A method and device for quantitatively characterizing pore structure of a carbonate reservoir

Abstract

The present application relates to a kind of carbonate reservoir pore structure quantitative characterization method and device, the method comprises: to the different scale digital image of the carbonate reservoir obtained, respectively, pore structure feature extraction is obtained corresponding binary image;For each scale binary image, at least part of the area of binary image is regarded as target area, based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics and pore shape characteristics of target area, search is carried out in different scale binary image by similarity matching algorithm, and pore key area is screened and obtained;Based on the multiscale comprehensive fractal dimension function and different scale pore key area of pre-construction, the multiscale comprehensive fractal dimension of carbonate reservoir is calculated to obtain.The present application realizes the comprehensive quantitative characterization of multiscale pore structure, solves the correlation and fusion characterization problem between multiscale images, and can provide data support for effectively solving the productivity prediction and evaluation of carbonate reservoir.

Description

Technical Field

[0001] This invention relates to a method and apparatus for quantitative characterization of the pore structure of carbonate reservoirs. Background Technology

[0002] Carbonate reservoirs are highly heterogeneous, especially the pore-cavity-furcation spaces that form the oil and gas reservoirs. These spaces are diverse and complex, exhibiting pore-scale characteristics and heterogeneity across multiple length scales, making quantitative characterization of carbonate reservoir structure extremely challenging. To better understand the characteristics of carbonate reservoir structure, information on the reservoir structure across multiple scales is necessary, broadly ranging from nanometers to millimeters. These characteristics are often controlled by the geometry and connectivity of the reservoir space. Therefore, quantitative characterization of reservoir pore structure is crucial for further clarifying structure-activity relationships and optimizing performance, thus improving the production evaluation of carbonate micro-reservoirs. Summary of the Invention

[0003] To enrich the methods for characterizing the pore structure of carbonate reservoirs and achieve quantitative characterization of their pore structure, this invention proposes a method and apparatus for quantitative characterization of carbonate reservoir pore structure. The technical solution proposed in this invention is as follows:

[0004] In a first aspect, embodiments of the present invention provide a method for quantitative characterization of the pore structure of carbonate reservoirs, comprising:

[0005] Pore ​​structure features were extracted from the acquired digital images of carbonate reservoirs at different scales to obtain corresponding binary images; wherein, the digital images at different scales include macroscopic digital images captured by high-definition optical imaging, mesoscopic digital images captured by optical microscopy, and microscopic digital images captured by scanning electron microscopy.

[0006] The binary image at each scale is calculated based on the pre-constructed feature function to obtain the corresponding pore distribution features, pore variation features, pore fractal features and pore shape features;

[0007] For each scale of binary image, at least a portion of the binary image is taken as the target region. Based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics and pore shape characteristics of the target region, a similarity matching algorithm is used to search for and filter out key pore regions in binary images at different scales.

[0008] Based on the pre-constructed multi-scale integrated fractal dimension function and the key pore regions at different scales, the multi-scale integrated fractal dimension of the carbonate reservoir is calculated.

[0009] In one or more embodiments, for each scale of binary image, at least a portion of the binary image is taken as the target region. Based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region, a similarity matching algorithm is used to search for key pore regions in binary images at different scales, including:

[0010] Perform the following operations on the binary image at each scale:

[0011] At least a portion of the binary image is taken as the target region;

[0012] Based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region, a similarity matching algorithm is used to select the search region with the highest comprehensive similarity to the target region in binary images at different scales, thereby obtaining the corresponding target region.

[0013] The Euclidean distances between the target region and the target region at different scales are determined respectively, and the region with the smallest Euclidean distance is selected to obtain the key pore region.

[0014] In one or more embodiments, the step of selecting the search region with the highest comprehensive similarity to the target region in binary images at different scales based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region, thereby obtaining the corresponding target region, includes:

[0015] For each scale of the binary image, the binary image is divided into grids to obtain a grid image;

[0016] Calculate the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of each search region in the grid image;

[0017] Based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region, and the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the search region, the similarity of pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics between the target region and the search region are calculated respectively. The comprehensive similarity between the target region and each of the search regions is then calculated using the following formula:

[0018]

[0019] Where DC(q,t) represents the comprehensive similarity between the target region and the search region, q is the target region, t is the search region, and S is the search region. xLet γ be the similarity of pore distribution characteristics, D be the similarity of pore variation characteristics, G be the similarity of pore shape characteristics, and λ be the similarity of pore shape characteristics. a , λ b , λ c and λ d These are the weights for the similarity of pore distribution features, pore variation features, pore fractal features, and pore shape features, respectively.

[0020] In binary images at different scales, the search regions with the highest comprehensive similarity to the target region are selected to obtain the corresponding target regions.

[0021] In one or more embodiments, the pore distribution characteristics are calculated using the following formula:

[0022]

[0023] Among them, S x (r1, r2) represents the pore distribution characteristics, where r1 and r2 are any two points in an image at the same scale, P(r1) is the phase function of point r1, and P(r2) is the phase function of point r2.

[0024] In one or more embodiments, the porosity variation characteristics are calculated using the following formula:

[0025]

[0026] Where γ(r) represents the porosity variation characteristic, r is the equivalent radius of the pores, and N(r) is the number of two points at a distance r in the image at the same scale. i Let P(r) be the radius of the i-th pore. i ) is r i The phase function value.

[0027] In one or more embodiments, the pore fractal characteristics are calculated using the following formula:

[0028]

[0029] Among them, D F The fractal characteristics of the pores are given by r, which is the equivalent radius of the pores. N'(r) is the number of pores with a diameter greater than r, and a is a constant.

[0030] In one or more embodiments, the pore shape feature is calculated using the following formula:

[0031]

[0032] Among them, G T S represents the shape characteristics of the pores. T C is the area of ​​the T-th pore region;T Let be the perimeter of the T-th pore region.

[0033] In one or more embodiments, the calculation of the multi-scale comprehensive fractal dimension of the carbonate reservoir based on a pre-constructed multi-scale comprehensive fractal dimension function and the key porosity regions at different scales includes:

[0034] Based on the following formula, the multi-scale comprehensive fractal dimension of the carbonate reservoir is obtained by calculating the pore size segments in the key pore regions at different scales:

[0035]

[0036] Where: d z B represents the multi-scale comprehensive fractal dimension of carbonate reservoirs. i d represents the proportion of the pore volume of the critical pore region at the i-th scale to the total pore volume, where i = 1, 2, ..., m, m is the number of scales, and d ij Let b be the fractal dimension of the j-th pore size segment in the porosity critical region at the i-th scale, where j = 1, 2, ..., l, and l is the number of pore size segments divided in the porosity critical region at the i-th scale. ij It represents the proportion of the pore volume of the j-th pore diameter segment in the pore critical region at the i-th scale to the total pore volume of the pore critical region at that scale.

[0037] In one or more embodiments, the method further includes:

[0038] The radius of the pore region in the binary image is searched using the maximum circle search method, and the radius of the pore region of the single pore structure is determined by the following formula:

[0039]

[0040] Where R is the radius of the pore region, δd is the actual size represented by each pixel, and N r The number of pixels representing the maximum radius of the inscribed circle in the pore region.

[0041] In one or more embodiments, the method further includes:

[0042] The area of ​​the pore region in the binary image is determined based on the following formula:

[0043]

[0044] Where S is the area of ​​the pore region, δd is the actual size represented by each pixel, n×n is the number of grids in the pore region, and y ij Let y' be the ordinate of the leftmost point in each row. ij Let y be the ordinate of the rightmost point in each row.ij -y' ij +1 represents the number of pixels in each grid row, where i = 1, 2, ..., n.

[0045] In one or more embodiments, the step of extracting pore structure features from digital images of the acquired carbonate reservoir at different scales to obtain corresponding binary images includes:

[0046] Digital images of carbonate reservoirs at every scale:

[0047] The digital images are preprocessed to obtain preprocessed digital images;

[0048] An iterative image thresholding algorithm is used to identify the pore structure in the preprocessed digital image to obtain a binary image.

[0049] In one or more embodiments, the step of using an iterative image thresholding algorithm to identify the pore structure of the preprocessed digital image to obtain a binary image includes:

[0050] Based on the minimum grayscale value PF in the preprocessed digital image out | min and maximum grayscale value PF out | max The initial threshold T0 is determined by the following formula;

[0051]

[0052] The preprocessed digital image is segmented into a target part and a background part according to the initial threshold. Based on the gray values ​​of the target part and the background part, the updated threshold is obtained by updating the threshold using the following formula.

[0053]

[0054]

[0055] Among them, PF out | O PF is the average grayscale value of all pixels in the target area. out | G PF is the average grayscale value of all pixels in the background area. out (i,j) represents the gray value of point (i,j) in the image, N(i,j) is the weight coefficient of point (i,j), and T K T is the threshold before the update. K+1 The updated threshold;

[0056] The preprocessed digital image is segmented based on the updated threshold, and the process of updating the threshold is repeated until the preset condition is met to obtain the optimal threshold.

[0057] The preprocessed digital image is segmented based on the optimal threshold to obtain a binary image.

[0058] In one or more embodiments, the step of preprocessing the digital images to obtain preprocessed digital images includes:

[0059] The feature components of the digital image are extracted based on the following formula:

[0060]

[0061] Where v is the feature component, L is the image gray level, g is the pixel gray value, and i g Let g be the number of pixels at the image gray level g, and M and N be the total number of rows and columns of the image matrix corresponding to the digital image, respectively.

[0062] Based on the aforementioned feature components, the digital image is enhanced using the following formula to obtain the preprocessed digital image:

[0063]

[0064] Where Z(g) is the enhancement function and E is the enhancement parameter.

[0065] In a second aspect, the present invention provides a device for quantitative characterization of the pore structure of carbonate reservoirs, comprising:

[0066] The feature extraction module is used to extract pore structure features from digital images of carbonate reservoirs at different scales to obtain corresponding binary images; wherein, the digital images at different scales include macroscopic digital images captured by high-definition optical imaging, mesoscopic digital images captured by optical microscopy, and microscopic digital images captured by scanning electron microscopy.

[0067] The first calculation module is used to calculate the binary image at each scale based on the pre-constructed feature function to obtain the corresponding pore distribution features, pore variation features, pore fractal features and pore shape features;

[0068] The search module is used to select at least a portion of the binary image as the target region for each scale of binary image, and search for key pore regions in binary images at different scales based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics and pore shape characteristics of the target region, using a similarity matching algorithm.

[0069] The second calculation module is used to calculate the multi-scale comprehensive fractal dimension of the carbonate reservoir based on the pre-constructed multi-scale comprehensive fractal dimension function and the key pore regions at different scales.

[0070] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the quantitative characterization method for the pore structure of carbonate reservoirs as described in the first aspect.

[0071] Fourthly, the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0072] Memory, used to store computer programs;

[0073] A processor, when executing a program stored in memory, implements the quantitative characterization method for the pore structure of carbonate reservoirs as described in the first aspect.

[0074] Based on the above technical solution, the beneficial effects of the present invention compared with the prior art are as follows:

[0075] The quantitative characterization method for pore structure of carbonate reservoirs provided in this invention lays a data foundation for the fusion and characterization of multi-scale pore structures by extracting feature parameters of pore structure from multi-scale pore structure images. Based on pre-constructed feature functions, calculations are performed on binary images at each scale to obtain corresponding pore distribution features, pore variation features, pore fractal features, and pore shape features. Then, based on the determined pore distribution features, pore variation features, pore fractal features, and pore shape features, cross-scale image feature region localization can be achieved. By combining pore distribution features, pore variation features, pore fractal features, and pore shape features, and using a similarity matching algorithm to achieve the search for key pore regions at different scales, the automatic search for key pore regions in carbonate reservoir pore images at the millimeter-micrometer-nanometer scales can be effectively realized. This invention constructs a multi-scale comprehensive fractal dimension of carbonate reservoirs, which can reflect the influence of the fractal characteristics of pores of different pore sizes on the overall fractal characteristics of carbonate reservoirs. It realizes a comprehensive quantitative characterization of multi-scale pore structure and ultimately solves the problem of correlation and fusion characterization between multi-scale images. It can provide data support for effectively solving the problem of production capacity prediction and evaluation of carbonate reservoirs.

[0076] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0077] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0078] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0079] Figure 1 This is a schematic flowchart of the quantitative characterization method for the pore structure of carbonate reservoirs provided in this embodiment of the invention;

[0080] Figure 2 This is a schematic diagram illustrating the relationship between the longest axis and the shortest axis of the pore boundary provided in an embodiment of the present invention.

[0081] Figure 3 This is a grid division result of the preprocessed digital image F2 provided in the embodiment of the present invention; wherein, (a) is a 10*10 grid macroscopic image, (b) is a 10*10 grid mesoscopic image, (c) is a 10*10 grid microscopic image, (d) is a 100*100 grid macroscopic image, (e) is a 100*100 grid mesoscopic image, and (f) is a 100*100 grid microscopic image;

[0082] Figure 4 This is a search map of key regions in a 10*10 grid image provided in an embodiment of the present invention; wherein, (a) is a macro-mesoscopic search map, (b) is a mesoscopic-microscopic search map, and (c) is a macro-microscopic search map;

[0083] Figure 5 This is a key region search map in a 100*100 grid image provided by an embodiment of the present invention; wherein, (a) is a macro-mesoscopic search map, (b) is a mesoscopic-microscopic search map, and (c) is a macro-microscopic search map;

[0084] Figure 6 These are comparison diagrams of the search and positioning area and the actual sampling area provided in the embodiments of the present invention; wherein, (a) is a macro-mesoscopic search and positioning comparison diagram, (b) is a mesoscopic-microscopic search and positioning comparison diagram, and (c) is a macro-microscopic search and positioning comparison diagram.

[0085] Figure 7 This is a schematic diagram of the structure of the quantitative characterization device for the pore structure of carbonate reservoirs provided in this embodiment of the invention;

[0086] Figure 8This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0087] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0088] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0089] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0090] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0091] Carbonate reservoirs are highly heterogeneous, especially the pore-cavity-fracture spaces that form the oil and gas reservoir. These spaces are diverse and complex, exhibiting pore-scale characteristics and heterogeneity across multiple length scales, making quantitative characterization of carbonate reservoir structure extremely challenging. To better understand the characteristics of carbonate reservoir structure, information on the reservoir structure across multiple scales, roughly from nanometers to millimeters, is necessary. These characteristics are often controlled by the geometry and connectivity of the reservoir space. Quantitative characterization of reservoir structure is crucial for further clarifying structure-activity relationships and optimizing performance, thus improving the production evaluation of carbonate micro-reservoirs. Traditional image-based observation methods for carbonate reservoir pore structure, primarily using optical microscopes, thin sections, and scanning electron microscopes, can directly provide qualitative descriptions and semi-quantitative observations of the reservoir's micro-pore structure, offering advantages such as intuitive observation and mature technology. The inventors discovered three main problems with existing image-based methods for characterizing the pore structure of carbonate reservoirs: first, the image sources are limited, failing to grasp the distribution characteristics of pore structures from multi-scale image features; second, the correlation features and positional matching relationships between multi-scale images are unclear, and no correspondence relationship has been established between images of different scales; and third, the pore structure features from multiple scales and data sources do not reflect the overall structural characteristics of carbonate reservoirs, making it difficult to achieve a comprehensive quantitative characterization of multi-scale pore structures and failing to meet the inventors' expectations. Therefore, to understand the different characteristics of pore structures at different scales as presented in images, the inventors further developed a multi-scale structural information association and fusion function for pore structures, achieving a comprehensive quantitative characterization of multi-scale pore structures. This invention proposes a quantitative characterization method for the pore structure of carbonate reservoirs based on multi-scale image features.

[0092] Invention patent CN201610908816.3, "A Method for Characterizing the Pore Structure of Carbonate Rocks Based on Rock Classification and Multi-Scale Digital Cores," describes a method for characterizing the pore structure of carbonate rocks based on rock classification and multi-scale digital cores. First, core images of different resolutions are obtained using CT scanning technology. Then, the obtained digital cores are binary segmented, and the optimized digital cores of different resolutions are combined to obtain multi-scale digital cores. The pore structure parameters of the digital cores are evaluated and compared with experimentally measured data. Finally, the obtained multi-scale digital cores are divided into regions based on rock bedding, pore structure, etc., and these regions are then classified by property. The classification results are summarized and filled into regions with the same properties. This invention combines digital cores of different scales, which can characterize the physical properties of the medium while meeting its resolution requirements. Simultaneously, this invention performs lithological classification on the obtained multi-scale digital cores, which can greatly reduce the difficulty of analysis and calculation, laying the foundation for processing more complex and heterogeneous cores. This method only realizes the image difference features of pore structure from different CT resolutions, and does not realize multi-scale image analysis research across orders of magnitude.

[0093] Invention patent CN202210159608.3, "Method and System for Identifying Pores and Fractures Based on Two-Dimensional Core Scanning Images," belongs to the field of oil and gas reservoir geological exploration technology. Specifically, it relates to a method and system for identifying pores and fractures based on two-dimensional core scanning images. The aim is to solve the problems of existing technologies being unable to accurately and efficiently segment pores and fractures, and unable to quantitatively analyze pore and fracture characteristics. The method includes scanning to acquire an initial two-dimensional core planar image, filtering it to obtain a first two-dimensional core planar image, then performing image segmentation to obtain a second two-dimensional core planar image; extracting the center coordinates of all pixels in each pore space to obtain the centroid of the corresponding pore space, and establishing a pore and fracture identification function; if the value of the pore and fracture identification function is greater than a preset characterization value, the pore space is determined to be a fracture; otherwise, it is determined to be a pore. This invention can obtain accurate pore and fracture distribution information, providing effective guidance for reservoir oil and gas exploitation. However, this method mainly focuses on understanding the characteristics of pore structure from macroscopic images, without considering the identification and characterization of pore structure in mesoscopic and microscopic images.

[0094] The invention patent CN202210096371.9, "A Method for Extracting Digital Core Structure from Low-Permeability Sandstone and Conglomerate Reservoirs," describes a method for extracting core casting thin-section images of the target reservoir, statistically analyzing the grain size of mineral components, and determining the number of reservoir core structure types. It then acquires digital core images of the target reservoir, slices them, plots grayscale histograms of the corresponding core voxels, and statistically analyzes the grayscale value ranges of voxels for different core structure types. The method determines the number of thresholds required for digital core structure analysis, establishes the relationship between the grayscale values ​​of voxels for different core structure types, and uses the maximum inter-class spacing method to perform multi-threshold optimization calculations on the digital core slices to obtain the optimal threshold for the sandstone and conglomerate reservoir digital core. Based on the optimal threshold results, it performs digital core threshold segmentation to obtain the voxel lattice of different core structure types, thus completing the digital core structure extraction. This invention enables accurate extraction of rock cuttings and interstitial materials, and effectively characterizes the three-dimensional structure of digital cores, including framework particles, matrix cement, and pore fractures. This method mainly focuses on identifying the pore structure of the core at the scale of mesoscopic images, neglecting the identification and characterization of pore structure in macroscopic and microscopic images.

[0095] Example 1

[0096] This invention provides a method for quantitative characterization of the pore structure of carbonate reservoirs, including: multi-scale image pore structure feature extraction, cross-scale image feature region localization, and comprehensive quantitative characterization of multi-scale pore structure. By performing feature analysis and data fusion on multi-scale images, a quantitative characterization of the pore structure of carbonate reservoirs based on multi-scale image features is achieved, providing more accurate data for the production evaluation of carbonate reservoirs.

[0097] Reference Figure 1 As shown, the method specifically includes:

[0098] S101. Pore structure features are extracted from the acquired digital images of carbonate reservoirs at different scales to obtain corresponding binary images; wherein, the digital images at different scales include macroscopic digital images captured by high-definition optical imaging, mesoscopic digital images captured by optical microscopy, and microscopic digital images captured by scanning electron microscopy.

[0099] The multi-scale image pore structure feature extraction described above mainly involves data obtained by taking macroscopic digital images using high-definition optical imaging, mesoscopic digital images using optical microscopy, and microscopic digital images using scanning electron microscopy. Macroscopic digital images are typically at the mm level, mesoscopic digital images are typically at the um level, and microscopic digital images are typically at the nm level.

[0100] S102. Calculate the binary image at each scale based on the pre-constructed feature function to obtain the corresponding pore distribution features, pore variation features, pore fractal features, and pore shape features;

[0101] The aforementioned characteristic functions include the autocorrelation function S. x (r1,r2), variogram γ(r), fractal dimension function, and shape characteristic function. To characterize the porosity distribution, an autocorrelation function S is constructed. x (r1, r2), this function can reflect the distribution characteristics of multi-scale pores; to characterize the pore variation characteristics, this invention constructs a variation function γ(r); to characterize the fractal characteristics of pores, this invention constructs a fractal dimension function; to reflect the pore shape characteristics, a shape characteristic function is established. Based on the constructed autocorrelation function S x By calculating the binary image using (r1,r2), the variation function γ(r), the fractal dimension function, and the shape feature function, the corresponding pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics can be obtained.

[0102] S103. For each scale of binary image, at least a portion of the binary image is taken as the target region. Based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics and pore shape characteristics of the target region, a similarity matching algorithm is used to search for and filter out key pore regions in binary images at different scales.

[0103] S104. Based on the pre-constructed multi-scale comprehensive fractal dimension function and the key pore regions at different scales, the multi-scale comprehensive fractal dimension of the carbonate reservoir is calculated.

[0104] This invention relates to experimental data analysis and characterization methods in the geological field, particularly a quantitative characterization method for the pore structure of carbonate reservoirs based on multi-scale image features. Specifically, it first extracts feature parameters of the pore structure from multi-scale images to obtain image information of the pore structure at multiple scales, effectively distinguishing pores from the framework. Subsequently, it constructs a multi-scale structural information association and fusion function for the pore structure, formulates a search strategy for locating key regions of pore structure development features, and achieves cross-scale image feature region localization. It combines pore distribution features, pore variation features, pore fractal features, and pore shape features within a certain range, and uses a similarity matching algorithm to achieve different scales. The search for key pore regions between different areas at the same scale can effectively achieve automatic searching of key regions in carbonate reservoir pore images at the millimeter-micrometer-nanometer scales. Finally, a multi-scale comprehensive fractal dimension of carbonate reservoirs is constructed, which can reflect the degree of influence of the fractal characteristics of pores of different diameter segments on the overall fractal characteristics of carbonate reservoirs. This achieves a comprehensive quantitative characterization of multi-scale pore structure, ultimately solving the problem of correlation and fusion characterization between multi-scale images. It can provide data support for effectively solving the problem of carbonate reservoir productivity prediction and evaluation, and further provide new technical means for developing research theories and technical models of carbonate reservoir pore structure development characteristics.

[0105] The multi-scale image pore structure feature extraction involved in this invention mainly utilizes these three types of digital images to achieve multi-scale image pore structure feature extraction. The multi-scale image pore structure feature extraction mainly includes three steps: digital image preprocessing, pore structure identification, and feature parameter calculation.

[0106] In an optional embodiment, step S101 above, which involves extracting pore structure features from digital images of the acquired carbonate reservoir at different scales to obtain corresponding binary images, includes:

[0107] Digital images of carbonate reservoirs at every scale:

[0108] S1011. Perform preprocessing on the digital images to obtain preprocessed digital images;

[0109] S1012. An iterative image thresholding algorithm is used to identify the pore structure of the preprocessed digital image to obtain a binary image.

[0110] As described above, traditional image enhancement methods for digital image preprocessing are not very effective, have low peak signal-to-noise ratios, limited applicability, and fail to effectively utilize global information to enhance image details. This invention proposes an image enhancement method that combines global and local approaches, employing power-law transform with varying gamma values—an improved gamma-corrected image enhancement method. In this method, a pixel is considered a bright spot if its grayscale value is greater than the average brightness; a pixel is considered a dark spot if its grayscale value is less than the average brightness; and a pixel is considered a smooth spot if its grayscale value equals the average brightness. The average brightness of the image is extracted as a feature component. In an optional embodiment, the preprocessing of the digital image to obtain a preprocessed digital image includes:

[0111] The feature components of the digital image are extracted based on the following formula:

[0112]

[0113] Where v is the feature component, L is the image gray level, g is the pixel gray value, and i g Let g be the number of pixels at the image gray level g, and M and N be the total number of rows and columns of the image matrix corresponding to the digital image, respectively.

[0114] Based on the aforementioned feature components, the digital image is enhanced using the following formula to obtain the preprocessed digital image:

[0115]

[0116] Where Z(g) is the enhancement function and E is the enhancement parameter;

[0117] To minimize the enhancement of pixels that are significantly brighter or darker, while maximizing the enhancement of relatively smoother pixels, a nonlinear transformation function as shown in the above formula is used to quantize the enhancement magnitude of pixels. Here, Z(g) has a range of (0,1), and E mainly controls the slope of the enhancement function Z(g), typically set to 1, but can be adjusted according to actual conditions. When the grayscale value is closer to the average value v, Z(g) is closer to 0.5, indicating that the pixel is relatively smooth and has relatively low contrast; when the grayscale value is further away from the average value v, Z(g) is closer to 0 or 1, indicating that the pixel is darker or brighter and has higher contrast. The expression for the grayscale PF of the image after improved gamma correction is:

[0118] PF out =cPF in (1.5-Z(g))

[0119] Equation 3;

[0120] Among them, PF inand PF out These are the grayscale values ​​of the input and output images, respectively. `c` is a parameter controlling the shape of the conversion curve; it is usually set to 1, but its value can be increased appropriately if the correction is not ideal. Median filtering has a relatively ideal denoising effect, filtering out noise points with significant pixel value differences and effectively preventing blurring of image details such as contours and edges. It is better than mean filtering at suppressing salt-and-pepper noise and impulse interference, and it can also ensure edge clarity, thus realizing the entire process of digital image preprocessing, transforming the original digital image F1 into the preprocessed digital image F2.

[0121] The key to pore structure identification as described above is finding the optimal segmentation threshold for the image and binarizing it. Finally, morphological operations are performed on the binary image to remove defects. An iterative image thresholding segmentation algorithm is used to segment the preprocessed digital image F2 to obtain a binary image F3. In an optional embodiment, step S1012 above, which involves using an iterative image thresholding algorithm to identify pore structures in the preprocessed digital image to obtain a binary image, includes:

[0122] S10121, Based on the minimum grayscale value PF in the preprocessed digital image out | min and maximum grayscale value PF out | max The initial threshold T0 is determined by the following formula;

[0123]

[0124] S10122. The preprocessed digital image is segmented into a target part and a background part according to the initial threshold. Based on the gray values ​​of the target part and the background part, the updated threshold is obtained by updating the threshold using the following formula.

[0125]

[0126]

[0127] Among them, PF out | O PF is the average grayscale value of all pixels in the target area. out | G PF is the average grayscale value of all pixels in the background area. out (i,j) represents the gray value of point (i,j) in the image, N(i,j) is the weight coefficient of point (i,j), and T K T is the threshold before the update. K+1 The updated threshold;

[0128] S10123. Segment the preprocessed digital image based on the updated threshold, and repeat the process of updating the threshold until the preset condition is met to obtain the optimal threshold.

[0129] If T is not reached K =T K+1 Then the above S10122-S10123 cycle will repeat. When T K =T K+1 Then the loop ends, and the optimal threshold is obtained for segmentation.

[0130] S10124. The preprocessed digital image is segmented based on the optimal threshold to obtain a binary image.

[0131] Pore ​​structure segmentation image through phase function To define, for a two-phase system considering only pores and framework, when When located in a pore, the value is 1; when... When located in the skeletal phase, the value is 0.

[0132] As described above, the feature parameter calculation employs the maximum circle search method to perform a radius search on the pore region (white area) in the binary image F3. If the number of pixels with the maximum inscribed circle radius of the pore region is N... r The radius of the pore region is denoted by R. In an optional embodiment, the method further includes:

[0133] S10131. The radius of the pore region in the binary image is searched using the maximum circle search method, and the radius R of the pore region of the single pore structure is determined by the following formula:

[0134]

[0135] Where R is the radius of the pore region, δd is the actual size represented by each pixel, and N r The number of pixels is the maximum inscribed circle radius of the pore region. The longest and shortest axes of the boundary of a single pore structure can also be determined. The longest axis is defined as the Euclidean distance between two farthest points on the outer boundary of the pore region. The line segment connecting these points is called the longest axis of the cell cavity boundary. The shortest axis is defined as the line segment perpendicular to the longest axis, denoted as d. The method for identifying the outer boundary of the pore region can be found in existing technologies and will not be repeated here.

[0136] As described above for feature parameter calculation, the porosity region area represents the area of ​​the region formed by the set of pixels within the white closed area in the image, denoted by S. The porosity region is divided into an n×n grid, with the leftmost point of each row being P(x). i ,y ij The rightmost point is Q(x).i ,y' ij If y = y, then the grid line contains y pixels. ij -y' ij +1, In an optional embodiment, the method further includes:

[0137] S10132. Determine the area of ​​the pore region in the binary image based on the following formula:

[0138]

[0139] Where S is the area of ​​the pore region, δd is the actual size represented by each pixel, n×n is the number of grids in the pore region, and y ij Let y' be the ordinate of the leftmost point in each row. ij Let y be the ordinate of the rightmost point in each row. ij -y' ij +1 represents the number of pixels in each grid row, where i = 1, 2, ..., n.

[0140] The aperture region, denoted by C, represents the side length of the region formed by the outermost set of pixels within the white closed region of an image. The perimeter of the aperture region is the length used to calculate the chain code. The feature parameter calculations described above also include:

[0141] The perimeter of the pore region is calculated by traversing the contour of the pore region point by point. Specifically, the perimeter C is initialized to 0, and all chain code elements of a single contour are traversed. If the chain code value is odd, then... If the chain code value is even, then C = C + 1. For each profile chain code, perform the above operation, summing the perimeters of all profiles to obtain the total perimeter C of the pore region. For macroscopic pores, the corresponding pore region perimeter is 2C / π.

[0142] The cross-scale image feature region localization described above mainly includes two steps: feature function construction and key region search for pores.

[0143] As described above, in order to characterize the pore distribution features, an autocorrelation function S is constructed. x (r1,r2), this function can reflect the statistical characteristics of multi-scale porosity, autocorrelation function S x (r1,r2) is defined as the probability that any two points in an image of the same scale are distributed in the same aperture.

[0144] In an optional embodiment, the autocorrelation function S x (r1, r2) represents the correlation between any two points in an image at the same scale. For a two-phase system considering only pores and the framework, the pore distribution characteristics are calculated using the following formula:

[0145]

[0146] Among them, S x (r1, r2) represents the pore distribution characteristics, where r1 and r2 are any two points in an image at the same scale, specifically any two points at a distance r in the same scale image. P(r1) is the phase function of point r1, and P(r2) is the phase function of point r2. When the distance r = 0, the autocorrelation function S x The value of (r1,r2) is the face gap of the image; when the distance r increases to a certain value, the autocorrelation function tends to stabilize.

[0147] To reflect the degree of variation of regionalized variables within a certain distance range in a specific direction, a variogram function γ(r) is constructed. The variogram function of an image at the same scale is closely related to its structure. The variogram function γ(r) is an essential function for evaluating the structural properties of an image. In an optional embodiment, the porosity variation characteristics are calculated using the following formula:

[0148]

[0149] Where γ(r) represents the porosity variation characteristic, r is the equivalent radius of the pores, and N(r) is the number of two points at a distance of r in the same scale image. i Let P(r) be the radius of the i-th pore. i ) is r i The phase function value.

[0150] In order to characterize the fractal features of pores, a fractal dimension function D is constructed. F In the binary image F2, the perimeter and area of ​​each pore are measured. The slope of the double logarithm plot of area and perimeter is twice the slope of the fractal dimension function D. F The value of . Based on the description of fractals, the relationship between the pore size distribution fractal and the fractal dimension can be obtained. In an optional embodiment, the pore fractal characteristics are calculated using the following formula:

[0151]

[0152] Among them, D F The fractal characteristics of the pores are given by r, which is the equivalent radius of the pores, N'(r) is the number of pores with a diameter greater than r, and a is a constant, usually taken as the natural logarithm.

[0153] To reflect the shape characteristics of the pores, a shape characteristic function G is established. T Because the pore region shape of carbonate reservoirs is very complex, in order to ensure that the simplified pores have the same geometric characteristics as the real pores, it is necessary to evaluate the shape characteristics of the real pore region, that is, to obtain the shape characteristic function G of the pore region. TIn an optional embodiment, the pore shape feature is calculated using the following formula:

[0154]

[0155] Among them, G T S represents the shape characteristics of the pores. T C is the area of ​​the T-th pore region; T Let be the perimeter of the T-th pore region.

[0156] The pore key region search described above combines pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics within a certain range, and uses a similarity matching algorithm to achieve pore key region search between digital images of different scales and between different regions of digital images of the same scale. Let q be the target region, t be the search region, and S be the pore key region search area. x γ represents the similarity of pore distribution features between the search region and the target region, γ represents the similarity of pore variation features between the search region and the target region, D represents the similarity of pore fractal features between the search region and the target region, and G represents the similarity of pore shape features between the search region and the target region. This invention uses the search for key pore regions between digital images of different scales as an example for illustration.

[0157] In an optional embodiment, step S103 above, for each scale of binary image, takes at least a portion of the binary image as the target region, and searches for key pore regions in binary images at different scales using a similarity matching algorithm based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region, including:

[0158] Perform the following operations on the binary image at each scale:

[0159] S1031. Taking at least a portion of the binary image as the target region, based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics and pore shape characteristics of the target region, a similarity matching algorithm is used to select the search region with the highest comprehensive similarity to the target region in binary images at different scales to obtain the corresponding target region.

[0160] In the search for key pore regions at different scales, firstly, parameters for pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics are manually set, and the system automatically matches the target region q on the image at the specified scale. Alternatively, a specified region image can be selected as the target region q. The pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region are calculated using Equations 9-12, respectively. Subsequently, region images of the corresponding scale are selected as search regions t in images at different scales. For each scale image, taking a macroscopic digital image as an example, the macroscopic, mesoscopic, and microscopic digital images are divided into grids to obtain corresponding grid images. Region images of the corresponding scale are selected as search regions t in the grid images corresponding to the mesoscopic scale and the digital images corresponding to the microscopic scale, respectively. Specifically, each grid in the grid image at each scale can be traversed as the search region. By combining the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics within the same search area, the comprehensive similarity between the search area and the target area is calculated. A first target area with the highest comprehensive similarity to the target area is selected from the mesoscale grid image, and a second target area with the highest comprehensive similarity to the target area is selected from the microscale grid image. These first and second target areas are the target areas corresponding to the macroscale digital image. Similarly, the target areas corresponding to the mesoscale digital image and the target areas corresponding to the microscale digital image can be obtained.

[0161] S1032. Determine the Euclidean distance between the target area and the target area at different scales, and filter out the area with the smallest Euclidean distance to obtain the key pore area.

[0162] The pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics within the same target area are combined and analyzed using the Euclidean distance L. qt’ The size is used to sort the target area distances between different scales, and the closest one is the key area of ​​the pore that needs to be searched and located.

[0163] Specifically, for each scale of digital image, the Euclidean distance between the target region and each destination region is calculated. The destination region with the smallest Euclidean distance to the target region is selected to obtain the porosity key region corresponding to that scale of digital image. Euclidean distance is used as the measure of comprehensive similarity DC, calculated by measuring the Euclidean distance L from the target region q to the destination region t'. qt’ Euclidean distance L qt’ This represents the actual distance between two regions. This distance incorporates pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics. Euclidean distance L qt’ The expression is:

[0164]

[0165] In the above formula, q1, q2, ..., q M The pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region are represented by t'1, t'2, ..., t' M These represent the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region, respectively, and M represents the number of target regions.

[0166] In an optional embodiment, step S1031 above, based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region, uses a similarity matching algorithm to select the search region with the highest comprehensive similarity to the target region in binary images at different scales to obtain the corresponding target region, including:

[0167] S10311. For each scale of the binary image, the binary image is divided into grids to obtain a grid image;

[0168] S10312. Calculate the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of each search region in the grid image;

[0169] Select a region image of the corresponding scale as the search region t in images of different scales; specifically, each grid in a grid image can be used as the search region. Calculate the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of each search region using Equations 9-12.

[0170] S10313. Based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region, and the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the search region, calculate the similarity of pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics between the target region and the search region, respectively, and calculate the comprehensive similarity between the target region and each of the search regions using the following formula:

[0171]

[0172] Where DC(q,t) represents the comprehensive similarity between the target region and the search region, q is the target region, t is the search region, and S is the search region. x Let γ be the similarity of pore distribution characteristics, D be the similarity of pore variation characteristics, G be the similarity of pore shape characteristics, and λ be the similarity of pore shape characteristics. a , λ b , λc and λ d These are the weights for the similarity of pore distribution features, pore variation features, pore fractal features, and pore shape features, respectively. The similarity of pore distribution features, pore variation features, pore fractal features, and pore shape features are all calculated using similarity calculation formulas, which will not be elaborated here.

[0173] S10314. Select the search region with the highest comprehensive similarity to the target region from binary images at different scales to obtain the corresponding target region.

[0174] For each scale of image, taking a macroscopic digital image as an example, the macroscopic, mesoscopic, and microscopic digital images are divided into grids to obtain corresponding grid images. Regions of the corresponding scale are selected as search regions *t* from the grid images corresponding to the mesoscopic and microscopic scales, respectively. Specifically, each grid in the grid image at each scale can be used as a search region. The pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics within the same search region are combined to calculate the comprehensive similarity between the search region and the target region. The first target region with the highest comprehensive similarity to the target region is selected from the mesoscopic grid images, and the second target region with the highest comprehensive similarity to the target region is selected from the microscopic grid images. The first and second target regions are the target regions corresponding to the macroscopic digital images. Similarly, the target regions corresponding to the mesoscopic and microscopic digital images can be obtained.

[0175] As described above, the comprehensive quantitative characterization of multi-scale pore structure involves calculating the pore volume of the digital image F2. This is based on the assumption that the image is a cuboid with a two-dimensional base and the maximum gray level of each pixel as its height. Each pixel can be considered a cylinder with its own base and its own gray level as its height. The sum of the volumes of all cylinders constitutes the skeleton volume, and the remaining volume is the pore volume. The pore volume ratio reflects the influence of the fractal characteristics of pores at different pore sizes on the overall fractal characteristics of the carbonate reservoir. Therefore, by using the pore volume of each pore size segment as a weight, the fractal dimension of pores at different scales is calculated using a weighted average, resulting in the multi-scale comprehensive fractal dimension d of the carbonate reservoir. z In an optional embodiment, the calculation of the multi-scale comprehensive fractal dimension of the carbonate reservoir based on the pre-constructed multi-scale comprehensive fractal dimension function and the porosity key regions at different scales in step S104 above includes:

[0176] S1041. Based on the following formula, the pore size segments in the key pore regions at different scales are calculated to obtain the multi-scale comprehensive fractal dimension of the carbonate reservoir:

[0177]

[0178] Where: d z B represents the multi-scale comprehensive fractal dimension of carbonate reservoirs. i d represents the proportion of the pore volume of the pore-critical region at the i-th scale to the total pore volume. The total pore volume refers to the total pore volume in the digital image at that scale, i = 1, 2, ..., m, where m is the number of scales, and d ij Let b be the fractal dimension of the j-th pore size segment in the porosity critical region at the i-th scale, where j = 1, 2, ..., l, and l is the number of pore size segments divided in the porosity critical region at the i-th scale. ij It represents the proportion of the pore volume of the j-th pore diameter segment in the pore critical region at the i-th scale to the total pore volume of the pore critical region at that scale.

[0179] The quantitative characterization method for carbonate reservoir pore structure based on multi-scale image features provided in this invention mainly achieves the following functions and effects: First, by preprocessing multi-scale pore structure images, the image information of pore structure in multi-scale images can be obtained, thereby effectively distinguishing pores from the skeleton and extracting feature parameters of pore structure, laying a data foundation for the fusion and characterization of multi-scale pore structures. Second, by constructing a multi-scale structural information association and fusion function for pore structure, a key region localization search strategy for pore structure development features is formulated, which can realize cross-scale image feature region localization, and identify pore distribution features, pore change features, and pore structure characteristics within a certain range. By combining fractal features and pore shape features, and employing a similarity matching algorithm to search for key pore regions across different scales and within the same scale, this method effectively enables automatic searching of key regions in carbonate reservoir pore images at the millimeter-micrometer-nanometer scales. Thirdly, it constructs a multi-scale comprehensive fractal dimension for carbonate reservoirs, reflecting the influence of fractal features of pores at different diameters on the overall fractal characteristics of the carbonate reservoir. This achieves a comprehensive quantitative characterization of multi-scale pore structure, ultimately solving the problem of correlation and fusion characterization between multi-scale images. This provides data support for effectively predicting and evaluating the productivity of carbonate reservoirs. Compared with traditional methods, this quantitative characterization method for carbonate reservoir pore structure based on multi-scale image features is scientific, reliable, and practical.

[0180] Example 2

[0181] To provide a clearer explanation of the quantitative characterization method for the pore structure of carbonate reservoirs provided in this embodiment of the invention, this invention applies this quantitative characterization method to a certain carbonate reservoir to quantitatively characterize the pore structure, as detailed below.

[0182] A quantitative characterization method for the pore structure of carbonate reservoirs based on multi-scale image features specifically includes: 1) multi-scale image pore structure feature extraction; 2) cross-scale image feature region localization; and 3) comprehensive quantitative characterization of multi-scale pore structure. Through feature analysis and data fusion of multi-scale images, a quantitative characterization of the pore structure of carbonate reservoirs based on multi-scale image features is achieved, providing more accurate data for carbonate reservoir yield evaluation.

[0183] The multi-scale image pore structure feature extraction 1 described above mainly involves data obtained using high-resolution optical imaging at the macroscopic scale, optical microscopy at the mesoscopic scale, and scanning electron microscopy at the microscopic scale. Macroscopic digital images are typically at the mm level, mesoscopic digital images at the μm level, and microscopic digital images at the nm level. Therefore, the multi-scale image pore structure feature extraction 1 of this invention primarily utilizes these three types of digital images to extract pore structure features from multi-scale images. The multi-scale image pore structure feature extraction 1 mainly includes three steps: digital image preprocessing 1.1, pore structure identification 1.2, and feature parameter calculation 1.3.

[0184] As described in the digital image preprocessing section 1.1 above, traditional image enhancement methods are not very effective, have low peak signal-to-noise ratios, limited applicability, and do not effectively utilize global information to enhance image details. This invention proposes an image enhancement method that uses power-law transformation with varying gamma values, based on a combination of global and local methods. This is an improved gamma-corrected image enhancement method. When the gray value of a pixel in a digital image is greater than the average brightness, the pixel is considered a bright spot; when the gray value is less than the average brightness, the pixel is considered a dark spot; and when the gray value is equal to the average brightness, the pixel is considered a smooth spot. The average brightness of the image is extracted as a feature component, and the calculation formula for the feature component is shown in Equation 1 above.

[0185] To minimize the enhancement of pixels that are too bright or too dark, while maximizing the enhancement of relatively smooth pixels, a nonlinear transformation function is used to quantize the enhancement magnitude of the pixels. The expression for the enhancement function Z(g) is shown in Equation 2 above.

[0186] When the grayscale value is closer to the average value v, Z(g) is closer to 0.5, indicating that the pixel is relatively smooth and has relatively low contrast. When the grayscale value is further away from the average value v, Z(g) is closer to 0 or 1, indicating that the pixel is darker or brighter and has higher contrast. The grayscale PF expression for the image after improved gamma correction is shown in Equation 3 above. Median filtering has a relatively ideal denoising effect, which can filter out noise points with obvious pixel value differences and effectively prevent blurring of image details such as contours and edges. It is better than mean filtering in suppressing salt-and-pepper noise and impulse interference, and can also ensure the clarity of edges, thus realizing the entire process of digital image preprocessing 1.1, transforming the original digital image F1 into the preprocessed digital image F2.

[0187] As described above, the key to pore structure identification 1.2 is finding the optimal segmentation threshold for the image and binarizing it. Finally, morphological operations are performed on the binary image to eliminate defects. An iterative image thresholding segmentation algorithm is used to segment the digital image F2, resulting in a binary image F3. The steps of the iterative image thresholding segmentation algorithm can be referred to in steps S10121-S10124 above to obtain the optimal threshold for segmentation. After thresholding and binarizing the image, the resulting target boundary is often very uneven. Mathematical morphology is used to smooth the object contour. Opening operations are used to remove pepper-like noise, and closing operations are used to remove sandstone noise. Continuous use of opening and closing operations can significantly improve the foreground and background noise in the binary image. The pore structure segmentation image is processed through a phase function. To define, for a two-phase system considering only pores and framework, when When located in a pore, the value is 1; when... When located in the skeletal phase, the value is 0.

[0188] As described in section 2.1 of the feature parameter calculation, the radius search method is used to search for the aperture region (white region) in the binary image F3. If the number of pixels with the maximum inscribed circle radius of the aperture region is N... r The radius of the pore region, denoted by R, is calculated using Equation 7 above.

[0189] The longest axis of the boundary is defined as the Euclidean distance between the two farthest points on the outer boundary of the pore region. If the longest axis is a useful descriptor, it is best applied to boundaries with a single pair of farthest points. When there are more than one pair of farthest points, they should be close to each other and it is the primary parameter determining the boundary shape. The line segment connecting these points is called the longest axis of the cell cavity boundary. The minor axis is defined as the line segment perpendicular to the longest axis, denoted as d. The relationship between the longest axis and the minor axis of the pore boundary is as follows: Figure 2 As shown,

[0190] The area of ​​the pore region represents the area of ​​the region formed by the set of pixels within the white closed area in the image, denoted by S. The pore region is divided into an n×n grid, with the leftmost point in each row being P(x). i ,y ij The rightmost point is Q(x). i ,y' ij If y = y, then the grid line contains y pixels. ij -y' ij +1, the area S of the pore region is calculated using Equation 8 above.

[0191] The porosity region, denoted by C, represents the side length of the region formed by the outermost set of pixels within the white closed area of ​​an image. The perimeter of the porosity region is the length of the chain code used for calculation. The perimeter is calculated by traversing the contour of the porosity region point by point and statistically analyzing the contour length. Initializing the perimeter C = 0, we iterate through all chain code elements of a single contour. If the chain code value is odd, then... If the chain code value is even, then C = C + 1. For each profile chain code, perform the above operation, summing the perimeters of all profiles to obtain the total perimeter C of the pore region. For macroscopic pores, the corresponding pore region perimeter is 2C / π.

[0192] The cross-scale image feature region localization described above mainly includes two steps: feature function construction (2.1) and key region search (2.2).

[0193] As described in section 2.1, to characterize the pore distribution features, an autocorrelation function S is constructed. x (r1,r2), this function can reflect the statistical characteristics of multi-scale porosity, autocorrelation function S x (r1, r2) is defined as the probability that any two points taken in an image of the same scale are distributed within the same aperture. The autocorrelation function S x (r1,r2) represents the correlation between any two points in an image at the same scale. For a two-phase system that only considers pores and skeleton, its expression is shown in Equation 9 above.

[0194] To reflect the degree of variation of regionalized variables within a certain distance range in a certain direction, a variogram function γ(r) is constructed. The variogram function of an image at the same scale is closely related to its structure. The variogram function γ(r) is an essential function for evaluating the structural properties of an image. The expression for the variogram function γ(r) is shown in Equation 10 above.

[0195] In order to characterize the fractal features of pores, a fractal dimension function D is constructed. F In the binary image F2, the perimeter and area of ​​each pore are measured. The slope of the double logarithm plot of area and perimeter is twice the slope of the fractal dimension function D. FThe value of is calculated using the formula above, 11.

[0196] To reflect the shape characteristics of the pores, a shape characteristic function G is established. T Because the shape of the pore region in carbonate reservoirs is very complex, in order to make the simplified pores have the same geometric characteristics as the real pores, it is necessary to evaluate the shape characteristics of the real pore region, that is, to construct the shape characteristic function G of the pore region as shown in Equation 12 above. T .

[0197] As described in section 2.2, the key region search combines the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics within a certain search area. A similarity matching algorithm is then used to search for key pore regions at different scales and within different regions at the same scale. Let q be the target region, t be the search region, and S be the pore region. x Let γ represent the similarity of pore distribution features between the search region and the target region, γ represent the similarity of pore variation features between the search region and the target region, D represent the similarity of pore fractal features between the search region and the target region, and G represent the similarity of pore shape features between the search region and the target region. Using Equation 14 above, the comprehensive similarity DC(q,t) between the two region images can be calculated. The target region t' corresponding to each scale of the digital image is obtained by filtering through the comprehensive similarity DC(q,t).

[0198] Euclidean distance is chosen as the measure of comprehensive similarity (DC) by calculating the Euclidean distance L from the target region q to the destination region t'. qt’ Euclidean distance L qt’ This represents the actual distance between two regions. This distance incorporates pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics. Euclidean distance L qt’ It can be calculated from Equation 13 above.

[0199] In the search for key pore regions at different scales, firstly, parameters for pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics are manually set, and the system automatically matches the target region q on the image at the specified scale. Alternatively, a specified region image is selected as the target region q. The pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region are calculated using Equations 9-12, respectively. Subsequently, region images of corresponding scales are selected as search regions t in images at different scales. Finally, the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics within a certain range, i.e., the aforementioned search region, are combined and measured using Euclidean distance L. qt’ The size is used to sort the target area distances between different scales, and the closest one is the key area of ​​the pore that needs to be searched and located.

[0200] As described above, the comprehensive quantitative characterization of multi-scale pore structure involves calculating the pore volume of the digital image F2. This is based on the assumption that the image is a cuboid with a two-dimensional base and the maximum gray level of each pixel as its height. Each pixel can be considered a cylinder with its own base and its own gray level as its height. The sum of the volumes of all cylinders is the skeleton volume, and the remaining volume is the pore volume. The pore volume ratio reflects the influence of the fractal characteristics of pores at different diameters on the overall fractal characteristics of the carbonate reservoir. Therefore, by using the pore volume of each diameter segment as a weight, the fractal dimension of pores at different scales is calculated using a weighted average. The result is the comprehensive multi-scale fractal dimension dz of the carbonate reservoir, as shown in Equation 15 above.

[0201] As described above, the cross-scale image feature region localization 2 first involves dividing the preprocessed digital image F2 into grids of 10*10 and 100*100, respectively. Figure 3 As shown, (a) is a 10*10 grid macroscopic image, (b) is a 10*10 grid mesoscopic image, (c) is a 10*10 grid microscopic image, (d) is a 100*100 grid macroscopic image, (e) is a 100*100 grid mesoscopic image, and (f) is a 100*100 grid microscopic image.

[0202] In the search for key regions in a 10x10 grid image, the search area in the macroscopic image is determined based on the mesoscopic image. (Refer to...) Figure 4 As shown, firstly, the mesoscopic image is selected as the target region. Then, the region image matrix [1,1] in the macroscopic image is used as the search region, and a comprehensive similarity calculation is performed on the two region images. After completing the search of the region image matrix [1,1] in the macroscopic image, the region image matrix [1,2] in the macroscopic image is used as the search region, and so on, until the search of the region image matrix [10,10] in the macroscopic image is completed. The search results are as follows. Figure 4 As shown in (a). In the key region search of a 10*10 grid image, the search for the corresponding region in the mesoscopic image is determined based on the microscopic image. First, the microscopic image is selected as the target region. Then, the region image matrix [1,1] in the mesoscopic image is used as the search region, and a comprehensive similarity calculation is performed on the two region images. After completing the search of the region image matrix [1,1] in the mesoscopic image, the region image matrix [1,2] in the mesoscopic image is used as the search region, and so on, until the search of the region image matrix [10,10] in the mesoscopic image is completed. The search results are as follows. Figure 4As shown in (b). In the key region search of the 10*10 grid image, the search area in the corresponding macroscopic image is determined based on the microscopic image. First, the microscopic image is selected as the target region, and then the region image matrix [1,1] in the macroscopic image is used as the search region, and a comprehensive similarity calculation is performed on the two region images. After completing the search of the region image matrix [1,1] in the macroscopic image, the region image matrix [1,2] in the macroscopic image is used as the search region, and so on, until the search of the region image matrix [10,10] in the macroscopic image is completed. The search results are as follows. Figure 4 As shown in (c). From Figure 4 As can be seen from (a), the highest comprehensive similarity value is 0.6104, corresponding to the 5th row and 7th column of the search matrix, indicating that the region image matrix [5,7] in the macroscopic image has the highest comprehensive similarity with the mesoscopic image. From Figure 4 As shown in (b), the highest comprehensive similarity value is 0.5824, corresponding to the 10th row and 10th column of the search matrix. This indicates that the region image matrix [10,10] in the mesoscopic image has the highest comprehensive similarity to the one in the microscopic image. Figure 4 As can be seen in (c), the maximum comprehensive similarity value is 0.6099, which corresponds to the 7th row and 9th column of the search matrix. This indicates that the comprehensive similarity between the region image matrix [7,9] in the macroscopic image and the microscopic image is the highest.

[0203] In key region search of a 100*100 grid image, the search area in the macroscopic image is determined based on the mesoscopic image. (Refer to...) Figure 5 As shown, firstly, the mesoscopic image is selected as the target region. Then, the region image matrix [1,1] in the macroscopic image is used as the search region, and a comprehensive similarity calculation is performed on the two region images. After completing the search of the region image matrix [1,1] in the macroscopic image, the region image matrix [1,2] in the macroscopic image is used as the search region, and so on, until the search of the region image matrix [100,100] in the macroscopic image is completed. The search results are as follows. Figure 5 As shown in (a). In the key region search of a 100*100 grid image, the search for the corresponding region in the mesoscopic image is determined based on the microscopic image. First, the microscopic image is selected as the target region. Then, the region image matrix [1,1] in the mesoscopic image is used as the search region, and a comprehensive similarity calculation is performed on the two region images. After completing the search of the region image matrix [1,1] in the mesoscopic image, the region image matrix [1,2] in the mesoscopic image is used as the search region, and so on, until the search of the region image matrix [100,100] in the mesoscopic image is completed. The search results are as follows. Figure 5As shown in (b). In the key region search of a 100*100 grid image, the search area in the corresponding macroscopic image is determined based on the microscopic image. First, the microscopic image is selected as the target region. Then, the region image matrix [1,1] in the macroscopic image is used as the search region, and a comprehensive similarity calculation is performed on the two region images. After completing the search of the region image matrix [1,1] in the macroscopic image, the region image matrix [1,2] in the macroscopic image is used as the search region, and so on, until the search of the region image matrix [100,100] in the macroscopic image is completed. The search results are as follows. Figure 5 As shown in (c).

[0204] from Figure 5 As shown in (a), the highest comprehensive similarity value is 0.8220, corresponding to the search matrix at row 81, column 35, indicating that the region image matrix [81, 35] in the macroscopic image has the highest comprehensive similarity to the mesoscopic image. Figure 5 As can be seen from (b), the highest comprehensive similarity value is 0.8216, corresponding to the search matrix at row 17, column 77, indicating that the region image matrix [17, 77] in the mesoscopic image has the highest comprehensive similarity to that in the microscopic image. Figure 5 As can be seen in (c), the maximum comprehensive similarity value is 0.7644, which corresponds to the search matrix in row 85 and column 49. This indicates that the comprehensive similarity between the region image matrix [85,49] in the macroscopic image and the microscopic image is the highest.

[0205] To verify the accuracy of the method of this invention in locating feature regions across scale images, the search results for key regions in 10*10 grid images, the search results for key regions in 100*100 grid images, and the regions where the actual extracted images were located were compared. The comparison diagram between the searched and located regions and the actual sampled regions is shown below. Figure 6 As shown. Figure 6 (a) is to search for the sampling region corresponding to the macroscopic scale through mesoscopic images. Figure 6 (b) is to search for the sampling region corresponding to the mesoscale through microscopic images. Figure 6 (c) This involves searching for the corresponding sampling region at the macroscopic scale using microscopic images. In the image, the darkest area represents the actual sampling region, the darker areas represent 10x10 grid search regions, and the lighter areas represent 100x100 grid search regions. From... Figure 6 As can be seen in (a), when searching for the sampling region corresponding to the macroscopic scale using mesoscopic images, using a 100*100 grid for the search results in a more accurate localized region compared to the actual sampling region. Figure 6As can be seen in (b), when searching for the sampling region corresponding to the mesoscale using microscopic images, a 100*100 grid is used for the search, and the calculated localization region is closer to the actual sampling region. From Figure 6 As shown in (c), when searching for the corresponding sampling region at the macroscopic scale using a microscopic image, a 100*100 grid results in a more accurate localized region compared to the actual sampling region. This is primarily because the actual sampling size is smaller, and a 100*100 grid is more efficient than a 10*10 grid, leading to a search region that is closer to the actual region. When using macroscopic images as the target image, using mesoscopic images as the target region is more reliable than using microscopic images. This is mainly because the pore structure characteristics at the mesoscopic scale are closer to those at the macroscopic scale than at the microscopic scale.

[0206] Example 3

[0207] This invention provides a device for quantitative characterization of the pore structure of carbonate reservoirs, referring to... Figure 7 As shown, it includes:

[0208] The feature extraction module 201 is used to extract pore structure features from digital images of different scales of the acquired carbonate reservoir to obtain corresponding binary images; wherein, the digital images of different scales include macroscopic digital images captured by high-definition optical imaging, mesoscopic digital images captured by optical microscopes, and microscopic digital images captured by scanning electron microscopes.

[0209] The first calculation module 202 is used to calculate the binary image at each scale based on the pre-constructed feature function to obtain the corresponding pore distribution features, pore variation features, pore fractal features and pore shape features;

[0210] The search module 203 is used to, for each scale of binary image, take at least a portion of the binary image as the target region, and search for key pore regions in binary images at different scales based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics and pore shape characteristics of the target region, and filter them by using a similarity matching algorithm.

[0211] The second calculation module 204 is used to calculate the multi-scale comprehensive fractal dimension of the carbonate reservoir based on the pre-constructed multi-scale comprehensive fractal dimension function and the key pore regions at different scales.

[0212] The quantitative characterization device for the pore structure of carbonate reservoirs provided in this embodiment of the invention has a similar implementation principle and technical effect to any of the aforementioned method embodiments, and will not be repeated here.

[0213] Example 4

[0214] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the quantitative characterization method for the pore structure of carbonate reservoirs as described in any of the foregoing method embodiments.

[0215] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; or it may exist independently and not assembled into the device / apparatus. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of the present invention.

[0216] According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0217] Example 5

[0218] This invention provides an electronic device, with reference to... Figure 8 As shown, it includes a processor 111, a communication interface 112, a memory 113, and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114.

[0219] Memory 113 is used to store computer programs;

[0220] When the processor 111 executes the program stored in the memory 113, it implements the quantitative characterization method of carbonate reservoir pore structure as described in any of the aforementioned method embodiments.

[0221] The electronic device provided in this embodiment of the invention has a similar implementation principle and technical effect to any of the aforementioned method embodiments, and will not be repeated here.

[0222] The aforementioned memory 113 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 113 has storage space for program code used to perform any of the method steps described above. For example, the storage space for program code may include individual program codes for implementing the various steps in the methods described above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, optical discs (CDs), memory cards, or floppy disks. Such computer program products are typically portable or fixed storage units. The storage unit may have storage segments or storage spaces arranged similarly to the memory 113 in the aforementioned electronic device. The program code may be compressed, for example, in a suitable form. Typically, the storage unit includes programs for performing the method steps according to embodiments of the invention, i.e., code that can be read by, for example, processor 111, which, when run by the electronic device, causes the electronic device to perform the various steps in the methods described above.

[0223] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. The terms "upper," "lower," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention.

[0224] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other. This invention is not limited to any single aspect, nor to any single embodiment, nor to any combination and / or substitution of these aspects and / or embodiments. Each aspect and / or embodiment of this invention can be used alone, or in combination with one or more other aspects and / or other embodiments.

[0225] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for quantitative characterization of the pore structure of carbonate reservoirs, characterized in that, include: Pore ​​structure features were extracted from the acquired digital images of carbonate reservoirs at different scales to obtain corresponding binary images; wherein, the digital images at different scales include macroscopic digital images captured by high-definition optical imaging, mesoscopic digital images captured by optical microscopy, and microscopic digital images captured by scanning electron microscopy. The binary image at each scale is calculated based on the pre-constructed feature function to obtain the corresponding pore distribution features, pore variation features, pore fractal features and pore shape features; Perform the following operations on the binary image at each scale: At least a portion of the binary image is taken as the target region; Based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region, a similarity matching algorithm is used to select the search region with the highest comprehensive similarity to the target region in binary images at different scales, thereby obtaining the corresponding target region. The Euclidean distances between the target region and the target region at different scales are determined respectively, and the region with the smallest Euclidean distance is selected to obtain the pore key region; Based on the pre-constructed multi-scale integrated fractal dimension function and the key pore regions at different scales, the multi-scale integrated fractal dimension of the carbonate reservoir is calculated.

2. The method for quantitative characterization of the pore structure of carbonate reservoirs according to claim 1, characterized in that, Based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region, a similarity matching algorithm is used to select the search region with the highest comprehensive similarity to the target region in binary images at different scales, thereby obtaining the corresponding target region, including: For each scale of the binary image, the binary image is divided into grids to obtain a grid image; Calculate the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of each search region in the grid image; Based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region, and the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the search region, the similarity of pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics between the target region and the search region are calculated respectively. The comprehensive similarity between the target region and each of the search regions is then calculated using the following formula: ； in, For the comprehensive similarity between the target region and the search region, For the target area, For the search area, For pore distribution characteristics similarity, Similarity of pore variation characteristics For pore fractal feature similarity, For pore shape feature similarity, , , and These are the weights for the similarity of pore distribution features, pore variation features, pore fractal features, and pore shape features, respectively. In binary images at different scales, the search regions with the highest comprehensive similarity to the target region are selected to obtain the corresponding target regions.

3. The method for quantitative characterization of the pore structure of carbonate reservoirs according to claim 1, characterized in that, The porosity distribution characteristics are calculated using the following formula: ； in, Pore ​​distribution characteristics, and For any two points in an image of the same scale, For point The phase function, For point The phase function.

4. The method for quantitative characterization of pore structure in carbonate reservoirs according to claim 1, characterized in that, The porosity variation characteristics are calculated using the following formula: ； in, Characteristics of porosity variation The equivalent radius of the pore is . For images of the same scale, the distance is The number of two points, For the first The radius of each pore, for The phase function value.

5. The method for quantitative characterization of the pore structure of carbonate reservoirs according to claim 1, characterized in that, The pore fractal characteristics are calculated using the following formula: ； in, It is a fractal feature of pores. The equivalent radius of the pore is . The number of pores with a diameter greater than r. It is a constant.

6. The method for quantitative characterization of the pore structure of carbonate reservoirs according to claim 1, characterized in that, The pore shape characteristics are calculated using the following formula: ； in, Features of pore shape, For the first The area of ​​each pore region; For the first The perimeter of each pore region.

7. The method for quantitative characterization of the pore structure of carbonate reservoirs according to claim 1, characterized in that, The multi-scale comprehensive fractal dimension of the carbonate reservoir is calculated based on the pre-constructed multi-scale comprehensive fractal dimension function and the key porosity regions at different scales, including: Based on the following formula, the multi-scale comprehensive fractal dimension of the carbonate reservoir is obtained by calculating the pore size segments in the key pore regions at different scales: ； in: The multi-scale comprehensive fractal dimension of carbonate reservoirs. For the first The proportion of pore volume in key pore regions at each scale to the total pore volume. , For scale quantity, For the first The first pore-critical region at each scale The fractal dimension of each aperture segment, , For the first The number of pore diameter segments divided into key regions of pore size at each scale. For the first The first pore-critical region at each scale The proportion of pore volume in a pore size segment to the pore volume in the critical region of that pore size.

8. The method for quantitative characterization of the pore structure of carbonate reservoirs according to claim 1, characterized in that, The method further includes: The radius of the pore region in the binary image is searched using the maximum circle search method, and the radius of the pore region of the single pore structure is determined by the following formula: ； in, The radius of the pore region is 1. The actual size represented by each pixel. The number of pixels representing the maximum radius of the inscribed circle in the pore region.

9. The method for quantitative characterization of the pore structure of carbonate reservoirs according to claim 1, characterized in that, The method further includes: The area of ​​the pore region in the binary image is determined based on the following formula: ； in, The area of ​​the pore region. The actual size represented by each pixel. The number of grids within the pore region. Let be the y-coordinate of the leftmost point in each row. Let be the y-coordinate of the rightmost point in each row. The number of pixels contained in each row of the grid. .

10. The method for quantitative characterization of the pore structure of carbonate reservoirs according to claim 1, characterized in that, The process of extracting pore structure features from digital images of the acquired carbonate reservoir at different scales to obtain corresponding binary images includes: Digital images of carbonate reservoirs at every scale: The digital images are preprocessed to obtain preprocessed digital images; An iterative image thresholding algorithm is used to identify the pore structure in the preprocessed digital image to obtain a binary image.

11. The method for quantitative characterization of the pore structure of carbonate reservoirs according to claim 10, characterized in that, The step of using an iterative image thresholding algorithm to identify the pore structure in the preprocessed digital image to obtain a binary image includes: Based on the minimum gray value in the preprocessed digital image and maximum grayscale value The initial threshold is determined by the following formula. ; ； The preprocessed digital image is segmented into a target part and a background part according to the initial threshold. Based on the gray values ​​of the target part and the background part, the updated threshold is obtained by updating the threshold using the following formula. ； in, The average grayscale value of all pixels in the target area. This represents the average grayscale value of all pixels in the background area. For the image The grayscale value of a dot. for The weighting coefficient of the point. The threshold before the update. The updated threshold; The preprocessed digital image is segmented based on the updated threshold, and the process of updating the threshold is repeated until the preset condition is met to obtain the optimal threshold. The preprocessed digital image is segmented based on the optimal threshold to obtain a binary image.

12. The method for quantitative characterization of the pore structure of carbonate reservoirs according to claim 10, characterized in that: The step of preprocessing the digital images to obtain preprocessed digital images includes: The feature components of the digital image are extracted based on the following formula: ； in, For characteristic components, For image gray levels, For pixel grayscale values, Image gray levels The number of pixels, , These represent the total number of rows and columns of the image matrix corresponding to the digital image, respectively. Based on the aforementioned feature components, the digital image is enhanced using the following formula to obtain the preprocessed digital image: ； in, To enhance the function, To enhance the parameters.

13. A device for quantitative characterization of the pore structure of carbonate reservoirs, characterized in that, include: The feature extraction module is used to extract pore structure features from digital images of carbonate reservoirs at different scales to obtain corresponding binary images; wherein, the digital images at different scales include macroscopic digital images captured by high-definition optical imaging, mesoscopic digital images captured by optical microscopy, and microscopic digital images captured by scanning electron microscopy. The first calculation module is used to calculate the binary image at each scale based on the pre-constructed feature function to obtain the corresponding pore distribution features, pore variation features, pore fractal features and pore shape features; The search module performs the following operations for each scale of binary image: at least a portion of the binary image is taken as the target region; based on the pore distribution characteristics, pore variation characteristics, pore fractal characteristics, and pore shape characteristics of the target region, a similarity matching algorithm is used to select the search region with the highest comprehensive similarity to the target region in binary images at different scales, thus obtaining the corresponding target region; the Euclidean distance between the target region and the target region at different scales is determined, and the region with the smallest Euclidean distance is selected to obtain the pore key region; The second calculation module is used to calculate the multi-scale comprehensive fractal dimension of the carbonate reservoir based on the pre-constructed multi-scale comprehensive fractal dimension function and the key pore regions at different scales.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the quantitative characterization method for the pore structure of carbonate reservoirs as described in any one of claims 1-12.

15. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the quantitative characterization method for the pore structure of carbonate reservoirs as described in any one of claims 1-12.

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