Microstructure Physical Property Calculation Method and Device Based on Rock Thin Section Microscope Images

Abstract

The present application discloses a method and device for calculating microstructural physical properties based on rock thin-section microscope images, relating to the technical fields of rock physical property analysis and image processing. The method includes: performing grayscale processing on a target rock thin-section microscope image; performing magnification processing on the grayscale-processed image according to the bicubic interpolation algorithm to obtain a second rock thin-section microscope image; performing an intercepting operation on the image part containing microstructures therein; determining the median of the grayscale values of all pixel points in the intercepted image, and performing binarization processing on the intercepted image according to the median to obtain a fourth rock thin-section microscope image; determining the physical properties of the microstructures in the target rock thin-section microscope image according to all pixel points with a grayscale value of 1 in the fourth rock thin-section microscope image. The present application can maintain the clarity of the rock thin-section microscope image while magnifying it, reduce the degree of image distortion, and improve the accuracy of microstructural physical property calculation.

Description

Technical Field

[0001] This application relates to the technical fields of rock physical property analysis and image processing, and particularly to a method and device for calculating microstructural physical properties based on thin-section microscope images of rocks. Background Art

[0002] In the fields of geological or geotechnical engineering research, massive rocks are often cut and polished into thin slices dozens of micrometers thick and observed under a microscope to assist researchers or engineers in understanding information such as the mineral composition and deformation characteristics of rock masses in a certain area. Processing the collected digital images can also obtain physical property parameters such as rock porosity and permeability. This method is faster and less costly than general physical experiment methods and is currently widely used in related fields. A series of microstructures often develop inside rocks, such as fractures, cataclastic zones formed by the fragmentation of individual grains, and fault gouge zones formed by intense rock fragmentation. They control the distribution of underground fluids and the stability of underground rock masses. Microstructure types such as fractures have strong permeability and play a guiding role for fluids. On the contrary, microstructures such as fault gouge have poor permeability and can block fluids. In addition, when a large number of microstructures develop inside a rock mass, its mechanical strength is quite different from that of an intact rock mass. The distribution of rock microstructures underground often has a certain scale. Therefore, accurately and quickly evaluating the physical properties of microstructures in rock thin sections based on image processing methods is of great significance for underground mineral exploration and the evaluation of geological disasters related to energy development.

[0003] The current main rock digital image processing technologies include the following: ① The process-based rock physical property analysis method calculates porosity and permeability by forward modeling sedimentation, compaction, and diagenesis processes during rock formation and corrects the results using actual rock microstructures. However, this method is only applicable to sandstones with a specific particle size range and undeformed ones; ② Conduct three-dimensional pore reconstruction of rocks based on stochastic algorithms or geostatistics and obtain permeability through seepage simulation. The results obtained by this method have high accuracy, but seepage simulation usually takes a long time and is not suitable for the rapid evaluation of rock physical properties at engineering sites; ③ Calculate permeability through the specific surface area recognition results of thin-section microscope images. This technology usually uses empirical mathematical relationships obtained from the experimental results of certain specific samples and does not have universality.

[0004] The above different technical means have different effects on the identification of rock structures and the calculation of physical properties. However, they are currently mainly applied to undeformed rocks without microstructures inside. In deformed rocks, due to the relatively small size of the microstructural zones within the field of view, the traversal of pixel points always includes both the mother rock and the microstructures when calculating the physical property parameters of the microstructures. This will undoubtedly bring relatively large errors to the calculation results, and directly magnifying the image of the microstructural part will cause image distortion. In this case, being able to accurately outline the range of microstructures in the microscopic image of a rock thin section and calculate its porosity and permeability is an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this application is to provide a method and device for calculating the physical properties of microstructures based on microscopic images of rock thin sections, which can maintain the clarity of the microscopic image of the rock thin section while magnifying it, reduce the degree of image distortion, and improve the accuracy of calculating the physical properties of microstructures.

[0006] To achieve the above purpose, this application provides the following solutions:

[0007] In the first aspect, this application provides a method for calculating the physical properties of microstructures based on microscopic images of rock thin sections, including:

[0008] Performing grayscale processing on the target microscopic image of the rock thin section to obtain the first microscopic image of the rock thin section;

[0009] Performing magnification processing on the first microscopic image of the rock thin section according to the bicubic interpolation algorithm to obtain the second microscopic image of the rock thin section;

[0010] Performing an intercepting operation on the image part containing microstructures in the second microscopic image of the rock thin section to obtain the third microscopic image of the rock thin section;

[0011] Determining the median of the grayscale values of all pixel points in the third microscopic image of the rock thin section, and performing binarization processing on the third microscopic image of the rock thin section according to the median to obtain the fourth microscopic image of the rock thin section; the grayscale value of each pixel point in the fourth microscopic image of the rock thin section is 1 or 0;

[0012] Determining the physical properties of the microstructures in the target microscopic image of the rock thin section according to all pixel points with a grayscale value of 1 in the fourth microscopic image of the rock thin section.

[0013] In the second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the steps of the method for calculating the physical properties of microstructures based on microscopic images of rock thin sections described in the first aspect.

[0014] In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for calculating microstructural physical properties based on a thin-section microscope image of a rock described in the first aspect are implemented.

[0015] In a fourth aspect, the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the method for calculating microstructural physical properties based on a thin-section microscope image of a rock described in the first aspect are implemented.

[0016] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0017] The present application provides a method and device for calculating microstructural physical properties based on a thin-section microscope image of a rock. The method includes: performing gray processing on a target thin-section microscope image of a rock to obtain a first thin-section microscope image of a rock; performing magnification processing on the first thin-section microscope image according to the bicubic interpolation algorithm to obtain a second thin-section microscope image; performing an intercepting operation on an image part including microstructures in the second thin-section microscope image to obtain a third thin-section microscope image; determining the median of the gray values of all pixel points in the third thin-section microscope image, and performing binarization processing on the third thin-section microscope image according to the median to obtain a fourth thin-section microscope image; determining the physical properties of the microstructures in the target thin-section microscope image according to all pixel points with a gray value of 1 in the fourth thin-section microscope image. Through the above solution, the present application provides a method capable of performing physical property analysis on microstructures in a rock based on a thin-section microscope image of a rock. Among them, the bicubic interpolation algorithm is combined to magnify the image, that is, during the image magnification process, the bicubic interpolation algorithm is used to enhance the image, thereby avoiding the problem of image distortion caused by direct magnification, maintaining the clarity of the image, being conducive to accurately identifying the microstructures in the thin-section of the rock and accurately intercepting the microstructural part in the image, and improving the accuracy of microstructural physical property calculation. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is an application environment diagram of the method for calculating microstructural physical properties based on a thin-section microscope image of a rock in an embodiment of the present application;

[0020] Figure 2Schematic flow chart of a microstructural physical property calculation method based on a rock thin section microscope image provided by an embodiment of the present application;

[0021] Figure 3 Schematic flow chart of another microstructural physical property calculation method based on a rock thin section microscope image provided by an embodiment of the present application;

[0022] Figure 4 Original grayscale image of a sample containing a cataclasite zone provided by an embodiment of the present application;

[0023] Figure 5 Schematic diagram of the principle of the bilinear interpolation algorithm provided by an embodiment of the present application;

[0024] Figure 6 Enhanced effect schematic diagram of Figure 4 by the bilinear interpolation algorithm provided by an embodiment of the present application;

[0025] Figure 7 Result schematic diagram of Figure 6 after binaryzation processing provided by an embodiment of the present application;

[0026] Figure 8 Schematic diagram of the structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0027] The present application relates to the identification of microstructures and the calculation of physical properties in rock thin section microscope images (referred to as rock thin section images). Specifically, it relates to a method for accurately locating microstructures such as fractures and deformation zones in rock thin section microscope images based on an image enhancement algorithm and capable of calculating their porosity and permeability, which solves the defect in current mainstream digital core technologies that local microstructures in deformed rocks cannot be identified and considered in physical property calculations, and helps to accurately predict the distribution of underground mineral deposits and the stability of rock masses.

[0028] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0029] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0030] The microstructural physical property calculation method based on a rock thin section microscope image provided by the embodiments of the present application can be applied to, for example, Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, placed on the cloud or other servers. The terminal 102 can send the target rock thin section microscope image to the server 104. After receiving the target rock thin section microscope image, the server 104 performs grayscale processing on the target rock thin section microscope image to obtain the first rock thin section microscope image; according to the bicubic interpolation algorithm, the first rock thin section microscope image is magnified to obtain the second rock thin section microscope image; the image part containing microstructures in the second rock thin section microscope image is intercepted to obtain the third rock thin section microscope image; the median of the grayscale values of all pixel points in the third rock thin section microscope image is determined, and the third rock thin section microscope image is binarized according to the median to obtain the fourth rock thin section microscope image; according to all the pixel points with a grayscale value of 1 in the fourth rock thin section microscope image, the physical properties of the microstructures in the target rock thin section microscope image are determined. The server 104 can feedback the physical properties of the microstructures in the obtained target rock thin section microscope image to the terminal 102. In addition, in some embodiments, the method for calculating the physical properties of microstructures based on rock thin section microscope images can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly process the target rock thin section microscope image, or the server 104 can obtain the target rock thin section microscope image from the data storage system and process it.

[0031] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0032] In an exemplary embodiment, refer to Figure 2 and Figure 3 , a method for calculating the physical properties of microstructures based on rock thin section microscope images is provided. This method is executed by a computer device, and can be specifically executed separately by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of the present application, taking this method applied to Figure 1 the server 104 in

[0033] Step 201: Perform grayscale processing on the target rock thin-section microscope image to obtain the first rock thin-section microscope image. That is, the first rock thin-section microscope image is a grayscale image. Refer to Figure 4 .

[0034] An optical microscope can be used to collect the electron image of the rock cast thin section.

[0035] Step 202: Enlarge the first rock thin-section microscope image according to the bicubic interpolation algorithm to obtain the second rock thin-section microscope image.

[0036] Based on the "bicubic interpolation" algorithm, the grayscale image of the micro-structure-containing area is enhanced, which can ensure that the enlarged image still maintains clarity. Subsequently, binarization processing is carried out to calculate porosity and permeability.

[0037] Step 203: Intercept the image part containing micro-structures in the second rock thin-section microscope image to obtain the third rock thin-section microscope image. This step intercepts the area containing micro-structures.

[0038] In some instances, the interception operation can be completed manually.

[0039] Step 204: Determine the median of the grayscale values of all pixel points in the third rock thin-section microscope image, and perform binarization processing on the third rock thin-section microscope image according to the median to obtain the fourth rock thin-section microscope image; the grayscale value (abbreviation: pixel value) of each pixel point in the fourth rock thin-section microscope image is 1 or 0.

[0040] Step 205: Determine the physical properties of the micro-structures in the target rock thin-section microscope image according to all pixel points with a grayscale value of 1 in the fourth rock thin-section microscope image.

[0041] In this embodiment, the grayscale image (the first rock thin-section microscope image) is enhanced based on the bicubic interpolation algorithm. In image processing, when the computer operation speed is fast enough, the bicubic interpolation algorithm is more favored than the bilinear interpolation algorithm or the nearest neighbor interpolation algorithm. During the image resampling process, compared with the bilinear interpolation algorithm that only considers 4 pixels (2×2), the bicubic interpolation algorithm considers 16 pixels (4×4). The image using bicubic interpolation resampling may have different interpolation artifacts, and the algorithm parameters need to be adjusted to achieve the best processing effect.

[0042] The bicubic interpolation algorithm is further explained below.

[0043] Refer to Figure 5 , assuming that at the four corner points (0,0), (1,0), (0,1), and (1,1) of the unit square, the function values are knownf and its derivatives f x 、 f y and f xy , then the pixel value of any point after interpolation can be expressed as:

[0044] (1);

[0045] in, Indicates a point The pixel value of i "and" j " is the power index, which means x of i Power, y of j Power.

[0046] The interpolation problem involves determining 16 coefficients a ij .Will p ( x , y ) and function value f After matching, we can get the following four formulas:

[0047] (2);

[0048] (3);

[0049] (4);

[0050] (5).

[0051] Similarly, x and y The derivative in the direction can be expressed by the following 8 formulas:

[0052] (6);

[0053] (7);

[0054] (8);

[0055] (9);

[0056] (10);

[0057] (11);

[0058] (12);

[0059] (13).

[0060] x and y The mixed partial derivatives in the

[0061] (14);

[0062] (15);

[0063] (16);

[0064] (17).

[0065] Thus, based on the formulas (2) to (17), the function values and derivative values of the four corner points, the algorithm coefficients a ij ( i = 0 to 3, j = 0 to 3) can be solved.

[0066] Refer to Figure 6 , Figure 6 which is the schematic diagram of the enhanced effect of Figure 4 through the bilinear interpolation algorithm.

[0067] As an optional implementation manner, in step 204, according to the median, the third thin-section microscope image of the rock is binarized to obtain a fourth thin-section microscope image of the rock, which specifically includes:

[0068] Step 204.02, determining the median as the gray threshold.

[0069] Step 204.03, according to the gray threshold, binarize all pixel points in the third thin-section microscope image of the rock to obtain a binarized thin-section microscope image of the rock.

[0070] Step 204.04, determine whether the separation degree of the microstructures in the binarized thin-section microscope image of the rock meets the requirements; if so, determine the binarized thin-section microscope image of the rock as the fourth thin-section microscope image of the rock.

[0071] Step 204.05, if not, select a value within the numerical range centered on the median, and determine the value as the gray threshold, and return to step 204.03.

[0072] In some embodiments, the size of the numerical range described in step 204.05 can be determined according to actual needs.

[0073] As an optional implementation, step 204.03 specifically includes:

[0074] According to the grayscale threshold, a first operation is performed on each pixel point in the third rock thin section microscope image. The first operation specifically includes: determining whether the grayscale value of the pixel point is less than the grayscale threshold point; if so, setting the grayscale value of the pixel point to 1; otherwise, setting the grayscale value of the pixel point to 0; when all pixels in the third rock thin section microscope image complete the first operation, a binarized rock thin section microscope image is obtained.

[0075] In this embodiment, microstructure refers to pores. The purpose of binarization is to facilitate the counting of the number of pixels where pores are located in the image. By continuously fine-tuning the grayscale threshold, the pixels corresponding to the rock skeleton particles and pores are completely separated or separated to a certain degree. Figure 7 , Figure 7 For Figure 6 Schematic diagram of the results after binarization processing.

[0076] As an optional implementation, the physical property includes face ratio; step 205 specifically includes:

[0077] Step 205.01: Count the number of all pixels with a grayscale value of 1 in the fourth rock thin section microscope image to obtain a first number.

[0078] Step 205.02: Determine the face ratio of the microstructure in the target rock thin section microscope image as the ratio of the first number to the number of all pixels in the fourth rock thin section microscope image.

[0079] That is, step 205 traverses all the pixels in the image (the fourth rock thin section microscope image) to find the points with a pixel value equal to "1", accumulates them and divides them by the total number of image pixels. The result is the face ratio of the microstructure, see formula (18):

[0080] (18);

[0081] in, is the face rate, is the pixel point corresponding to the pore ( x , y ), is the total number of pixels in the image (the fourth rock thin section microscope image).

[0082] As an alternative embodiment, the physical properties further include permeability; step 205 specifically further includes:

[0083] Step 205.03: Determine the permeability of the microstructures in the target rock thin-section microscope image according to the porosity and permeability calculation formula of the microstructures in the target rock thin-section microscope image. The permeability calculation formula is:

[0084] (19);

[0085] Wherein, represents permeability; represents porosity; represents the pore geometry factor (if it is circular in cross-section, this value is 2); represents the specific surface area of the sample, which can be obtained by the nitrogen adsorption method; represents the formation factor.

[0086] Wherein, the formation factor The calculation formula is:

[0087] (20);

[0088] Wherein, represents the cementation factor, which ranges from 1.5 to 2 from weakly consolidated sandstone to consolidated sandstone.

[0089] In this embodiment, the calculation of permeability is realized by using the improved Kozeny-Carman equation and Archie's formula.

[0090] In this embodiment, the bicubic interpolation algorithm is applied to magnify the image to ensure the fidelity of the image at the microstructural development site, so as to observe more details. Through gray conversion and binarization processing, the porosity and permeability of the sample are calculated in combination with the improved Kozeny-Carman equation. The algorithms involved in the above image processing can be written in the Python language.

[0091] As an alternative embodiment, the target rock thin-section microscope image is an image of a target rock thin section collected by an optical microscope.

[0092] The present application also provides an application scenario, which applies the above-mentioned method for calculating microstructural physical properties based on rock thin-section microscope images. Specifically: The method for calculating microstructural physical properties based on rock thin-section microscope images provided in this embodiment can be applied to the scenario of rock microstructural physical property analysis. The scenario of rock microstructural physical property analysis includes a link for collecting rock thin-section microscope images and a link for analyzing rock microstructural physical properties; the target rock thin-section microscope image enters the link for analyzing rock microstructural physical properties from the link for collecting rock thin-section microscope images. The method for calculating microstructural physical properties based on rock thin-section microscope images provided in this embodiment belongs to the link for analyzing rock microstructural physical properties. Specifically, in the link for analyzing rock microstructural physical properties, the target rock thin-section microscope image is subjected to grayscale processing to obtain a first rock thin-section microscope image; according to the bicubic interpolation algorithm, the first rock thin-section microscope image is magnified to obtain a second rock thin-section microscope image; the image part containing microstructures in the second rock thin-section microscope image is intercepted to obtain a third rock thin-section microscope image; the median of the grayscale values of all pixel points in the third rock thin-section microscope image is determined, and the third rock thin-section microscope image is binarized according to the median to obtain a fourth rock thin-section microscope image; according to all the pixel points with a grayscale value of 1 in the fourth rock thin-section microscope image, the physical properties of the microstructures in the target rock thin-section microscope image are determined.

[0093] The present application has the following beneficial effects:

[0094] A method for identifying microstructures and calculating physical properties of rock thin-section images based on image processing algorithms is proposed, which can improve the resolution of the microstructural development area in deformed rock thin-section images and calculate its porosity and permeability, and has very good effects. Compared with the image processing method for undeformed rock thin-sections, the present application can effectively enhance the image clarity of the area containing microstructures by combining traditional grayscale processing and binarization techniques with the bicubic interpolation algorithm, greatly improving the calculation accuracy of the deformed rock porosity and permeability, and has guiding significance for evaluating the underground fluid distribution and rock mass stability in areas with strong tectonic deformation.

[0095] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 8As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to the target rock thin-section microscope image. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for calculating the microstructure physical properties based on the rock thin-section microscope image.

[0096] Those skilled in the art can understand that Figure 8 The structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0097] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0098] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0099] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0100] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0101] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0102] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity in description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0103] In this article, specific examples are used to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for calculating microstructural physical properties based on thin-section microscope images of rocks, characterized in that, The microstructural physical property calculation method based on the thin-section microscope image of rock includes: Performing gray-scale processing on the target thin-section microscope image of rock to obtain the first thin-section microscope image of rock; Performing magnification processing on the first thin-section microscope image of rock according to the bicubic interpolation algorithm to obtain the second thin-section microscope image of rock; Performing an intercepting operation on the image part containing microstructures in the second thin-section microscope image of rock to obtain the third thin-section microscope image of rock; Determining the median of the gray-scale values of all pixel points in the third thin-section microscope image of rock, and performing binarization processing on the third thin-section microscope image of rock according to the median to obtain the fourth thin-section microscope image of rock; the gray-scale value of each pixel point in the fourth thin-section microscope image of rock is 1 or 0; Determining the physical properties of the microstructures in the target thin-section microscope image of rock according to all pixel points with gray-scale value of 1 in the fourth thin-section microscope image of rock; The physical properties include porosity; determining the physical properties of the microstructures in the target thin-section microscope image of rock according to all pixel points with gray-scale value of 1 in the fourth thin-section microscope image of rock specifically includes: Counting the number of all pixel points with gray-scale value of 1 in the fourth thin-section microscope image of rock to obtain the first number; Determining the ratio of the first number to the number of all pixel points in the fourth thin-section microscope image of rock as the porosity of the microstructures in the target thin-section microscope image of rock; The physical properties further include permeability; determining the physical properties of the microstructures in the target thin-section microscope image of rock according to all pixel points with gray-scale value of 1 in the fourth thin-section microscope image of rock specifically further includes: Determining the permeability of the microstructures in the target thin-section microscope image of rock according to the porosity and permeability calculation formula of the microstructures in the target thin-section microscope image of rock, and the permeability calculation formula is: ； Among them, represents the permeability; represents the porosity; represents the pore geometry factor; represents the specific surface area of the sample; represents the formation factor.

2. The microstructural physical property calculation method based on the thin section microscope image of rock according to claim 1, characterized in that, Performing binarization processing on the third thin-section microscope image of rock according to the median to obtain the fourth thin-section microscope image of rock, specifically including: Determining the median as the gray-scale threshold; Performing binarization processing on all pixel points in the third thin-section microscope image of rock according to the gray-scale threshold to obtain the binarized thin-section microscope image of rock; Judging whether the separation degree of the microstructures in the binarized thin-section microscope image of rock meets the requirements; if so, determining the binarized thin-section microscope image of rock as the fourth thin-section microscope image of rock; if not, selecting a value within the numerical range centered on the median, and determining the value as the gray-scale threshold, and returning to the step "Performing binarization processing on all pixel points in the third thin-section microscope image of rock according to the gray-scale threshold to obtain the binarized thin-section microscope image of rock"; 3. The microstructural physical property calculation method based on the rock thin section microscope image according to claim 2, characterized in that Performing binarization processing on all pixel points in the third thin-section microscope image of rock according to the gray-scale threshold to obtain the binarized thin-section microscope image of rock, specifically including: According to the grayscale threshold, perform a first operation on each pixel point in the third thin rock slice microscopic image. The first operation specifically includes: determining whether the grayscale value of the pixel point is less than the grayscale threshold. If so, set the grayscale value of the pixel point to 1; if not, set the grayscale value of the pixel point to 0. After all pixel points in the third thin rock slice microscopic image have completed the first operation, a binary-processed thin rock slice microscopic image is obtained.

4. The microstructural physical property calculation method based on the thin section microscope image of rock according to claim 1, characterized in that Formation factor The calculation formula is as follows: ； Among them, represents the cementing factor.

5. The microstructural physical property calculation method based on the thin section microscope image of rock according to claim 1, wherein The target thin rock slice microscopic image is an image of a target thin rock slice collected by an optical microscope.

6. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method for calculating microstructural physical properties based on a thin rock slice microscopic image according to any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for calculating microstructural physical properties based on a thin rock slice microscopic image according to any one of claims 1-5.

8. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for calculating microstructural physical properties based on a thin rock slice microscopic image according to any one of claims 1-5.

Images