A method, device and electronic equipment for simulating core pore network distribution

Abstract

The embodiments of this specification provide a method, device, and electronic device for simulating the distribution of a core pore network. The method comprises: obtaining three-dimensional matrix data of a core sample; the three-dimensional matrix data comprises a plurality of skeleton pixels and a plurality of pore pixels; selecting any point in the pore pixel as the center of a sphere, searching for an inscribed sphere tangent to the boundary of the skeleton pixel closest to the pore pixel, and obtaining the inscribed spheres corresponding to the plurality of pore pixels; filtering out redundant spheres in the inscribed spheres corresponding to the plurality of pore pixels to obtain a plurality of maximum spheres; wherein the redundant spheres are inscribed spheres contained within other inscribed spheres in the inscribed spheres; determining the minimum sphere between the maximum spheres that have an intersecting relationship among the plurality of maximum spheres; using the maximum sphere as a pore and the minimum sphere as a pore throat to simulate the pore network of the core sample, thereby improving the accuracy of obtaining the distribution of the rock pore network.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of oil production technology, and in particular to a method, device, and electronic equipment for simulating core pore network distribution. Background Art

[0002] Calculating physical parameters such as reservoir physical properties and liquid fluidity in underground porous media is an extremely important task in reservoir physics engineering, hydrophysics, and even environmental engineering. Through experiments, some macroscopic parameters such as capillary pressure and relative permeability can be obtained. These two parameters are crucial in the process of CO2 flooding to enhance oil recovery. However, these experimental techniques often make it difficult to restore the entire displacement process. For example, experiments on three-phase fluids in porous media are extremely difficult, especially in low-saturation areas.

[0003] Currently, the common practice in the petroleum industry is to study the pore structure of rocks to determine the relative permeability of two-phase liquids in porous media. Pores are a series of empty geometric volumes enclosed by curved surfaces. Extracting these empty geometric volumes from porous media and establishing pore structure models is the basis for analyzing pore structure characteristics. Currently, research on rock pore structure mostly relies on experimental methods and image scanning techniques, such as slice scanning.

[0004] Slice scanning involves cutting a series of thin slices along a specific direction into a sample, numbering them sequentially, and then sequentially scanning and imaging the slices using a magnification system such as a scanning electron microscope (SEM). This method essentially reshapes the two-dimensional pore structure obtained from the slices into a specific order to obtain a three-dimensional pore structure model.

[0005] This method requires extreme caution during sample preparation, avoiding disturbance of the sample as much as possible, paying attention to the integrity of the slice surface, and the distance between adjacent slices should not be too large to avoid spatial discontinuity. Therefore, this test method has a high time cost and high requirements for sample preparation operations, which affects the accuracy of the obtained rock pore network distribution. Summary of the Invention

[0006] The purpose of the embodiments of this specification is to provide a method, device and electronic equipment for simulating the distribution of core pore network, so as to improve the accuracy of obtaining the distribution of rock pore network.

[0007] To solve the above problems, an embodiment of the present specification provides a method for simulating the distribution of a core pore network, the method comprising: obtaining three-dimensional matrix data of a core sample; the three-dimensional matrix data comprising a plurality of skeleton pixels and a plurality of pore pixels; selecting any point in the pore pixel as the center of a sphere, searching for an inscribed sphere tangent to the boundary of the skeleton pixel closest to the pore pixel, and obtaining a plurality of inscribed spheres corresponding to the pore pixels; filtering out redundant spheres in the inscribed spheres corresponding to the plurality of pore pixels, and obtaining a plurality of maximum spheres; wherein the redundant spheres are inscribed spheres contained within other inscribed spheres in the inscribed spheres; determining a minimum sphere between the maximum spheres having an intersecting relationship among the plurality of maximum spheres; using the maximum sphere as a pore and the minimum sphere as a pore throat to simulate the pore network of the core sample.

[0008] To solve the above problems, an embodiment of this specification also provides a simulation device for the distribution of core pore networks, which includes: an acquisition module for acquiring three-dimensional matrix data of a core sample; the three-dimensional matrix data includes multiple skeleton pixels and multiple pore pixels; an inscribed sphere search module for selecting any point in the pore pixel as the center of a sphere, searching for an inscribed sphere that is tangent to the boundary of the skeleton pixel closest to the pore pixel, and obtaining multiple inscribed spheres corresponding to the pore pixels; a filtering module for filtering out redundant spheres in the inscribed spheres corresponding to the multiple pore pixels, and obtaining multiple maximum spheres; wherein the redundant spheres are inscribed spheres that are contained inside other inscribed spheres in the inscribed spheres; a determination module for determining the minimum sphere between the maximum spheres with an intersecting relationship among the multiple maximum spheres; and a simulation module for taking the maximum sphere as a pore and the minimum sphere as a pore throat to simulate the pore network of the core sample.

[0009] To solve the above problems, an embodiment of this specification also provides an electronic device, including: a memory for storing a computer program; a processor for executing the computer program to achieve: obtaining three-dimensional matrix data of a core sample; the three-dimensional matrix data includes multiple skeleton pixels and multiple pore pixels; selecting any point in the pore pixel as the center of a sphere, finding an inscribed sphere tangent to the skeleton pixel boundary closest to the pore pixel, and obtaining multiple inscribed spheres corresponding to the pore pixels; filtering out redundant spheres in the inscribed spheres corresponding to the multiple pore pixels to obtain multiple maximum spheres; wherein the redundant spheres are inscribed spheres in the inscribed spheres that are contained inside other inscribed spheres; determining the minimum sphere between the maximum spheres with an intersecting relationship among the multiple maximum spheres; using the maximum sphere as a pore and the minimum sphere as a pore throat to simulate the pore network of the core sample.

[0010] It can be seen from the technical solutions provided in the above embodiments of this specification that in the embodiments of this specification, three-dimensional matrix data of a core sample can be obtained; the three-dimensional matrix data includes multiple skeleton pixels and multiple pore pixels; any point in the pore pixel is selected as the center of a sphere, and an inscribed sphere tangent to the boundary of the skeleton pixel closest to the pore pixel is found to obtain multiple inscribed spheres corresponding to the pore pixels; redundant spheres in the inscribed spheres corresponding to the multiple pore pixels are filtered out to obtain multiple maximum spheres; wherein the redundant spheres are inscribed spheres contained in other inscribed spheres among the inscribed spheres; the minimum sphere between the maximum spheres with an intersecting relationship among the multiple maximum spheres is determined; the maximum sphere is used as a pore and the minimum sphere is used as a pore throat to simulate the pore network of the core sample, thereby improving the accuracy of obtaining the rock pore network distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 This is a flow chart of a method for simulating core pore network distribution according to an embodiment of this specification;

[0013] Figure 2 This is a functional structure diagram of an electronic device according to an embodiment of this specification;

[0014] Figure 3 This is a functional structural diagram of a simulation device for core pore network distribution according to an embodiment of this specification. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this specification.

[0016] Reservoir microstructural properties determine their macro-engineering properties. Understanding the internal structural characteristics of reservoirs at a microscopic level is crucial for mechanistically understanding and analyzing their macro-engineering properties and behavioral characteristics. Rocks and soils are naturally porous, loose materials containing numerous irregular pores. The size and distribution of these pores directly influence the permeability, water retention, deformation, and strength of rocks and soils. Therefore, studying pore properties can provide a deeper understanding of their engineering properties and their changing patterns at a microscopic level, ultimately contributing to optimized engineering design and operational safety.

[0017] Pores are a series of empty geometric bodies surrounded by curved surfaces. Extracting these empty geometric bodies from porous media and establishing a pore structure model is the basis for analyzing pore structure characteristics. Currently, research on rock pore structure mostly uses experimental methods and image scanning technologies such as slice scanning and CT scanning.

[0018] Slice scanning involves cutting a series of thin slices along a specific direction into the sample, numbering them sequentially, and then sequentially scanning and imaging the slices using a magnification system such as a scanning electron microscope (SEM). This method essentially reshapes the two-dimensional pore structure obtained from the slices in a specific order to obtain a three-dimensional pore structure model. This method requires extreme caution during sample preparation, minimizing sample disturbance. Attention must be paid to ensuring the integrity of the slice surface and that the spacing between adjacent slices is not too large to avoid spatial discontinuities. Therefore, this test method is time-consuming and requires high sample preparation procedures, which can affect the accuracy of the obtained rock pore network distribution.

[0019] CT scanning is a non-destructive imaging technology that can obtain images of the microscopic pore structure of soil. By transferring the images to a computer and setting a certain threshold through image processing software to convert the images into binary images, information such as the number, size, shape, and position of pores can be obtained. This method does not destroy the pore structure and skeleton of the sample and can easily obtain micron-level non-destructive pore structure. However, image scanning is greatly affected by the quality of sample preparation and test equipment. The scanned images are limited by image processing technology, which affects the accuracy of the obtained rock pore network distribution.

[0020] If it were possible to obtain three-dimensional matrix data of a core sample, abstract the pores in the core sample as spheres, and perform corresponding processing on these spheres to simulate the pore network distribution of the core sample, it would be possible to solve the low accuracy of rock pore network distribution obtained in existing technologies and improve the accuracy of rock pore network distribution acquisition. Based on this, embodiments of this specification provide a method, device, and electronic device for simulating core pore network distribution.

[0021] See also Figure 1. This specification embodiment provides a method for simulating the distribution of core pore networks. In the embodiment of this specification, the subject that executes the simulation method of the distribution of core pore networks may be an electronic device with a logical operation function, and the electronic device may be a server. The server may be an electronic device with a certain computing and processing capability. It may have a network communication unit, a processor, a memory, etc. Of course, the server is not limited to the above-mentioned electronic device with a certain entity, and it may also be software running in the above-mentioned electronic device. The server may also be a distributed server, which may be a system with multiple processors, memories, network communication modules, etc. operating in collaboration. Alternatively, the server may also be a server cluster formed by several servers. The method may include the following steps.

[0022] S110: Acquire three-dimensional matrix data of a core sample; the three-dimensional matrix data includes a plurality of skeleton pixels and a plurality of pore pixels.

[0023] In the field of image processing, a color image is represented by a three-dimensional matrix. If you're using MATLAB, you can use imread to read an image, and the matrix will be visible in the workspace. However, a three-dimensional matrix cannot be displayed directly. A three-dimensional array has three faces, corresponding to the colors red, green, and blue, respectively. The data within each face represents the intensity of each color, and the elements within each face correspond to the pixels in the image. Let the resulting matrix be X, a three-dimensional matrix (256, 256, 3), where X(:,:,1) represents a two-dimensional matrix for red, X(:,:,2) represents a two-dimensional matrix for green, and X(:,:,3) represents a two-dimensional matrix for blue.

[0024] In some embodiments, an image can be composed of multiple pixels. For example, an image displayed on a display is the result of powering each light-emitting element on the display screen that can display a different color. Ultimately, the combination of these light-emitting elements on the screen reproduces the image. When the image displayed on the display screen is at its native resolution, each element on the display screen corresponding to each pixel in the image.

[0025] If the image is color, the color displayed by each light-emitting element after power is applied depends on the RGB value of the corresponding pixel in the image. The RGB color model is an industry standard that creates a variety of colors by varying and superimposing the three color channels: red (R), green (G), and blue (B). RGB stands for the colors of the three channels, also known as the three primary colors. This color model encompasses nearly all colors perceptible by human vision and is one of the most widely used color systems. In a commonly used RGB standard, the amount of each color, R, G, and B, is represented by a decimal number between 0 and 255 (corresponding to the binary numbers 00000000 to 11111111). In another RGB standard commonly used on web pages, the RGB value of a pixel is represented by a 6-digit hexadecimal number, such as #000000. Those skilled in the art will readily appreciate that the amount of each RGB color in a pixel is represented by a decimal number between 0 and 255, which can be converted to a 6-digit hexadecimal number, meaning there is a one-to-one correspondence between the different representations. Generally speaking, the final displayed color of a pixel is obtained by mixing corresponding amounts of red (R), green (G), and blue (B) in these standards.

[0026] In some embodiments, the three-dimensional matrix data may include skeleton pixels corresponding to the rock skeleton in the core sample, and pore pixels corresponding to the pores in the core sample.

[0027] In some embodiments, obtaining the three-dimensional matrix data of the core sample may include: scanning the core sample to obtain a grayscale image of the core sample; and obtaining the three-dimensional matrix data of the core sample based on the grayscale image. Specifically, the core sample may be CT scanned to obtain a two-dimensional grayscale image of the core sample, and then obtaining the three-dimensional matrix data of the core sample based on the grayscale image.

[0028] In some embodiments, a grayscale image is an image in which each pixel has a single sampled color. This type of image is typically displayed as a grayscale scale ranging from darkest black to brightest white, although theoretically, these samples can be any shade of color, even varying in brightness. Unlike black-and-white images, which in computer graphics only have two colors, black and white, grayscale images have many levels of color depth between black and white. Specifically, for a grayscale image, the grayscale color displayed by each light-emitting element on a display after power is applied depends on the RGB values ​​of the corresponding pixel in the image. In this case, the RGB values ​​of these grayscale pixels exhibit a certain pattern. In a commonly used standard, the R, G, and B values ​​of the grayscale are equal. Using this standard, grayscale can typically be divided into 256 levels, representing the color depth of a point in a black-and-white image. Grayscale values ​​can also be represented in other ways, such as using 1-byte data. In this case, the value of each bit in this 1-byte data has a certain correspondence with the RGB value.

[0029] In some embodiments, the grayscale image can be preprocessed, and the three-dimensional matrix data of the core sample can be obtained based on the preprocessed grayscale image. Specifically, the preprocessing includes Gaussian filtering, binarization, etc. Among them, Gaussian filtering is a linear smoothing filter, which is suitable for eliminating Gaussian noise and is widely used in the noise reduction process of image processing. In layman's terms, Gaussian filtering is the process of weighted averaging of the entire image. The value of each pixel is obtained by weighted averaging of itself and other pixel values ​​in the neighborhood. The specific operation of Gaussian filtering is: use a template (or convolution, mask) to scan each pixel in the image, and replace the value of the center pixel of the template with the weighted average grayscale value of the pixels in the neighborhood determined by the template. Binarization is the process of setting the grayscale value of the pixel on the image to 0 or 255, that is, the process of making the entire image present an obvious black and white effect.

[0030] Due to external interference during image acquisition, the captured image may exhibit flaws such as dark or bright colors, lack of contrast, and image blur, which can lead to significant errors in image analysis. Therefore, it is necessary to perform image preprocessing to improve its quality without destroying the useful information contained in the image. In some embodiments, this preprocessing may also include adjusting the brightness and contrast of the grayscale image and performing sharpening. Specifically, brightness adjustment is a point-by-point processing technique that adds or subtracts a constant from the grayscale value of each pixel in the image. The appropriateness of image brightness can be determined using the image's grayscale value histogram. The grayscale histogram provides the distribution probability of the grayscale values ​​of all pixels in the image within the grayscale range [0, 255]. There are various methods for adjusting contrast, such as linear transformation, nonlinear transformation, and histogram flattening. The purpose of image sharpening is to emphasize image edge information and enhance edge features for easier recognition by the human eye and machines. Image sharpening can remove the "blur" that causes poor image quality and make the highlights more clearly defined.

[0031] In some embodiments, the server can acquire the three-dimensional matrix data of the core sample using any method. For example, a user can directly send the three-dimensional matrix data of the core sample to the server, which can receive it. Alternatively, an electronic device other than the server can send the three-dimensional matrix data of the core sample to the server, which can receive it. Alternatively, the server can have a scanning function that scans the core sample to obtain a grayscale image of the core sample, and then obtain the three-dimensional matrix data of the core sample based on the grayscale image. In the embodiments of this specification, the method used by the server to acquire the physical parameters and the particle size distribution is not limited.

[0032] S120: Select any point in the pore pixel as the sphere center, and search for an inscribed sphere that is tangent to the boundary of the skeleton pixel closest to the pore pixel, to obtain the inscribed spheres corresponding to the plurality of pore pixels.

[0033] In some embodiments, for each pore pixel, a point can be randomly selected within the pore pixel and used as the center of a sphere to search for an inscribed sphere tangent to the boundary of the skeleton pixel closest to the pore pixel. Specifically, the radius of the sphere corresponding to the center of the sphere can be continuously increased until the sphere surface touches the boundary of the skeleton pixel closest to the pore pixel. The sphere tangent to the boundary of the skeleton pixel closest to the pore pixel is then used as the inscribed sphere, thereby obtaining the inscribed spheres corresponding to the multiple pore pixels. At this point, the pore pixels in the three-dimensional matrix data of the core sample are filled with different inscribed spheres.

[0034] S130: Filter out redundant spheres in the inscribed spheres corresponding to the plurality of pore pixels to obtain a plurality of maximum spheres; wherein the redundant spheres are inscribed spheres in the inscribed spheres that are contained inside other inscribed spheres.

[0035] In some embodiments, the inscribed spheres of different pore pixels may intersect, be tangent to each other, be separated from each other, or be contained within each other. Intersection of the inscribed spheres means that the two inscribed spheres partially overlap; tangency of the inscribed spheres means that the outer surfaces of the two inscribed spheres have and have a single intersection; separation of the inscribed spheres means that there is no intersection between the two inscribed spheres, and the straight-line distance between the centers of the two inscribed spheres is greater than the sum of the radii of the two inscribed spheres; and containment of the inscribed spheres means that one inscribed sphere is contained within the other, and the two inscribed spheres have no intersection or only a single intersection.

[0036] In some embodiments, an inscribed sphere included in other inscribed spheres among the multiple inscribed spheres can be deleted, that is, the smaller inscribed sphere among the inscribed spheres with an inclusion relationship is deleted, and the deleted inscribed sphere is marked as the largest sphere.

[0037] S140: Determine a minimum sphere between the maximum spheres having an intersection relationship among the plurality of maximum spheres.

[0038] In some embodiments, since redundant spheres in the inscribed spheres corresponding to the plurality of pore pixels are filtered out, the relationships among the remaining maximum spheres may be intersecting, tangent, or detached. Maximum spheres with intersecting relationships may be found, and the minimum sphere among the plurality of maximum spheres with intersecting relationships may be determined.

[0039] In some embodiments, determining the minimum sphere between the maximum spheres having an intersection relationship among the multiple maximum spheres may include: connecting the centers of the two intersecting maximum spheres using a straight line, and using the connecting line between the centers of the two intersecting maximum spheres as a baseline; measuring the distance between the two intersecting maximum spheres along the baseline, and using the distance as the minimum sphere diameter.

[0040] S150: The largest sphere is used as a pore and the smallest sphere is used as a pore throat to simulate and obtain a pore network of the core sample.

[0041] In some embodiments, the largest sphere may be used as the pores of the core sample, and the smallest sphere may be used as the pore throats of the core sample to simulate and obtain the pore network of the core sample.

[0042] It can be seen from the technical solutions provided in the above embodiments of this specification that in the embodiments of this specification, three-dimensional matrix data of a core sample can be obtained; the three-dimensional matrix data includes multiple skeleton pixels and multiple pore pixels; any point in the pore pixel is selected as the center of a sphere, and an inscribed sphere tangent to the boundary of the skeleton pixel closest to the pore pixel is found to obtain multiple inscribed spheres corresponding to the pore pixels; redundant spheres in the inscribed spheres corresponding to the multiple pore pixels are filtered out to obtain multiple maximum spheres; wherein the redundant spheres are inscribed spheres contained in other inscribed spheres among the inscribed spheres; the minimum sphere between the maximum spheres with an intersecting relationship among the multiple maximum spheres is determined; the maximum sphere is used as a pore and the minimum sphere is used as a pore throat to simulate the pore network of the core sample, thereby improving the accuracy of obtaining the rock pore network distribution.

[0043] Figure 2 This is a functional structure diagram of an electronic device according to an embodiment of this specification. The electronic device may include a memory and a processor.

[0044] In some embodiments, the memory can be used to store the computer program and / or module, and the processor realizes various functions of the simulation method of core pore network distribution by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required for a function; the data storage area can store data created according to the use of the user terminal. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0045] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor can execute the computer instructions to implement the following steps: obtaining three-dimensional matrix data of the core sample; the three-dimensional matrix data includes multiple skeleton pixels and multiple pore pixels; randomly selecting a point in the pore pixel as the center of a sphere, searching for an inscribed sphere tangent to the skeleton pixel boundary closest to the pore pixel, and obtaining multiple inscribed spheres corresponding to the pore pixels; filtering out redundant spheres in the inscribed spheres corresponding to the multiple pore pixels, and obtaining multiple maximum spheres; wherein the redundant spheres are inscribed spheres contained in other inscribed spheres among the inscribed spheres; determining the minimum sphere between the maximum spheres with an intersecting relationship among the multiple maximum spheres; using the maximum sphere as a pore and the minimum sphere as a pore throat to simulate the pore network of the core sample.

[0046] In the embodiments of this specification, the functions and effects specifically achieved by the electronic device can be explained in comparison with other embodiments and will not be repeated here.

[0047] Figure 3 This is a functional structural diagram of a device for simulating core pore network distribution according to an embodiment of this specification. The device may specifically include the following structural modules.

[0048] An acquisition module 310 is configured to acquire three-dimensional matrix data of a core sample; the three-dimensional matrix data includes a plurality of skeleton pixels and a plurality of pore pixels;

[0049] An inscribed sphere search module 320 is configured to select any point in a pore pixel as the sphere center and search for an inscribed sphere that is tangent to the skeleton pixel boundary closest to the pore pixel, thereby obtaining inscribed spheres corresponding to a plurality of pore pixels;

[0050] A filtering module 330 is configured to filter out redundant spheres in the inscribed spheres corresponding to the plurality of pore pixels to obtain a plurality of maximum spheres; wherein the redundant spheres are inscribed spheres that are contained within other inscribed spheres in the inscribed spheres;

[0051] A determination module 340 is configured to determine a minimum sphere among the plurality of maximum spheres that have an intersection relationship;

[0052] The simulation module 350 is configured to simulate the pore network of the core sample by using the largest sphere as the pore and the smallest sphere as the pore throat.

[0053] In some embodiments, the acquisition module 310 may include: a scanning unit for scanning a core sample to obtain a grayscale image of the core sample; and an acquisition unit for obtaining three-dimensional matrix data of the core sample based on the grayscale image.

[0054] In some embodiments, the acquisition module 310 may further include: a preprocessing unit for preprocessing the grayscale image; correspondingly, the acquisition unit is used to obtain the three-dimensional matrix data of the core sample based on the preprocessed grayscale image.

[0055] It should be noted that the various embodiments in this specification are described in a progressive manner. References to the same or similar parts of the various embodiments are sufficient. Each embodiment focuses on the differences from the other embodiments. In particular, since the apparatus and device embodiments are generally similar to the method embodiments, their descriptions are relatively simple. For relevant details, references to the descriptions of the method embodiments are sufficient.

[0056] After reading this specification, those skilled in the art can conceive of any combination of some or all of the embodiments listed in this specification without creative work, and these combinations are also within the scope of disclosure and protection of this specification.

[0057] In the 1990s, technological improvements could be clearly distinguished as either hardware improvements (for example, improvements to circuit structures like diodes, transistors, and switches) or software improvements (improvements to process flows). However, with the advancement of technology, many process flow improvements today can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can "integrate" a digital system on a PLD through their own programming, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this type of programming is mostly performed using "logic compiler" software. This is similar to the software compiler used when developing programs. Before compilation, the original code must also be written in a specific programming language, called a hardware description language (HDL). There is not just one HDL, but many, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog2. Those skilled in the art will also understand that by simply programming the method flow in one of these hardware description languages ​​and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0058] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0059] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that this specification can be implemented by means of software plus the necessary general hardware platform. Based on this understanding, the technical solution of this specification, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of this specification.

[0060] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0061] This specification is applicable to numerous general-purpose and special-purpose computer system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments that include any of the above.

[0062] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0063] Although the present specification has been described through embodiments, those skilled in the art will appreciate that there are many modifications and variations to the present specification without departing from the spirit of the present specification. It is intended that the appended claims include these modifications and variations without departing from the spirit of the present specification.

Claims

1. A method for simulating core pore network distribution, characterized in that: The method comprises: Acquire three-dimensional matrix data of a core sample; the three-dimensional matrix data includes a plurality of skeleton pixels and a plurality of pore pixels; the pores represent a series of empty geometric bodies surrounded by curved surfaces; A point in the pore pixel is randomly selected as the sphere center, and an inscribed sphere that is tangent to the boundary of the skeleton pixel closest to the pore pixel is found to obtain a plurality of inscribed spheres corresponding to the pore pixels; the method comprising: continuously increasing the radius of the sphere corresponding to the sphere center until the sphere surface touches the boundary of the skeleton pixel closest to the pore pixel, and using a sphere that is tangent to the boundary of the skeleton pixel closest to the pore pixel as the inscribed sphere, thereby obtaining the inscribed spheres corresponding to the plurality of pore pixels; Filtering out redundant spheres from the inscribed spheres corresponding to the plurality of pore pixels to obtain a plurality of maximum spheres, including: deleting smaller inscribed spheres from the inscribed spheres that have a containment relationship; marking the deleted inscribed spheres as maximum spheres; the redundant spheres are inscribed spheres from the inscribed spheres that are contained within other inscribed spheres and two inscribed spheres that have a containment relationship have only one intersection point; the relationships between the plurality of maximum spheres may include intersection, tangency, and separation; Determining a minimum sphere between intersecting maximum spheres among the plurality of maximum spheres; the method comprising: connecting the centers of the two intersecting maximum spheres with a straight line, using the connecting line between the centers of the two intersecting maximum spheres as a reference line; measuring a distance between the two intersecting maximum spheres along the reference line, and using the distance as a minimum sphere diameter; The largest sphere is used as the pore and the smallest sphere is used as the pore throat to simulate the pore network of the core sample.

2. The method according to claim 1, characterized in that The three-dimensional matrix data of the core sample is obtained by: Scanning the core sample to obtain a grayscale image of the core sample; Three-dimensional matrix data of the core sample is obtained based on the grayscale image.

3. The method according to claim 2, characterized in that The method further comprises: Preprocessing the grayscale image; Accordingly, three-dimensional matrix data of the core sample is obtained based on the preprocessed grayscale image.

4. The method according to claim 3, characterized in that The preprocessing includes Gaussian filtering or binarization.

5. The method according to claim 1, characterized in that The step of selecting any point in the pore pixel as the sphere center and finding an inscribed sphere tangent to the boundary of the skeleton pixel closest to the pore pixel comprises: The radius of the sphere corresponding to the sphere center is continuously increased until the sphere surface touches the boundary of the skeleton pixel closest to the pore pixel, and the sphere tangent to the boundary of the skeleton pixel closest to the pore pixel is used as the inscribed sphere.

6. A device for simulating core pore network distribution, characterized in that: The device comprises: An acquisition module, configured to acquire three-dimensional matrix data of a core sample; the three-dimensional matrix data includes a plurality of skeleton pixels and a plurality of pore pixels; the pores represent a series of empty geometric bodies surrounded by curved surfaces; An inscribed sphere search module is configured to select any point in a pore pixel as a sphere center and search for an inscribed sphere that is tangent to the boundary of the skeleton pixel closest to the pore pixel, thereby obtaining a plurality of inscribed spheres corresponding to the pore pixels. The module includes: continuously increasing the radius of the sphere corresponding to the sphere center until the sphere surface touches the boundary of the skeleton pixel closest to the pore pixel, and using a sphere that is tangent to the boundary of the skeleton pixel closest to the pore pixel as the inscribed sphere, thereby obtaining the inscribed spheres corresponding to the plurality of pore pixels. A filtering module is configured to filter out redundant spheres from the inscribed spheres corresponding to the plurality of pore pixels to obtain a plurality of maximum spheres. The filtering module includes: deleting smaller inscribed spheres from the inscribed spheres that have a containment relationship; marking the deleted inscribed spheres as maximum spheres. The redundant spheres are inscribed spheres that are contained within other inscribed spheres, and two inscribed spheres that have a containment relationship have only one intersection point. The relationships between the plurality of maximum spheres may include intersection, tangency, and separation. a determination module configured to determine a minimum sphere between the intersecting maximum spheres among the plurality of maximum spheres, comprising: connecting the centers of the two intersecting maximum spheres using a straight line, using the connecting line between the centers of the two intersecting maximum spheres as a reference line; and measuring a distance between the two intersecting maximum spheres along the reference line, using the distance as a minimum sphere diameter; A simulation module is used to simulate the pore network of the core sample by taking the largest sphere as the pore and the smallest sphere as the pore throat.

7. The device according to claim 6, characterized in that The acquisition module includes: A scanning unit, configured to scan the core sample to obtain a grayscale image of the core sample; An acquisition unit is used to obtain three-dimensional matrix data of the core sample based on the grayscale image.

8. The device according to claim 7, characterized in that The acquisition module also includes: A preprocessing unit, configured to preprocess the grayscale image; Correspondingly, the acquisition unit is used to obtain the three-dimensional matrix data of the core sample based on the preprocessed grayscale image.

9. An electronic device, characterized in that: include: memory for storing computer programs; A processor is configured to execute the computer program to: obtain three-dimensional matrix data of a core sample; The three-dimensional matrix data includes multiple skeleton pixels and multiple pore pixels; the pores represent a series of empty geometric bodies surrounded by curved surfaces; any one point in the pore pixels is selected as the center of a sphere, and an inscribed sphere tangent to the boundary of the skeleton pixel closest to the pore pixel is found to obtain multiple inscribed spheres corresponding to the pore pixels; the method includes: continuously increasing the radius of the sphere corresponding to the center of the sphere until the surface of the sphere touches the boundary of the skeleton pixel closest to the pore pixel, and taking the sphere tangent to the boundary of the skeleton pixel closest to the pore pixel as the inscribed sphere, thereby obtaining the multiple inscribed spheres corresponding to the pore pixels; filtering out redundant spheres in the inscribed spheres corresponding to the multiple pore pixels to obtain multiple maximum spheres; the method includes: deleting the smaller spheres in the inscribed spheres with a containment relationship; small inscribed sphere; marking the deleted inscribed sphere as the maximum sphere; the redundant sphere is an inscribed sphere contained in other inscribed spheres among the inscribed spheres and the two inscribed spheres with a containment relationship have only one intersection point; the relationship between the multiple maximum spheres may include intersection, tangency and separation; determining the minimum sphere between the maximum spheres with an intersection relationship among the multiple maximum spheres; including: connecting the centers of the two intersecting maximum spheres with a straight line, and taking the connecting line between the centers of the two intersecting maximum spheres as a baseline; measuring the distance between the two intersecting maximum spheres along the baseline, and taking the distance as the minimum sphere diameter; taking the maximum sphere as a pore and the minimum sphere as a pore throat, and simulating the pore network of the core sample.

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