Shale pore type identification and quantitative characterization method based on digital core

Abstract

The invention provides a shale pore type identification method based on a digital core, and the method comprises the steps: carrying out the gray adjustment processing and image noise reduction processing of an original gray image of a to-be-identified shale sample, and obtaining a standard gray image; organic matter and transition zone images in the standard grayscale image are extracted, organic matter identification processing is carried out, and an organic matter connected body image is determined; extracting a pore image in the standard gray-scale image, performing pore recognition processing in combination with the organic matter connected body image, and determining an organic pore image and an inorganic pore image; and performing micro-crack identification processing on the inorganic pore image, and determining a micro-crack image and an inorganic pore image. By applying the method, the organic pores, the inorganic pores and the microcracks of the shale can be quantitatively identified, the pore structure of the shale can be quantitatively represented, and the structural parameters of the organic pores, the inorganic pores and the microcracks can be analyzed.

Description

Technical Field

[0001] 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

[0002] The shale reservoir space structure is complex, with small pores, mainly nanopores, and complex types, including organic pores, inorganic pores, and microcracks. The oil and gas in the organic pores of the shale reservoir exist in an adsorbed and dissolved state, while the oil and gas in the inorganic pores exist mostly in a free state. Accurate and quantitative characterization of the pore structure of different types of pores in shale is of great significance for studying the occurrence state of shale oil and gas, evaluating resource quality, and formulating reasonable development methods.

[0003] At present, fluid intrusion method (gas adsorption, mercury injection and nuclear magnetic resonance) is the mainstream method for quantitative characterization of pore structure of porous media materials. A large number of literatures use this technology to study the pore structure characteristics of shale, but these methods cannot distinguish between organic pores and inorganic pores in shale in theory. Organic pores can be identified by imaging, but they all need to be marked manually, which is inefficient and lacks automated identification methods. Moreover, they can only perform qualitative analysis and cannot perform quantitative characterization.

[0004] In view of the problems of the prior art, the present invention provides a method for identifying and quantitatively characterizing shale pore types based on digital cores. Summary of the invention

[0005] In view of the defects of the prior art, the present invention proposes a shale pore type identification method based on digital core, which comprises:

[0006] 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;

[0007] 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;

[0008] Extracting the pore image in the standard grayscale image, combining it with the organic matter interconnected body image, performing pore recognition processing, and determining the organic pore image and the inorganic pore image;

[0009] A microcrack recognition process is performed on the inorganic pore image to determine a microcrack image and an inorganic pore image.

[0010] 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:

[0011] 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;

[0012] or,

[0013] The shale sample to be identified is scanned by electronic computer tomography to obtain a three-dimensional original grayscale image, wherein the electronic computer tomography adopts nano-scale electronic computer tomography.

[0014] According to one embodiment of the present invention, grayscale adjustment processing is performed through the following steps:

[0015] 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;

[0016] 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 the original grayscale image after grayscale adjustment;

[0017] The grayscale adjustment expression is:

[0018]

[0019] 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.

[0020] According to one embodiment of the present invention, image noise reduction processing is performed through the following steps:

[0021] Determine a noise reduction window based on the original grayscale image after grayscale adjustment;

[0022] 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;

[0023] 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;

[0024] The noise reduction standard deviation calculation expression is:

[0025]

[0026] 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 in the noise reduction window; I min It is the minimum grayscale value within the noise reduction window.

[0027] According to one embodiment of the present invention, the organic matter interconnected body image is determined by the following steps:

[0028] According to the grayscale distribution of the standard grayscale image, the grayscale lower limit of the organic matter and transition zone pixels and the grayscale upper limit of the organic matter and transition zone pixels are determined to extract the organic matter and transition zone image;

[0029] Performing the organic matter identification process on the organic matter and transition zone images to determine the organic matter connected body image;

[0030] The organic matter identification process comprises the following steps:

[0031] For the organic matter and transition zone images, a connected body calculation method is used to calculate and obtain the organic matter and transition zone connected body images;

[0032] Based on the total area or total volume of the region of the organic matter and the connected body image of the transition zone, and the effective area or effective volume of the region of the organic matter and the connected body image of the transition zone, organic matter determination is performed to determine the organic matter connected body image.

[0033] According to one embodiment of the present invention, organic matter determination is performed through the following steps:

[0034] 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 or volume efficiency is calculated as the efficiency of the organic matter and transition zone connected body image;

[0035] An organic matter determination cutoff value is set, and the organic matter and transition zone connected body image with an efficiency less than the organic matter determination cutoff value are recorded as the transition zone connected body image, and the organic matter and transition zone connected body image with an efficiency greater than or equal to the organic matter determination cutoff value are recorded as the organic matter connected body image.

[0036] According to one embodiment of the present invention, the organic pore image and the inorganic pore image are determined by the following steps:

[0037] According to the grayscale distribution of the standard grayscale image, determine the grayscale lower limit and the grayscale upper limit of the pores to extract the pore image;

[0038] For the pore image, a connected body calculation method is used to calculate and obtain a pore connected body image;

[0039] Based on the pore interconnected body image and the organic matter interconnected body image, the pore identification process is performed to determine the organic pore image and the inorganic pore image.

[0040] According to one embodiment of the present invention, pore identification processing is performed through the following steps:

[0041] 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;

[0042] Calculating the perimeter of the pore expansion connected body in the pore expansion connected body image;

[0043] Taking the intersection of the boundary image and the organic matter connected body image as the intersection image, and calculating the intersection image area;

[0044] 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.

[0045] According to one embodiment of the present invention, the organic pore determination is performed by the following steps:

[0046] The area of ​​the intersection image is used as the dividend and the perimeter of the porous expansion connected body is used as the divisor to calculate the quotient value as the proportional coefficient of the porous connected body image;

[0047] Recording the pore interconnected body image with a scale factor equal to zero as the inorganic pore image, and recording the pore interconnected body image with a scale factor greater than zero and less than or equal to one as the organic pore image;

[0048] For the organic pore image, an organic edge pore cutoff value is set, and the organic pore image with a proportional coefficient greater than zero and less than or equal to the organic edge pore cutoff value is recorded as an organic edge pore image, and the organic pore image with a proportional coefficient greater than the organic edge pore cutoff value and less than or equal to one is recorded as an organic internal pore image.

[0049] According to one embodiment of the present invention, microcrack identification processing is performed through the following steps:

[0050] For the inorganic pore image, the maximum Feret diameter and the minimum Feret diameter are calculated;

[0051] The Feret minimum diameter is used as the dividend and the Feret maximum diameter is used as the divisor to calculate the quotient value as the diameter ratio of the inorganic pore image;

[0052] Setting a diameter cutoff value and a ratio cutoff value, recording an inorganic pore image that meets the microcrack determination condition as the microcrack image, and recording an inorganic pore image that does not meet the microcrack determination condition as the inorganic pore image;

[0053] The microcrack determination condition is: the maximum Feret diameter is greater than the diameter cutoff value and the diameter ratio is less than the ratio cutoff value.

[0054] According to another aspect of the present invention, a storage medium is provided, which contains a series of instructions for executing the steps of a shale pore type identification method based on digital core as described in any one of the above items.

[0055] According to another aspect of the present invention, there is also provided a shale pore type identification device based on digital cores, which executes a shale pore type identification method based on digital cores as described in any one of the above items, and the device comprises:

[0056] 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, so as to obtain a standard grayscale image;

[0057] 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;

[0058] A pore recognition module is used to extract the pore image in 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;

[0059] 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.

[0060] According to another aspect of the present invention, a method for quantitatively characterizing shale pore structure parameters based on digital cores is also provided, the method comprising:

[0061] 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 digital cores 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.

[0062] 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:

[0063] Porosity in organic matter r ':

[0064]

[0065] Organic pore porosity φr :

[0066]

[0067] Inorganic pore porosity φ i :

[0068]

[0069] Microcrack porosity φ f :

[0070]

[0071] Total porosity φ:

[0072]

[0073] Where: x i Is the i-th pore interconnected body image whether it is an organic pore image, and the value is 1 or 0; S i is the number of pixels of the i-th organic pore / inorganic pore / microcrack image; q i Whether the i-th organic matter and transition zone connected body image 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 of the original grayscale image; z i Whether the i-th pore interconnected body image is a microcrack image, the value is 1 or 0.

[0074] 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:

[0075] The total pore volume V corresponding to the original two-dimensional grayscale image i :

[0076] V i =p 2 ·S i

[0077] The total pore volume V corresponding to the original three-dimensional grayscale image i :

[0078] V i =p 3 ·S i

[0079] Organic pore volume corresponding to the original grayscale image of the two-dimensional image

[0080]

[0081] The organic pore volume corresponding to the original grayscale image of the three-dimensional image

[0082]

[0083] Inorganic pore volume corresponding to the original grayscale image of the two-dimensional image

[0084]

[0085] Inorganic pore volume corresponding to the original three-dimensional grayscale image

[0086]

[0087] Microcrack volume corresponding to the original two-dimensional grayscale image

[0088]

[0089] Microcrack volume corresponding to the original three-dimensional grayscale image

[0090]

[0091] Where: p is the actual length corresponding to a single pixel; S i is the number of pixels of 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 Whether the i-th pore interconnected body image is a microcrack image, the value is 1 or 0.

[0092] 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:

[0093] The pore equivalent radius r corresponding to the original two-dimensional grayscale image i :

[0094]

[0095] The pore equivalent radius r corresponding to the original three-dimensional grayscale image i :

[0096]

[0097] Where: V i is the total pore volume.

[0098] 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:

[0099] The shape factor f corresponding to the original two-dimensional grayscale image i :

[0100]

[0101] The shape factor f corresponding to the original three-dimensional grayscale image i :

[0102]

[0103] Where: S i is the number of pixels of 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.

[0104] 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:

[0105] The microcrack area S corresponding to the two-dimensional original grayscale image ssi :

[0106] S ssi =L i ×W i

[0107] The microcrack perimeter P corresponding to the original two-dimensional grayscale image pi :

[0108] P pi =2(L i +W i )

[0109] 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;

[0110] The microcrack volume S corresponding to the original three-dimensional grayscale image ssi :

[0111]

[0112] The microcrack surface area P corresponding to the three-dimensional original grayscale image pi :

[0113]

[0114] 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;

[0115] 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;

[0116] The number of microcracks in the original grayscale image is counted as M, then:

[0117] The microcrack density ρ corresponding to the original two-dimensional grayscale image L :

[0118]

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

[0120]

[0121] 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.

[0122] According to another aspect of the present invention, a storage medium is provided, which contains a series of instructions for executing the steps of a method for quantitatively characterizing shale pore structure parameters based on digital cores as described in any one of the above items.

[0123] 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 executes a method for quantitatively characterizing shale pore structure parameters based on digital cores as described above, and the device comprises:

[0124] 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 digital cores 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.

[0125] The present invention provides a method for identifying and quantitatively characterizing shale pore types based on digital cores, which has the following advantages over the prior art:

[0126] 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 clarity of the pore-organic matter boundary and the organic matter-rock skeleton boundary can be guaranteed while improving the local image quality within the pores, organic matter and rock skeleton.

[0127] 2. The present invention proposes a new organic matter determination method for shale digital images, which can identify organic matter and transition zones through the efficient images of organic matter and connected bodies in transition zones. It has high recognition accuracy, simple implementation, and high feasibility, and can quickly identify images of connected bodies of organic matter and images of connected bodies in transition zones.

[0128] 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, organic edge pore images and organic internal pore images are identified through organic edge pore cutoff values. The method has high recognition accuracy, simple implementation, and high feasibility, and can quickly identify organic edge pore images and organic internal pore images.

[0129] 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.

[0130] 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 microfracture structure characterization parameters of the shale sample to be identified.

[0131] By using the present invention, it is possible to 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 analyze the distribution of different types of storage spaces in shale, which is of great significance for shale reservoir quality evaluation and optimization of effective shale oil and gas development methods.

[0132] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose 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

[0133] 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:

[0134] Figure 1 A flowchart of a method for identifying shale pore types based on digital cores according to an embodiment of the present invention is shown;

[0135] Figure 2 A flowchart of the steps of a method for obtaining a standard grayscale image according to an embodiment of the present invention is shown;

[0136] Figure 3 A grayscale image of pixels in a noise reduction window according to an embodiment of the present invention is shown;

[0137] Figure 4 A flowchart of a method for determining an organic pore image and an inorganic pore image according to an embodiment of the present invention is shown;

[0138] Figure 5 shows a standard grayscale image according to an embodiment of the present invention;

[0139] Figure 6 An organic matter and transition zone image according to an embodiment of the present invention is shown;

[0140] Figure 7 An image of organic matter and transition zone connected bodies according to an embodiment of the present invention is shown;

[0141] Figure 8 A schematic diagram of the total area of ​​a region and the effective area of ​​a region according to an embodiment of the present invention is shown;

[0142] Fig. 9 shows an image of pores according to one embodiment of the present invention;

[0143] Fig.10 An image of a pore interconnector according to an embodiment of the present invention is shown;

[0144] Fig.11 A schematic diagram of a pore template according to an embodiment of the present invention is shown;

[0145] Fig.12 An image of a pore expansion interconnection according to an embodiment of the present invention is shown;

[0146] Fig.13 A diagram showing the relationship between the positions of pores and organic matter according to an embodiment of the present invention is shown;

[0147] Fig.14 A schematic diagram showing the Feret's maximum diameter and Feret's minimum diameter of pores and microcracks according to an embodiment of the present invention is shown;

[0148] Fig.15 A schematic diagram of organic holes and inorganic holes in an original grayscale image according to an embodiment of the present invention is shown;

[0149] Fig.16 A schematic diagram of a shale pore structure parameter calculation process according to an embodiment of the present invention is shown;

[0150] Fig.17 A schematic diagram of the equivalent morphology of microcracks after straightening according to an embodiment of the present invention is shown;

[0151] Fig.18 A schematic diagram of a microcrack ellipse according to an embodiment of the present invention is shown;

[0152] Fig.19 shows an original SEM scan image according to one embodiment of the present invention;

[0153] Fig. 20 shows a grayscale distribution diagram of a scanned image A according to an embodiment of the present invention;

[0154] Fig.21 shows an image B after grayscale adjustment according to an embodiment of the present invention;

[0155] Fig. 22 shows a grayscale distribution diagram of an image B after grayscale adjustment according to an embodiment of the present invention;

[0156] Fig.23 shows a denoised image C according to an embodiment of the present invention;

[0157] Fig.24 shows an organic matter and transition band image D according to an embodiment of the present invention;

[0158] Fig.25 Shows an organic matter and transition zone interconnector E according to an embodiment of the present invention;

[0159] Fig.26 shows an efficiency distribution diagram according to an embodiment of the present invention;

[0160] Fig. 27 An organic matter interconnected body image according to an embodiment of the present invention is shown;

[0161] Fig.28 An inorganic pore edge transition zone image according to an embodiment of the present invention is shown;

[0162] Fig.29 A schematic diagram of a pore image according to an embodiment of the present invention is shown;

[0163] Fig.30 shows a pore interconnected body image F according to an embodiment of the present invention;

[0164] Fig.31 The pore expansion interconnected body image G according to one embodiment of the present invention is shown;

[0165] Fig.32shows a pore expansion map boundary image H according to one embodiment of the present invention;

[0166] Fig.33 shows a boundary image and an organic matter intersection image according to an embodiment of the present invention;

[0167] Fig.34 A graph showing pore identification results according to an embodiment of the present invention is shown;

[0168] Fig.35 shows a total pore volume distribution diagram according to one embodiment of the present invention;

[0169] Fig.36 shows a pore volume distribution diagram of organic pores according to one embodiment of the present invention;

[0170] Fig.37 shows a pore volume distribution diagram of inorganic pores according to one embodiment of the present invention;

[0171] Fig.38 shows a total pore radius distribution graph according to one embodiment of the present invention;

[0172] Fig.39 shows a pore radius distribution diagram of organic pores according to one embodiment of the present invention;

[0173] Fig.40 shows a distribution diagram of inorganic pore radius according to one embodiment of the present invention;

[0174] Fig.41 shows a total pore shape factor distribution graph according to one embodiment of the present invention;

[0175] Fig.42 shows a distribution diagram of organic pore shape factors according to one embodiment of the present invention;

[0176] Fig.43 A graph of inorganic pore shape factor distribution according to one embodiment of the present invention is shown.

[0177] In the accompanying drawings, the same reference numerals are used for the same components. 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, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

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

[0180] The prior art (CN110132816A) provides a method for analyzing the pore structure of organic matter in Lower Paleozoic shale, which can observe and describe the pore development characteristics of different types of organic matter, including the morphology, size and distribution characteristics of different organic pores. However, it requires manual observation of the shape, occurrence, connectivity and other conditions of organic pores through a scanning electron microscope, measurement of pore size, and description of the pore development degree based on the number and size of pores. This method of manually marking organic pores is inefficient and cannot automatically identify organic pores.

[0181] The prior art (CN109387468A) provides a method and system for testing and analyzing characteristic parameters of nanopore structure of shale reservoirs, which uses a reconstructed three-dimensional image and an image processing method to statistically analyze, measure and calculate characteristic parameters of nanopore structure of shale. However, it does not disclose what image processing method is used to statistically measure and calculate characteristic parameters of nanopore structure of shale.

[0182] The prior art (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 of the Lower Silurian Longmaxi Formation in the Jiaoshiba area of ​​the Sichuan Basin. However, it has not disclosed a quantitative study of shale pore structure by image recognition methods.

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

[0184] In summary, there is currently a lack of quantitative characterization methods that consider the pore structure of different types of pores in shale. In response to the above-mentioned defects in the prior art, the present invention establishes a shale pore type identification and quantitative characterization method based on digital cores, which 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] Figure 1 A flowchart of the steps of a shale pore type identification method based on digital cores according to an embodiment of the present invention is shown.

[0186] like Figure 1 As shown, in step S101, a standard grayscale image is obtained. Specifically, grayscale adjustment processing and image noise reduction processing are performed on the original grayscale image of the shale sample to be identified to obtain a standard grayscale image.

[0187] In one embodiment, by Figure 2 The steps S201-S203 shown obtain a standard grayscale image.

[0188] like Figure 2 As shown, 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 computer tomography to obtain a two-dimensional or three-dimensional original grayscale image.

[0189] In one embodiment, the shale sample to be identified is scanned by a scanning electron microscope (SEM) to obtain a two-dimensional original grayscale image. By setting the resolution, the original grayscale image obtained by scanning can clearly observe the pores and cracks in the shale core, wherein the resolution of the scanning electron microscope is less than or equal to 10nm.

[0190] 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.

[0191] In practice, there are two problems with the original grayscale image. The first is that the brightness distribution is unreasonable and the contrast between the area of ​​interest and the background information is low. The second is that the scanned image has noise and needs further processing. In view of the above problems of the original grayscale image, the present invention improves the unreasonable brightness distribution problem of the original grayscale image through grayscale adjustment processing and improves the noise problem of the original grayscale image through image noise reduction processing.

[0192] like Figure 2 As shown, in step S202, the original grayscale image (image B) after grayscale adjustment is obtained.

[0193] In one embodiment, grayscale adjustment processing is performed through steps S2021-S2022.

[0194] 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 I, and according to the grayscale distribution, the grayscale value of the pores is recorded as I1, and the grayscale value of the rock skeleton is recorded as I2.

[0195] 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 original grayscale image after grayscale adjustment. Specifically, the grayscale adjustment expression is:

[0196]

[0197] 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.

[0198] 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.

[0199] like Figure 2 As shown, in step S203, a standard grayscale image (image C) is obtained.

[0200] In one embodiment, image noise reduction processing is performed through steps S2031-S2033 to obtain a standard grayscale image.

[0201] In step S2031, a noise reduction window is determined based on the original grayscale image after grayscale adjustment. Figure 3 The 3*3 noise reduction window shown.

[0202] In step S2032, the noise reduction standard deviation is calculated by using the noise reduction standard deviation calculation expression according to the maximum grayscale value in the noise reduction window and the minimum grayscale value in the noise reduction window. Figure 3 As shown, the maximum grayscale value and the minimum grayscale value in the noise reduction window are determined by the following expressions:

[0203] I max =maxI ij (2)

[0204] I min =min I ij (3)

[0205] In one embodiment, the noise reduction standard deviation calculation expression is:

[0206]

[0207] Where: σ is the standard deviation of noise reduction; m is the tolerance coefficient. The larger its value, the greater the boundary blur after noise reduction; the smaller its value, the clearer the boundary; σ0 is the preset standard deviation; I max is the maximum grayscale value in the noise reduction window; I min It is the minimum grayscale value within the noise reduction window.

[0208] In step S2033, based on the noise reduction standard deviation, the original grayscale image after grayscale adjustment is subjected to image noise reduction by using a Gaussian noise reduction kernel function to obtain a standard grayscale image. Specifically, the calculated noise reduction standard deviation is brought 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.

[0209] The Gaussian denoising kernel function is:

[0210]

[0211] Where: r is the distance vector.

[0212] Different from the denoising requirements of conventional digital images, the denoising of shale digital images must maintain the clarity of the pore-organic matter boundary and the organic matter-solid skeleton boundary while eliminating local noise in the image. Therefore, the present invention improves the denoising standard deviation in the Gaussian denoising kernel function and proposes a new image denoising processing method for shale digital images. According to the image denoising processing method proposed by the present invention, for pixels in pores, organic matter and rock skeleton, I max with I min The difference is small, the value of the noise reduction standard deviation σ is large, the noise reduction effect is strong, and the local image quality can be better improved; at the same time, for the pore-organic matter boundary and the organic matter-rock skeleton boundary, I max with I minThe difference is large, the standard deviation σ is small, the noise reduction effect is strong, and the clarity of the boundary can be guaranteed. Therefore, the image noise reduction processing method provided by the present invention can improve the local image quality in the pores, in the organic matter and in the rock skeleton while ensuring the clarity of the pore-organic matter boundary and the organic matter-rock skeleton boundary.

[0213] like Figure 1 As shown, in step S102, an organic matter interconnected body image is determined. Specifically, the organic matter and transition zone image in the standard grayscale image are extracted, and an organic matter recognition process is performed to determine the organic matter interconnected body image.

[0214] In one embodiment, by Figure 4 Steps S401-S403 shown determine the organic matter connected body image.

[0215] like Figure 4 As shown, in step S401, an organic matter and transition band image (image D) is obtained. Specifically, according to the grayscale distribution of the standard grayscale image, the grayscale lower limit and the grayscale upper limit of the organic matter and transition band pixels are determined to extract the organic matter and transition band image.

[0216] like Figure 5 As shown in Figure 4, organic matter usually contains one or more organic pores, and there is a transition zone from the periphery of inorganic pores to the rock skeleton. The transition zone and organic matter are basically consistent in grayscale, and it is difficult to divide them by grayscale value. However, there are obvious grayscale boundaries between pores, organic matter and rock skeleton in shale images. Therefore, in step S401, according to the grayscale distribution of the standard grayscale image, the threshold segmentation method can be used to extract the following Figure 6 Images of organic matter and transition zones are shown.

[0217] In one embodiment, the organic matter and transition band image f is extracted by the following expression:

[0218] f={x|I3<x<I4}(6)

[0219] Where: I3 is the lower limit of the grayscale of organic matter and transitional pixels, and I4 is the upper limit of the grayscale of organic matter and transitional pixels.

[0220] 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 the organic matter connected body image.

[0221] like Figure 4As shown, in step S402, an organic matter and transition zone connected body image (image E) is obtained. Specifically, for the organic matter and transition zone image, a connected body calculation method is used to calculate the organic matter and transition zone connected body image. Further, a connected body calculation method is used to calculate the individual organic matter or transition zone. The calculation result is shown in FIG. Figure 7 As shown ( Figure 7 The left image in the middle is a single organic matter, and the right image is a single transition zone). The image of the i-th organic matter and transition zone connected body is denoted as E i In one embodiment, the connected body calculation method may adopt a seed fill algorithm or a two-pass algorithm.

[0222] like Figure 4 As shown, in step S403, an organic matter connected body image is obtained. Specifically, 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, an organic matter determination is performed to determine the organic matter connected body image.

[0223] In one embodiment, organic matter determination is performed through steps S4031 - S4032 .

[0224] 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 Total area of ​​the region ( Figure 8 Left side) or the total volume of the region is S s1 , effective area of ​​the region ( Figure 8 Right side figure) or the effective volume of the area is S s2 ,but:

[0225]

[0226] Where: α is the efficiency of the connected image of organic matter and transition zone.

[0227] The present invention has observed a large number of shale scanning images and found that the shale organic matter contains 0, 1 or more organic pores, but overall, the total area (volume) of these pores is relatively small, and most of the area (volume) within the organic matter coverage range is organic matter. The transition zone is different. The transition zone is the transition pixel between the pores and the rock skeleton, and the area (volume) is relatively small overall. Therefore, the present invention determines the organic matter and the transition zone by the area ratio within the coverage area.

[0228] In step S4032, the organic matter determination cutoff value is set, and the organic matter and transition zone connected body image with an efficiency less than the organic matter determination cutoff value is recorded as the transition zone connected body image, and the 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 the 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, it is determined that the connected body image E of the organic matter and the transition zone i It is a transition band connected image.

[0229] Use q i Represents the image of organic matter and transition zone connected body E i Whether it is organic matter, that is:

[0230]

[0231] Traverse all organic matter and transition zone connected body images to complete the organic matter identification and judgment process.

[0232] The present invention recognizes organic matter and transition zones through the efficient images of organic matter and transition zone connected bodies, has high recognition accuracy, is simple to implement, and has high feasibility, and can quickly recognize images of organic matter connected bodies and images of transition zone connected bodies.

[0233] like Figure 1 As shown, in step S103, the organic pore image and the inorganic pore image are determined. Specifically, the pore image in the standard grayscale image is extracted, and the pore recognition process is performed in combination with the organic matter interconnected body image to determine the organic pore image and the inorganic pore image. Furthermore, after identifying the organic matter and the transition zone, the organic pores and the inorganic pores can be identified by judging the contact relationship between the pores and the organic matter and the transition zone.

[0234] In one embodiment, by Figure 4 Steps S404-S406 are shown to determine an organic pore image and an inorganic pore image.

[0235] like Figure 4 As shown, in step S404, a pore image is obtained. Specifically, according to the grayscale distribution of the standard grayscale image, the grayscale lower limit and the grayscale upper limit of the pore are determined to extract the pore image. Specifically, according to the grayscale distribution of the standard grayscale image, it is found that the grayscale value of the pore is the lowest. Through the threshold segmentation method, the following can be extracted: Fig. 9 Pore ​​image shown.

[0236] like Figure 4As shown, in step S405, a pore connected body image (image F) is obtained. Specifically, a connected body calculation method is used to calculate the pore connected body image. Further, a connected body calculation method is used to calculate individual pores. The calculation result is shown in FIG. Fig.10 As shown, the i-th pore interconnected 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.

[0237] like Figure 4 As shown, in step S406, an organic pore image and an inorganic pore image are determined. Specifically, 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.

[0238] In one embodiment, pore identification processing is performed through steps S4061-S4064.

[0239] 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 subjected to pore expansion 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 Fig.12 In one embodiment, the pore template T is as shown in FIG. Fig.11 shown.

[0240] The expanded image is recorded as the pore expansion connected body image G i ,but:

[0241]

[0242] Where: is the pore expansion operator.

[0243] The boundary image of the pore expansion connected body image is denoted as H i ,but:

[0244] H i =G i \F i (10)

[0245] Where: \ is the difference operation.

[0246] 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. iThe perimeter of the pore expansion connected body is L li .

[0247] In step S4063, the intersection of the boundary image and the organic matter interconnected 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 interconnected body image is used. i and organic matter interconnected 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 but:

[0248] P ij =E j ∩H i (11)

[0249] Where: ∩ is the intersection operator.

[0250] In step S4064, organic pore determination is performed by intersection image area and pore expansion connected body perimeter to determine an organic pore image and an inorganic pore image.

[0251] In one embodiment, organic pore determination is performed through steps S40641-S40643.

[0252] In step S40641, the intersection image area is used as the dividend and the perimeter of the porous expansion interconnected body is used as the divisor to calculate the quotient value, which is used as the proportional coefficient of the porous interconnected body image. Specifically, for the i-th porous interconnected body image F i Make a judgment and record the pore expansion connected body image G i The ratio of the intersection with the organic matter connected body image to the total pore perimeter is β i ,but:

[0253]

[0254] Furthermore, if β i Equal to 0, indicating that the pore connected body 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 interconnection image F i Organic pores.

[0255] In step S40642, the pore interconnected body image with a scale factor equal to zero is recorded as an 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 an organic pore image. Fig.13As shown in the figure, the white part in the frame is the intersection of the pore expansion interconnected body image and the organic matter interconnected body image. β represents the ratio of the intersection of pores and organic matter to the total pore perimeter, that is, the proportional coefficient, 0≤β≤1. For organic pores, β>0; for inorganic matter (non-organic pores), β=0; for organic edge pores, 0<β<1, and for organic internal pores, β=1.

[0256] In step S40643, for the organic pore image, the organic edge pore cutoff value is set, and the organic pore image with a proportional coefficient greater than zero and less than or equal to the organic edge pore cutoff value is recorded as the organic edge pore image, and the organic pore image with a proportional coefficient greater than the organic edge pore cutoff value and less than or equal to one is recorded as the organic internal pore image. Specifically, the determination of the organic edge pore requires setting the organic edge pore cutoff value β0. When 0<β≤β0, the pore is considered to be an organic edge pore; when β0<β≤1, the pore is considered to be an organic internal pore.

[0257] 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 is the internal pore image of organic matter. Fig.13 As shown, the left picture shows the internal pores of organic matter, the middle picture shows the edge pores of organic matter, and the right picture shows the non-organic pores.

[0258] 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:

[0259]

[0260]

[0261] Traverse all the pore connected body images F i , complete the organic pore determination.

[0262] 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, organic edge pore images and organic internal pore images are identified through organic edge pore cutoff values. The method has high recognition accuracy, simple implementation, and high feasibility, and can quickly identify organic edge pore images and organic internal pore images.

[0263] like Figure 1As shown, 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 a microcrack image and an inorganic pore image.

[0264] In one embodiment, microcrack identification processing is performed through steps S1041-S1043.

[0265] In step S1041, the Feret maximum diameter and Feret minimum diameter are calculated for the inorganic pore image. Specifically, the difference between pores and microcracks lies in the difference in aspect ratio. The present invention uses the Feret maximum diameter to approximate the length of the pore and the Feret minimum diameter to approximate the width of the pore. The Feret diameter is used to distinguish between inorganic pores and microcracks in shale. Fig.14 As shown, for microcracks ( Fig.14 For the right figure), the maximum Feret diameter and the minimum Feret diameter are quite different; while for the pores ( Fig.14 Left figure), the difference between Feret's maximum diameter and Feret's minimum diameter is small.

[0266] 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 value, which is used as the diameter ratio of the inorganic pore image. Specifically, the present invention uses two indicators to identify microcracks, one is the Feret maximum diameter, and the other is 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:

[0267]

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

[0269] In step S1043, a diameter cutoff value and a ratio cutoff value are set, and an inorganic pore image that meets the microcrack determination condition is recorded as a microcrack image, and an inorganic pore image that does not meet the microcrack determination condition is recorded as an inorganic pore image. The microcrack determination condition is: 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 max0 and 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, denoted as F i ', otherwise, the pore is judged to be an inorganic pore and is recorded as F i ”.

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

[0272]

[0273] Traverse all inorganic pore images and complete the identification of all microcracks.

[0274] 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 identification accuracy, simple implementation, and high feasibility, and can quickly identify micro-crack images and inorganic pore images.

[0275] According to another aspect of the present invention, a shale pore type identification device based on digital core is provided, which executes a shale pore type identification method based on digital core. 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 is used to perform grayscale adjustment and image noise reduction on the original grayscale image of the shale sample to be identified, and obtain a standard grayscale image. The organic matter identification module is used to extract the organic matter and transition zone images in the standard grayscale image, perform organic matter identification processing, and determine the organic matter interconnected body image. The pore identification module is used to extract the pore image in the standard grayscale image, and perform pore identification processing in combination with the organic matter interconnected body image to determine the organic pore image and the inorganic pore image. The microcrack identification module is used to perform microcrack identification processing on the inorganic pore image, and determine the microcrack image and the inorganic pore image.

[0277] The present invention provides a shale pore type identification method and device based on digital core, which obtains digital images of rock samples by imaging scanning, processes the images, and obtains organic pores, inorganic pores and microcracks (such as Fig.15 Furthermore, the present invention can also perform quantitative calculations on pores and microcracks to obtain pore structure parameters of different types of pore spaces.

[0278] According to another aspect of the present invention, a method for quantitatively characterizing shale pore structure based on digital core is also provided. Fig.16As shown, based on the total number of pixels N of the original grayscale image of the shale sample to be identified and the actual length p (image resolution) corresponding to a single pixel, 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.

[0279] 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:

[0280] Porosity in organic matter r '(Volume proportion of pores in organic matter):

[0281]

[0282] Organic pore porosity φ r :

[0283]

[0284] Inorganic pore porosity φ i :

[0285]

[0286] Microcrack porosity φ f :

[0287]

[0288] Total porosity φ:

[0289]

[0290] Where: x i Is the i-th pore interconnected body image whether it is an organic pore image, and the value is 1 or 0; S i is the number of pixels of the i-th organic pore / inorganic pore / microcrack image; q i Whether the i-th organic matter and transition zone connected body image 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 of the original grayscale image; z i Whether the i-th pore interconnected body image is a microcrack image, the value is 1 or 0.

[0291] In one embodiment, the pore volume characterization parameters include: total pore volume, organic pore volume, inorganic pore volume, and microcrack volume. Specifically, the pore volume characterization parameters of the shale sample to be identified are determined by the following expression:

[0292] The total pore volume V corresponding to the original two-dimensional grayscale image i :

[0293] V i =p 2 ·S i (twenty two)

[0294] The total pore volume V corresponding to the original three-dimensional grayscale image i :

[0295] V i =p 3 ·S i (twenty three)

[0296] After calculating the total pore volume, the pore volume distributions of organic pores, inorganic pores, and microcracks can be obtained respectively.

[0297] Organic pore volume corresponding to the original grayscale image of the two-dimensional image

[0298]

[0299] The organic pore volume corresponding to the original grayscale image of the three-dimensional image

[0300]

[0301] Inorganic pore volume corresponding to the original grayscale image of the two-dimensional image

[0302]

[0303] Inorganic pore volume corresponding to the original three-dimensional grayscale image

[0304]

[0305] Microcrack volume corresponding to the original two-dimensional grayscale image

[0306]

[0307] Microcrack volume corresponding to the original three-dimensional grayscale image

[0308]

[0309] Where: p is the actual length corresponding to a single pixel; S i is the number of pixels of 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 Whether the i-th pore interconnected body image is a microcrack image, the value is 1 or 0.

[0310] In one embodiment, the pore equivalent radius distribution parameter includes: pore equivalent radius. Specifically, the pore equivalent radius can be calculated according to the area (volume) and surface area (perimeter) of the pore, and the pore equivalent radius distribution parameter of the shale sample to be identified is determined by the following expression:

[0311] The pore equivalent radius r corresponding to the original two-dimensional grayscale image i :

[0312]

[0313] The pore equivalent radius r corresponding to the original three-dimensional grayscale image i :

[0314]

[0315] Where: V i is the total pore volume.

[0316] 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 pore is 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 pore, and the pore shape factor characterization parameter of the shale sample to be identified is determined by the following expression:

[0317] The shape factor f corresponding to the original two-dimensional grayscale image i :

[0318]

[0319] The shape factor f corresponding to the original three-dimensional grayscale image i :

[0320]

[0321] Where: S i is the number of pixels of 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.

[0322] 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 by the following expressions and steps:

[0323] like Fig.17 As shown, the shape of the microcrack is equivalent to a deformed rectangle (cuboid), so the area and perimeter of the crack can be used to calculate the length and width of the crack.

[0324] The microcrack area S corresponding to the two-dimensional original grayscale image ssi :

[0325] S ssi =L i ×W i (34)

[0326] The microcrack perimeter P corresponding to the original two-dimensional grayscale image pi :

[0327] P pi =2(L i +W i )(35)

[0328] 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.

[0329] The microcrack length corresponding to the original two-dimensional grayscale image:

[0330]

[0331] The microcrack width corresponding to the original two-dimensional grayscale image:

[0332]

[0333] The microcrack volume S corresponding to the original three-dimensional grayscale image ssi :

[0334]

[0335] The microcrack surface area P corresponding to the three-dimensional original grayscale image pi :

[0336]

[0337] 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.

[0338] 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. Specifically, Fig.18 As shown, the second-order moment of the microcrack is calculated to obtain the origin and principal axis (major axis) of the ellipse, and then the crack inclination angle θ is obtained.

[0339] The number of microcracks in the original grayscale image is counted as M, then:

[0340] The microcrack density ρ corresponding to the original two-dimensional grayscale image L :

[0341]

[0342] The microcrack density ρ corresponding to the original three-dimensional grayscale image L :

[0343]

[0344] 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.

[0345] According to another aspect of the present invention, a digital core-based shale pore structure quantitative characterization device is provided, which implements a digital core-based shale pore structure quantitative characterization method. The quantitative characterization device includes: a parameter quantitative characterization module.

[0346] 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.

[0347] The present invention proposes a new shale pore structure parameter quantitative characterization method and device 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.

[0348] The digital core-based shale pore type identification and quantitative characterization method provided by the present invention can also be used in conjunction with a computer-readable storage medium, on which a computer program is stored, and the computer program is executed to run a digital core-based shale pore type identification method and / or a digital core-based shale pore structure parameter quantitative characterization method. The computer program can run computer instructions, which include computer program codes, which can be in source code form, object code form, executable file, or some intermediate form.

[0349] Computer-readable storage media may include: any entity or device that can carry computer program code, recording media, USB flash drives, mobile hard disks, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0350] It should be noted that the content contained in computer-readable storage media can be appropriately increased or decreased based on the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, based on legislation and patent practices, computer-readable storage media do not include electrical carrier signals and telecommunications signals.

[0351] The present invention obtains shale scanning images through SEM / CT. First, according to the characteristics of shale digital images, a corresponding image preprocessing method is invented to improve the clarity of the image; secondly, through a graphics algorithm, a method for identifying shale organic matter, organic pores, inorganic pores, and microcracks is invented; finally, the structural parameters of different reservoir spaces identified are calculated and counted. The present invention solves the method of quantitative characterization of different types of shale reservoir space structures, which is of great significance to the evaluation of shale reservoir quality and the optimization of development methods.

[0352] In one embodiment, a method for identifying and quantitatively characterizing shale pore types based on digital cores proposed in the present invention is applied, and the original image is a SEM-Maps two-dimensional scanning image, such as Fig.19 As shown. The image resolution is 5000*3000 and the accuracy is 10nm. Fig.19 It can be seen that there are inorganic dissolution pores inside rock particles and organic pores inside organic matter. Fig.19 For image A.

[0353] Fig. 20The grayscale value distribution of the scanned image A is a typical double peak. Corresponding to the image, it can be known that the first peak is organic matter and the second peak is rock particles. The proportion of pixels below grayscale 70 is extremely low, while those above grayscale 150 are rock particles. Therefore, the grayscale adjustment expression (1) can be grayscale stretched in the range of 70-150, that is, I1=80 and I2=150 in formula (1).

[0354] The grayscale adjusted image is as follows Fig.21 As shown, it is recorded as image B, and its brightness distribution is as follows Fig. 22 As shown in Figure 2, it can be seen that after grayscale adjustment, the contrast of pores, organic matter and rock particles is enhanced.

[0355] Given the noise reduction parameters m = 30, σ0 = 0.5, and the noise reduction window is 3, the noise reduction template is calculated using formulas (2) and (3). The noise reduction algorithm is run 3 times, and the results are as follows: Fig.23 As shown, the left picture is the image after denoising, the middle picture is a part of the image after denoising, and the right picture is the image before denoising.

[0356] According to formula (6), here we take I3 = 11, I4 = 100, and the calculated organic matter and transition band image D is as follows: Fig.24 shown.

[0357] Calculate the organic matter and transition zone connected body. Here, the pixel connectivity condition is 8-connected. The calculated organic matter and transition zone connected body image E is as follows: Fig.25 As shown, different shades of color represent different connected bodies.

[0358] For organic matter determination, the area (volume) efficiency α is calculated according to formula (7), and its distribution is as follows: Fig.26 shown.

[0359] Given the organic matter determination cutoff value α0 = 0.7, the organic matter connectivity is obtained as follows Fig. 27 As shown, the transition zone at the edge of the inorganic pore is Fig.28 shown.

[0360] The grayscale threshold of the pores is set to 10, and the grayscale values ​​of 0-10 are identified as pores. The pore image is extracted, as shown in Fig.29 As shown, the pore interconnected body image is extracted, such as Fig.30 shown.

[0361] According to the calculation method provided by the present invention, the pore image G after expansion is as follows: Fig.31 As shown, the expanded pore boundary image H is Fig.32 shown.

[0362] The intersection image of image H and organic matter E is as follows: Fig.33As shown in the figure, the internal pores of organic pores, the edge pores of organic pores, and the inorganic pores are calculated as follows Fig.34 As shown, the left picture shows the internal pores of organic matter, the middle picture shows the edge pores of organic matter, and the right picture shows the inorganic pores.

[0363] According to the quantitative characterization method of shale pore structure parameters based on digital core provided by the present invention, the porosity of organic matter is calculated as follows: r '=3.56%, organic pore porosity φ r =0.39%, inorganic pore porosity φ i =0.22%, total porosity φ=0.61%.

[0364] The total pore volume distribution is Fig.35 As shown, the pore volume distribution of organic pores is Fig.36 As shown, the pore volume distribution of inorganic pores is Fig.37 The total pore radius distribution is shown in Fig.38 As shown in Figure 2, the pore radius distribution of organic pores is as follows: Fig.39 As shown in Figure 2, the pore radius distribution of inorganic pores is as follows: Fig.40 The total pore shape factor distribution is shown in Fig.41 As shown, the organic pore shape factor distribution is as follows Fig.42 As shown, the inorganic pore shape factor distribution is as follows Fig.43 shown.

[0365] In summary, the present invention provides a method for identifying and quantitatively characterizing shale pore types based on digital cores, which has the following advantages over the prior art:

[0366] 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 clarity of the pore-organic matter boundary and the organic matter-rock skeleton boundary can be guaranteed while improving the local image quality within the pores, organic matter and rock skeleton.

[0367] 2. The present invention proposes a new organic matter determination method for shale digital images, which can identify organic matter and transition zones through the efficient images of organic matter and connected bodies in transition zones. It has high recognition accuracy, simple implementation, and high feasibility, and can quickly identify images of connected bodies of organic matter and images of connected bodies in transition zones.

[0368] 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, organic edge pore images and organic internal pore images are identified through organic edge pore cutoff values. The method has high recognition accuracy, simple implementation, and high feasibility, and can quickly identify organic edge pore images and organic internal pore images.

[0369] 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.

[0370] 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 microfracture structure characterization parameters of the shale sample to be identified.

[0371] By using the present invention, it is possible to 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 analyze the distribution of different types of storage spaces in shale, which is of great significance for shale reservoir quality evaluation and optimization of effective shale oil and gas development methods.

[0372] It should be understood that the embodiments disclosed in the present invention are not limited to the specific structures, processing steps or materials disclosed herein, but should be extended to equivalent substitutions of these features understood by ordinary technicians in the relevant field. It should also be understood that the terms used herein are only used for the purpose of describing specific embodiments and are not meant to be limiting.

[0373] In the description of the present invention, unless otherwise specified, "plurality" means two or more than two; the orientations or positional relationships indicated by the terms "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0374] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0375] Certain terms are used throughout this application document to indicate specific system components. As those skilled in the art will recognize, different names can usually be used to indicate the same components, so this application document is not intended to distinguish those 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 form, and therefore should be interpreted as meaning "including but not limited to...". In addition, the terms "substantially", "substantially" or "approximately" that may be used in this article relate to the tolerances of the corresponding terms accepted by the industry. The term "coupling" as may be used in this article includes direct coupling and indirect coupling via other components, elements, circuits, or modules, wherein for indirect coupling, the components, elements, circuits, or modules between them do not change the information of the signal but can adjust its current level, voltage level, and / or power level. Inferred coupling (for example, one of the elements is coupled to another element by inference) includes direct and indirect coupling between two elements in the same way as "coupling".

[0376] The "one embodiment" or "embodiment" mentioned in the specification means that the specific features, structures or characteristics described in conjunction with the embodiment are included in at least one embodiment of the present invention. Therefore, the phrases "one embodiment" or "embodiment" appearing in various places throughout the specification do not necessarily refer to the same embodiment.

[0377] The embodiments of the present invention are given for the purpose 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 of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0378] Although the embodiments disclosed in the present invention are as above, the contents described are only embodiments adopted for facilitating the understanding of the present invention and are not intended to limit the present invention. Any technician in the technical field to which the present invention belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A shale pore type identification method based on digital core, characterized in that: The method comprises: 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; 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; Extracting the pore image in the standard grayscale image, combining it with the organic matter interconnected body image, performing pore recognition processing, and determining the organic pore image and the inorganic pore image; A microcrack recognition process is performed on the inorganic pore image to determine a microcrack image and an inorganic pore image.

2. The method for identifying shale pore types based on digital cores according to claim 1, characterized in that: The original grayscale image of the shale sample to be identified is obtained through the following steps: 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; or, The shale sample to be identified is scanned by electronic computer tomography to obtain a three-dimensional original grayscale image, wherein the electronic computer tomography adopts nano-scale electronic computer tomography.

3. A method for identifying shale pore types based on digital cores according to claim 1 or 2, characterized in that: Perform grayscale adjustment by following the steps below: 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, grayscale adjustment is performed on the original grayscale image using a grayscale adjustment expression to obtain the original grayscale image after grayscale adjustment; The grayscale adjustment expression is: 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.

4. A shale pore type identification method based on digital core according to any one of claims 1 to 3, characterized in that: Image noise reduction is performed through the following steps: Determine a noise reduction window based on the original grayscale image after grayscale adjustment; 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; 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; The noise reduction standard deviation calculation expression is: 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 in the noise reduction window; I min It is the minimum grayscale value within the noise reduction window.

5. A shale pore type identification method based on digital core according to any one of claims 1 to 4, characterized in that: The organic matter connectivity image is determined by the following steps: According to the grayscale distribution of the standard grayscale image, the grayscale lower limit of the organic matter and transition zone pixels and the grayscale upper limit of the organic matter and transition zone pixels are determined to extract the organic matter and transition zone image; Performing the organic matter identification process on the organic matter and transition zone images to determine the organic matter connected body image; The organic matter identification process comprises the following steps: For the organic matter and transition zone images, a connected body calculation method is used to calculate and obtain the organic matter and transition zone connected body images; Based on the total area or total volume of the region of the organic matter and the connected body image of the transition zone, and the effective area or effective volume of the region of the organic matter and the connected body image of the transition zone, organic matter determination is performed to determine the organic matter connected body image.

6. The method for identifying shale pore types based on digital cores according to claim 5, 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 or volume efficiency is calculated as the efficiency of the organic matter and transition zone connected body image; An organic matter determination cutoff value is set, and the organic matter and transition zone connected body image with an efficiency less than the organic matter determination cutoff value are recorded as the transition zone connected body image, and the organic matter and transition zone connected body image with an efficiency greater than or equal to the organic matter determination cutoff value are recorded as the organic matter connected body image.

7. A shale pore type identification method based on digital core according to any one of claims 1 to 6, characterized in that: The organic pore image and the inorganic pore image are determined by the following steps: According to the grayscale distribution of the standard grayscale image, determine the grayscale lower limit and the grayscale upper limit of the pores to extract the pore image; For the pore image, a connected body calculation method is used to calculate and obtain a pore connected body image; Based on the pore interconnected body image and the organic matter interconnected body image, the pore identification process is performed to determine the organic pore image and the inorganic pore image.

8. The method for identifying shale pore types based on digital cores according to claim 7, 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 calculating 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.

9. The method for identifying shale pore types based on digital cores according to claim 8, characterized in that: The organic pore determination is determined by the following steps: The area of ​​the intersection image is used as the dividend and the perimeter of the porous expansion connected body is used as the divisor to calculate the quotient value as the proportional coefficient of the porous connected body image; Recording the pore interconnected body image with a scale factor equal to zero as the inorganic pore image, and recording the pore interconnected body image with a scale factor greater than zero and less than or equal to one as the organic pore image; For the organic pore image, an organic edge pore cutoff value is set, and the organic pore image with a proportional coefficient greater than zero and less than or equal to the organic edge pore cutoff value is recorded as an organic edge pore image, and the organic pore image with a proportional coefficient greater than the organic edge pore cutoff value and less than or equal to one is recorded as an organic internal pore image.

10. A shale pore type identification method based on digital core according to any one of claims 1 to 9, characterized in that: Micro crack identification is done by following the steps below: For the inorganic pore image, the maximum Feret diameter and the minimum Feret diameter are calculated; The Feret minimum diameter is used as the dividend and the Feret maximum diameter is used as the divisor to calculate the quotient value as the diameter ratio of the inorganic pore image; Setting a diameter cutoff value and a ratio cutoff value, recording an inorganic pore image that meets the microcrack determination condition as the microcrack image, and recording an inorganic pore image that does not meet the microcrack determination condition as the inorganic pore image; The microcrack determination condition is: the maximum Feret diameter is greater than the diameter cutoff value and the diameter ratio is less than the ratio cutoff value.

11. A storage medium, characterized in that: It contains a series of instructions for executing the method steps as claimed in any one of claims 1-10.

12. A shale pore type identification device based on digital core, characterized in that: Execute the method according to any one of claims 1 to 10, wherein the type identification device comprises: A preprocessing module is used to perform grayscale adjustment and image noise reduction processing on the original grayscale image of the shale sample to be identified to obtain a standard grayscale image; 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; A pore recognition module is used to extract the pore image in 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; 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.

13. 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 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 the method as described in any one of claims 1 to 10, 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.

14. The method for quantitative characterization of shale pore structure based on digital core according to claim 13, characterized in that: The porosity characterization parameters of the shale sample to be identified are determined by the following expression: Porosity in organic matter r ': Organic pore porosity φ r : Inorganic pore porosity φ i : Microcrack porosity φ f : Total porosity φ: Where: x i Is the i-th pore interconnected body image whether it is an organic pore image, and the value is 1 or 0; S i is the number of pixels of the i-th organic pore / inorganic pore / microcrack image; q i Whether the i-th organic matter and transition zone connected body image 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 of the original grayscale image; z i Whether the i-th pore interconnected body image is a microcrack image, the value is 1 or 0.

15. The method for quantitative characterization of shale pore structure based on digital core according to claim 13, characterized in that: The pore volume characterization parameter of the shale sample to be identified is determined by the following expression: The total pore volume V corresponding to the original two-dimensional grayscale image i : V i =p 2 ·S i The total pore volume V corresponding to the original three-dimensional grayscale image i : V i =p 3 ·S i Organic pore volume corresponding to the original grayscale image of the two-dimensional image The organic pore volume corresponding to the original grayscale image of the three-dimensional image Inorganic pore volume corresponding to the original grayscale image of the two-dimensional image Inorganic pore volume corresponding to the original three-dimensional grayscale image Microcrack volume corresponding to the original two-dimensional grayscale image Microcrack volume corresponding to the original three-dimensional grayscale image Where: p is the actual length corresponding to a single pixel; S i is the number of pixels of 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 Whether the i-th pore interconnected body image is a microcrack image, the value is 1 or 0.

16. A method for quantitative characterization of shale pore structure based on digital core according to claim 15, characterized in that: The pore equivalent radius distribution parameter of the shale sample to be identified is determined by the following expression: The pore equivalent radius r corresponding to the original two-dimensional grayscale image i : The pore equivalent radius r corresponding to the original three-dimensional grayscale image i : Where: V i is the total pore volume.

17. The method for quantitatively characterizing shale pore structure based on digital core according to claim 13, characterized in that: The pore shape factor characterization parameter of the shale sample to be identified is determined by the following expression: The shape factor f corresponding to the original two-dimensional grayscale image i : The shape factor f corresponding to the original three-dimensional grayscale image i : Where: S i is the number of pixels of 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.

18. The method for quantitative characterization of shale pore structure based on digital core according to claim 13, characterized in that: The microcrack structure characterization parameters of the shale sample to be identified are determined by the following expressions and steps: The microcrack area S corresponding to the two-dimensional original grayscale image ssi : S ssi =L i ×W i The microcrack perimeter P corresponding to the original two-dimensional grayscale image pi : P pi =2(L i +W i ) 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; The microcrack volume S corresponding to the original three-dimensional grayscale image ssi : The microcrack surface area P corresponding to the three-dimensional original grayscale image pi : 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; 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; The number of microcracks in the original grayscale image is counted as M, then: The microcrack density ρ corresponding to the original two-dimensional grayscale image L : The microcrack density ρ corresponding to the original three-dimensional grayscale image L : 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.

19. A storage medium, characterized in that: It contains a series of instructions for executing the method steps as claimed in any one of claims 13-18.

20. A device for quantitative characterization of shale pore structure based on digital core, characterized in that: The method according to any one of claims 13 to 18 is performed, wherein the quantitative characterization device comprises: 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 of 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.

Images