Digital core-based shale pore type recognition method and quantitative characterization method

Abstract

The present invention relates to the technical field of oil and gas exploration and development. Provided are a digital core-based shale pore type recognition method and a quantitative characterization method. The digital core-based shale pore type recognition method comprises: on the basis of a gray image of a shale sample to be recognized, recognizing an organic matter and a transition zone to obtain an organic matter recognition result; and on the basis of the organic matter recognition result and the gray image, obtaining an organic pore image and an inorganic pore image as a shale pore type recognition result. The present invention can be used to quantitatively recognize organic pores, inorganic pores and micro-cracks of shale, quantitatively characterize shale pore structures, and analyze structural parameters of the organic pores, the inorganic pores and the micro-cracks, so as to perform statistical analysis of distribution of different types of reservoir spaces of shale, thereby being of great significance for shale reservoir quality evaluation and effective shale oil and gas development method optimization.

Description

Identification and quantitative characterization of shale pore types based on digital cores

[0001] Cross-references to related art

[0002] This application claims priority to Chinese patent application No. 202310858325.2, filed on July 13, 2023, entitled “Shale pore type identification and quantitative characterization method based on digital core,” the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present invention relates to the technical field of oil and gas exploration and development, and in particular to a method for identifying and quantitatively characterizing shale pore types based on digital cores. Background Art

[0004] Shale reservoirs possess a complex spatial structure, with small pore sizes, primarily nanopores, and a diverse range of pore types, including organic pores, inorganic pores, and microfractures. Oil and gas within organic pores in shale reservoirs exist in an adsorbed and dissolved state, while oil and gas within inorganic pores are primarily free. Accurately and quantitatively characterizing the pore structure of different pore types in shale is crucial for studying the occurrence of shale oil and gas, evaluating resource quality, and developing appropriate development strategies.

[0005] Currently, fluid intrusion methods (gas adsorption, mercury intrusion, and nuclear magnetic resonance) are the mainstream methods for quantitatively characterizing the pore structure of porous materials. Numerous studies have used these techniques to study shale pore structure characteristics. However, these methods are theoretically unable to distinguish between organic and inorganic pores in shale. While imaging techniques can identify organic pores, they require manual labeling, which is inefficient, lacks automated identification methods, and only allows for qualitative analysis, not quantitative characterization.

[0006] In response to the problems of the prior art, the present invention provides a method for identifying and quantitatively characterizing shale pore types based on digital cores.

[0007] Summary of the Invention

[0008] To solve the above problems, the present invention provides a shale pore type identification method based on digital cores, which comprises:

[0009] Based on the grayscale image of the shale sample to be identified, the organic matter and transition zone are identified to obtain the organic matter identification result;

[0010] Based on the organic matter identification result and the grayscale image, an organic pore image and an inorganic pore image are obtained as a shale pore type identification result.

[0011] According to one embodiment of the present invention, organic matter and transition zone are identified by the following steps:

[0012] According to the grayscale distribution of the grayscale image, a grayscale lower limit and a grayscale upper limit corresponding to the organic matter and the transition zone are obtained, thereby obtaining an image of a connected body of the organic matter and the transition zone;

[0013] Based on the total area or total volume of the organic matter and transition zone connected body image and the effective area or effective volume of the organic matter and transition zone connected body image, organic matter determination is performed to obtain the organic matter identification result and the transition zone identification result.

[0014] According to one embodiment of the present invention, organic matter determination is performed through the following steps:

[0015] Taking the effective area or the effective volume of the region as the dividend and the total area or the total volume of the region as the divisor, an area efficiency rate or a volume efficiency rate is obtained as the efficiency rate of the organic matter and transition zone connected body image;

[0016] Setting an organic matter determination threshold, recording organic matter and transition zone connected body images with an efficiency less than the organic matter determination threshold as transition zone connected body images as the transition zone recognition result;

[0017] The organic matter and transition zone connected body images with an efficiency greater than or equal to the organic matter determination threshold are recorded as organic matter connected body images as the organic matter recognition result.

[0018] According to one embodiment of the present invention, the organic pore image and the inorganic pore image are identified and obtained by the following steps:

[0019] Obtaining a pore-connected body image according to the grayscale distribution of the grayscale image;

[0020] Based on the pore interconnected body image and the organic matter interconnected body image, pore recognition processing is performed to determine the organic pore image and the inorganic pore image.

[0021] According to one embodiment of the present invention, pore identification processing is performed through the following steps:

[0022] Based on the expansion template, the pore-connected body image is subjected to pore expansion to obtain the pore-expanded connected body image, and a boundary image of the pore-expanded connected body image is determined;

[0023] Calculating the perimeter of the pore expansion connected body in the pore expansion connected body image;

[0024] Taking the intersection of the boundary image and the organic matter connected body image as an intersection image, and obtaining the intersection image area;

[0025] Organic pore determination is performed based on the intersection image area and the perimeter of the pore expansion connected body to determine the organic pore image and the inorganic pore image.

[0026] According to one embodiment of the present invention, organic pore determination is performed by the following steps:

[0027] Using the area of ​​the intersection image as the dividend and the perimeter of the porous expansion interconnected body as the divisor, a quotient is obtained as the scale coefficient of the porous interconnected body image;

[0028] The pore interconnected body image with a scale factor equal to zero is recorded as the inorganic pore image, and the pore interconnected body image with a scale factor greater than zero and less than or equal to one is recorded as the organic pore image.

[0029] According to one embodiment of the present invention, the method further comprises:

[0030] setting an organic matter edge pore threshold for the organic pore image;

[0031] The organic pore images with a proportional coefficient greater than zero and less than or equal to the organic edge pore threshold are recorded as organic edge pore images, and the organic pore images with a proportional coefficient greater than the organic edge pore threshold and less than or equal to one are recorded as organic internal pore images.

[0032] According to one embodiment of the present invention, the method further comprises:

[0033] For the inorganic pore image, calculating the maximum Feret diameter and the minimum Feret diameter;

[0034] Using the minimum Feret diameter as the dividend and the maximum Feret diameter as the divisor, a quotient is obtained as the diameter ratio of the inorganic pore image;

[0035] The diameter threshold and the ratio threshold are set, and the inorganic pore images that meet the microcrack determination conditions are recorded as microcrack images, and the inorganic pore images that do not meet the microcrack determination conditions are recorded as inorganic pore images.

[0036] According to one embodiment of the present invention, the microcrack determination condition is: the maximum Feret diameter is greater than the diameter threshold, and the diameter ratio is less than the ratio threshold.

[0037] According to one embodiment of the present invention, the grayscale image is a two-dimensional or three-dimensional original grayscale image of the shale sample to be identified.

[0038] According to one embodiment of the present invention, the method further comprises performing grayscale adjustment processing on the original grayscale image, wherein:

[0039] Determining the grayscale distribution of the original grayscale image to obtain a first grayscale value corresponding to the pores and a second grayscale value corresponding to the rock skeleton;

[0040] Based on the first grayscale value and the second grayscale value, an original grayscale image after grayscale adjustment is obtained.

[0041] According to one embodiment of the present invention, the method further comprises:

[0042] Determining a noise reduction window based on the original grayscale image after grayscale adjustment;

[0043] The noise reduction standard deviation is calculated based on the maximum grayscale value and the minimum grayscale value within the noise reduction window;

[0044] Based on the noise reduction standard deviation, image noise reduction is performed on the original grayscale image after grayscale adjustment.

[0045] According to one embodiment of the present invention, the method comprises:

[0046] For the original grayscale image of the shale sample to be identified, grayscale adjustment processing and image noise reduction processing are performed to obtain a standard grayscale image;

[0047] Extracting organic matter and transition zone images from the standard grayscale image, performing organic matter recognition processing, and determining an organic matter connected body image;

[0048] Extracting a pore image from the standard grayscale image, combining it with the organic matter interconnected body image, performing pore recognition processing, and determining an organic pore image and an inorganic pore image;

[0049] A microcrack identification process is performed on the inorganic pore image to determine a microcrack image and an inorganic pore image.

[0050] According to one embodiment of the present invention, the original grayscale image of the shale sample to be identified is obtained through the following steps:

[0051] Scanning the shale sample to be identified by a scanning electron microscope to obtain a two-dimensional original grayscale image, wherein the resolution of the scanning electron microscope is less than or equal to 10 nm;

[0052] or,

[0053] The shale sample to be identified is scanned by electronic computed tomography to obtain a three-dimensional original grayscale image, wherein the electronic computed tomography adopts nanoscale electronic computed tomography.

[0054] According to one embodiment of the present invention, grayscale adjustment processing is performed through the following steps:

[0055] Determining the grayscale distribution of the original grayscale image to obtain a first grayscale value corresponding to the pores and a second grayscale value corresponding to the rock skeleton;

[0056] Based on the first grayscale value and the second grayscale value, grayscale adjustment is performed on the original grayscale image using a grayscale adjustment expression to obtain an original grayscale image after grayscale adjustment;

[0057] The grayscale adjustment expression is:

[0058] Wherein: I' is the grayscale value of the original grayscale image after grayscale adjustment; I is the grayscale value of the original grayscale image; I1 is the first grayscale value; I2 is the second grayscale value.

[0059] According to one embodiment of the present invention, image noise reduction is performed through the following steps:

[0060] Determining a noise reduction window based on the original grayscale image after grayscale adjustment;

[0061] According to the maximum grayscale value and the minimum grayscale value in the noise reduction window, the noise reduction standard deviation is calculated by the noise reduction standard deviation calculation expression;

[0062] Based on the noise reduction standard deviation, performing image noise reduction on the original grayscale image after grayscale adjustment by using a Gaussian noise reduction kernel function to obtain the standard grayscale image;

[0063] The noise reduction standard deviation calculation expression is:

[0064] Where: σ is the standard deviation of noise reduction; m is the tolerance coefficient; σ0 is the preset standard deviation; I max is the maximum grayscale value within the noise reduction window; I min is the minimum grayscale value within the noise reduction window.

[0065] According to one embodiment of the present invention, the organic matter connected body image is determined by the following steps:

[0066] Determining the grayscale lower limit and the grayscale upper limit of the organic matter and transition zone pixels according to the grayscale distribution of the standard grayscale image, so as to extract the organic matter and transition zone image;

[0067] performing the organic matter identification processing on the organic matter and transition zone images to determine the organic matter connected body image;

[0068] The organic matter identification process comprises the following steps:

[0069] A connected body calculation method is used for the organic matter and transition zone image to calculate and obtain a connected body image of the organic matter and transition zone;

[0070] Based on the total area or total volume of the organic matter and transition zone connected body image and the effective area or effective volume of the organic matter and transition zone connected body image, organic matter determination is performed to determine the organic matter connected body image.

[0071] According to one embodiment of the present invention, the organic pore image and the inorganic pore image are determined by the following steps:

[0072] Determining the grayscale lower limit and the grayscale upper limit of the pores according to the grayscale distribution of the standard grayscale image to extract the pore image;

[0073] A connected body calculation method is used for the pore image to calculate and obtain a pore connected body image;

[0074] The pore recognition process is performed based on the pore interconnected body image and the organic matter interconnected body image to determine the organic pore image and the inorganic pore image.

[0075] According to another aspect of the present invention, there is also provided a shale pore type identification device based on digital cores, which performs the shale pore type identification method based on digital cores as described above, and the device comprises:

[0076] A preprocessing module is used to perform grayscale adjustment and image noise reduction on the original grayscale image of the shale sample to be identified to obtain a standard grayscale image;

[0077] An organic matter identification module is used to extract organic matter and transition zone images from the standard grayscale image, perform organic matter identification processing, and determine an organic matter connected body image;

[0078] A pore recognition module is used to extract the pore image from the standard grayscale image, combine it with the organic matter interconnected body image, perform pore recognition processing, and determine the organic pore image and the inorganic pore image;

[0079] The microcrack identification module is used to perform microcrack identification processing on the inorganic pore image to determine the microcrack image and the inorganic pore image.

[0080] According to another aspect of the present invention, a computer-readable storage medium is provided, which contains a series of instructions for executing the steps of the shale pore type identification method based on digital cores as described in any one of the above items.

[0081] According to another aspect of the present invention, a method for quantitatively characterizing shale pore structure parameters based on digital cores is provided, the method comprising:

[0082] Based on the total number of pixels in the grayscale image and the actual length corresponding to a single pixel, and in combination with the organic matter identification result, the organic pore image, and the inorganic pore image determined by any of the methods described above, the pore structure parameters of the shale sample to be identified are obtained, wherein the pore structure parameters include at least one of a porosity characterization parameter, a pore volume characterization parameter, a pore equivalent radius distribution parameter, a pore shape factor characterization parameter, and a microcrack structure characterization parameter.

[0083] According to one embodiment of the present invention, based on the total number of pixels in the original grayscale image of the shale sample to be identified and the actual length corresponding to a single pixel, combined with the organic pore image, the microcrack image and the inorganic pore image determined by a shale pore type identification method based on a digital core as described in any of the above items, the porosity characterization parameters, pore volume characterization parameters, pore equivalent radius distribution parameters, pore shape factor characterization parameters and microcrack structure characterization parameters of the shale sample to be identified are calculated.

[0084] According to one embodiment of the present invention, the porosity characterization parameter of the shale sample to be identified is determined by the following expression:

[0085] Porosity φ' in organic matter r :

[0086] Organic pore porosity φ r :

[0087] Inorganic pore porosity φ i :

[0088] Microcrack porosity φ f :

[0089] Total porosity φ:

[0090] Where: x i Is the image of the i-th pore interconnected body an organic pore image, and takes the value of 1 or 0; S i is the number of pixels in the i-th organic pore / inorganic pore / microcrack image; q i Whether the image of the i-th organic matter and transition zone connected body is an organic matter image, the value is 1 or 0; E i is the image of the i-th organic matter and transition zone connected body; N is the total number of pixels in the original grayscale image; z i Indicates whether the i-th pore interconnected body image is a microcrack image, and takes the value of 1 or 0.

[0091] According to one embodiment of the present invention, the pore volume characterization parameter of the shale sample to be identified is determined by the following expression:

[0092] The total pore volume V corresponding to the original two-dimensional grayscale image i : V i =p 2 ·S i

[0093] The total pore volume V corresponding to the three-dimensional original grayscale image i : V i =p 3 ·S i

[0094] Organic pore volume corresponding to the original two-dimensional grayscale image

[0095] Organic pore volume corresponding to the original three-dimensional grayscale image

[0096] Inorganic pore volume corresponding to the original two-dimensional grayscale image

[0097] Inorganic pore volume corresponding to the original three-dimensional grayscale image

[0098] Microcrack volume corresponding to the original two-dimensional grayscale image

[0099] The microcrack volume corresponding to the original three-dimensional grayscale image

[0100] Where: p is the actual length corresponding to a single pixel; S i is the number of pixels in the i-th organic pore / inorganic pore / microcrack image; x i Whether the i-th pore interconnected body image is an organic pore image, the value is 1 or 0; z i Indicates whether the i-th pore interconnected body image is a microcrack image, and takes the value of 1 or 0.

[0101] According to one embodiment of the present invention, the pore equivalent radius distribution parameter of the shale sample to be identified is determined by the following expression:

[0102] The pore equivalent radius r corresponding to the two-dimensional original grayscale image i :

[0103] The pore equivalent radius r corresponding to the three-dimensional original grayscale image i :

[0104] Where: V i is the total pore volume.

[0105] According to one embodiment of the present invention, the pore shape factor characterization parameter of the shale sample to be identified is determined by the following expression:

[0106] The shape factor f corresponding to the two-dimensional original grayscale image i :

[0107] The shape factor f corresponding to the original three-dimensional grayscale image i :

[0108] Where: S i is the number of pixels in the i-th organic pore / inorganic pore / microcrack image; P i is the number of pixels on the outer surface of the i-th organic hole / inorganic hole.

[0109] According to one embodiment of the present invention, the microfracture structure characterization parameters of the shale sample to be identified are determined by the following expressions and steps:

[0110] The microcrack area S corresponding to the two-dimensional original grayscale image ssi : S ssi =L i ×W i

[0111] The microcrack perimeter P corresponding to the two-dimensional original grayscale image pi : P pi =2(L i +W i )

[0112] By combining the microcrack area expression and microcrack perimeter expression corresponding to the two-dimensional original grayscale image, the microcrack length and microcrack width corresponding to the two-dimensional original grayscale image can be obtained;

[0113] The microcrack volume S corresponding to the three-dimensional original grayscale image ssi :

[0114] The microcrack surface area P corresponding to the three-dimensional original grayscale image pi :

[0115] By combining the microcrack volume expression and microcrack surface area expression corresponding to the three-dimensional original grayscale image, the microcrack length and microcrack width corresponding to the three-dimensional original grayscale image can be obtained;

[0116] The origin and major axis of the ellipse are obtained through the second-order moment of the microcrack, and then the microcrack inclination angle θ is obtained;

[0117] The number of microcracks in the original grayscale image is M, then:

[0118] The microcrack density ρ corresponding to the two-dimensional original grayscale image L :

[0119] The microcrack density ρ corresponding to the original three-dimensional grayscale image L :

[0120] Where: L i is the length of the i-th microcrack; W i is the width of the i-th microcrack; p is the actual length corresponding to a single pixel; N is the total number of pixels in the original grayscale image.

[0121] According to another aspect of the present invention, there is also provided a device for quantitatively characterizing shale pore structure parameters based on digital cores, which performs the method for quantitatively characterizing shale pore structure parameters based on digital cores as described above, and the device comprises:

[0122] Based on the total number of pixels in the original grayscale image of the shale sample to be identified and the actual length corresponding to a single pixel, combined with the organic pore image, the microcrack image and the inorganic pore image determined by a shale pore type identification method based on a digital core as described in any of the above items, the porosity characterization parameters, pore volume characterization parameters, pore equivalent radius distribution parameters, pore shape factor characterization parameters and microcrack structure characterization parameters of the shale sample to be identified are calculated.

[0123] According to another aspect of the present invention, a computer-readable storage medium is provided, which contains a series of instructions for executing the steps of the method for quantitatively characterizing shale pore structure parameters based on digital cores as described above.

[0124] The present invention provides a method for identifying and quantitatively characterizing shale pore types based on digital cores. Compared with existing technologies, it has the following advantages:

[0125] 1. The present invention improves the noise reduction standard deviation in the Gaussian noise reduction kernel function and proposes a new image noise reduction processing method for shale digital images. According to the image noise reduction processing method proposed by the present invention, the local image quality within pores, organic matter, and rock skeleton can be improved while ensuring the clarity of the pore-organic matter boundary and the organic matter-rock skeleton boundary.

[0126] 2. The present invention proposes a new organic matter determination method for shale digital images, which effectively identifies organic matter and transition zone through the images of connected bodies of organic matter and transition zone. It has high recognition accuracy, simple implementation, and high feasibility, and can quickly identify images of connected bodies of organic matter and connected bodies of transition zone.

[0127] 3. The present invention proposes a new organic pore determination method for shale digital images, which identifies inorganic pore images and organic pore images in pore connectivity images through proportional coefficients; for organic pore images, the organic edge pore images and organic internal pore images are identified through the organic edge pore threshold. This method has high recognition accuracy, simple implementation, and high feasibility, and can quickly identify organic edge pore images and organic internal pore images.

[0128] 4. The present invention proposes a new micro-crack identification and processing method for shale digital images, which uses two indicators to identify micro-cracks: one is the maximum Feret diameter, and the other is the ratio of the minimum Feret diameter to the maximum Feret diameter. It has high recognition accuracy, simple implementation, and high feasibility, and can quickly identify micro-crack images and inorganic pore images.

[0129] 5. The present invention proposes a new quantitative characterization method for shale pore structure parameters for shale digital images, which can quantitatively characterize the porosity characterization parameters, pore volume characterization parameters, pore equivalent radius distribution parameters, pore shape factor characterization parameters and microcrack structure characterization parameters of the shale sample to be identified.

[0130] The present invention can quantitatively identify organic pores, inorganic pores and microcracks in shale, quantitatively characterize the pore structure of shale, analyze the structural parameters of organic pores, inorganic pores and microcracks, and statistically calculate the distribution of different types of reservoir spaces in shale. This is of great significance for shale reservoir quality evaluation and the optimization of effective shale oil and gas development methods.

[0131] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0132] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0133] FIG1A shows a flowchart of a method for identifying shale pore types based on digital cores according to an embodiment of the present invention;

[0134] FIG1B shows a flowchart of a method for identifying shale pore types based on digital cores according to another embodiment of the present invention;

[0135] FIG2 shows a flow chart of steps of a method for obtaining a standard grayscale image according to an embodiment of the present invention;

[0136] FIG3 shows a grayscale image of pixels within a noise reduction window according to an embodiment of the present invention;

[0137] FIG4 shows a flow chart of steps of a method for determining an organic pore image and an inorganic pore image according to one embodiment of the present invention;

[0138] FIG5 shows a standard grayscale image according to an embodiment of the present invention;

[0139] FIG6 shows an image of organic matter and transition zones according to an embodiment of the present invention;

[0140] FIG7 shows an image of organic matter and transition zone connected bodies according to an embodiment of the present invention;

[0141] FIG8 shows a schematic diagram of the total area and effective area of ​​a region according to an embodiment of the present invention;

[0142] FIG9 shows a pore image according to one embodiment of the present invention;

[0143] FIG10 shows an image of a pore interconnected body according to an embodiment of the present invention;

[0144] FIG11 shows a schematic diagram of a pore template according to an embodiment of the present invention;

[0145] FIG12 shows an image of a pore-expanded interconnected body according to an embodiment of the present invention;

[0146] FIG13 shows a diagram showing the positional relationship between pores and organic matter according to an embodiment of the present invention;

[0147] FIG14 shows a schematic diagram of Feret's maximum diameter and Feret's minimum diameter of pores and microcracks according to an embodiment of the present invention;

[0148] FIG15 shows a schematic diagram of organic pores and inorganic pores in an original grayscale image according to one embodiment of the present invention;

[0149] FIG16 shows a schematic diagram of a shale pore structure parameter calculation process according to one embodiment of the present invention;

[0150] FIG17 shows a schematic diagram of the equivalent morphology of microcracks after straightening according to an embodiment of the present invention;

[0151] FIG18 shows a schematic diagram of a microcrack ellipse according to an embodiment of the present invention;

[0152] FIG19 shows an original SEM scan image according to one embodiment of the present invention;

[0153] FIG20 shows a grayscale distribution diagram of a scanned image A according to an embodiment of the present invention;

[0154] FIG21 shows an image B after grayscale adjustment according to an embodiment of the present invention;

[0155] FIG22 shows a grayscale distribution diagram of an image B after grayscale adjustment according to an embodiment of the present invention;

[0156] FIG23 shows a denoised image C according to an embodiment of the present invention;

[0157] FIG24 shows an organic matter and transition zone image D according to one embodiment of the present invention;

[0158] FIG25 shows an organic matter and transition zone interconnecting body E according to an embodiment of the present invention;

[0159] FIG26 shows an efficiency distribution diagram according to an embodiment of the present invention;

[0160] FIG27 shows an image of a connected organic matter body according to an embodiment of the present invention;

[0161] FIG28 shows an image of an inorganic pore edge transition zone according to an embodiment of the present invention;

[0162] FIG29 shows a schematic diagram of a pore image according to an embodiment of the present invention;

[0163] FIG30 shows a pore interconnected body image F according to an embodiment of the present invention;

[0164] FIG31 shows a pore expansion interconnected body image G according to an embodiment of the present invention;

[0165] FIG32 shows a pore expansion map boundary image H according to one embodiment of the present invention;

[0166] FIG33 shows a boundary image and an organic matter intersection image according to one embodiment of the present invention;

[0167] FIG34 shows a diagram of pore identification results according to one embodiment of the present invention;

[0168] FIG35 shows a total pore volume distribution graph according to one embodiment of the present invention;

[0169] FIG36 shows a pore volume distribution diagram of organic pores according to one embodiment of the present invention;

[0170] FIG37 shows a pore volume distribution diagram of inorganic pores according to one embodiment of the present invention;

[0171] FIG38 shows a total pore radius distribution diagram according to one embodiment of the present invention;

[0172] FIG39 shows a pore radius distribution diagram of organic pores according to one embodiment of the present invention;

[0173] FIG40 shows a distribution diagram of inorganic pore radius according to one embodiment of the present invention;

[0174] FIG41 shows a total pore shape factor distribution graph according to one embodiment of the present invention;

[0175] FIG42 shows a distribution diagram of organic pore shape factors according to one embodiment of the present invention;

[0176] FIG. 43 shows a graph of inorganic pore shape factor distribution according to one embodiment of the present invention.

[0177] In the accompanying drawings, the same reference numerals are used for the same parts. In addition, the accompanying drawings are not drawn according to the actual scale. DETAILED DESCRIPTION

[0178] To make the objectives, technical solutions and advantages of the present invention more clear, embodiments of the present invention are described in further detail below with reference to the accompanying drawings.

[0179] Prior art (CN112858136B) provides a quantitative evaluation method for the pore structure of shale organic matter, which uses carbon dioxide and nitrogen adsorption methods to determine the pore structure parameters of shale organic matter. Prior art (CN111563695A) provides a rapid evaluation method for the pore structure of mud shale, which uses nitrogen adsorption method to quantitatively characterize and evaluate the pore structure of mud shale. Prior art (CN108169099A) provides a quantitative calculation method for the pore structure of shale gas reservoirs based on nuclear magnetic resonance, which uses nuclear magnetic resonance method to quantitatively calculate the pore structure of shale gas reservoirs. Prior art (CN111398122A) provides a comprehensive characterization method for the heterogeneity characteristics of the full-scale pore structure of shale, which uses high-pressure mercury injection experiments, low-temperature nitrogen adsorption experiments, and low-temperature carbon dioxide adsorption experiments to comprehensively analyze the pore structure characteristics of shale. Existing technology (CN115753866A) provides a method for quantitatively characterizing shale pore structure. This method uses a combination of low-pressure nitrogen adsorption, high-pressure mercury porosimetry, and nuclear magnetic resonance to characterize the pore radius distribution of shale. However, from a theoretical perspective, this existing technology cannot distinguish between organic and inorganic pores in shale.

[0180] Existing technology (CN110132816A) provides a method for analyzing the pore structure of organic matter in Lower Paleozoic shales. This method can observe and describe the pore development characteristics of different types of organic matter, including the morphology, size, and distribution of different organic pores. However, this method requires manual observation of the shape, occurrence, and connectivity of organic pores using a scanning electron microscope, measurement of pore size, and description of pore development based on the number and size of pores. This manual labeling of organic pores is inefficient and cannot automatically identify organic pores.

[0181] Prior art (CN109387468A) provides a method and system for measuring and analyzing the characteristic parameters of nanopore structure in shale reservoirs. This method uses reconstructed three-dimensional images and image processing methods to statistically analyze, measure, and calculate the characteristic parameters of shale nanopore structure. However, the method does not disclose the image processing method used to statistically measure and calculate the characteristic parameters of shale nanopore structure.

[0182] The existing technology (Gou Qiyang, Xu Shang, Hao Fang, Lu Yangbo, Zhang Aihua, Wang Yuxuan, Cheng Xuan, Qing Jiawei. Nano-CT shale pore structure characterization method - taking JY-1 well as an example [J]. Acta Petrolei Sinica, 2018, 39(11):1253-1261.) constructed a 3D pore network model of shale and evaluated the porosity, pore characteristics and pore connectivity of shale samples from the Lower Silurian Longmaxi Formation in the Jiaoshiba area of ​​the Sichuan Basin. However, it does not disclose the quantitative study of shale pore structure using image recognition methods.

[0183] The existing technology (Huang Jiaguo, Xu Kaiming, Guo Shaobin, Guo Hewei. Comprehensive study of pore structure of shale reservoir based on SEM, NMR and X-CT [J]. Modern Geology, 2015, 29(01):198-205.) uses SEM, NMR and X-CT to conduct comprehensive study of pore structure of shale reservoir. However, it can only perform qualitative analysis and cannot quantitatively characterize the pore structure of shale reservoir.

[0184] In summary, there is currently a lack of quantitative characterization methods that consider the pore structure of different types of pores in shale. To address the above-mentioned shortcomings of the existing technology, the present invention establishes a shale pore type identification and quantitative characterization method based on digital cores. This method can automatically identify and quantitatively characterize the pore structure of organic pores, inorganic pores, and microcracks in shale, providing a basis for shale oil and gas reservoir quality evaluation and development method optimization.

[0185] FIG1A shows a flowchart of a method for identifying shale pore types based on digital cores according to an embodiment of the present invention.

[0186] As shown in FIG1A , in step S1 , organic matter and transition zone are identified based on the grayscale image of the shale sample to be identified, and an organic matter identification result is obtained.

[0187] It should be noted that in the present invention, the transition zone refers to the transition area between inorganic pores and inorganic matter in the grayscale image of the shale sample to be identified (for example: a circular band area surrounding the inorganic pores, an irregular ring band area surrounding the inorganic pores), and the grayscale value distribution coincides with that of organic matter.

[0188] In one embodiment, organic matter and transition zone are identified by the following steps: based on the grayscale distribution of the grayscale image, the grayscale lower limit and grayscale upper limit corresponding to the organic matter and transition zone are obtained, and then the organic matter and transition zone connected body image is obtained; based on the total area or total volume of the region of the organic matter and transition zone connected body image and the effective area or effective volume of the region of the organic matter and transition zone connected body image, the organic matter is judged, thereby obtaining the organic matter identification result and the transition zone identification result.

[0189] In one embodiment, organic matter determination is performed through the following steps: taking the effective area or effective volume of the region as the dividend and the total area or total volume of the region as the divisor, obtaining the area efficiency rate or volume efficiency rate as the efficiency rate of the organic matter and the transition zone connected body image; setting an organic matter determination threshold, recording the organic matter and the transition zone connected body image with an efficiency rate less than the organic matter determination threshold as the transition zone connected body image, as the transition zone identification result; recording the organic matter and the transition zone connected body image with an efficiency rate greater than or equal to the organic matter determination threshold as the organic matter connected body image, as the organic matter identification result.

[0190] As shown in FIG1A , in step S2 , an organic pore image and an inorganic pore image are obtained based on the organic matter identification result and the grayscale image as the shale pore type identification result.

[0191] In one embodiment, the organic pore image and the inorganic pore image are identified by the following steps: a pore interconnected body image is obtained according to the grayscale distribution of the grayscale image; and pore identification processing is performed based on the pore interconnected body image and the organic matter interconnected body image to determine the organic pore image and the inorganic pore image.

[0192] In one embodiment, pore identification processing is performed through the following steps: based on the expansion template, the pore expansion is performed on the pore interconnected body image to obtain the pore expansion interconnected body image, and the boundary image of the pore expansion interconnected body image is determined; the pore expansion interconnected body perimeter in the pore expansion interconnected body image is calculated; the intersection of the boundary image and the organic matter interconnected body image is taken as the intersection image to obtain the intersection image area; organic pores are judged based on the intersection image area and the pore expansion interconnected body perimeter to determine the organic pore image and the inorganic pore image.

[0193] In one embodiment, the organic pore determination is performed by the following steps: using the intersection image area as the dividend and the perimeter of the pore expansion interconnected body as the divisor to obtain a quotient as the scale coefficient of the pore interconnected body image; recording the pore interconnected body image with a scale coefficient equal to zero as an inorganic pore image, and recording the pore interconnected body image with a scale coefficient greater than zero and less than or equal to one as an organic pore image.

[0194] In one embodiment, the method further includes: setting an organic matter edge pore threshold for the organic matter pore image; recording the organic matter edge pore image with a proportional coefficient greater than zero and less than or equal to the organic matter edge pore threshold as an organic matter edge pore image, and recording the organic matter internal pore image with a proportional coefficient greater than the organic matter edge pore threshold and less than or equal to one as an organic matter internal pore image.

[0195] In one embodiment, the method further includes: calculating the maximum Feret diameter and the minimum Feret diameter for the inorganic pore image; using the minimum Feret diameter as the dividend and the maximum Feret diameter as the divisor to obtain a quotient as the diameter ratio of the inorganic pore image; setting a diameter threshold and a ratio threshold, recording the inorganic pore image that meets the microcrack judgment conditions as a microcrack image, and recording the inorganic pore image that does not meet the microcrack judgment conditions as an inorganic pore image.

[0196] In one embodiment, the microcrack determination condition is: the maximum Feret diameter is greater than a diameter threshold, and the diameter ratio is less than a ratio threshold.

[0197] In one embodiment, the grayscale image is a two-dimensional or three-dimensional original grayscale image of the shale sample to be identified.

[0198] In one embodiment, a shale pore type identification method based on digital cores further includes grayscale adjustment processing of the original grayscale image, wherein: the grayscale distribution of the original grayscale image is determined to obtain a first grayscale value corresponding to the pore and a second grayscale value corresponding to the rock skeleton; based on the first grayscale value and the second grayscale value, the original grayscale image after grayscale adjustment is obtained.

[0199] In one embodiment, a shale pore type identification method based on digital cores further includes: determining a noise reduction window based on an original grayscale image after grayscale adjustment; calculating a noise reduction standard deviation based on a maximum grayscale value and a minimum grayscale value within the noise reduction window; and performing image noise reduction on the original grayscale image after grayscale adjustment based on the noise reduction standard deviation.

[0200] FIG1B shows a flowchart of a method for identifying shale pore types based on digital cores according to another embodiment of the present invention.

[0201] As shown in FIG1B , in step S101 , a standard grayscale image is obtained. Specifically, grayscale adjustment and image noise reduction are performed on the original grayscale image of the shale sample to be identified to obtain a standard grayscale image.

[0202] In one embodiment, a standard grayscale image is obtained through steps S201 - S203 shown in FIG. 2 .

[0203] As shown in FIG2 , in step S201 , an original grayscale image (image A) is obtained. Specifically, the shale sample to be identified is scanned by a scanning electron microscope or a computerized tomography to obtain a two-dimensional or three-dimensional original grayscale image.

[0204] In one embodiment, a shale sample to be identified is scanned using a scanning electron microscope (SEM) to obtain a two-dimensional raw grayscale image. By setting the resolution, the raw grayscale image can clearly observe the pores and cracks in the shale core, wherein the resolution of the SEM is less than or equal to 10 nm.

[0205] In one embodiment, the shale sample to be identified is scanned by electronic computed tomography (CT) to obtain a three-dimensional original grayscale image. By setting the resolution, the scanned original grayscale image can clearly observe the pores and cracks in the shale core, wherein the electronic computed tomography uses nanoscale electronic computed tomography.

[0206] In practice, raw grayscale images suffer from two problems: first, irrational brightness distribution, resulting in low contrast between the region of interest and the background; and second, the presence of noise in the scanned image, requiring further processing. To address these issues with raw grayscale images, the present invention improves the irrational brightness distribution of raw grayscale images through grayscale adjustment and reduces noise through image noise reduction.

[0207] As shown in FIG. 2 , in step S202 , the original grayscale image (image B) after grayscale adjustment is obtained.

[0208] In one embodiment, grayscale adjustment processing is performed through steps S2021-S2022.

[0209] In step S2021, the grayscale distribution of the original grayscale image is determined to obtain a first grayscale value corresponding to the pores and a second grayscale value corresponding to the rock skeleton. Specifically, the grayscale value of the original grayscale image is 1. Based on the grayscale distribution, the grayscale value that is clearly a pore is recorded as I1, and the grayscale value that is clearly a rock skeleton is recorded as I2.

[0210] In step S2022, based on the first grayscale value and the second grayscale value, the grayscale adjustment expression is used to adjust the grayscale of the original grayscale image to obtain the grayscale-adjusted original grayscale image. Specifically, the grayscale adjustment expression is:

[0211] Wherein: I' is the grayscale value of the original grayscale image after grayscale adjustment; I is the grayscale value of the original grayscale image; I1 is the first grayscale value; I2 is the second grayscale value.

[0212] In the present invention, after the grayscale adjustment process, the contrast of the original grayscale image after the grayscale adjustment is increased, which is conducive to further identifying pores and organic matter.

[0213] As shown in FIG. 2 , in step S203 , a standard grayscale image (image C) is obtained.

[0214] In one embodiment, image noise reduction processing is performed through steps S2031 - S2033 to obtain a standard grayscale image.

[0215] In step S2031, a noise reduction window is determined based on the original grayscale image after grayscale adjustment. Specifically, a 3*3 noise reduction window as shown in FIG3 can be selected.

[0216] In step S2032, the noise reduction standard deviation is calculated based on the maximum grayscale value and the minimum grayscale value in the noise reduction window by using the noise reduction standard deviation calculation expression. Specifically, as shown in FIG3 , the maximum grayscale value and the minimum grayscale value in the noise reduction window are determined by the following expression: max =max I ij (2) I min =min I ij (3)

[0217] In one embodiment, the noise reduction standard deviation calculation expression is:

[0218] Where: σ is the standard deviation of noise reduction; m is the tolerance coefficient. The larger its value, the greater the boundary fuzziness after noise reduction, and the smaller its value, the clearer the boundary; σ0 is the preset standard deviation; I max is the maximum grayscale value within the noise reduction window; I min is the minimum grayscale value within the noise reduction window.

[0219] In step S2033, based on the noise reduction standard deviation, the original grayscale image after grayscale adjustment is subjected to image noise reduction using a Gaussian noise reduction kernel function to obtain a standard grayscale image. Specifically, the calculated noise reduction standard deviation is substituted into the Gaussian noise reduction kernel function to perform image noise reduction on the original grayscale image after grayscale adjustment to obtain a standard grayscale image.

[0220] The Gaussian denoising kernel function is:

[0221] Where: r is the distance vector.

[0222] Different from the noise reduction requirements of conventional digital images, the noise reduction of shale digital images must maintain the clarity of the pore-organic matter boundary and the organic matter-solid skeleton boundary while eliminating local image noise. Therefore, the present invention improves the noise reduction standard deviation in the Gaussian noise reduction kernel function and proposes a new image noise reduction processing method for shale digital images. According to the image noise reduction processing method proposed by the present invention, for pixels within the pores, organic matter, and rock skeleton, I max with I min The smaller the difference, the larger the value of the noise reduction standard deviation σ, the stronger the noise reduction effect, and the better the local image quality. At the same time, for the pore-organic matter boundary and the organic matter-rock skeleton boundary, I max with I min The larger the difference, the smaller the standard deviation σ, and the stronger the noise reduction effect, the clearer the boundaries. Therefore, the image noise reduction method provided by the present invention can improve the quality of local images within pores, organic matter, and the rock skeleton while ensuring the clarity of pore-organic matter boundaries and organic matter-rock skeleton boundaries.

[0223] As shown in FIG1B , in step S102 , an organic matter connected body image is determined. Specifically, organic matter and transition zone images are extracted from the standard grayscale image, and organic matter recognition processing is performed to determine the organic matter connected body image.

[0224] In one embodiment, the organic matter interconnected body image is determined through steps S401 - S403 shown in FIG. 4 .

[0225] As shown in Figure 4, in step S401, an organic matter and transition zone image (Image D) is obtained. Specifically, based on the grayscale distribution of the standard grayscale image, the grayscale lower limit and grayscale upper limit of the organic matter and transition zone pixels are determined to extract the organic matter and transition zone image.

[0226] As shown in Figure 5, organic matter typically contains one or more organic pores, while the periphery of the inorganic pores forms a transition zone to the rock skeleton. The transition zone and the organic matter have essentially the same grayscale, making it difficult to distinguish them based on grayscale values. However, in shale images, there are distinct grayscale boundaries between the pores, organic matter, and rock skeleton. Therefore, in step S401, based on the grayscale distribution of the standard grayscale image, a threshold segmentation method is used to extract the organic matter and transition zone images, as shown in Figure 6.

[0227] In one embodiment, the organic matter and transition zone image f is extracted by the following expression: f = {x|I3<x<I4} (6)

[0228] Where: I3 is the lower limit of the grayscale of organic matter and transition zone pixels, and I4 is the upper limit of the grayscale of organic matter and transition zone pixels.

[0229] After the organic matter and transition zone images are obtained in step S401 , organic matter identification processing is performed on the organic matter and transition zone images in steps S402 - S403 to determine an organic matter connected body image.

[0230] As shown in FIG4 , in step S402 , an organic matter and transition zone connected body image (image E) is obtained. Specifically, a connected body calculation method is used for the organic matter and transition zone image to obtain an organic matter and transition zone connected body image. Further, a connected body calculation method is used to calculate individual organic matter or transition zones. The calculation results are shown in FIG7 (the left image in FIG7 is individual organic matter, and the right image is individual transition zones). The i-th organic matter and transition zone connected body image is denoted as E. i In one embodiment, the connected body calculation method may adopt a seed fill algorithm or a two-pass algorithm.

[0231] As shown in FIG4 , in step S403 , an organic matter connected volume image is obtained. Specifically, organic matter determination is performed based on the total area or total volume of the organic matter and transition zone connected volume image and the effective area or effective volume of the organic matter and transition zone connected volume image to determine the organic matter connected volume image.

[0232] In one embodiment, organic matter determination is performed through steps S4031 - S4032 .

[0233] In step S4031, the effective area or effective volume of the region is used as the dividend and the total area or total volume of the region is used as the divisor to calculate the area efficiency or volume efficiency as the efficiency of the organic matter and transition zone connected body image. Specifically, the organic matter and transition zone connected body image E i The total area of ​​the region (left side of Figure 8) or the total volume of the region is S s1 , the effective area of ​​the region (right side of Figure 8) or the effective volume of the region is S s2 ,but:

[0234] Where: α is the efficiency of the connected body image of organic matter and transition zone.

[0235] By observing a large number of shale scan images, the present invention has determined that shale organic matter contains zero, one, or many organic pores. However, overall, the total area (volume) of these pores is relatively small, and the vast majority of the area (volume) within the organic matter coverage area is organic matter. This is different in the transition zone, which is the transition pixel between pores and the rock skeleton and has a relatively small area (volume) overall. Therefore, the present invention uses the area percentage within the coverage area to determine organic matter and transition zone content.

[0236] In step S4032, an organic matter determination cutoff value (organic matter determination threshold) is set, and an organic matter and transition zone connected body image with an efficiency less than the organic matter determination cutoff value is recorded as a transition zone connected body image, and an organic matter and transition zone connected body image with an efficiency greater than or equal to the organic matter determination cutoff value is recorded as an organic matter connected body image. Specifically, let α0 be the organic matter determination cutoff value for determining organic matter and transition zone. When α≥α0, the organic matter and transition zone connected body image E is determined. i is the image of the connected body of organic matter; when α<α0, the image E of the connected body of organic matter and transition zone is determined i It is the image of the connected body in the transition zone.

[0237] Use q i Represents the image of organic matter and transition zone connected body E i Whether it is organic matter, that is:

[0238] Traverse all organic matter and transition zone connected body images to complete the organic matter identification and judgment process.

[0239] The present invention recognizes organic matter and transition zone through the efficiency of organic matter and transition zone connected body images, has high recognition accuracy, simple implementation, and high feasibility, and can quickly recognize organic matter connected body images and transition zone connected body images.

[0240] As shown in Figure 1B, in step S103, the organic pore image and the inorganic pore image are determined. Specifically, the pore image is extracted from the standard grayscale image and combined with the organic interconnected body image to perform pore identification processing to determine the organic pore image and the inorganic pore image. Furthermore, after identifying the organic matter and transition zone, the organic and inorganic pores can be identified by determining the contact relationship between the pores, the organic matter, and the transition zone.

[0241] In one embodiment, the organic pore image and the inorganic pore image are determined through steps S404 - S406 shown in FIG. 4 .

[0242] As shown in Figure 4 , in step S404, a pore image is obtained. Specifically, based on the grayscale distribution of the standard grayscale image, the lower and upper grayscale limits of the pores are determined to extract the pore image. Specifically, based on the grayscale distribution of the standard grayscale image, it is found that the pores have the lowest grayscale value. Using the threshold segmentation method, the pore image shown in Figure 9 can be extracted.

[0243] As shown in FIG4 , in step S405 , a pore connected body image (image F) is obtained. Specifically, a connected body calculation method is used for the pore image to calculate the pore connected body image. Further, the connected body calculation method is used to calculate the individual pores. The calculation results are shown in FIG10 . The i-th pore connected body image is denoted as F i In one embodiment, the connected body calculation method may adopt a seed fill algorithm or a two-pass algorithm.

[0244] As shown in Figure 4, in step S406, an organic pore image and an inorganic pore image are determined. Specifically, pore recognition processing is performed based on the pore interconnected body image and the organic matter interconnected body image to determine the organic pore image and the inorganic pore image.

[0245] In one embodiment, pore identification processing is performed through steps S4061-S4064.

[0246] In step S4061, a pore expansion connected body image (image G) is obtained. Specifically, based on the expansion template, the pore expansion connected body image is performed to obtain the pore expansion connected body image, and the boundary image of the pore expansion connected body image is determined. Specifically, for the i-th pore image F i , expand according to the pore template T, and obtain the pore expansion connected body image shown in Figure 12. In one embodiment, the pore template T is shown in Figure 11.

[0247] The expanded image is recorded as the pore expansion connected body image G i ,but:

[0248] Where: is the pore expansion operator.

[0249] The boundary image of the pore expansion connected body image is denoted as H i , then: H i =G i \F i (10)

[0250] Where: \ is the difference operation.

[0251] In step S4062, the perimeter of the pore expansion connected body in the pore expansion connected body image is calculated. Specifically, the pore expansion connected body image G after pore expansion is calculated. i The perimeter of the pore expansion connected body is L li .

[0252] In step S4063, the intersection of the boundary image and the organic matter connected body image is taken as the intersection image, and the area of ​​the intersection image is calculated. Specifically, the boundary image H of the pore expansion connected body image is used. i and organic matter connected body image E j Take the intersection and record the result as the intersection image P ij , and calculate the intersection image P ij The area of ​​the intersection image is S ij Then: P ij =E j ∩H i (11)

[0253] Where: ∩ is the intersection operator.

[0254] In step S4064, organic pore determination is performed by combining the intersection image area and the perimeter of the pore expansion connected body to determine the organic pore image and the inorganic pore image.

[0255] In one embodiment, organic pore determination is performed through steps S40641-S40643.

[0256] In step S40641, the intersection image area is used as the dividend and the perimeter of the porous expansion connected body is used as the divisor to calculate the quotient value, which is used as the scale coefficient of the porous connected body image. Specifically, for the i-th porous connected body image F i Make a judgment and record the pore expansion connected body image G i The ratio of the intersection of the organic matter connected body image to the total pore perimeter is β i ,but:

[0257] Furthermore, if β i Equal to 0, indicating that the pore-connected image F i If the intersection with all organic matter images is an empty set, then the pore connected body image F is marked i is a non-organic pore; otherwise, the pore interconnected body image F i Organic pores.

[0258] In step S40642, pore interconnectedness images with a scale factor equal to zero are recorded as inorganic pore images, and pore interconnectedness images with a scale factor greater than zero and less than or equal to one are recorded as organic pore images. Specifically, as shown in Figure 13, the white portion within the frame represents the intersection of the pore expansion interconnectedness image and the organic matter interconnectedness image. β represents the ratio of the intersection of the pore and organic matter to the total pore perimeter, i.e., the scale factor, 0 ≤ β ≤ 1. For organic pores, β > 0; for inorganic (non-organic pores), β = 0; for organic edge pores, 0 < β < 1, and for organic internal pores, β = 1.

[0259] In step S40643, an organic edge pore cutoff value (organic edge pore threshold) is set for the organic pore image. Organic pore images with a proportionality coefficient greater than zero and less than or equal to the organic edge pore cutoff value are recorded as organic edge pore images, while organic pore images with a proportionality coefficient greater than the organic edge pore cutoff value and less than or equal to one are recorded as organic internal pore images. Specifically, determining organic edge pores requires setting an organic edge pore cutoff value β0. When 0 < β ≤ β0, the pore is considered an organic edge pore; when β0 < β ≤ 1, the pore is considered an organic internal pore.

[0260] Furthermore, if 0<β i ≤β0, then mark the pore connected body image F i is the organic matter edge hole image, when β0<β i When ≤1, mark the pore connected body image F i As shown in Figure 13, the left image shows the pores inside the organic matter, the middle image shows the pores at the edge of the organic matter, and the right image shows the non-organic pores.

[0261] Use x i Represents the pore-connected body image F i Whether it is an organic hole, y i Represents the pore-connected body image F i Whether it is an internal pore of organic matter, then:

[0262] Traverse all the pore connected body images F i , complete the organic pore determination.

[0263] The present invention proposes a new organic pore determination method for shale digital images, which identifies inorganic pore images and organic pore images in pore connectivity images through proportional coefficients; for organic pore images, the organic edge pore images and organic internal pore images are identified through the organic edge pore cutoff value. The method has high recognition accuracy, simple implementation, and high feasibility, and can quickly identify organic edge pore images and organic internal pore images.

[0264] As shown in FIG1B , in step S104 , a microcrack image and an inorganic pore image are determined. Specifically, a microcrack identification process is performed on the inorganic pore image to determine the microcrack image and the inorganic pore image.

[0265] In one embodiment, microcrack identification processing is performed through steps S1041 - S1043 .

[0266] In step S1041, the maximum and minimum Feret diameters are calculated for the inorganic pore image. Specifically, the difference between pores and microcracks lies in their aspect ratios. This paper uses the maximum Feret diameter to approximate the pore length and the minimum Feret diameter to approximate the pore width, employing the Feret diameter to distinguish inorganic pores from microcracks in shale. As shown in Figure 14 , for microcracks (right image of Figure 14 ), the difference between the maximum and minimum Feret diameters is significant; whereas for pores (left image of Figure 14 ), the difference between the maximum and minimum Feret diameters is smaller.

[0267] In step S1042, the Feret minimum diameter is used as the dividend and the Feret maximum diameter is used as the divisor to calculate the quotient, which is used as the diameter ratio of the inorganic pore image. Specifically, the present invention uses two indicators to identify microcracks: the Feret maximum diameter and the ratio of the Feret minimum diameter to the Feret maximum diameter. max is the maximum Feret diameter, RF min is the Feret minimum diameter, γ is the ratio of the two, then:

[0268] Where: γ is the diameter ratio of the inorganic pore image.

[0269] In step S1043, a diameter cutoff value (diameter threshold) and a ratio cutoff value (ratio threshold) are set. Inorganic pore images that meet the microcrack determination criteria are recorded as microcrack images, while inorganic pore images that do not meet the microcrack determination criteria are recorded as inorganic pore images. The microcrack determination criteria are: the Feret maximum diameter is greater than the diameter cutoff value and the diameter ratio is less than the ratio cutoff value.

[0270] Set the diameter cutoff value RF max0and the ratio cutoff value γ0, when the i-th inorganic pore image satisfies RF maxi >RF max0 When γ<γ0, the pore is judged to be a microcrack, which is recorded as F i ', otherwise, the pore is judged to be an inorganic pore and recorded as F i ”.

[0271] Use z i Represents the pore-connected body image F i Whether it is micro cracks, then:

[0272] Traverse all inorganic pore images and complete the identification of all microcracks.

[0273] The present invention proposes a new microcrack identification and processing method for shale digital images, which uses two indicators to identify microcracks: one is the maximum Feret diameter, and the other is the ratio of the minimum Feret diameter to the maximum Feret diameter. It has high recognition accuracy, simple implementation, and high feasibility, and can quickly identify microcrack images and inorganic pore images.

[0274] It should be noted that, in actual applications, parameters such as the grayscale lower limit and grayscale upper limit of organic matter and transition zone, the organic matter judgment threshold, the grayscale lower limit and grayscale upper limit of pores, the organic matter edge pore threshold, the diameter threshold, the ratio threshold, the first grayscale value, the second grayscale value, the tolerance coefficient, the preset standard deviation, the noise reduction window, etc. can be selected or determined according to actual needs and actual conditions. The present invention does not limit the value range or determination process of parameters such as the grayscale lower limit and grayscale upper limit of organic matter and transition zone, the organic matter judgment threshold, the grayscale lower limit and grayscale upper limit of pores, the organic matter edge pore threshold, the diameter threshold, the ratio threshold, the first grayscale value, the second grayscale value, the tolerance coefficient, the preset standard deviation, the noise reduction window, etc.

[0275] According to another aspect of the present invention, a shale pore type identification device based on digital cores is provided, which performs a shale pore type identification method based on digital cores. The type identification device includes: a preprocessing module, an organic matter identification module, a pore identification module, and a microfracture identification module.

[0276] The preprocessing module performs grayscale adjustment and image noise reduction on the original grayscale image of the shale sample to be identified, generating a standard grayscale image. The organic matter identification module extracts organic matter and transition zone images from the standard grayscale image, performs organic matter identification processing, and determines an organic matter interconnected volume image. The pore identification module extracts the pore image from the standard grayscale image and, combined with the organic matter interconnected volume image, performs pore identification processing to determine organic pore images and inorganic pore images. The microfracture identification module performs microfracture identification processing on the inorganic pore image to determine microfracture images and inorganic pore images.

[0277] The present invention provides a digital core-based shale pore type identification method and device. This method obtains digital images of rock samples through imaging scanning and processes these images to identify organic pores, inorganic pores, and microcracks in the shale (as shown in Figure 15). Furthermore, the present invention can quantitatively calculate pores and microcracks to obtain pore structure parameters for different pore types.

[0278] According to another aspect of the present invention, a method for quantitative characterization of shale pore structure based on digital cores is also provided. Based on the total number of pixels in the grayscale image and the actual length corresponding to a single pixel, and combined with the organic matter identification results, organic pore images, and inorganic pore images determined by a shale pore type identification method based on digital cores, pore structure parameters of the shale sample to be identified are obtained, wherein the pore structure parameters include at least one of a porosity characterization parameter, a pore volume characterization parameter, a pore equivalent radius distribution parameter, a pore shape factor characterization parameter, and a microfracture structure characterization parameter.

[0279] As shown in Figure 16, based on the total number of pixels N in the original grayscale image of the shale sample to be identified and the actual length p corresponding to a single pixel (image resolution), combined with the organic pore image, microcrack image, and inorganic pore image determined by a shale pore type identification method based on digital cores, the porosity characterization parameters, pore volume characterization parameters, pore equivalent radius distribution parameters, pore shape factor characterization parameters, and microcrack structure characterization parameters of the shale sample to be identified are calculated.

[0280] In one embodiment, the porosity characterization parameters include: porosity in organic matter, organic pore porosity, inorganic pore porosity, microfracture porosity, and total porosity. Specifically, the porosity characterization parameters of the shale sample to be identified are determined by the following expression:

[0281] Porosity φ' in organic matter r (Volume percentage of pores in organic matter):

[0282] Organic pore porosity φ r :

[0283] Inorganic pore porosity φ i :

[0284] Microcrack porosity φ f :

[0285] Total porosity φ:

[0286] Where: x i Is the image of the i-th pore interconnected body an organic pore image, and takes the value of 1 or 0; S i is the number of pixels in the i-th organic pore / inorganic pore / microcrack image; q i Whether the image of the i-th organic matter and transition zone connected body is an organic matter image, the value is 1 or 0; E i is the image of the i-th organic matter and transition zone connected body; N is the total number of pixels in the original grayscale image; z i Indicates whether the i-th pore interconnected body image is a microcrack image, and takes the value of 1 or 0.

[0287] In one embodiment, the pore volume characterization parameters include: total pore volume, organic pore volume, inorganic pore volume, and microfracture volume. Specifically, the pore volume characterization parameters of the shale sample to be identified are determined by the following expression:

[0288] The total pore volume V corresponding to the original two-dimensional grayscale image i : V i =p 2 ·S i (twenty two)

[0289] The total pore volume V corresponding to the three-dimensional original grayscale image i : V i =p 3 ·S i (twenty three)

[0290] After the total pore volume is calculated, the pore volume distribution of organic pores, inorganic pores, and microcracks can be obtained separately.

[0291] Organic pore volume corresponding to the original two-dimensional grayscale image

[0292] Organic pore volume corresponding to the original three-dimensional grayscale image

[0293] Inorganic pore volume corresponding to the original two-dimensional grayscale image

[0294] Inorganic pore volume corresponding to the original three-dimensional grayscale image

[0295] Microcrack volume corresponding to the original two-dimensional grayscale image

[0296] Microcrack volume corresponding to the original three-dimensional grayscale image

[0297] Where: p is the actual length corresponding to a single pixel; S i is the number of pixels in the i-th organic pore / inorganic pore / microcrack image; x i Whether the i-th pore interconnected body image is an organic pore image, the value is 1 or 0; z i Indicates whether the i-th pore interconnected body image is a microcrack image, and takes the value of 1 or 0.

[0298] In one embodiment, the pore equivalent radius distribution parameter includes: pore equivalent radius. Specifically, the pore equivalent radius can be calculated based on the pore area (volume) and surface area (perimeter), and the pore equivalent radius distribution parameter of the shale sample to be identified is determined by the following expression:

[0299] The pore equivalent radius r corresponding to the two-dimensional original grayscale image i :

[0300] The pore equivalent radius r corresponding to the three-dimensional original grayscale image i :

[0301] Where: V i is the total pore volume.

[0302] In one embodiment, the pore shape factor characterization parameter includes: pore shape factor. Specifically, the shape factor is used to describe the degree to which the pores are close to a circle (sphere), that is, the shape factor of a circle (sphere) is 1. The shape factor can be calculated using the area (volume) and perimeter (surface area) of the pores. The pore shape factor characterization parameter of the shale sample to be identified is determined by the following expression:

[0303] The shape factor f corresponding to the two-dimensional original grayscale image i :

[0304] The shape factor f corresponding to the original three-dimensional grayscale image i :

[0305] Where: S i is the number of pixels in the i-th organic pore / inorganic pore / microcrack image; P i is the number of pixels on the outer surface of the i-th organic hole / inorganic hole.

[0306] In one embodiment, the microcrack structure characterization parameters include: microcrack length, microcrack width, microcrack inclination, and microcrack density. Specifically, the microcrack structure characterization parameters of the shale sample to be identified are determined using the following expressions and steps:

[0307] As shown in FIG17 , the shape of the microcrack is equivalent to a deformed rectangle (cuboid), so the length and width of the crack can be calculated using the area and perimeter of the crack.

[0308] The microcrack area S corresponding to the two-dimensional original grayscale image ssi : S ssi =L i ×W i (34)

[0309] The microcrack perimeter P corresponding to the two-dimensional original grayscale image pi : P pi =2(L i +W i ) (35)

[0310] By combining the microcrack area expression (34) and the microcrack perimeter expression (35) corresponding to the two-dimensional original grayscale image, the microcrack length and microcrack width corresponding to the two-dimensional original grayscale image can be obtained.

[0311] The microcrack length corresponding to the original two-dimensional grayscale image:

[0312] The width of microcracks corresponding to the original two-dimensional grayscale image:

[0313] The microcrack volume S corresponding to the three-dimensional original grayscale image ssi :

[0314] The microcrack surface area P corresponding to the three-dimensional original grayscale image pi :

[0315] By combining the microcrack volume expression (38) and the microcrack surface area expression (39) corresponding to the three-dimensional original grayscale image, the microcrack length and microcrack width corresponding to the three-dimensional original grayscale image can be obtained. Furthermore, the microcrack length and microcrack width corresponding to the three-dimensional original grayscale image can be obtained through Newton (Newton dynamics plug-in) iteration.

[0316] The origin and major axis of the ellipse are obtained from the second moment of the microcrack, and the microcrack inclination angle θ is obtained. Specifically, as shown in Figure 18, the origin and major axis (major axis) of the ellipse are obtained from the second moment of the microcrack, and the crack inclination angle θ is obtained.

[0317] The number of microcracks in the original grayscale image is M, then:

[0318] The microcrack density ρ corresponding to the two-dimensional original grayscale image L :

[0319] The microcrack density ρ corresponding to the original three-dimensional grayscale image L :

[0320] Where: L i is the length of the i-th microcrack; W i is the width of the i-th microcrack; p is the actual length corresponding to a single pixel; N is the total number of pixels in the original grayscale image.

[0321] According to another aspect of the present invention, a digital core-based quantitative characterization device for shale pore structure is provided, which implements a digital core-based quantitative characterization method for shale pore structure. The quantitative characterization device includes a parameter quantitative characterization module.

[0322] The parameter quantitative characterization module is used to calculate the porosity characterization parameters, pore volume characterization parameters, pore equivalent radius distribution parameters, pore shape factor characterization parameters and microcrack structure characterization parameters of the shale sample to be identified based on the total number of pixels in the original grayscale image of the shale sample to be identified and the actual length corresponding to a single pixel, combined with the organic pore image, microcrack image and inorganic pore image.

[0323] The present invention proposes a new quantitative characterization method and device for shale pore structure parameters for shale digital images, which can quantitatively characterize the porosity characterization parameters, pore volume characterization parameters, pore equivalent radius distribution parameters, pore shape factor characterization parameters and microcrack structure characterization parameters of the shale sample to be identified.

[0324] The digital core-based shale pore type identification and quantitative characterization method provided by the present invention may also be used in conjunction with a computer-readable storage medium storing a computer program. The computer program is executed to implement the digital core-based shale pore type identification method and / or the digital core-based shale pore structure parameter quantitative characterization method. A computer program is capable of executing computer instructions, which include computer program code. The computer program code may be in source code form, object code form, executable file, or some intermediate form.

[0325] Computer-readable storage media may include: any entity or device that can carry computer program code, recording media, USB flash drives, mobile hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0326] It should be noted that the content contained in computer-readable storage media can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable storage media do not include electrical carrier signals and telecommunications signals.

[0327] This invention uses SEM / CT to obtain shale scan images. First, a corresponding image preprocessing method is developed based on the characteristics of shale digital images, improving image clarity. Second, a method for identifying shale organic matter, organic pores, inorganic pores, and microfractures is developed using a graphics algorithm. Finally, the structural parameters of the different identified reservoir spaces are calculated and statistically analyzed. This invention provides a method for quantitatively characterizing the structure of different types of shale reservoir spaces, which is of great significance for shale reservoir quality evaluation and the optimization of development methods.

[0328] In one embodiment, the digital core-based shale pore type identification and quantitative characterization method proposed in the present invention was applied. The original image was a two-dimensional SEM-Maps scan image, as shown in Figure 19. The image resolution was 5000 x 3000, with an accuracy of 10 nm. As can be seen in Figure 19, the rock particles contain inorganic solution pores, while organic matter contains organic pores. Figure 19 is denoted as Image A.

[0329] Figure 20 shows the grayscale distribution of scanned image A, which is a typical bimodal image. Corresponding to the image, we can see that the first peak represents organic matter, and the second peak represents rock particles. Pixels with grayscales below 70 account for a very low percentage, while those with grayscales above 150 represent rock particles. Therefore, the grayscale adjustment expression (1) can be stretched to the range of 70-150, i.e., taking I1 = 80 and I2 = 150 in equation (1).

[0330] The image after grayscale adjustment is shown in Figure 21, denoted as Image B, and its brightness distribution is shown in Figure 22. It can be seen that after grayscale adjustment, the contrast of pores, organic matter, and rock particles is enhanced.

[0331] Given denoising parameters m = 30, σ0 = 0.5, and a denoising window of 3, the denoising template is calculated using formulas (2) and (3). The denoising algorithm is run three times, and the results are shown in Figure 23. The left image is the denoised image, the middle image is a partial denoised image, and the right image is the image before denoising.

[0332] According to formula (6), where I3=11 and I4=100, the calculated organic matter and transition zone image D is shown in FIG24 .

[0333] The connected bodies of organic matter and transition zone are calculated. Here, the pixel connectivity condition is 8-connected. The calculated connected body image E of organic matter and transition zone is shown in FIG25 , where different shades of color represent different connected bodies.

[0334] For organic matter determination, the area (volume) efficiency α is calculated according to formula (7), and its distribution is shown in Figure 26.

[0335] Given an organic matter determination cutoff value α0 = 0.7, the organic matter interconnected body is shown in FIG27 , and the inorganic pore edge transition zone is shown in FIG28 .

[0336] The grayscale value threshold of the pores is set to 10, and grayscale 0-10 is identified as pores. The pore image is extracted, as shown in Figure 29, and the pore connected body image is extracted, as shown in Figure 30.

[0337] According to the calculation method provided by the present invention, the expanded pore image G is shown in FIG31 , and the expanded pore boundary image H is shown in FIG32 .

[0338] The intersection image of image H and organic matter E is shown in Figure 33. Given the organic matter edge pore threshold β0 = 1, the calculated organic pore internal pores, organic matter edge pores, and inorganic pores are shown in Figure 34. The left image is the organic matter internal pores, the middle image is the organic matter edge pores, and the right image is the inorganic pores. In addition, given the diameter threshold RF max0 =100um, proportional threshold γ0=5, used to determine microcracks.

[0339] According to the quantitative characterization method of shale pore structure parameters based on digital core provided by the present invention, the porosity φ' of organic matter is calculated. r =3.56%, organic pore porosity φ r =0.39%, inorganic pore porosity φ i =0.22%, total porosity φ=0.61%.

[0340] The total pore volume distribution is shown in Figure 35, the pore volume distribution of organic pores is shown in Figure 36, the pore volume distribution of inorganic pores is shown in Figure 37, the total pore radius distribution is shown in Figure 38, the pore radius distribution of organic pores is shown in Figure 39, the pore radius distribution of inorganic pores is shown in Figure 40, the total pore shape factor distribution is shown in Figure 41, the organic pore shape factor distribution is shown in Figure 42, and the inorganic pore shape factor distribution is shown in Figure 43.

[0341] In summary, the present invention provides a method for identifying and quantitatively characterizing shale pore types based on digital cores. Compared with existing technologies, it has the following advantages:

[0342] 1. The present invention improves the noise reduction standard deviation in the Gaussian noise reduction kernel function and proposes a new image noise reduction processing method for shale digital images. According to the image noise reduction processing method proposed by the present invention, the local image quality within pores, organic matter, and rock skeleton can be improved while ensuring the clarity of the pore-organic matter boundary and the organic matter-rock skeleton boundary.

[0343] 2. The present invention proposes a new organic matter determination method for shale digital images, which effectively identifies organic matter and transition zone through the images of connected bodies of organic matter and transition zone. It has high recognition accuracy, simple implementation, and high feasibility, and can quickly identify images of connected bodies of organic matter and connected bodies of transition zone.

[0344] 3. The present invention proposes a new organic pore determination method for shale digital images, which identifies inorganic pore images and organic pore images in pore connectivity images through proportional coefficients; for organic pore images, the organic edge pore images and organic internal pore images are identified through the organic edge pore threshold. This method has high recognition accuracy, simple implementation, and high feasibility, and can quickly identify organic edge pore images and organic internal pore images.

[0345] 4. The present invention proposes a new micro-crack identification and processing method for shale digital images, which uses two indicators to identify micro-cracks: one is the maximum Feret diameter, and the other is the ratio of the minimum Feret diameter to the maximum Feret diameter. It has high recognition accuracy, simple implementation, and high feasibility, and can quickly identify micro-crack images and inorganic pore images.

[0346] 5. The present invention proposes a new quantitative characterization method for shale pore structure parameters for shale digital images, which can quantitatively characterize the porosity characterization parameters, pore volume characterization parameters, pore equivalent radius distribution parameters, pore shape factor characterization parameters and microcrack structure characterization parameters of the shale sample to be identified.

[0347] The present invention can quantitatively identify organic pores, inorganic pores and microcracks in shale, quantitatively characterize the pore structure of shale, analyze the structural parameters of organic pores, inorganic pores and microcracks, and statistically calculate the distribution of different types of reservoir spaces in shale. This is of great significance for shale reservoir quality evaluation and the optimization of effective shale oil and gas development methods.

[0348] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should extend to equivalent substitutions of these features understood by those skilled in the relevant art. It should also be understood that the terminology used herein is for the purpose of describing specific embodiments only and is not intended to be limiting.

[0349] In the description of the present invention, unless otherwise specified, "plurality" means two or more; terms such as "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," and "tail" indicate positions or relationships based on those shown in the accompanying drawings. These terms are intended solely to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, terms such as "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0350] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "connected" and "connection" should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, or integral connection; mechanical connection, electrical connection; direct connection, or indirect connection through an intermediary. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0351] Certain terms are used throughout this application document to indicate specific system components. As will be appreciated by those skilled in the art, different names may be used to indicate the same component, and thus this application document is not intended to distinguish between components that are only different in name but not in function. In this application document, the terms "comprise," "include," and "have" are used in an open format and should therefore be interpreted as meaning "including, but not limited to...". In addition, the terms "substantially," "substantially," or "approximately" that may be used herein refer to industry-accepted tolerances for the corresponding terms. The term "coupling," as used herein, includes direct coupling and indirect coupling via another component, element, circuit, or module, wherein for indirect coupling, the intervening component, element, circuit, or module does not change the information of the signal but can adjust its current level, voltage level, and / or power level. Inferred coupling (e.g., one element is coupled to another element by inference) includes direct and indirect coupling between two elements in the same manner as "coupling."

[0352] References in this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "one embodiment" or "an embodiment" in various places throughout this specification do not necessarily refer to the same embodiment.

[0353] The embodiments of the present invention are presented for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described in order to better illustrate the principles of the invention and its practical application and to enable those skilled in the art to understand the invention and design various embodiments with various modifications as suited for specific applications.

[0354] Although the embodiments disclosed herein are as described above, the contents described herein are merely embodiments for facilitating understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art of the present invention may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed herein. However, the scope of patent protection of the present invention shall still be subject to the scope defined by the appended claims.

Claims

1. A shale pore type identification method based on digital core, characterized in that: The method comprises: Based on the grayscale image of the shale sample to be identified, the organic matter and the transition zone are identified to obtain the organic matter identification result; Based on the organic matter identification result and the grayscale image, an organic pore image and an inorganic pore image are obtained as shale pore type identification results.

2. The shale pore type identification method according to claim 1, characterized in that: Organic matter and transition zones are identified by the following steps: According to the grayscale distribution of the grayscale image, a grayscale lower limit and a grayscale upper limit corresponding to the organic matter and the transition zone are obtained, and then an image of the connected body of the organic matter and the transition zone is obtained; Based on the total area or total volume of the organic matter and transition zone connected body image and the effective area or effective volume of the organic matter and transition zone connected body image, organic matter determination is performed to obtain the organic matter identification result and transition zone identification result.

3. The shale pore type identification method according to claim 2, characterized in that: The organic matter determination is carried out through the following steps: Taking the effective area of ​​the region or the effective volume of the region as the dividend and the total area of ​​the region or the total volume of the region as the divisor, the area efficiency rate or the volume efficiency rate is obtained as the efficiency rate of the organic matter and transition zone connected body image; Setting an organic matter determination threshold, recording the organic matter and transition zone connected body image whose efficiency is less than the organic matter determination threshold as the transition zone connected body image as the transition zone recognition result; The organic matter and transition zone connected body images with an efficiency greater than or equal to the organic matter determination threshold are recorded as organic matter connected body images as the organic matter identification result.

4. The shale pore type identification method according to claim 3, characterized in that: The organic pore image and the inorganic pore image are identified by the following steps: Obtaining a pore interconnected body image according to the grayscale distribution of the grayscale image; Based on the pore interconnected body image and the organic matter interconnected body image, pore identification processing is performed to determine the organic pore image and the inorganic pore image.

5. The shale pore type identification method according to claim 4, characterized in that: The pore identification process is performed by the following steps: Based on the expansion template, the pore-connected body image is subjected to pore expansion to obtain a pore-expanded connected body image, and a boundary image of the pore-expanded connected body image is determined; Calculating the perimeter of the pore expansion connected body in the pore expansion connected body image; Taking the intersection of the boundary image and the organic matter connected body image as the intersection image, and obtaining the intersection image area; Organic pore determination is performed based on the intersection image area and the perimeter of the pore expansion interconnected body to determine the organic pore image and the inorganic pore image.

6. The shale pore type identification method according to claim 5, characterized in that: The organic pore determination is determined by the following steps: Taking the area of ​​the intersection image as the dividend and the perimeter of the porous expansion interconnected body as the divisor, a quotient is obtained as the scale coefficient of the porous interconnected body image; The pore interconnected body image with a scale factor equal to zero is recorded as the inorganic pore image, and the pore interconnected body image with a scale factor greater than zero and less than or equal to one is recorded as the organic pore image.

7. The shale pore type identification method according to claim 6, characterized in that: The method further comprises: Setting an organic matter edge pore threshold for the organic pore image; The organic pore image with a proportional coefficient greater than zero and less than or equal to the organic edge pore threshold is recorded as an organic edge pore image, and the organic pore image with a proportional coefficient greater than the organic edge pore threshold and less than or equal to one is recorded as an organic internal pore image.

8. The shale pore type identification method according to any one of claims 1 to 7, characterized in that: The method further comprises: For the inorganic pore image, the maximum Feret diameter and the minimum Feret diameter are calculated; Taking the minimum Feret diameter as the dividend and the maximum Feret diameter as the divisor, a quotient is obtained as the diameter ratio of the inorganic pore image; The diameter threshold and the ratio threshold are set, and the inorganic pore images that meet the microcrack determination conditions are recorded as microcrack images, and the inorganic pore images that do not meet the microcrack determination conditions are recorded as inorganic pore images.

9. The shale pore type identification method according to claim 8, characterized in that: The microcrack determination condition is: the maximum Feret diameter is greater than the diameter threshold, and the diameter ratio is less than the ratio threshold.

10. The shale pore type identification method according to any one of claims 1 to 9, characterized in that: The grayscale image is a two-dimensional or three-dimensional original grayscale image of the shale sample to be identified.

11. The shale pore type identification method according to claim 10, characterized in that: The method further comprises performing grayscale adjustment processing on the original grayscale image, wherein: Determine the grayscale distribution of the original grayscale image to obtain a first grayscale value corresponding to the pores and a second grayscale value corresponding to the rock skeleton; Based on the first grayscale value and the second grayscale value, an original grayscale image after grayscale adjustment is obtained.

12. The shale pore type identification method according to claim 11, characterized in that: The method further comprises: Determine a noise reduction window based on the original grayscale image after grayscale adjustment; The noise reduction standard deviation is calculated based on the maximum grayscale value and the minimum grayscale value in the noise reduction window. Based on the noise reduction standard deviation, image noise reduction is performed on the original grayscale image after grayscale adjustment.

13. A computer-readable storage medium, characterized in that: It contains a series of instructions for executing the method steps as claimed in any one of claims 1-12.

14. A method for quantitative characterization of shale pore structure parameters based on digital core, characterized in that: The method comprises: Based on the total number of pixels in the grayscale image and the actual length corresponding to a single pixel, and in combination with the organic matter identification result, the organic pore image and the inorganic pore image determined by the method as described in any one of claims 1 to 12, the pore structure parameters of the shale sample to be identified are obtained, wherein the pore structure parameters include at least one of porosity characterization parameters, pore volume characterization parameters, pore equivalent radius distribution parameters, pore shape factor characterization parameters and microfracture structure characterization parameters.

15. A computer-readable storage medium, characterized in that: It contains a series of instructions for executing the method steps as claimed in claim 14.

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