Pore throat identification method and device, electronic equipment and storage medium

Abstract

The invention discloses a pore throat identification method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a scanning electron microscope image, and performing pore throat extraction on the scanning electron microscope image to obtain at least one region to be identified; determining a throat type corresponding to the to-be-identified area according to the minimum radius and the average radius of the throat of the to-be-identified area; determining a pore shape factor of the to-be-identified area, and determining a pore type corresponding to the to-be-identified area according to the pore shape factor of the to-be-identified area; and if it is determined that the pore type of the target to-be-identified area is a strip type, determining that the target to-be-identified area is a crack, and determining the crack type of the target to-be-identified area according to the central axis angle of the target to-be-identified area. By means of the technical scheme, qualitative and quantitative evaluation of the pore throat structure of the tight sandstone can be achieved, and data support is provided for tight sandstone reservoir evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a pore throat identification method, device, electronic equipment and storage medium. Background Art

[0002] Tight sandstone reservoirs are an important part of my country's oil and gas resources. Tight sandstone reservoirs mostly develop micron-scale pores and nanometer-scale throats, with large pore throat radius differences, irregular shapes, complex structures, and strong microscopic heterogeneity, which greatly affect the seepage capacity of the reservoirs. Quantitative description of the microscopic pore throat structure characteristics can provide a basis for the differentiated characterization of tight sandstone reservoir quality, and can also lay the foundation for the classification of tight sandstone reservoirs and the study of development strategies, which is of great significance.

[0003] Conventional experimental methods (mercury injection, nuclear magnetic resonance, displacement, etc.) have problems such as long experimental cycle and low precision in identifying pore throat structure. With the widespread application of CT (Computed Tomography) technology in the oil industry, a pore throat segmentation method based on multi-scale three-dimensional CT image processing has emerged. However, due to the complex pore structure and irregular shape of tight sandstone reservoirs, there are multiple throats connecting the pores, which easily leads to mis-segmentation and under-segmentation of pore throats. Summary of the invention

[0004] The present invention provides a pore throat identification method, device, electronic equipment and storage medium to achieve qualitative and quantitative evaluation of the pore throat structure of tight sandstone, and provide data support for tight sandstone reservoir evaluation.

[0005] In a first aspect, an embodiment of the present invention provides a pore throat identification method, the method comprising:

[0006] Determine a scanning electron microscope image, and perform pore throat extraction on the scanning electron microscope image to obtain at least one area to be identified;

[0007] Determining the throat type corresponding to the area to be identified according to the minimum radius and the average radius of the throat of the area to be identified;

[0008] Determining a pore shape factor of the area to be identified, and determining a pore type corresponding to the area to be identified according to the pore shape factor of the area to be identified;

[0009] If it is determined that the pore type of the target area to be identified is a strip type, the target area to be identified is determined to be a crack, and the crack type of the target area to be identified is determined according to the central axis angle of the target area to be identified.

[0010] In a second aspect, an embodiment of the present invention further provides a pore throat identification device, the device comprising:

[0011] A pore throat extraction module is used to determine a scanning electron microscope image and perform pore throat extraction on the scanning electron microscope image to obtain at least one area to be identified;

[0012] A throat type determination module, used to determine the throat type corresponding to the area to be identified according to the minimum radius and average radius of the throat of the area to be identified;

[0013] A pore type determination module, used to determine the pore shape factor of the area to be identified, and determine the pore type corresponding to the area to be identified according to the pore shape factor of the area to be identified;

[0014] The crack type determination module is used to determine that the target area to be identified is a crack if the pore type of the target area to be identified is a strip type, and determine the crack type of the target area to be identified according to the central axis angle of the target area to be identified.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a pore throat identification method as described in any one of the embodiments of the present invention is implemented.

[0016] In a fourth aspect, an embodiment of the present invention further provides a storage medium storing computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to execute a pore throat identification method as described in any one of the embodiments of the present invention.

[0017] The technical solution of the embodiment of the present invention extracts pore throats from scanning electron microscope images to obtain the area to be identified, determines the minimum radius and average radius of the area to be identified, determines the throat type based on the minimum radius and average radius, determines the pore shape factor of the area to be identified, determines the pore type based on the pore shape factor, determines the type of strip-type pores as fractures, and determines the fracture type based on the central axis angle of the area to be identified corresponding to the fracture. The problem of low precision and poor recognition effect of the pore throat structure recognition method in the prior art is solved, and the qualitative and quantitative evaluation of the pore throat structure of dense sandstone is realized, the precision and reliability of pore throat feature recognition are improved, and data support is provided for the evaluation of dense sandstone reservoirs.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 is a flow chart of a pore throat identification method provided in Example 1 of the present invention;

[0021] Figure 2 is a schematic diagram of pore type classification provided by the first embodiment of the present invention;

[0022] Figure 3 is a flow chart of a pore throat identification method provided in Embodiment 2 of the present invention;

[0023] Figure 4 It is a schematic diagram of a scanning electron microscope image, a binarized image, a denoised image and a candidate region provided by the second embodiment of the present invention;

[0024] Figure 5 is a structural schematic diagram of a pore throat identification device provided in Embodiment 3 of the present invention;

[0025] Figure 6 It is a structural schematic diagram of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1 A flow chart of a pore throat identification method is provided for the first embodiment of the present invention. This embodiment is applicable to the case of identifying pore throat characteristics of tight sandstone reservoirs. The method can be executed by a pore throat identification device. The pore throat identification device can be implemented in the form of hardware and / or software. The pore throat identification device can be configured in an electronic device.

[0030] like Figure 1 As shown, the method includes:

[0031] S110, determining a scanning electron microscope image, and performing pore throat extraction on the scanning electron microscope image to obtain at least one area to be identified.

[0032] Among them, the scanning electron microscope image refers to a two-dimensional SEM (Scanning Electron Microscope) image (hereinafter referred to as SEM image). Pore throat extraction can be a process of extracting connected domains from SEM and selecting areas that meet the pore throat scale characteristics in each connected domain, and taking the areas that meet the pore throat scale characteristics as the areas to be identified, so as to perform subsequent throat type classification and pore type classification.

[0033] Further, pore throat identification can be performed based on SEM images of different scales, such as 30 μm and below, 100 μm, and 500 μm, etc., but this embodiment does not limit the specific image scale of the SEM image. This embodiment studies the pore throat structure based on multi-scale and large number of SEM images, which can maximize the accuracy and reliability of pore throat characterization.

[0034] Furthermore, in this embodiment, after the area to be identified is determined, throat identification is performed on the area to be identified, and throat identification can adopt a watershed segmentation method, a central axis algorithm, and a grayscale expansion and erosion method, etc. After the throat is identified and the throat position is determined, multiple cuts are performed at the throat to obtain pores, so as to subsequently identify the throat type and pore type.

[0035] S120: Determine the throat type corresponding to the area to be identified according to the minimum radius and the average radius of the throat of the area to be identified.

[0036] Among them, taking the identification of the throat according to the central axis algorithm as an example, the minimum radius and average radius of the throat can be determined according to the cross-sectional length of each pixel point on the central axis of the throat, and this embodiment does not limit the determination method of the minimum radius and the average radius. Based on the connectivity and morphological characteristics between the pores of the tight reservoir, the throat types can include necking type, (bend) lamellar throat and pore reduction throat.

[0037] In this embodiment, the throat types are divided based on the minimum radius and the average radius of the throat, thereby achieving quantitative characterization based on the throat radius and qualitative classification of the throat.

[0038] Furthermore, S120 may include:

[0039] A1. If it is determined that the difference between the average radius and the minimum radius of the throat of the to-be-identified area is greater than or equal to a first preset difference threshold, and the number of pixel points corresponding to the throat is greater than or equal to a preset number threshold, then it is determined that the throat type corresponding to the to-be-identified area is a necking throat;

[0040] A2. If it is determined that the difference between the average radius and the minimum radius of the throat of the to-be-identified area is greater than or equal to a first preset difference threshold, and the number of consecutive side-by-side pixel points in the throat is greater than or equal to a preset consecutive value, then it is determined that the throat type corresponding to the to-be-identified area is a lamellar throat or a curved lamellar throat;

[0041] A3. If it is determined that the difference between the average radius and the minimum radius of the throat of the to-be-identified area is less than a second preset difference threshold, it is determined that the throat type corresponding to the to-be-identified area is a pore-reducing throat.

[0042] Among them, the first preset difference threshold and the second preset difference threshold can be the same or different, and the specific size of the first preset difference threshold and the second preset difference threshold can be determined according to the image scale of the SEM image, and the first preset difference threshold and the second preset difference threshold corresponding to the SEM images of different image scales are different. The preset quantity threshold can be determined based on the pixel points of the laryngeal region corresponding to each area to be identified in the SEM image where the area to be identified is located. For example, the preset quantity threshold can be the average value or mode of the pixel points of the laryngeal region corresponding to each area to be identified in the SEM image. It can also be determined according to empirical values ​​according to different scales of the SEM image. This embodiment does not limit the method for determining the preset quantity threshold. The preset continuous value can be determined according to the empirical value. For example, the preset continuous value can be 3.

[0043] In this embodiment, three screenings are performed based on the minimum radius and average radius of the throat to determine the throat type. First, if the difference between the average radius and the minimum radius of the throat is greater than or equal to the first preset difference threshold, it can be considered that the average radius of the throat is much larger than the minimum radius. If the number of pixel points corresponding to the throat is greater than or equal to the preset number threshold, it can be determined that the number of pixel points corresponding to the throat is large, and the throat is determined to be a necking throat. Secondly, if the average radius of the throat is much larger than the minimum radius, and there are more than a preset continuous value of continuous side-by-side pixel points, the throat is determined to be a (curved) sheet throat. Finally, if the difference between the average radius and the minimum radius of the throat is less than the second preset difference threshold, it can be considered that although the average radius of the throat is greater than the minimum radius, it is not much greater than the minimum radius. At this time, the throat is determined to be a pore-reducing throat.

[0044] Furthermore, after determining the throat type of each area to be identified in a large number of multi-scale SEM images, a statistical analysis of the number and proportion of different throat types can be performed, and distribution statistics of the radius or diameter of different throat types can be performed.

[0045] S130, determining a pore shape factor of the area to be identified, and determining a pore type corresponding to the area to be identified according to the pore shape factor of the area to be identified.

[0046] The pore shape factor is used to describe the surface profile and geometric shape of the pores. The pore types can include quasi-circular, quasi-elliptical, and stripe.

[0047] In this embodiment, due to the irregularity of the pore cross section, the pore types are divided based on the pore shape factor, thereby achieving quantitative characterization of the pore shape characteristics and qualitative classification of the pore types.

[0048] Furthermore, S130 may include:

[0049] B1. Determine the pore throat boundary area and the pore throat boundary perimeter of the area to be identified, and determine the pore shape factor according to the pore throat boundary area and the pore throat boundary perimeter.

[0050] B2. If it is determined that the pore shape factor is less than or equal to the first shape factor threshold, determine that the pore type corresponding to the area to be identified is a strip type.

[0051] B3. If it is determined that the pore shape factor is greater than the first shape factor threshold and less than or equal to the second shape factor threshold, determine that the pore type corresponding to the area to be identified is elliptical.

[0052] B4. If it is determined that the pore shape factor is greater than the second shape factor threshold and less than or equal to the third shape factor threshold, then it is determined that the pore type corresponding to the region to be identified is circular-like.

[0053] Among them, the first shape factor threshold is less than the second shape factor threshold, and the second shape factor threshold is less than the third shape factor threshold.

[0054] Specifically, the pore shape factor can be calculated by the following formula: Among them, A represents the pore throat boundary area, and L represents the pore throat boundary perimeter.

[0055] Each shape factor threshold can be determined based on empirical values, or can be obtained by statistical analysis of the pore shape factors and pore types of a large number of typical pores. The specific setting method of the shape factor threshold in this embodiment is not limited. Exemplarily, the first shape factor threshold can be 0.3, the second shape factor can be 0.6, and the third shape factor can be 1. That is, when the pore shape factor Q ≤ 0.3, it is determined that the pore type of this pore is strip-shaped; when the pore shape factor 0.3 < Q ≤ 0.6, it is determined that the pore type of this pore is elliptical-like; when the pore shape factor Q > 0.6, it is determined that the pore type of this pore is circular-like.

[0056] Exemplarily, Figure 2 A schematic diagram of pore type division is provided, as Figure 2 shown. Although the shapes of each pore are irregular, the shape of the circular-like pore is closer to a circle, the shape of the elliptical-like pore is closer to an ellipse, and the strip-shaped pore is long and narrow.

[0057] Further, after determining the pore types of each region to be identified in a large number of SEM images with multiple scales, statistical analysis of the quantity and proportion of different pore types can be carried out, and statistical distributions of the radius or diameter and area of different pore types can be performed.

[0058] S140. If it is determined that the pore type of the target region to be identified is strip-shaped, then it is determined that the target region to be identified is a crack, and according to the central axis angle of the target region to be identified, the crack type of the target region to be identified is determined.

[0059] Among them, for strip-shaped pores, it is determined that they are cracks, and further classification of crack types is carried out. For example, it can be determined that pores with a pore shape factor Q ≤ 0.3 are cracks. The central axis angle can be the included angle between the tangential direction of the central axis and the horizontal direction. The crack types can include bedding plane fractures, low-angle fractures, and high-angle fractures.

[0060] Further, S140 can further include:

[0061] C1. If it is determined that the central axis angle is less than a first angle threshold, it is determined that the crack type of the target area to be identified is a layer-parallel crack.

[0062] C2. If it is determined that the central axis angle is greater than or equal to the first angle threshold and less than the second angle threshold, it is determined that the crack type of the target area to be identified is a low-angle crack.

[0063] C3. If it is determined that the central axis angle is greater than or equal to the second angle threshold, the crack type of the target area to be identified is determined to be a high-angle crack.

[0064] Wherein, the first angle threshold is smaller than the second angle threshold. Exemplarily, the first angle threshold may be 5°, and the second angle threshold may be 30°.

[0065] Furthermore, after determining the crack type, the crack length, crack width, crack curvature, number of crack branches, crack branch length, crack branch angle, crack shape factor, crack equidistant point distribution, number of mesh crack nodes, number of bifurcations, path length and other parameters can be calculated and statistically analyzed to achieve quantitative characterization of the cracks.

[0066] The technical solution of the embodiment of the present invention extracts pore throats from scanning electron microscope images to obtain the area to be identified, determines the minimum radius and average radius of the area to be identified, determines the throat type based on the minimum radius and average radius, determines the pore shape factor of the area to be identified, determines the pore type based on the pore shape factor, determines the type of strip-type pores as fractures, and determines the fracture type based on the central axis angle of the area to be identified corresponding to the fracture. The problem of low precision and poor recognition effect of the pore throat structure recognition method in the prior art is solved, and the qualitative and quantitative evaluation of the pore throat structure of dense sandstone is realized, the precision and reliability of pore throat feature recognition are improved, and data support is provided for the evaluation of dense sandstone reservoirs.

[0067] Embodiment 2

[0068] Figure 3 A flow chart of a pore throat identification method provided for Example 2 of the present invention. Based on the above-mentioned embodiments, this embodiment of the present invention further specifies the process of determining the area to be identified in the scanning electron microscope image and the process of determining the central axis of the area to be identified.

[0069] like Figure 3 As shown, the method includes:

[0070] S210, binarizing the scanning electron microscope image to obtain a binarized image.

[0071] In this embodiment, the SEM is binarized to improve the accuracy of pore throat identification.

[0072] Furthermore, S210 may include:

[0073] D1. Determine an initial grayscale value threshold value according to the maximum grayscale value and the minimum grayscale value of the scanning electron microscope image.

[0074] D2. According to the initial gray value threshold, the scanning electron microscope image is segmented to obtain a target image and a background image.

[0075] D3. Re-determine the gray value threshold according to the average gray value of the target image and the average gray value of the background image.

[0076] D4. Re-performing the image segmentation operation on the scanning electron microscope image according to the re-determined gray value threshold value, until the re-determined gray value threshold value satisfies the iteration stop condition.

[0077] D5. Binarize the scanning electron microscope image according to the re-determined gray value threshold to obtain a binary image.

[0078] In this embodiment, the gray value threshold obtained based on iterative calculation is used for binarization processing, which can ensure that the optimal gray value threshold of the SEM image is obtained, thereby further improving the accuracy of pore throat identification.

[0079] Specifically, the initial gray value threshold can be calculated by the following formula: Among them, R min Represents the minimum gray value, R max Represents the maximum grayscale value. After the SEM image is segmented according to the initial grayscale value threshold, the target image and the background image are obtained, and the average grayscale values ​​of the target image and the background image are calculated respectively. Specifically, the average grayscale value can be calculated by the following formula: Among them, Ro represents the average gray value of the target image, R G represents the average gray value of the background image, R(i,j) represents the gray value of the pixel with pixel coordinates (i,j) in the SEM image, N(i,j) represents the number of pixels with pixel value R(i,j), T k Represents the gray value threshold. When image segmentation is performed based on the initial gray value threshold, T k =To. Recalculate the gray value threshold The operation of performing image segmentation according to the gray value threshold is repeatedly performed until the re-determined gray value threshold satisfies the iteration stopping condition.

[0080] Among them, the re-determined grayscale value threshold satisfies the iteration stop condition, which can be that the difference between the grayscale value thresholds determined twice before and after is less than or equal to the preset value, or that the grayscale value thresholds determined twice before and after are the same. This embodiment does not limit this.

[0081] Furthermore, the binary image can be subjected to image denoising to prevent noise points in the image from affecting pore identification and data statistics.

[0082] S220, performing pore throat extraction on the binary image to obtain at least one candidate region.

[0083] In this embodiment, the connected region in the binary image is used as the candidate region. Figure 4 is a schematic diagram of a scanning electron microscope image, a binary image, a de-noised image and a candidate region provided in this embodiment, such as Figure 4 As shown in the figure, 4(a) is a SEM image, 4(b) is a binarized image obtained by binarization according to the gray value threshold obtained by iterative calculation, 4(c) is a denoised image after denoising the binarized image, and 4(d) is each candidate region obtained by connected domain identification of the denoised image.

[0084] S230 . Determine a region to be identified in each candidate region according to a pore radius threshold.

[0085] The pore radius threshold may be determined based on empirical values ​​and the image scale of the SEM, or may be obtained by statistics based on sample images of classic pore throats, which is not limited in this embodiment.

[0086] Specifically, the radius of each candidate area is determined, and the candidate areas whose difference between the radius and the pore radius threshold is larger than the preset radius difference are eliminated, and the remaining candidate areas are used as areas to be identified to identify throats, pores and cracks.

[0087] S240: Determine the throat type corresponding to the area to be identified according to the minimum radius and the average radius of the throat of the area to be identified.

[0088] The specific manner of classifying the throat types according to the minimum radius and the average radius of the throat has been described in the above embodiments, and will not be repeated in this embodiment.

[0089] S250, determining a pore shape factor of the area to be identified, and determining a pore type corresponding to the area to be identified according to the pore shape factor of the area to be identified.

[0090] The specific manner of classifying pore types according to pore shape factors has been described in the above embodiments, and will not be repeated in this embodiment.

[0091] S260: If it is determined that the pore type of the target area to be identified is a strip type, then the target area to be identified is determined to be a crack.

[0092] S270 , determining a crack image, and performing binarization processing on the crack image to obtain a binarized image matching the crack image.

[0093] Specifically, the crack image is also subjected to binarization and denoising processing to obtain a binarized image of the crack image.

[0094] S280: Perform edge detection on the binary image that matches the crack image to obtain crack edges.

[0095] The edge detection may adopt a method such as Canny edge detection, and this embodiment does not limit the specific algorithm of edge detection.

[0096] S290. Determine the central axis of the crack according to the crack edge and the skeletonization algorithm.

[0097] Among them, the skeletonization algorithm can convert the crack edge pixel set obtained by edge detection into a skeleton representation of the crack image, in which the edge line is shrunk into a line with a width of 1 pixel, and the central axis of the crack image is obtained.

[0098] S2100, determining the angle of the central axis according to the pixel point corresponding to the maximum radius of the crack on the central axis or the inflection point of the central axis.

[0099] Specifically, the pixel point or inflection point corresponding to the maximum radius of the crack on the central axis can be determined, the tangent of the central axis can be determined at the pixel point or inflection point corresponding to the maximum radius of the crack, and the angle of the central axis can be determined based on the tangent direction and horizontal direction of the central axis.

[0100] S2110. Determine the crack type of the target area to be identified according to the central axis angle of the target area to be identified.

[0101] The specific method of classifying crack types according to the central axis angle has been described in the above embodiment, and will not be repeated in this embodiment.

[0102] The technical solution of this embodiment, by binarizing multi-scale SEM images and removing noise from pore throat boundaries, automatically segmenting pores and throats, dividing throat types based on quantitative measurement of throat radius, dividing pore types based on shape factors, and identifying microcracks, can achieve qualitative and quantitative characterization of throats, pores, cracks and pore sizes of different forms in dense sandstone.

[0103] Embodiment 3

[0104] Figure 5This is a schematic diagram of the structure of a pore throat identification device provided in Example 3 of the present invention. Figure 5 As shown, the device includes: a pore throat extraction module 310, a throat type determination module 320, a pore type determination module 330 and a fracture type determination module 340. Among them:

[0105] The pore throat extraction module 310 is used to determine a scanning electron microscope image and perform pore throat extraction on the scanning electron microscope image to obtain at least one area to be identified;

[0106] A throat type determination module 320, configured to determine the throat type corresponding to the area to be identified according to the minimum radius and the average radius of the throat of the area to be identified;

[0107] A pore type determination module 330 is used to determine a pore shape factor of the area to be identified, and determine a pore type corresponding to the area to be identified according to the pore shape factor of the area to be identified;

[0108] The crack type determination module 340 is used to determine that the target area to be identified is a crack if the pore type of the target area to be identified is a strip type, and determine the crack type of the target area to be identified according to the central axis angle of the target area to be identified.

[0109] The technical solution of the embodiment of the present invention extracts pore throats from scanning electron microscope images to obtain the area to be identified, determines the minimum radius and average radius of the area to be identified, determines the throat type based on the minimum radius and average radius, determines the pore shape factor of the area to be identified, determines the pore type based on the pore shape factor, determines the type of strip-type pores as fractures, and determines the fracture type based on the central axis angle of the area to be identified corresponding to the fracture. The problem of low precision and poor recognition effect of the pore throat structure recognition method in the prior art is solved, and the qualitative and quantitative evaluation of the pore throat structure of dense sandstone is realized, the precision and reliability of pore throat feature recognition are improved, and data support is provided for the evaluation of dense sandstone reservoirs.

[0110] Based on the above embodiment, the pore throat extraction module 310 includes:

[0111] A binarization processing unit, used for performing binarization processing on the scanning electron microscope image to obtain a binarized image;

[0112] A pore throat extraction unit, used for performing pore throat extraction on the binary image to obtain at least one candidate region;

[0113] The radius screening unit is used to determine the area to be identified in each candidate area according to the pore radius threshold.

[0114] Based on the above embodiment, the binarization processing unit is specifically used for:

[0115] Determining an initial grayscale value threshold value according to a maximum grayscale value and a minimum grayscale value of the scanning electron microscope image;

[0116] According to the initial gray value threshold, the scanning electron microscope image is segmented to obtain a target image and a background image;

[0117] Re-determine the gray value threshold according to the average gray value of the target image and the average gray value of the background image;

[0118] According to the re-determined gray value threshold, re-performing the image segmentation operation on the scanning electron microscope image until the re-determined gray value threshold satisfies the iteration stop condition;

[0119] The scanning electron microscope image is binarized according to the re-determined gray value threshold to obtain a binarized image.

[0120] Based on the above embodiment, the laryngeal type determination module 320 includes:

[0121] A necking throat judgment unit, configured to determine that the throat type corresponding to the area to be identified is a necking throat if the difference between the average radius and the minimum radius of the throat in the area to be identified is greater than or equal to a first preset difference threshold, and the number of pixel points corresponding to the throat is greater than or equal to a preset number threshold;

[0122] a curved lamellar throat judging unit, configured to determine that the throat type corresponding to the area to be identified is a lamellar throat or a curved lamellar throat if the difference between the average radius and the minimum radius of the throat in the area to be identified is greater than or equal to a first preset difference threshold, and the number of consecutive side-by-side pixel points in the throat is greater than or equal to a preset consecutive value;

[0123] The pore-reducing throat judgment unit is used to determine that the throat type corresponding to the to-be-identified area is a pore-reducing throat if the difference between the average radius and the minimum radius of the throat in the to-be-identified area is less than a second preset difference threshold.

[0124] Based on the above embodiment, the pore type determination module 330 includes:

[0125] A pore shape factor determination unit, used to determine the pore throat boundary area and the pore throat boundary perimeter of the area to be identified, and determine the pore shape factor according to the pore throat boundary area and the pore throat boundary perimeter;

[0126] a strip-type pore judging unit, configured to determine that the pore type corresponding to the to-be-identified area is a strip-type pore if it is determined that the pore shape factor is less than or equal to a first shape factor threshold;

[0127] a quasi-ellipse pore judging unit, configured to determine that the pore type corresponding to the to-be-identified area is a quasi-ellipse if it is determined that the pore shape factor is greater than the first shape factor threshold and less than or equal to the second shape factor threshold;

[0128] a quasi-circular pore judging unit, configured to determine that the pore type corresponding to the to-be-identified area is quasi-circular if it is determined that the pore shape factor is greater than the second shape factor threshold and less than or equal to a third shape factor threshold;

[0129] The first shape factor threshold is smaller than the second shape factor threshold, and the second shape factor threshold is smaller than the third shape factor threshold.

[0130] Based on the above embodiment, the device further includes:

[0131] A crack image processing module, used for determining a crack image and performing a binarization process on the crack image to obtain a binarized image matching the crack image;

[0132] A crack edge determination module, used for performing edge detection on a binary image matching the crack image to obtain a crack edge;

[0133] A central axis determination module, used to determine the central axis of the crack according to the crack edge and the skeletonization algorithm;

[0134] The central axis angle determination module is used to determine the central axis angle according to the pixel point corresponding to the maximum radius of the crack on the central axis or the inflection point of the central axis.

[0135] Based on the above embodiment, the crack type determination module 340 includes:

[0136] a layer fracture judging unit, configured to determine that the crack type of the target area to be identified is a layer fracture if it is determined that the central axis angle is less than a first angle threshold;

[0137] A low-angle crack judgment unit, configured to determine that the crack type of the target area to be identified is a low-angle crack if it is determined that the central axis angle is greater than or equal to a first angle threshold and less than a second angle threshold;

[0138] a high-angle crack judging unit, configured to determine that the crack type of the target area to be identified is a high-angle crack if it is determined that the central axis angle is greater than or equal to a second angle threshold;

[0139] The first angle threshold is smaller than the second angle threshold.

[0140] The pore throat identification device provided in the embodiment of the present invention can execute the pore throat identification method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0141] Embodiment 4

[0142] Figure 6 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0143] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0144] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0145] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the pore throat identification method.

[0146] In some embodiments, the pore throat identification method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the pore throat identification method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the pore throat identification method in any other appropriate manner (e.g., by means of firmware).

[0147] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0148] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0149] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0150] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0151] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0152] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0153] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0154] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A pore throat identification method, characterized in that: include: Determine a scanning electron microscope image, and perform pore throat extraction on the scanning electron microscope image to obtain at least one area to be identified; Determining the throat type corresponding to the area to be identified according to the minimum radius and the average radius of the throat of the area to be identified; Determining a pore shape factor of the area to be identified, and determining a pore type corresponding to the area to be identified according to the pore shape factor of the area to be identified; If it is determined that the pore type of the target area to be identified is a strip type, the target area to be identified is determined to be a crack, and the crack type of the target area to be identified is determined according to the central axis angle of the target area to be identified.

2. The method according to claim 1, characterized in that: Performing pore throat extraction on the scanning electron microscope image to obtain at least one area to be identified includes: Binarizing the scanning electron microscope image to obtain a binary image; Performing pore throat extraction on the binary image to obtain at least one candidate region; According to the pore radius threshold, the area to be identified is determined in each candidate area.

3. The method according to claim 2, characterized in that Binarization is performed on the scanning electron microscope image to obtain a binary image, including: Determining an initial grayscale value threshold value according to a maximum grayscale value and a minimum grayscale value of the scanning electron microscope image; According to the initial gray value threshold, the scanning electron microscope image is segmented to obtain a target image and a background image; Re-determine the gray value threshold according to the average gray value of the target image and the average gray value of the background image; According to the re-determined gray value threshold, re-performing the image segmentation operation on the scanning electron microscope image until the re-determined gray value threshold satisfies the iteration stop condition; The scanning electron microscope image is binarized according to the re-determined gray value threshold to obtain a binarized image.

4. The method according to claim 1, characterized in that Determining the throat type corresponding to the area to be identified according to the minimum radius and the average radius of the throat of the area to be identified includes: If it is determined that the difference between the average radius and the minimum radius of the throat of the to-be-identified area is greater than or equal to a first preset difference threshold, and the number of pixel points corresponding to the throat is greater than or equal to a preset number threshold, then it is determined that the throat type corresponding to the to-be-identified area is a necking throat; If it is determined that the difference between the average radius and the minimum radius of the throat of the to-be-identified area is greater than or equal to a first preset difference threshold, and the number of consecutive side-by-side pixel points in the throat is greater than or equal to a preset consecutive value, then it is determined that the throat type corresponding to the to-be-identified area is a lamellar throat or a curved lamellar throat; If it is determined that the difference between the average radius and the minimum radius of the throat of the to-be-identified area is less than a second preset difference threshold, it is determined that the throat type corresponding to the to-be-identified area is a pore-reducing throat.

5. The method according to claim 1, characterized in that Determining the pore shape factor of the area to be identified, and determining the pore type corresponding to the area to be identified according to the pore shape factor of the area to be identified, including: Determine the pore throat boundary area and the pore throat boundary perimeter of the area to be identified, and determine the pore shape factor according to the pore throat boundary area and the pore throat boundary perimeter; If it is determined that the pore shape factor is less than or equal to the first shape factor threshold, determining that the pore type corresponding to the to-be-identified area is a strip type; If it is determined that the pore shape factor is greater than the first shape factor threshold and less than or equal to the second shape factor threshold, then determining that the pore type corresponding to the to-be-identified area is a quasi-ellipse type; If it is determined that the pore shape factor is greater than the second shape factor threshold and less than or equal to the third shape factor threshold, then determining that the pore type corresponding to the to-be-identified area is a quasi-circular type; The first shape factor threshold is smaller than the second shape factor threshold, and the second shape factor threshold is smaller than the third shape factor threshold.

6. The method according to claim 1, characterized in that After determining that the target area to be identified is a crack, the method further includes: Determine a crack image, and perform binarization processing on the crack image to obtain a binarized image matching the crack image; Performing edge detection on the binary image matched with the crack image to obtain the crack edge; Determining the center axis of the crack based on the crack edge and the skeletonization algorithm; The angle of the central axis is determined according to the pixel point corresponding to the maximum radius of the crack on the central axis or the inflection point of the central axis.

7. The method according to claim 1, characterized in that Determining the crack type of the target area to be identified according to the central axis angle of the target area to be identified includes: If it is determined that the central axis angle is less than the first angle threshold, then the crack type of the target area to be identified is determined to be a layer-parallel crack; If it is determined that the central axis angle is greater than or equal to the first angle threshold and less than the second angle threshold, then the crack type of the target area to be identified is determined to be a low-angle crack; If it is determined that the central axis angle is greater than or equal to the second angle threshold, then the crack type of the target area to be identified is determined to be a high-angle crack; The first angle threshold is smaller than the second angle threshold.

8. A pore throat identification device, characterized in that: include: A pore throat extraction module is used to determine a scanning electron microscope image and perform pore throat extraction on the scanning electron microscope image to obtain at least one area to be identified; A throat type determination module, used to determine the throat type corresponding to the area to be identified according to the minimum radius and average radius of the throat of the area to be identified; A pore type determination module, used to determine the pore shape factor of the area to be identified, and determine the pore type corresponding to the area to be identified according to the pore shape factor of the area to be identified; The crack type determination module is used to determine that the target area to be identified is a crack if the pore type of the target area to be identified is a strip type, and determine the crack type of the target area to be identified according to the central axis angle of the target area to be identified.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the pore throat identification method as described in any one of claims 1-7 is implemented.

10. A storage medium storing computer executable instructions, characterized in that: The computer executable instructions are used to perform the pore throat identification method as described in any one of claims 1 to 7 when executed by a computer processor.

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