the pore boundary so as to complete the construction of the digital core for the target shale sample.
In a third aspect, the present application discloses an electronic device, comprising:
a memory for storing a computer program;
a processor configured to execute the computer program to implement the steps of the shale digital core construction method disclosed in the foregoing disclosure.
In a fourth aspect, the present application discloses a computer readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the steps of the shale digital core construction method disclosed in the foregoing disclosure.
When the shale digital core is constructed, firstly, an FIB-SEM image and an MAPS image of a target shale sample are obtained, different components in the FIB-SEM image are segmented according to the gray gradient and the gray scale of the FIB-SEM image based on a maximum entropy algorithm, a target three-dimensional image corresponding to the target shale sample is generated, the aperture of the target three-dimensional image is optimized based on the MAPS image to obtain an optimized three-dimensional image, finally, the pores in the optimized three-dimensional image are subjected to morphological corrosion and expansion operation by using preset structural elements, a corresponding pore boundary is determined by using a Boolean superposition algorithm, and then, the corresponding pore type is determined according to the distribution condition of the different components of the pore boundary to complete the construction of the digital core aiming at the target shale sample. It can be seen that when the shale digital core is constructed, firstly, an FIB-SEM image and an MAPS image of a target shale sample are obtained, further, different components in the FIB-SEM image are segmented according to the gray gradient and the gray scale of the FIB-SEM image based on a maximum entropy algorithm, a target three-dimensional image corresponding to the target shale sample is generated according to the FIB-SEM image after the components are segmented, the aperture of the target three-dimensional image is optimized according to the MAPS image, finally, three-dimensional morphological corrosion operation is performed on the pores of the optimized three-dimensional image, then three-dimensional morphological expansion operation is performed, a corresponding pore boundary is determined by using a Boolean superposition algorithm, and finally, a corresponding pore type is determined according to the distribution condition of different components of the pore boundary, so that the construction of the digital core for the target shale sample is completed. Therefore, when the shale digital core is constructed, different components in the FIB-SEM image are segmented according to the gray gradient and the gray scale of the FIB-SEM image based on the maximum entropy algorithm, and the characteristics of heterogeneity of shale reservoir minerals and organic matter distribution are considered to automatically segment the different components in the FIB-SEM image; on the other hand, the corresponding pore boundaries are determined by using morphological operation and a Boolean superposition algorithm, and finally, the organic pores and the inorganic pores are determined in a three-dimensional space according to the distribution condition of different components of the pore boundaries. In conclusion, the characteristics of heterogeneity of shale reservoir minerals and organic matter distribution are considered when the shale multi-component digital core is constructed, so that multi-component automatic segmentation of the shale digital core and accurate identification of three-dimensional space pore types are achieved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a shale digital core construction method provided by the present application;
fig. 2 is a flowchart of a specific shale digital core construction method provided by the present application;
FIG. 3 is an exemplary illustration of a segmented FIB-SEM image corresponding to a three-dimensional image of a target provided herein;
FIG. 4 is a schematic diagram of the optimization of the aperture distribution of a three-dimensional image of an object provided herein;
FIG. 5 is a diagram of an example of identifying organic pores and inorganic pores provided herein;
fig. 6 is a schematic structural diagram of a shale digital core construction apparatus provided by the present application;
fig. 7 is a block diagram of an electronic device provided in the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, the construction of a shale multi-component digital core mainly comprises image segmentation and pore type identification. For the image segmentation method, the related art has some problems: manual threshold segmentation methods are time consuming and subject to subjective judgment by the operator. The OTSU algorithm determines the gray threshold of an image by selecting the maximum between-class variance and the minimum within-class variance of gray distribution, which is a common method in image segmentation, but the calculation speed is slow, and if the prior information of the threshold is known, the calculation speed is accelerated. The maximum entropy algorithm utilizes the maximum information quantity of each component to determine the optimal segmentation threshold, the method processes the whole information of the image, the model is stable, but the model ignores the boundary information of the image. A watershed algorithm is an image region segmentation algorithm based on mathematical morphology, pixel points with similar spatial positions and gray values are connected with one another to form a closed contour, and therefore region segmentation of an image is achieved. For pore type identification methods, organic and inorganic pores are typically determined by means of SEM images in combination with spectral analysis by determining the elemental content in the mineral or organic matter at the pore boundaries. The method can analyze the pore type in the two-dimensional image, but the method is time-consuming, does not consider the influence of organic matters and mineral heterogeneity in the three-dimensional pore space, and cannot identify organic pores and inorganic pores in the three-dimensional space. Therefore, the shale digital core construction method provided by the application can realize the multi-component automatic segmentation of the shale digital core and the accurate identification of the three-dimensional space pore type by considering the characteristic of heterogeneity of shale reservoir mineral and organic matter distribution when the shale multi-component digital core is constructed.
The embodiment of the invention discloses a shale digital core construction method, which comprises the following steps of:
step S11: and acquiring a FIB-SEM image and a MAPS image of the target shale sample.
In this embodiment, a FIB-SEM image and a MAPS image of the target shale sample are obtained. 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, and the FIB-SEM images are obtained to facilitate subsequent segmentation of different components in the FIB-SEM images, and a three-dimensional image of the target shale sample is generated according to the several FIB-SEM images. Further, the MAPS images (i.e., the automatically acquired images) are corresponding images of the target shale sample at different resolution ratios, wherein a low-resolution sub-image of the MAPS image is used for determining the fracture of the target shale sample, and a high-resolution sub-image of the MAPS image is used for determining the porosity of the target shale sample. According to the technical scheme, the FIB-SEM image and the MAPS image of the target shale sample are obtained so as to be convenient for subsequently segmenting different components in the FIB-SEM image, generate the target three-dimensional image corresponding to the target shale sample, and further optimize the aperture of the target three-dimensional image based on the MAPS image.
Step S12: and segmenting different components in the FIB-SEM image according to the gray scale gradient and the gray scale of the FIB-SEM image based on a maximum entropy algorithm, and generating a target three-dimensional image corresponding to the target shale sample.
In the embodiment, different components in the FIB-SEM image are segmented according to the gray scale gradient and the gray scale of the FIB-SEM image based on a maximum entropy algorithm, and a target three-dimensional image corresponding to the target shale sample is generated. It can be understood that four different components, namely organic matters, skeleton minerals, pyrite and pores, exist in the target shale sample, the gray gradients of the different components are different, the different components in the FIB-SEM image are further segmented according to the gray gradients and the gray levels of the FIB-SEM image by a maximum entropy algorithm, and a target three-dimensional image corresponding to the target shale sample is constructed through the FIB-SEM image after component segmentation. According to the technical scheme, when the shale digital core is constructed, different components in the FIB-SEM image are segmented according to the gray gradient and the gray scale of the FIB-SEM image based on a maximum entropy algorithm, and the characteristics of heterogeneity of mineral and organic matter distribution of a shale reservoir stratum are considered to automatically segment the different components in the FIB-SEM image.
Step S13: and optimizing the aperture of the target three-dimensional image based on the MAPS image to obtain an optimized three-dimensional image.
In this embodiment, the aperture of the target three-dimensional image is optimized based on the MAPS image to obtain an optimized three-dimensional image. It is understood that the high resolution and low resolution sub-images of the MAPS image are used together to determine porosity of the target shale sample, further optimizing the pore size of the target three-dimensional image based on the MAPS image. By the technical scheme, the optimized three-dimensional image is determined so as to determine the corresponding pore type according to the distribution condition of different components of the pore boundary.
Step S14: performing morphological corrosion and expansion operation on the pores in the optimized three-dimensional image by using a preset structural element, determining a corresponding pore boundary by using a Boolean superposition algorithm, and determining a corresponding pore type according to the distribution condition of different components of the pore boundary so as to complete the construction of the digital core for the target shale sample.
In this embodiment, performing morphological erosion and expansion operations on pores in the optimized three-dimensional image by using a preset structural element, determining a corresponding pore boundary by using a boolean superposition algorithm, and then determining a corresponding pore type according to distribution conditions of different components of the pore boundary, so as to complete construction of a digital core for the target shale sample. Specifically, selecting a proper preset structural element to perform three-dimensional morphological corrosion operation on the pores of the optimized three-dimensional image, performing three-dimensional morphological expansion operation, determining a corresponding pore boundary by using a Boolean superposition algorithm, and finally determining a corresponding pore type according to the distribution condition of different components of the pore boundary so as to complete the construction of the digital core for the target shale sample. According to the technical scheme, the corresponding pore boundaries are determined by using morphological operation and a Boolean superposition algorithm, and finally, organic pores and inorganic pores are determined in a three-dimensional space according to the distribution condition of different components of the pore boundaries.
As can be seen, in the embodiment, when a shale digital core is constructed, an FIB-SEM image and an MAPS image of a target shale sample are obtained, different components in the FIB-SEM image are further segmented according to a gray gradient and a gray scale of the FIB-SEM image based on a maximum entropy algorithm, a target three-dimensional image corresponding to the target shale sample is generated according to the FIB-SEM image after the components are segmented, a pore diameter of the target three-dimensional image is optimized according to the MAPS image, a pore of the optimized three-dimensional image is subjected to three-dimensional morphological erosion operation, a three-dimensional morphological dilation operation is performed, a corresponding pore boundary is determined by using a boolean superposition algorithm, and a corresponding pore type is determined according to a distribution condition of different components of the pore boundary, so that the construction of the digital core for the target shale sample is completed. Therefore, when the shale digital core is constructed, different components in the FIB-SEM image are segmented according to the gray gradient and the gray scale of the FIB-SEM image based on the maximum entropy algorithm, and the characteristics of heterogeneity of shale reservoir minerals and organic matter distribution are considered to automatically segment the different components in the FIB-SEM image; and on the other hand, determining corresponding pore boundaries by using morphological operation and a Boolean superposition algorithm, and finally determining organic pores and inorganic pores in a three-dimensional space according to the distribution condition of different components of the pore boundaries. In conclusion, the method and the device can automatically segment the multi-component of the shale digital core and accurately identify the three-dimensional space pore type by considering the characteristic of the heterogeneity of the distribution of minerals and organic matters of the shale reservoir during the construction of the shale multi-component digital core.
Referring to fig. 2, the embodiment of the invention discloses a specific shale digital core construction method, and compared with the previous embodiment, the embodiment further explains and optimizes the technical scheme.
Step S21: and acquiring a FIB-SEM image and a MAPS image of the target shale sample.
Step S22: according to the gray scale gradient at each pixel in the FIB-SEM image and the gray scale determines a gray scale gradient distribution histogram and a gray scale distribution histogram, respectively.
In this embodiment, the determining a gray scale gradient distribution histogram and a gray scale distribution histogram according to the gray scale gradient and the gray scale at each pixel in the FIB-SEM image includes: determining the gray gradient of each pixel of the FIB-SEM image by utilizing Sobel operators representing four directions based on a digital difference method; accumulating the gray gradients corresponding to different gray levels, carrying out weighted average processing to obtain average gradients corresponding to different gray levels, and determining a gray gradient distribution histogram according to the average gradients corresponding to different gray levels; the gray distribution histogram is determined using the imhist function in Matlab. Specifically, the gray gradient of each pixel of the FIB-SEM image is calculated by using a digital difference method, a Sobel gradient operator is selected to process the focused ion beam scanning electron microscope image, and the sum of absolute values of the gray gradients of the Sobel operator in the horizontal direction, the vertical direction, the diagonal direction and the anti-diagonal direction is calculated, namely the amplitude of the image gray gradient. The gray gradients corresponding to each gray level are accumulated, and then weighted average processing is performed, so that the average gradient under each gray scale can be obtained:
wherein q is j A number of pixels representing a jth gray scale; j represents the pixel level in the image; x is the number of ij ,y ij Respectively representing the position coordinates of the ith pixel in the jth gray scale.
Step S23: and determining an optimal gray scale segmentation threshold value according to the gray scale gradient distribution histogram and the gray scale distribution histogram based on a maximum entropy algorithm, and segmenting different components in the FIB-SEM image according to the optimal gray scale segmentation threshold value.
In this embodiment, the determining an optimal grayscale segmentation threshold according to the grayscale gradient and the grayscale distribution histogram based on a maximum entropy algorithm, and segmenting different components in the FIB-SEM image according to the optimal grayscale segmentation threshold includes: determining an optimal gray segmentation threshold value in the gray distribution histogram between the maximum gray gradient value and the minimum gray gradient value of the gray gradient distribution histogram based on the maximum sum of entropy values among different components; and determining the gray scale distribution corresponding to different components according to the optimal gray scale segmentation threshold, and segmenting different components in the FIB-SEM image according to the gray scale distribution corresponding to different components. Specifically, in the gray gradient distribution histogram, the minimum gray gradient region represents the same component, and the maximum gray gradient region represents a different component, and then the gray threshold segmentation point of the two components is between the two. And 3 pairs of LV (LV 1, LV2 and LV 3) and LP (LP 1, LP2 and LP 3) are selected. Based on the maximum entropy algorithm, three optimal gray segmentation threshold values corresponding to the gray distribution histogram are respectively searched among (LV 1, LP 1), (LV 2, LP 2) and (LV 3, LP 3), so that the sum of the entropy values of all components is maximum, and the corresponding objective function can be expressed as:
wherein LV1, LV2, LV3 and LP1, LP2, LP3 respectively represent 3 groups of minimum gray scale gradient values and 3 groups of maximum gray scale gradient values in the gray scale gradient histogram; e 0 (k) Is an entropy value; p (i) is the gray distribution of the whole gray map; p is a radical of 0 (K)，p 1 (K)，p 2 (K)，p 3 (K) The distribution intervals of the four components correspond to gray level distribution respectively. The gray value corresponding to the maximum value of the above formula is the optimal segmentation threshold selected by the method, an example graph of a segmentation gray distribution histogram image corresponding to a target three-dimensional image is shown in fig. 3, and the three optimal gray segmentation thresholds divide four components in a focused ion beam scanning electron microscope image, wherein the graph (a) and the graph (d) represent pores obtained by segmentation; the diagrams (b) and (e) represent the divided pyrites; the graph (c) and the graph (f) represent the organic matters obtained by segmentation, the transparent area in the graph is the skeleton mineral, and the graph (a), the graph (b) and the graph (c) represent the three-dimensional space distribution of the pores, the pyrites and the organic matters in the shale sample; the graphs (d), (e) and (f) represent the typical cross-sectional pore, pyrite and organic matter planar distributions in shale samples.
Step S24: and optimizing the aperture of the target three-dimensional image based on the MAPS image to obtain an optimized three-dimensional image.
In this embodiment, the optimizing the aperture of the target three-dimensional image based on the MAPS image to obtain an optimized three-dimensional image includes: marking pore clusters in the target three-dimensional image by using a 26-point connected domain marking method, determining pixels marked by the same pore clusters as pores of the target three-dimensional image, and then determining a first equivalent pore radius according to the pores of the target three-dimensional image; extracting pore distribution characteristics in the MAPS image by using a binarization function in Matlab and determining a second equivalent pore radius according to the pore distribution characteristics; and performing morphological expansion operation on the pores by using structural elements with different sizes, and taking the root mean square error of the radius of the second equivalent pore and the radius of the first equivalent pore as a constraint condition. And taking the three-dimensional image expansion result corresponding to the minimum root mean square error as the optimized three-dimensional image. Specifically, based on the concept of pore clusters, the pore clusters are marked by using a 26-point connected domain marking method, and all pixels marked by the same pore cluster are regarded as one pore. Counting the volume of each pore and equating the volume to a sphere, wherein the radius of the first equivalent pore is as follows:
wherein k represents the kth cluster of pores, V k The kth pore cluster volume is indicated. Based on MAPS subimages under high and low resolution, the self-contained binarization function of Matlab software is utilized to extract the aperture distribution characteristics of each subimage in the MAPS image, and the second equivalent pore radius is as follows:
wherein g represents the g-th pore cluster, S g Represents the g-th aperture cluster area. In order to solve the contradiction between the size and the resolution of the MAPS image and the target three-dimensional image, morphological expansion operation is carried out on the pores by using structural elements with different sizes, and the mean square heel error of the radius of the second equivalent pore and the radius of the first equivalent pore is used as a constraint condition. The objective function is:
min{||R MAPS -R FIB-SEM || 2 }；
wherein R is MAPS Representing the pore size distribution of the MAPS image and the high-resolution sub-image acquisition thereof; r FIB-SEM Representing the resulting pore size distribution of the three-dimensional image of the target. In one embodiment, a schematic diagram of the optimization of the aperture distribution of the target three-dimensional image is shown in fig. 4, in which (a) a cumulative aperture distribution curve of the target three-dimensional image and a cumulative aperture distribution curve determined by the MAPS image are shown respectively. Graph (b) represents that the root mean square error of MAPS pore size distribution and target three-dimensional image pore size distribution changes with the size of three-dimensional morphological structural elements in the three-dimensional morphological corrosion processThe black asterisks in the figure represent the dimensions of the selected structural elements. Panel (c) represents the result plot of pore size distribution after MAPS confinement.
Step S25: performing morphological corrosion and expansion operation on the pores in the optimized three-dimensional image by using preset structural elements, determining a corresponding pore boundary by using a Boolean superposition algorithm, and determining a corresponding pore type according to the distribution condition of different components of the pore boundary so as to complete the construction of the digital core for the target shale sample.
In this embodiment, the performing morphological erosion and expansion operations on the pores in the optimized three-dimensional image by using a preset structural element, determining a corresponding pore boundary by using a boolean superposition algorithm, and then determining a corresponding pore type according to distribution conditions of different components of the pore boundary to complete construction of the digital core for the target shale sample, includes: marking pore clusters in the optimized three-dimensional image by using a 26-point connected domain marking method, and determining pixels marked by the same pore clusters as pores of the optimized three-dimensional image; and judging that the pores of the optimized three-dimensional image are organic pores or inorganic pores according to the distribution probability of the organic matters and the framework minerals at the pore boundary so as to complete the construction of the digital core aiming at the target shale sample. Specifically, based on the concept of pore clusters, all pore clusters are marked by using a 26-point connected domain marking method, cubic structural elements with structural elements of 2 × 2 × 2 are selected, three-dimensional morphological corrosion operation and three-dimensional morphological expansion operation are performed on each pore cluster marked in the digital core, and the boundary corresponding to each pore cluster is calculated by using a Boolean superposition algorithm. And determining the pore type of the three-dimensional pore space according to the relative sizes of the organic matters at the boundary of each pore cluster and the distribution probability of the skeleton minerals. In one embodiment, an example graph identifying organic pores and inorganic pores is shown in fig. 5, graph (a) representing a three-dimensional morphological identification of organic pore three-dimensional distribution features; the graph (b) represents the three-dimensional morphological recognition of the typical section distribution characteristics of organic pores; graph (c) represents three-dimensional morphology identifying inorganic pore three-dimensional distribution characteristics; graph (d) represents a three-dimensional morphological characterization of a typical cross-sectional distribution of inorganic pores. Fig. (e) and (g) are the original grayscale images of the focused ion beam scanning electron microscope images. Fig. (f) and (h) are the results of segmenting the typical image. Py in the figure represents pyrite; IP represents inorganic porosity; OP represents an organic pore; OM represents organic matter; SF stands for organic matter shrinkage joints.
As can be seen, in this embodiment, different components in the FIB-SEM image are segmented according to the gray scale gradient and the gray scale of the FIB-SEM image based on a maximum entropy algorithm, and the different components in the FIB-SEM image are automatically segmented in consideration of the characteristics of heterogeneity of shale reservoir minerals and organic matter distribution; on the other hand, determining the corresponding pore boundary by using morphological operation and a Boolean superposition algorithm, and finally determining organic pores and inorganic pores in a three-dimensional space according to the distribution condition of different components of the pore boundary. In conclusion, the characteristics of heterogeneity of shale reservoir minerals and organic matter distribution are considered when the shale multi-component digital core is constructed, so that multi-component automatic segmentation of the shale digital core and accurate identification of three-dimensional space pore types are achieved.
Referring to fig. 6, an embodiment of the present application discloses a shale digital core construction apparatus, including:
the image acquisition module 11 is used for acquiring a FIB-SEM image and a MAPS image of the target shale sample;
the image segmentation module 12 is configured to segment different components in the FIB-SEM image according to the gray scale gradient and the gray scale of the FIB-SEM image based on a maximum entropy algorithm, and generate a target three-dimensional image corresponding to the target shale sample;
an aperture optimization module 13, configured to optimize an aperture of the target three-dimensional image based on the MAPS image to obtain an optimized three-dimensional image;
the pore type identification module 14 is configured to perform morphological erosion and expansion operations on pores in the optimized three-dimensional image by using a preset structural element, determine a corresponding pore boundary by using a boolean superposition algorithm, and then determine a corresponding pore type according to a distribution condition of different components of the pore boundary, so as to complete construction of a digital core for the target shale sample.
As can be seen, in the embodiment, when a shale digital core is constructed, an FIB-SEM image and an MAPS image of a target shale sample are obtained, different components in the FIB-SEM image are further segmented according to a gray gradient and a gray scale of the FIB-SEM image based on a maximum entropy algorithm, a target three-dimensional image corresponding to the target shale sample is generated according to the FIB-SEM image after the components are segmented, a pore diameter of the target three-dimensional image is optimized according to the MAPS image, a pore of the optimized three-dimensional image is subjected to three-dimensional morphological erosion operation, a three-dimensional morphological dilation operation is performed, a corresponding pore boundary is determined by using a boolean superposition algorithm, and a corresponding pore type is determined according to a distribution condition of different components of the pore boundary, so that the construction of the digital core for the target shale sample is completed. Therefore, when the shale digital core is constructed, different components in the FIB-SEM image are segmented according to the gray gradient and the gray scale of the FIB-SEM image based on the maximum entropy algorithm, and the characteristics of heterogeneity of shale reservoir minerals and organic matter distribution are considered to automatically segment the different components in the FIB-SEM image; and on the other hand, determining the corresponding pore boundary by using morphological operation and a Boolean superposition algorithm, and finally determining organic pores and inorganic pores in a three-dimensional space according to the distribution condition of different components of the pore boundary. In conclusion, the characteristics of heterogeneity of shale reservoir minerals and organic matter distribution are considered when the shale multi-component digital core is constructed, so that multi-component automatic segmentation of the shale digital core and accurate identification of three-dimensional space pore types are achieved.
In some embodiments, the image segmentation module 12 specifically includes:
a gray gradient and gray level determining unit for determining a gray gradient distribution histogram and a gray level distribution histogram according to the gray level gradient and the gray level at each pixel in the FIB-SEM image;
and the component segmentation unit is used for determining an optimal gray scale segmentation threshold value according to the gray scale gradient distribution histogram and the gray scale distribution histogram based on a maximum entropy algorithm, and segmenting different components in the FIB-SEM image according to the optimal gray scale segmentation threshold value.
In some specific embodiments, the gray gradient and gray level determining unit is specifically configured to: determining the gray gradient of each pixel of the FIB-SEM image by utilizing Sobel operators representing four directions based on a digital difference method; accumulating the gray gradients corresponding to different gray levels, carrying out weighted average processing to obtain average gradients corresponding to different gray levels, and determining a gray gradient distribution histogram according to the average gradients corresponding to different gray levels; the gray distribution histogram is determined using the imhist function in Matlab.
In some embodiments, the component segmentation unit is specifically configured to: determining an optimal gray scale segmentation threshold value in the gray scale distribution histogram between the maximum gray scale gradient value and the minimum gray scale gradient value of the gray scale gradient distribution histogram based on the maximum sum of entropy values among different components; and determining the gray scale distribution corresponding to different components according to the optimal gray scale segmentation threshold, and segmenting different components in the FIB-SEM image according to the gray scale distribution corresponding to different components.
In some embodiments, the aperture optimization module 13 specifically includes:
the first pore radius determining unit is used for marking pore clusters in the target three-dimensional image by using a 26-point connected domain marking method, determining pixels marked by the same pore clusters as pores of the target three-dimensional image, and then determining a first equivalent pore radius according to the pores of the target three-dimensional image;
the second pore radius determining unit is used for extracting the pore distribution characteristics in the MAPS image by using a binarization function in Matlab and determining a second equivalent pore radius according to the pore distribution characteristics;
and the optimization unit is used for performing morphological expansion operation on the pores by using structural elements with different sizes and taking the root mean square error of the radius of the second equivalent pore and the radius of the first equivalent pore as a constraint condition. And taking the three-dimensional image expansion result corresponding to the minimum root-mean-square error as the optimized three-dimensional image.
In some specific embodiments, the shale digital core construction apparatus further comprises:
and the pore determining module is used for marking the pore clusters in the optimized three-dimensional image by using a 26-point connected domain marking method and determining the pixels marked by the same pore clusters as the pores of the optimized three-dimensional image.
In some embodiments, the pore type identification module 14 is specifically configured to: and judging that the pores of the optimized three-dimensional image are organic pores or inorganic pores according to the distribution probability of the organic matters and the framework minerals at the pore boundaries so as to complete the construction of the digital core aiming at the target shale sample.
Fig. 7 illustrates an electronic device 20 according to an embodiment of the present disclosure. The 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 is configured to store a computer program, and the computer program is loaded and executed by the processor 21 to implement relevant steps in the shale digital core construction method disclosed in any one of the foregoing embodiments. In addition, the electronic device 20 in this embodiment may be specifically an electronic computer.
In this embodiment, the power supply 23 is used to provide voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and a communication protocol followed by the communication interface is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited herein; the input/output interface 25 is configured to obtain external input data or output data to the outside, and a specific interface type thereof may be selected according to specific application requirements, which is not specifically limited herein.
In addition, the memory 22 is used as a carrier for resource storage, and may be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., and the resources stored thereon may include an operating system 221, a computer program 222, etc., and the storage manner may be a transient storage manner or a permanent storage manner.
The operating system 221 is used for managing and controlling each hardware device on the electronic device 20, and the computer program 222 may be Windows Server, netware, unix, linux, or the like. The computer programs 222 may further include computer programs that can be used to perform other specific tasks in addition to the computer programs that can be used to perform the shale digital core construction method disclosed in any of the preceding embodiments and executed by the electronic device 20.
Further, the present application also discloses a computer readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the shale digital core construction method disclosed above. For the specific steps of the method, reference may be made to corresponding contents disclosed in the foregoing embodiments, and details are not repeated here.
Finally, it should also be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The shale digital core construction method, the shale digital core construction device, the shale digital core construction equipment and the shale digital core construction medium are introduced in detail, specific examples are applied in the method to explain the principle and the implementation mode of the shale digital core construction method, and the descriptions of the specific examples are only used for helping to understand the method and the core idea of the shale digital core construction method; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A shale digital core construction method is characterized by comprising the following steps:
obtaining an FIB-SEM image and an MAPS image of a target shale sample;
based on a maximum entropy algorithm, segmenting different components in the FIB-SEM image according to the gray gradient and the gray scale of the FIB-SEM image, and generating a target three-dimensional image corresponding to the target shale sample;
optimizing the aperture of the target three-dimensional image based on the MAPS image to obtain an optimized three-dimensional image;
performing morphological corrosion and expansion operation on the pores in the optimized three-dimensional image by using a preset structural element, determining a corresponding pore boundary by using a Boolean superposition algorithm, and determining a corresponding pore type according to the distribution condition of different components of the pore boundary so as to complete the construction of the digital core for the target shale sample.
2. The shale digital core construction method according to claim 1, wherein the segmenting different components in the FIB-SEM image according to the gray scale gradient and the gray scale of the FIB-SEM image based on a maximum entropy algorithm comprises:
determining a gray scale gradient distribution histogram and a gray scale distribution histogram according to the gray scale gradient and the gray scale of each pixel in the FIB-SEM image;
and determining an optimal gray scale segmentation threshold value according to the gray scale gradient distribution histogram and the gray scale distribution histogram based on a maximum entropy algorithm, and segmenting different components in the FIB-SEM image according to the optimal gray scale segmentation threshold value.
3. The method for constructing shale digital core according to claim 2, wherein the determining a gray scale gradient distribution histogram and a gray scale distribution histogram according to the gray scale gradient and gray scale at each pixel in the FIB-SEM image comprises:
determining the gray gradient of each pixel of the FIB-SEM image by utilizing a Sobel operator representing four directions based on a digital difference method;
accumulating the gray gradients corresponding to different gray levels, carrying out weighted average processing to obtain average gradients corresponding to different gray levels, and determining a gray gradient distribution histogram according to the average gradients corresponding to different gray levels; the gray distribution histogram is determined using the imhist function in Matlab.
4. The shale digital core construction method according to claim 2, wherein the determining an optimal gray scale segmentation threshold value according to the gray scale gradient distribution histogram and the gray scale distribution histogram based on a maximum entropy algorithm, and segmenting different components in the FIB-SEM image according to the optimal gray scale segmentation threshold value comprises:
determining an optimal gray scale segmentation threshold value in the gray scale distribution histogram between the maximum gray scale gradient value and the minimum gray scale gradient value of the gray scale gradient distribution histogram based on the maximum sum of entropy values among different components;
and determining the gray scale distribution corresponding to different components according to the optimal gray scale segmentation threshold, and segmenting different components in the FIB-SEM image according to the gray scale distribution corresponding to different components.
5. The method for constructing shale digital core according to claim 1, wherein the optimizing the aperture of the target three-dimensional image based on the MAPS image to obtain an optimized three-dimensional image comprises:
marking pore clusters in the target three-dimensional image by using a 26-point connected domain marking method, determining pixels marked by the same pore clusters as pores of the target three-dimensional image, and then determining a first equivalent pore radius according to the pores of the target three-dimensional image;
extracting pore distribution characteristics in the MAPS image by using a binarization function in Matlab, and determining a second equivalent pore radius according to the pore distribution characteristics;
and performing morphological expansion operation on the pores by using structural elements with different sizes, and taking the root mean square error of the radius of the second equivalent pore and the radius of the first equivalent pore as a constraint condition. And taking the three-dimensional image expansion result corresponding to the minimum root-mean-square error as the optimized three-dimensional image.
6. The shale digital core construction method according to claim 1, wherein before performing morphological erosion and expansion operations on pores in the optimized three-dimensional image by using preset structural elements and determining corresponding pore boundaries by using a boolean superposition algorithm, the method further comprises:
and marking pore clusters in the optimized three-dimensional image by using a 26-point connected domain marking method, and determining pixels marked by the same pore clusters as pores of the optimized three-dimensional image.
7. The shale digital core construction method according to any one of claims 1 to 6, wherein the determining a corresponding pore type according to the distribution of different components of the pore boundary to complete the construction of the digital core for the target shale sample comprises:
and judging that the pores of the optimized three-dimensional image are organic pores or inorganic pores according to the distribution probability of the organic matters and the framework minerals at the pore boundary so as to complete the construction of the digital core aiming at the target shale sample.
8. A shale digital core construction device is characterized by comprising:
the image acquisition module is used for acquiring an FIB-SEM image and an MAPS image of the target shale sample;
the image segmentation module is used for segmenting different components in the focused ion beam scanning electron microscope image according to the gray gradient and the gray scale of the FIB-SEM image based on a maximum entropy algorithm and generating a target three-dimensional image corresponding to the target shale sample;
the aperture optimization module is used for optimizing the aperture of the target three-dimensional image based on the MAPS image so as to obtain an optimized three-dimensional image;
and the pore type identification module is used for performing morphological corrosion and expansion operation on pores in the optimized three-dimensional image by using a preset structural element, determining a corresponding pore boundary by using a Boolean superposition algorithm, and then determining a corresponding pore type according to the distribution condition of different components of the pore boundary so as to complete the construction of the digital core for the target shale sample.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the shale digital core construction method as claimed in any of claims 1 to 7.
10. A computer-readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the steps of the shale digital core construction method as claimed in any of claims 1 to 7.
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| CN116519731B (en) * | 2023-07-03 | 2023-08-25 | 中国石油大学(华东) | Shale oil movable limit determination method based on molecular dynamics simulation |
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