METHOD FOR DETECTING SHALE PORE TYPE BASED ON DIGITAL DRILL CORE SAMPLES AND A METHOD FOR QUANTITATIVE CHARACTERIZATION

Abstract

The present invention proposes a method for identifying the shale pore type based on a digital drill core and a method for quantitative characterization, relating to the technical field of oil and gas exploration and development. The method comprises identifying the organic matter and a transition zone based on a grayscale image of a shale sample to be identified, thereby obtaining a result for the identification of the organic matter; and obtaining an image of the organic pores and an image of the inorganic pores based on the result of the organic matter identification and the grayscale image, which serve as the result for the identification of the shale pore type.The present invention enables the quantitative identification of organic pores, inorganic pores, and microfractures in shale, as well as the quantitative characterization of shale pore structures. Furthermore, structural parameters of organic pores, inorganic pores, and microfractures can be analyzed, allowing for a statistical distribution of the different types of reservoir spaces in shale rocks. The present invention is of considerable importance for assessing the quality of shale deposits and optimizing effective development strategies for shale oil and gas.

Description

Cross-reference to related patent applications

[0001] The present application claims priority over Chinese patent application No. 202310858325.2 entitled “Digital core-based shale pore type recognition method and quantitative characterization method”, which was filed on July 13, 2023 and the entire contents of which are incorporated herein by reference. Technical field

[0002] The present invention relates to the technical field of petroleum and natural gas exploration and development, and in particular to a method for identifying the shale pore type based on digital drill cores and a method for quantitative characterization. Technical background

[0003] Shale deposit spaces exhibit complex structures with small pore sizes, consisting primarily of nanometer-sized pores. Various pore types exist, including organic pores, inorganic pores, and microfractures. In shale deposits, oil and gas are present in organic pores in an adsorbed / dissolved state, while in inorganic pores, oil and gas are predominantly in a free state. Accurate quantitative characterization of the different shale pore structures is crucial for investigating oil and gas occurrences in shale, assessing resource quality, and formulating an appropriate development strategy.

[0004] Currently, liquid intrusion techniques (such as gas adsorption, mercury injection, and nuclear magnetic resonance) are common methods for the quantitative characterization of pore structures in porous media. Numerous studies have employed these techniques to investigate the properties of shale pore structures. However, theoretically, these methods are unable to distinguish between organic and inorganic pores in shale rocks. While imaging techniques can identify organic pores, they typically rely on manual annotations, which are not as efficient as automated identification methods. Furthermore, these methods can only perform qualitative analysis, not quantitative characterization.

[0005] In view of the aforementioned problems of the prior art, the present invention proposes a method for identifying the schist pore type based on digital drill cores and a method for quantitative characterization. Brief description of the invention

[0006] To solve the aforementioned technical problems, the present invention proposes a method for identifying the shale pore type based on digital drill cores, comprising the following: Identifying the organic matter and the transition zone based on a grayscale image of a shale sample to be identified, thereby obtaining a result for the identification of the organic matter; and Obtaining an image of the organic pores and an image of the inorganic pores based on the result for identifying the organic matter and the grayscale image, which serve as the result for identifying the shale pore type.

[0007] According to one embodiment of the present invention, the organic substance and the transition zone are identified by the following steps: Preservation of the lower and upper grayscale boundaries corresponding to the organic matter and the transition zone, based on the grayscale distribution of the grayscale image, thereby obtaining an image with contiguous components of the organic matter and the transition zone; and

[0008] Performing the identification of organic matter based on a total regional area or total regional volume and an effective regional area or effective regional volume in the image of the contiguous components of organic matter and the transition zone, thereby obtaining the result for the identification of organic matter and a result for the identification of the transition zone.

[0009] According to one embodiment of the present invention, the identification of the organic substance is carried out by the following steps: Taking the effective regional area or effective regional volume as the dividend and the total regional area or total regional volume as the divisor to obtain an area effectiveness ratio or a volume effectiveness ratio, which serves as the effectiveness ratio of the picture of the contiguous components of organic matter and the transition zone;

[0010] Establishing a threshold for the identification of organic matter and recording the image of the coherent components of the organic matter and the transition zone with an effectiveness ratio that is less than the threshold for the identification of organic matter, as an image of the coherent components of the transition zone, which serves as the result for the identification of the transition zone; and

[0011] Recording the image of the contiguous components of the organic matter and the transition zone with an effectiveness ratio greater than or equal to the threshold for the identification of the organic matter, as an image of the contiguous components of the organic matter that serves as the result for the identification of the organic matter.

[0012] According to one embodiment of the present invention, the image of the organic pores and the image of the inorganic pores are identified by the following steps: Obtaining an image of the contiguous components of pores based on the grayscale distribution of the grayscale image; and

[0013] Performing the identification of pores based on the image of the related components of pores and the image of the related components of organic matter, in order to obtain the image of organic pores and the image of inorganic pores.

[0014] According to one embodiment of the present invention, pore identification is carried out by the following steps: Performing a pore dilation on the image of the connected components of pores based on a dilation template to obtain an image of the dilated connected components of pores and an image of their boundaries;

[0015] Calculating the perimeter of a dilated connected component of pores in the image of the dilated connected component of pores;

[0016] Taking an intersection of the image of the boundaries and the image of the connected components of the organic substance as the image of the intersection, in order to obtain an area of ​​the image of the intersection; and

[0017] Performing the identification of organic pores based on the area of ​​the image of the intersection and the extent of the dilated connected component of pores to determine the image of organic pores and the image of inorganic pores.

[0018] According to one embodiment of the present invention, the identification of the organic pores is carried out by the following steps: Taking the area of ​​the image of the intersection as the dividend and the perimeter of the expanded connected component of the pores as the divisor, to obtain a quotient that serves as the proportionality coefficient of the image of the connected components of pores; and

[0019] Recording the image of the connected components of pores with a proportional coefficient equal to zero as the image of the inorganic pores and the image of the connected components with a proportional coefficient greater than zero and less than or equal to one as the image of the organic pores.

[0020] According to one embodiment of the present invention, the method further comprises: Establishing a threshold for the organic matter content of the pores located at the edge for the organic pore image; and

[0021] Recording the image of the organic pores with a proportional coefficient greater than zero and less than or equal to the threshold for the organic matter of the pores located at the edge as an image of the organic matter of the pores located at the edge and the image of the organic pores with a proportional coefficient greater than the threshold for the organic matter of the pores located at the edge and less than or equal to one as an image of the organic matter of the pores located at the inside.

[0022] According to one embodiment of the present invention, the method further comprises: Calculating the maximum and minimum Feret diameters for the image of inorganic pores;

[0023] Taking the minimum Feret diameter as the dividend and the maximum Feret diameter as the divisor to obtain a quotient that serves as the diameter ratio of the image of the inorganic pores; and

[0024] Setting a diameter threshold and a ratio threshold, and recording the image of the inorganic pores that meets the condition for the identification of microfractures as the image of microfractures, and the image of the inorganic pores that does not meet the condition for the identification of microfractures as the image of inorganic pores.

[0025] According to one embodiment of the present invention, the condition for identifying the microfracture is that the maximum Feret diameter is larger than the diameter threshold and the diameter ratio is smaller than the ratio threshold.

[0026] According to one embodiment of the present invention, the greyscale image is an original 2D or 3D greyscale image of the slate sample to be identified.

[0027] According to one embodiment of the present invention, the method further comprises adjusting the grayscale of the original grayscale image, wherein: a 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 matrix; and An original grayscale image is obtained after grayscale adjustment based on the first grayscale value and the second grayscale value.

[0028] According to one embodiment of the present invention, the method further comprises: Determining a denoising window based on the original grayscale image after grayscale adjustment; Calculating a standard deviation of the denoising based on a maximum grayscale value and a minimum grayscale value within the noise window; and

[0029] Performing denoising of the original grayscale image after grayscale adjustment based on the standard deviation of the denoising.

[0030] According to one embodiment of the present invention, the method further comprises: Performing grayscale adjustment and image denoising on the original grayscale image of the slate sample to be identified, in order to obtain the standard grayscale image; Extracting the image of the organic matter and the transition zone from the standard grayscale image to perform the identification of the organic matter and to determine the image of the related components of the organic matter; Extracting images of pores from the standard grayscale image and performing pore identification based on the image of the contiguous components of the organic matter to determine the image of the organic pores and the image of the inorganic pores; and Performing an identification of microfractures on the image of inorganic pores in order to determine the image of the microfracture and the image of the inorganic pores.

[0031] According to one embodiment of the present invention, the original grayscale image of the slate sample to be identified is obtained by the following steps: Scanning the slate sample to be identified using a scanning electron microscope to obtain a 2D original grayscale image, wherein the resolution of the scanning electron microscope is less than or equal to 10 nm; or Scanning the slate sample to be identified with a computed tomography scanner to obtain a 3D original grayscale image, using nanoscale computed tomography.

[0032] According to one embodiment of the present invention, the grayscale adjustment is performed by the following steps: Determining the grayscale distribution of the original grayscale image to obtain the first grayscale value corresponding to the pores and the second grayscale value corresponding to the rock matrix; Adjusting the original grayscale image based on the first and second grayscale values ​​using a grayscale adjustment equation to obtain the original grayscale image after grayscale adjustment; and The equation for grayscale adjustment is: I'={0,I≤I1I−I1I2−I1,I1<I≤l2255,l> l1 where I' denotes the grayscale value of the original grayscale image after grayscale adjustment; I denotes the grayscale value of the original grayscale image; I1 denotes the first grayscale value; and I2 denotes the second grayscale value.

[0033] According to one embodiment of the present invention, image denoising is carried out by the following steps: Determining the denoising window based on the original grayscale image after grayscale adjustment; Calculating a standard deviation of the denoising based on the maximum grayscale value and the minimum grayscale value within the denoising window using an equation for the standard deviation of the denoising; Performing a standard deviation denoising on the original grayscale image after grayscale adjustment using a Gaussian denoising kernel function to obtain the standard grayscale image; and The equation for the standard deviation of the denoising is: σ=mσ0Imax−Imin where σ denotes the standard deviation of the denoising; m denotes a tolerance coefficient; σ0 denotes a preset standard deviation; I max denotes the maximum grayscale value within the denoising window; and I min denotes the minimum grayscale value within the denoising window.

[0034] According to one embodiment of the present invention, the image of the interconnected components of the organic substance is determined by the following steps: Determining the lower and upper grayscale limits for pixels of the organic matter and the transition zone based on the grayscale distribution of the standard grayscale image to obtain the image of the organic matter and the transition zone; and

[0035] Performing the identification of the organic matter on the image of the organic matter and the transition zone in order to determine the image of the related components of the organic matter, the identification of the organic substance is carried out by the following steps: Applying a connected component algorithm to the organic matter and transition zone image to obtain the connected component image of the organic matter and transition zone; and

[0036] Performing the identification of organic matter based on the total regional area or total regional volume and the effective regional area or effective regional volume of the image of the contiguous components of organic matter and the transition zone to obtain the image of the contiguous components of organic matter.

[0037] According to one embodiment of the present invention, the image of the organic pores and the image of the inorganic pores are determined by the following steps: Determining the lower and upper grayscale limits for pores to obtain the image of the pores based on the grayscale distribution of the standard grayscale image; Applying the connected components algorithm to the pore image to obtain the connected components image of pores; and Performing an identification of pores based on images of the related components of pores and organic matter to determine the image of organic pores and the image of inorganic pores.

[0038] According to a further aspect of the present invention, a device for detecting the shale pore type based on a digital drill core is provided, configured to perform the aforementioned method for detecting the shale pore type based on a digital drill core. The device comprises: a preprocessing module configured to perform grayscale adjustment processing and image denoising processing on the original grayscale image of the slate sample to be identified, resulting in a standard grayscale image; an organic matter identification module configured to extract the organic matter and transition zone image from the standard grayscale image, thus performing organic matter identification to determine the image of the contiguous components of the organic matter; a module for identifying pores, configured to extract the pore image from the standard grayscale image and to identify the pores based on the image of the contiguous components of the organic matter, thereby identifying the organic pores and the inorganic pores; and a module for identifying microfractures, configured to perform an identification of microfractures on the image of inorganic pores in order to determine the image of microfractures and the image of inorganic pores.

[0039] According to a further aspect of the present invention, a computer-readable storage medium is also provided on which a series of instructions for carrying out the aforementioned method for identifying the shale pore type based on a digital drill core are stored.

[0040] According to yet another aspect of the present invention, a method for the quantitative characterization of the parameters of the shale pore structure based on a digital drill core comprises the following: Obtaining pore structure parameters of the slate sample to be identified based on a total number of pixels in the grayscale image and an actual length of a pixel, and in combination with the result for identifying the organic matter, the image of the organic pores, and the image of the inorganic pores obtained by the above method, wherein the pore structure parameters include at least one parameter for characterizing the porosity, parameters for characterizing the pore volume, parameters for the distribution of the equivalent pore radius, parameters for characterizing the form factor of the pores, and parameters for characterizing the microfracture structure.

[0041] According to one embodiment of the present invention, the parameters for characterizing the porosity, the parameters for characterizing the pore volume, the parameters for the distribution of the equivalent pore radius, the parameters for characterizing the form factor of the pores and the parameters for characterizing the microfracture structure of the slate sample to be identified are calculated on the basis of the total number of pixels in the original grayscale image of the slate sample to be identified and the actual length of a pixel, as well as in combination with the image of the organic pores, the image of the microfractures and the image of the inorganic pores determined by the method for recognizing the slate pore type based on a digital drill core.

[0042] According to one embodiment of the present invention, the parameters for characterizing the porosity of the slate sample to be identified are determined using the following equations:

[0043] Porosity within organic matter ϕr' (Volume fraction of the pores within the organic matter): ϕr'=∑xiSi∑qiEi

[0044] Porosity of organic pores ϕ r : ϕr=∑xiSiN

[0045] Porosity of inorganic pores ϕ i : ϕi=∑(1−xi)(1−zi)SiN

[0046] Porosity of microfractures ϕ f : ϕf=∑(1−xi)ziSiN

[0047] Total porosity ϕ: ϕ=∑SiN where: xi with a value of 1 or 0, indicating whether the i-th image of the connected components of the pores is an image of the organic pores; S iindicates the number of pixels in the i-th image of the organic pores / inorganic pores / microfractures; q i with a value of 1 or 0, indicating whether the i-th image of the connected components of the organic matter and the transition zone is an image of the organic matter; E i the i-th image of the contiguous components of the organic matter and the transition zone; N is the total number of pixels in the original grayscale image; and z i with a value of 1 or 0 indicates whether the i-th image of the connected components of the pores is an image of the microfractures.

[0048] According to one embodiment of the present invention, the parameters for characterizing the pore volume of the slate sample to be identified are determined using the following equations:

[0049] Total pore volume V i corresponding to the two-dimensional original grayscale image: Vi=p2⋅Si

[0050] Total pore volume Vi corresponding to the three-dimensional original grayscale image: Vi=p3⋅Si

[0051] Volume of organic pores V ri corresponding to the two-dimensional original grayscale image: Vri=xi⋅p2⋅Si

[0052] Volume of organic pores V ri corresponding to the three-dimensional original grayscale image: Vri=xi⋅p3⋅Si

[0053] Volume of inorganic pores V ii corresponding to the two-dimensional original grayscale image: Vii=(1−xi)⋅p2⋅Si

[0054] Volume of inorganic pores V ii corresponding to the three-dimensional original grayscale image: Vii=(1−xi)⋅p3⋅Si

[0055] Volume of microfractures V fi corresponding to the two-dimensional original grayscale image: Vfi=(1−xi)⋅zi⋅p2⋅Si

[0056] Volume of the microfracture corresponding to the three-dimensional original grayscale image: Vfi=(1−xi)⋅zi⋅p3⋅Si where p represents the actual length corresponding to one pixel; S i indicates the number of pixels in the i-th image of the organic pores / inorganic pores / microfractures; x i with a value of 1 or 0, indicating whether the i-th image of the connected components of the pores is an image of the organic pores; and z i with a value of 1 or 0 indicates whether the i-th image of the connected components of the pores is an image of the microfractures.

[0057] According to one embodiment of the present invention, the parameters of the distribution of the equivalent pore radius of the slate sample to be identified are determined using the following equations: Equivalent pore radius r i corresponding to the two-dimensional original grayscale image: ri=Viπ Equivalent pore radius r i corresponding to the three-dimensional original grayscale image: ri=3Vi4π3 where V i indicates the total pore volume.

[0058] According to one embodiment of the present invention, the parameters for characterizing the form factor of the pores of the slate sample to be identified are determined using the following equations: Form factor f i corresponding to the two-dimensional original grayscale image: fi=4πSiPi2 Form factor f i corresponding to the three-dimensional original grayscale image: fi=Pi336πSi2 where S i indicates the number of pixels in the i-th image of the organic pores / inorganic pores / microfractures; and P i is the number of pixels on the peripheral surface of the i-th organic pore / inorganic pore.

[0059] According to one embodiment of the present invention, the parameters for characterizing the microfracture structure of the slate sample to be identified are determined using the following equations and steps: Area of ​​microfractures S ssi corresponding to the two-dimensional original grayscale image: Sssi=Li×Wi Extent of microfractures P pi corresponding to the two-dimensional original grayscale image: Ppi=2(Li+Wi)

[0060] By simultaneously solving the equation for the area of ​​the microfracture and the equation for the perimeter of the microfracture, which correspond to the two-dimensional original grayscale image, the length and width of the microfracture, which correspond to the two-dimensional original grayscale image, can be determined.

[0061] Volume of microfractures S ssi corresponding to the three-dimensional original grayscale image: Sssi=Li2×Wi

[0062] Area of ​​the microfracture P pi corresponding to the three-dimensional original grayscale image: Ppi=2Li2+4Li×Wi

[0063] By simultaneously solving the expression for the volume of the microfracture and the expression for the area of ​​the microfracture, which correspond to the three-dimensional original grayscale image, the lengths and widths of the microfracture, which correspond to the three-dimensional original grayscale image, can be determined.

[0064] The second-order moments of the microfracture are calculated to obtain the origin and the principal axis of the ellipse, from which the inclination angle θ of the fracture is determined.

[0065] Assuming that the number of microfractures counted in the original grayscale image is M, the microfracture density ρ corresponding to the two-dimensional original grayscale image is calculated. L calculated: ρL=MN×p2 the density of microfractures ρ L , which corresponds to the three-dimensional original grayscale image, is calculated: ρL=MN×p3

[0066] Where L i indicates the length of the i-th microfracture; W i specifies the width of the i-th microfracture; p specifies the actual length of a pixel; and N specifies the total number of pixels in the original grayscale image.

[0067] According to yet another aspect of the present invention, a device for the quantitative characterization of the schist pore structure based on a digital drill core is also provided, which performs the above-described method for the quantitative characterization of the schist pore structure based on a digital drill core.The quantitative characterization device is configured to calculate the parameters for characterizing porosity, pore volume, equivalent pore radius distribution, pore form factor, and microfracture structure of the slate sample to be identified, based on the total number of pixels of the original grayscale image of the slate sample to be identified and the actual length of a pixel, in combination with the organic pore image, microfracture image, and inorganic pore image determined by the slate pore type identification method described above based on a digital core sample.

[0068] According to a further aspect of the present invention, a computer-readable storage medium is also provided on which a series of instructions for carrying out the aforementioned method for the quantitative characterization of the schist pore structure are stored on the basis of a digital core.

[0069] The present invention proposes a method for identifying the shale pore type based on a digital drill core and a method for quantitative characterization. Compared with the prior art, the present invention has the following advantages. 1 The present invention improves the standard deviation of the Gaussian denoising kernel function and proposes a novel image processing method for denoising digital images of slate. According to the image processing method for denoising proposed in the present invention, the local image quality within the pores, the organic matter, and the rock matrix can be improved, ensuring the clarity of the boundaries between pores and organic matter as well as between organic matter and the rock matrix. 2. The present invention proposes a novel method for identifying organic matter in digital images of slate. The organic matter and the transition zone can be identified based on the effectiveness ratios of the images of the contiguous components for the organic matter and the transition zone. This method is easy to implement, highly practical, and can quickly identify images of the contiguous components of the organic matter and the transition zone with high identification accuracy. 3. The present invention proposes a novel method for identifying organic pores in digital images of slate, which can identify images of inorganic pores and images of organic pores within images of the contiguous components of pores using a proportional coefficient. For images of organic pores, images of marginal and inward pores composed of organic matter can be identified based on a threshold value for marginal organic pores. This method is easy to implement, highly practical, and can quickly identify images of marginal and inward pores composed of organic matter with high identification accuracy. 4. The present invention proposes a novel method for identifying and processing microfractures in digital images of slate. Two indicators are used to identify microfractures: the maximum Feret diameter and the ratio of minimum Feret diameter to maximum Feret diameter. This method is easy to implement, highly practical, and enables the rapid identification of microfracture images and images of inorganic pores with high identification accuracy. 5. The present invention proposes a novel method for the quantitative characterization of parameters of the slate pore structure for digital images of slate, with which the quantitative characterization of porosity parameters, pore volume parameters, parameters for the distribution of the equivalent pore radius, pore form factor parameters and microfracture structure parameters for slate samples to be identified is achieved.

[0070] The present invention enables the quantitative identification of organic pores, inorganic pores, and microfractures in shale, as well as the quantitative characterization of shale pore structures. Furthermore, structural parameters of organic pores, inorganic pores, and microfractures can be analyzed, allowing for a statistical distribution of the different types of reservoir spaces in shale rocks. The present invention is of considerable importance for assessing the quality of shale deposits and optimizing effective development strategies for shale oil and gas.

[0071] Further features and advantages of the present invention are set forth in the following description and will partly become apparent from the description or can be deduced from the implementation of the present invention. The objective and other advantages of the present invention can be realized and achieved through the structure particularly highlighted in the description, the claims, and the drawings. Brief description of the drawings

[0072] The accompanying drawings serve to further understand the present invention and are part of the description. Together with the embodiments of the present invention, the drawings serve to illustrate the present invention, but do not constitute a limitation of the present invention. In the drawings: Fig. Figure 1A is a flowchart showing the steps of a method for identifying the shale pore type based on a digital drill core according to an embodiment of the present invention; Fig. Figure 1B is a flowchart showing the steps of the method for identifying the shale pore type based on a digital drill core according to another embodiment of the present invention; Fig. Figure 2 is a flowchart showing the steps for obtaining a standard grayscale image according to an embodiment of the present invention; Fig. Figure 3 shows a pixel grayscale value in a denoising window according to an embodiment of the present invention; Fig. Figure 4 is a flowchart showing the steps for determining the images of the organic pores and the inorganic pores according to an embodiment of the present invention; Fig. Figure 5 shows a standard grayscale image according to an embodiment of the present invention; Fig. Figure 6 shows an image of the organic substance and the transition zone according to an embodiment of the present invention; Fig. Figure 7 shows an image of the interconnected components of the organic substance and the transition zone according to an embodiment of the present invention; Fig. Figure 8 schematically shows a total regional area and an effective regional area according to one embodiment of the present invention; Fig. Figure 9 shows an image of the pores according to an embodiment of the present invention; Fig. Figure 10 shows an image of the interconnected components of pores according to an embodiment of the present invention; Fig. Figure 11 schematically shows a pore template according to an embodiment of the present invention; Fig. Figure 12 shows an image of the dilated connected components of pores according to an embodiment of the present invention; Fig. Figure 13 shows the positions of the pores in relation to the organic substance according to an embodiment of the present invention; Fig. Figure 14 schematically shows a maximum ferret diameter and a minimum ferret diameter of pores and microfractures according to an embodiment of the present invention; Fig. Figure 15 schematically shows organic pores and inorganic pores in an original grayscale image according to an embodiment of the present invention; Fig. 16 is a flowchart showing the steps for calculating the parameters of the shale pore structure according to an embodiment of the present invention; Fig. Figure 17 schematically shows the morphology of a microfracture after equivalent straightening according to an embodiment of the present invention; Fig. Figure 18 schematically shows an ellipse representing a microfracture according to an embodiment of the present invention; Fig. Figure 19 shows an original SEM scan image according to an embodiment of the present invention; Fig. Figure 20 shows a grayscale distribution of the scanned image A according to an embodiment of the present invention; Fig. Figure 21 shows image B after grayscale adjustment according to an embodiment of the present invention; Fig. 22 shows a greyscale distribution of image B after greyscale adjustment according to an embodiment of the present invention; Fig. 23 shows the denoised image C according to an embodiment of the present invention; Fig. Figure 24 shows image D of the organic substance and the transition zone according to an embodiment of the present invention; Fig. Figure 25 shows the interconnected components E of the organic substance and the transition zone according to an embodiment of the present invention; Fig. Figure 26 shows a distribution of the effectiveness ratio according to an embodiment of the present invention; Fig. 27 shows an image of the interconnected components of the organic substances according to an embodiment of the present invention; Fig. Figure 28 shows an image of the transition zone of the pores located at the edge of an inorganic matrix according to an embodiment of the present invention; Fig. Figure 29 schematically shows an image of the pores according to an embodiment of the present invention; Fig. Figure 30 shows an image of the connected components F of pores according to an embodiment of the present invention; Fig. 31 shows an image of the dilated connected components G of pores according to an embodiment of the present invention; Fig. Figure 32 shows an image of the dilated pore boundaries H according to an embodiment of the present invention; Fig. Figure 33 shows an image of the intersection between the boundary image and the organic substance according to an embodiment of the present invention; Fig. Figure 34 shows a result for the identification of the pores according to an embodiment of the present invention; Fig. Figure 35 shows a volume distribution of the total pores according to an embodiment of the present invention; Fig. Figure 36 shows a volume distribution of the organic pores according to an embodiment of the present invention; Fig. Figure 37 shows a volume distribution of the inorganic pores according to an embodiment of the present invention; Fig. Figure 38 shows a radius distribution of the total pores according to an embodiment of the present invention; Fig. Figure 39 shows a radius distribution of the organic pores according to an embodiment of the present invention; Fig. Figure 40 shows a radius distribution of the inorganic pores according to an embodiment of the present invention; Fig. 41 shows a form factor distribution of the total pores according to an embodiment of the present invention; Fig. Figure 42 shows a shape factor distribution of the organic pores according to an embodiment of the present invention; and Fig. Figure 43 shows a form factor distribution of the inorganic pores according to an embodiment of the present invention.

[0073] The same reference symbols are used in the drawings to denote the same components. The drawings are not necessarily to scale. Detailed description of the embodiments

[0074] In order to clarify the objectives, technical solutions and advantages of the present invention, the embodiments of the present invention are described in more detail below with reference to the accompanying drawings.

[0075] CN112858136B provides a method for the quantitative evaluation of the pore structures of organic matter in shale, which determines the pore structure parameters of organic matter in shale using carbon dioxide and nitrogen adsorption methods. CN111563695A provides a method for the rapid evaluation of shale pore structures, which uses the nitrogen adsorption method for the quantitative characterization and evaluation of mud shale pore structures. CN108169099A provides a quantitative method for calculating the pore structures in shale gas reservoirs based on nuclear magnetic resonance, whereby nuclear magnetic resonance is used for the quantitative calculation of pore structures in shale gas reservoirs.CN111398122A presents a comprehensive method for characterizing the heterogeneous properties of full-size schist pore structures, which thoroughly analyzes the schist pore structure properties through a high-pressure mercury intrusion experiment, a low-temperature nitrogen adsorption experiment, and a low-temperature carbon dioxide adsorption experiment. CN115753866A provides a method for the quantitative characterization of schist pore structure, which combines low-pressure nitrogen adsorption, high-pressure mercury intrusion, and nuclear magnetic resonance to obtain the pore radius distribution properties in schist. However, from a theoretical perspective, the aforementioned methods are unable to distinguish between organic and inorganic pores in schist.

[0076] CN110132816A provides a method for analyzing the pore structures of organic substances in Lower Paleozoic shales, enabling the observation and description of the pore development characteristics of various types of organic substances, including the morphology, size, and distribution properties of different organic pores. However, this method requires careful manual observation of the shape, occurrence, connectivity, etc., of organic pores using scanning electron microscopy to measure pore size and describe pore development based on the number and size of the pores. Therefore, such a manual method for annotating organic pores is inefficient and incapable of automatically identifying them.

[0077] CN109387468A discloses a method for testing and analyzing characteristic parameters of nanopore structures in shale deposits and a corresponding system that uses reconstructed 3D images to statistically analyze and measure the characteristic parameters of shale nanopore structures using an image processing method. However, it does not disclose which image processing method is used for the statistical measurement and calculation of the characteristic parameters of shale nanopore structures.

[0078] In the prior art (Gou Qiyang, Xu Shang, Hao Fang, Lu Yangbo, Zhang Aihua, Wang Yuxuan, Cheng Xuan, Qing Jiawei, Characterization method of shale pore structure based on nano-CT: a case study of Well JY-1. Acta Petrolei Sinica (2018, 39(11): 1253-1261), a 3D pore network model for shale was created to evaluate the porosity, pore properties, and pore connectivity of shale samples from the Lower Silurian Longmaxi Formation in the Jiaoshiba area of ​​the Sichuan Basin, China. However, the use of image recognition techniques for the quantitative investigation of the shale pore structures is not disclosed.

[0079] In the state of the art (Huang Jiaguo, Xu Kaiming, Guo Shaobin, Guo Hewei, Comprehensive Study on Pore Structures of Shale Reservoirs Based on SEM, NMR and X-CT. Geoscience, 2015, 29(01):198-205.), the pore structures of shale deposits are investigated in detail using SEM, NMR, and X-CT. However, this study can only perform a qualitative analysis and cannot quantitatively characterize the pore structures of shale deposits.

[0080] In summary, there is currently no method for the quantitative characterization of pore structures that takes into account the different types of pore structures in shale. To address the aforementioned shortcomings of the prior art, the present invention proposes a method for detecting the shale pore type based on a digital drill core and a method for quantitative characterization that can automatically identify and quantitatively characterize the structures of organic pores, inorganic pores, and microfractures in shale, thereby providing a basis for assessing the quality of oil and gas reservoirs in shale rocks and optimizing development strategies.

[0081] Fig. Figure 1A is a flowchart showing the steps of the method for identifying the shale pore type based on a digital drill core according to another embodiment of the present invention;

[0082] As in Fig. As shown in 1A, in step S1 organic substances and transition zones are identified based on a greyscale image of a slate sample to be identified, thus obtaining a result for the identification of the organic substance.

[0083] It should be noted that the transition zone in the present invention refers to a transition area between inorganic pores and inorganic matrix, such as a circular ring belt region surrounding an inorganic pore or an irregular ring belt region surrounding an inorganic pore, in the greyscale image of the slate sample to be identified, wherein the greyscale distribution of the transition area overlaps with the greyscale distribution of the organic substance.

[0084] In one embodiment, the identification of the organic matter and the transition zone is performed by the following steps: obtaining lower and upper grayscale boundaries corresponding to the organic matter and the transition zone based on the grayscale distribution of the grayscale image, thereby obtaining an image of the contiguous components of the organic matter and the transition zone; and performing the identification of the organic matter based on a total regional area or total regional volume and an effective regional area or effective regional volume in the image of the contiguous components of the organic matter and the transition zone, thereby obtaining a result for the identification of the organic matter and a result for the identification of the transition zone.

[0085] In one embodiment, the identification of organic matter is carried out by the following steps: taking the effective regional area or effective regional volume as the dividend and the total regional area or total regional volume as the divisor to obtain an area efficiency ratio or a volume efficiency ratio, which serves as the efficiency ratio of the image of the contiguous components of organic matter and the transition zones; setting a threshold for the identification of organic matter; recording the image of the contiguous components of organic matter and the transition zones with an efficiency ratio that is less than the threshold as the image of the contiguous components of the transition zone, which serves as the result of the identification of transition zones;and recording the image of the coherent components of the organic matter and the transition zones with an effectiveness ratio greater than or equal to the threshold, as an image of the coherent components of the organic matter, which serves as the result of the identification of the organic matter.

[0086] As in Fig. As shown in 1A, in step S2, based on the result of the identification of the organic matter and the grayscale image, an image of the organic pores and an image of the inorganic pores are obtained, which serve as the result of the identification of the shale pore type.

[0087] In one embodiment, the image of the organic pores and the image of the inorganic pores are identified by the following steps: obtaining the image of the contiguous components of the pores based on the grayscale distribution of the grayscale image; and performing the identification of the pores based on the image of the contiguous components of the pores and the organic matter to obtain the image of the organic pores and the image of the inorganic pores.

[0088] In one embodiment, the identification of the pores is carried out by the following steps: performing a pore dilation on the image of the connected components of pores based on a dilation template to obtain an image of the dilated connected components of pores; determining an image of the boundaries of the image of the dilated connected components of pores; calculating a perimeter of the dilated connected component of pores in the image of the dilated connected components of pores; taking an intersection of the image of the boundaries and the image of the connected component of the organic matter as an intersection image to obtain an area of ​​the intersection image;Performing an identification of the organic pore based on the area of ​​the image of the overlap and the extent of the dilated connected component of the pores in order to determine the image of the organic pore and the image of the inorganic pore.

[0089] In one embodiment, the identification of the organic pores is carried out by the following steps: taking the area of ​​the image of the intersection as the dividend and the perimeter of the dilated connected component of the pores as the divisor to obtain a quotient that serves as the proportional coefficient of the image of the connected components of the pores; and plotting the image of the connected components of the pores with a proportional coefficient equal to zero as the image of the inorganic pores, and the image with a proportional coefficient greater than zero and less than or equal to one as the image of the organic pores.

[0090] In one embodiment, the method further comprises: setting a threshold for the periphery pores of the organic substance for the organic pore image; and recording the organic pore image with a proportional coefficient greater than zero and less than or equal to the threshold for the periphery pores of the organic substance as the periphery pores image, and the image with a proportional coefficient greater than the threshold for the periphery pores of the organic substance and less than or equal to one as the intrinsic pores image.

[0091] In one embodiment, the method further comprises: calculating the maximum and minimum Feret diameters for the inorganic pore image; taking the minimum Feret diameter as the dividend and the maximum Feret diameter as the divisor to obtain a quotient that serves as the diameter ratio of the inorganic pore image; setting a diameter threshold and a ratio threshold; and recording the inorganic pore image that meets a condition for microfracture identification as the microfracture image, and the inorganic pore image that does not meet the condition as the inorganic pore image.

[0092] In one embodiment, the condition for the identification of microfractures is that the maximum Feret diameter is larger than the diameter threshold and the diameter ratio is smaller than the ratio threshold.

[0093] In one embodiment, the grayscale image refers to the original 2D or 3D grayscale image of the slate sample to be identified.

[0094] In one embodiment, the method for detecting the shale pore type based on a digital drill core further comprises adjusting the grayscale values ​​of the original grayscale image, wherein the grayscale distribution of the original grayscale image is determined to obtain a first grayscale value corresponding to the pores and a second grayscale value corresponding to the rock matrix; and wherein the original grayscale image is obtained after grayscale adjustment based on the first grayscale value and the second grayscale value.

[0095] In one embodiment, the method for detecting the shale pore type based on a digital drill core further comprises: determining a denoising window based on the original grayscale image after grayscale adjustment; calculating a standard deviation of the denoising based on a maximum grayscale value and a minimum grayscale value within the denoising window; and performing image denoising on the original grayscale image after grayscale adjustment based on the standard deviation of the denoising.

[0096] Fig. Figure 1B is a flowchart showing the steps of the method for identifying the shale pore type based on a digital drill core according to another embodiment of the present invention.

[0097] As in Fig. As shown in Figure 1B, a standard grayscale image is obtained in step S101. Specifically, the original grayscale image of the slate sample to be identified undergoes grayscale adjustment and image denoising to obtain the standard grayscale image.

[0098] In one embodiment, the standard grayscale image is obtained by steps S201-S203, as shown in Fig. 2 shown. As in Fig. As shown in Figure 2, the original grayscale image (Figure A) is obtained in step S201. Specifically, the slate sample to be identified is scanned with a scanning electron microscope (SEM) or computed tomography (CT) to obtain a 2D or 3D original grayscale image.

[0099] In one embodiment, the slate sample to be identified is scanned with a SEM to obtain a 2D original grayscale image. By adjusting the resolution, pores and fractures in the slate core can be clearly visualized on the scanned original grayscale image where the SEM resolution is less than or equal to 10 nm.

[0100] In one embodiment, the slate sample to be identified is scanned using CT to obtain a 3D original grayscale image. By adjusting the resolution, pores and fractures in the slate core can be clearly visualized on the scanned original grayscale image, with the CT employing computed tomography at the nanoscale.

[0101] In practice, the original grayscale image has two significant limitations. First, the brightness distribution is implausible because the contrast between the area of ​​interest and the background information is low. Second, the scanned image contains noise that requires post-processing. To overcome these limitations of the original grayscale image, the present invention improves the implausible brightness distribution by grayscale adjustment and reduces the noise by image denoising.

[0102] As in Fig. As shown in Figure 2, the original grayscale image is obtained after grayscale adjustment (Figure B) in step S202. In one embodiment, the grayscale adjustment is performed by steps S2021-S2022.

[0103] In step S2021, the grayscale distribution of the original grayscale image is determined to obtain the first grayscale value, corresponding to the pores, and the second grayscale value, corresponding to the rock matrix. Specifically, the grayscale value of the original grayscale image is denoted as I. Based on the grayscale distribution, the grayscale value corresponding to the pores is denoted as I1, and the one corresponding to the rock matrix is ​​denoted as I2.

[0104] In step S2022, the original grayscale image is adjusted based on the first and second grayscale values ​​using a grayscale adjustment equation to obtain the original grayscale image after the adjustment. Specifically, the grayscale adjustment equation is as follows: I'={0,I≤I1I−I1I2−I1,I1<I≤l2255,l> l1 where I' denotes the grayscale value of the original grayscale image after grayscale adjustment; I denotes the grayscale value of the original grayscale image; I1 denotes the first grayscale value; and I2 denotes the second grayscale value.

[0105] In the present invention, the contrast of the original grayscale image is improved after grayscale adjustment, which facilitates the further identification of pores and organic matter.

[0106] As in Fig. As shown in Figure 2, the standard grayscale image (Figure C) is obtained in step S203. In one embodiment, image denoising is performed in steps S2031-S2033 to obtain the standard grayscale image.

[0107] In step S2031, the denoising window is determined based on the original grayscale image after grayscale adjustment. Specifically, a 3×3 denoising window can be selected, as shown in Fig. 3 shown.

[0108] In step S2032, a standard deviation of the denoising is calculated based on the maximum and minimum grayscale values ​​within the denoising window, using an equation for the standard deviation of the denoising. Specifically, as shown in Fig. Figure 3 shows that the maximum grayscale value and the minimum grayscale value within the denoising window are determined using the following equations: Imax = max Iij Imin=min Iij

[0109] In one embodiment, the equation for the standard deviation of the denoising is: σ=mσ0Imax−Imin where σ denotes the standard deviation of the denoising; m denotes a tolerance coefficient, with a larger tolerance coefficient resulting in a less precise boundary after denoising, and a smaller tolerance coefficient resulting in a sharper boundary; σ0 denotes a preset standard deviation; I max denotes the maximum grayscale value within the denoising window; and I min denotes the minimum grayscale value within the denoising window.

[0110] In step S2033, based on the standard deviation of the denoising after grayscale adjustment, image denoising is performed on the original grayscale image using a Gaussian denoising kernel function to obtain the standard grayscale image. Specifically, the calculated standard deviation of the denoising is inserted into the Gaussian denoising kernel function to denoise the original grayscale image after grayscale adjustment, resulting in a standard grayscale image.

[0111] The Gaussian denoising kernel function is: G(r)=12πσe−‖r‖2σ2 where r denotes a distance vector.

[0112] In contrast to conventional denoising of digital images, denoising of digital images of slate must eliminate local image noise while simultaneously preserving the clarity of the boundaries between pores and organic matter, as well as between organic matter and the solid matrix. Therefore, the present invention improves the standard deviation of the denoising in the Gaussian denoising kernel function and proposes a novel image processing method for denoising specifically for digital images of slate. According to the image processing method for denoising proposed in the present invention, the difference between I max and I minFor pixels within pores, organic matter, and the rock matrix, the noise reduction standard deviation (σ) is low, so it should be large, which can lead to a strong noise reduction effect and improve local image quality. At the boundaries between pores and organic matter, as well as between organic matter and the rock matrix, the difference between I max and I min large, and therefore the standard deviation of the denoising σ should be small, which can also produce a strong denoising effect and ensure the clarity of the boundaries. Thus, the image processing method for denoising according to the present invention can improve the local image quality within the pores, the organic matter, and the rock matrix, while simultaneously ensuring the clarity of the boundaries between pores and organic matter, as well as between organic matter and the rock matrix.

[0113] As in Fig. As shown in Figure 1B, in step S102 the image of the contiguous components of the organic matter is determined. In particular, the image of the organic matter and the transition zone is extracted from the standard grayscale image to identify the organic matter and determine the image of the contiguous components of the organic matter.

[0114] In one embodiment, the image of the interconnected components of the organic substance is represented by the Fig. The 4 steps shown, S401-S403, are determined.

[0115] As in Fig. As shown in Figure 4, the image of the organic matter and the transition zone (image D) is obtained in step S401. Specifically, based on the grayscale distribution of the standard grayscale image, the lower and upper grayscale limits for pixels of the organic matter and the transition zone are determined to obtain the image of the organic matter and the transition zone.

[0116] As in Fig. As shown in Figure 5, the organic matter typically contains one or more organic pores, while the inorganic pore is surrounded by a transition zone that extends to the rock matrix. The transition zone and the organic matter have similar grayscale values, making it difficult to distinguish them based on grayscale alone. However, in images of shale, clear grayscale boundaries are visible between pores, organic matter, and the rock matrix. Therefore, in step S401, based on the grayscale distribution of the standard grayscale image, the following is determined... Fig. Figure 6 shows the organic matter and transition zone extracted by threshold segmentation.

[0117] In one embodiment, the image of the organic substance and the transition zone f is extracted using the following equation: f={x|I3 <x<l4|} where I3 denotes the lower grayscale limit for pixels of the organic matter and the transition zone, and I4 denotes the upper grayscale limit for pixels of the organic matter and the transition zone.

[0118] After the image of the organic matter and the transition zone has been obtained by step S401, steps S402-S403 are used to identify the organic matter on this image in order to determine the image of the related components of the organic matter.

[0119] As in Fig. As shown in Figure 4, in step S402 the image of the connected components of the organic matter and the transition zone (image E) is obtained. Specifically, a connected component algorithm is applied to the image of the organic matter and the transition zone to obtain the image of the connected components of the organic matter and the transition zone. Furthermore, the connected component algorithm is used to obtain the organic matter or the transition zone separately, with the results shown in Figure 4. Fig. Figure 7 is shown, with the left image showing the organic matter and the right image showing the transition zone. The i-th image of the connected components of the organic matter and the transition zone is labeled E i This is referred to as such. In one embodiment, the algorithm for related components can, for example, be a seed-fill algorithm or a two-pass algorithm.

[0120] As in Fig. As shown in Figure 4, the image of the contiguous components of organic matter is obtained in step S403. Specifically, the identification of organic matter is performed based on the total regional area or volume and the effective regional area or volume of the image of the contiguous components of organic matter and the transition zone, in order to obtain the image of the contiguous components of organic matter.

[0121] In one embodiment, the identification of the organic substance is carried out in steps S4031-S4032.

[0122] In step S4031, an effective area ratio or effective volume ratio is calculated by taking the effective regional area or effective regional volume as the dividend and the total regional area or total regional volume as the divisor, which serves as the effectiveness ratio of the image of the contiguous components of the organic matter and the transition zone. Specifically, the total regional area (left image in Fig. 8) or the volume of the image of the connected components of the organic matter and the transition zone E i equal S s1 and the effective regional area (right image in Fig. 8) or the volume equals S s2 : α=Ss2Ss1 where α denotes the effectiveness ratio of the image of the connected components of the organic substance and the transition zone.

[0123] Based on the extensive observation of scanned slate images within the scope of the present invention, it can be determined that the organic matter in the slate can contain zero, one, or more organic pores. However, the total area (total volume) occupied by these pores is relatively small, with the organic matter constituting the largest part of the area (volume) within the organic matter detection area. In contrast, the transition zone consists of transition pixels between the pores and the rock matrix with a relatively small total area (a relatively small total volume). Therefore, the present invention distinguishes between organic matter and the transition zone based on the area fraction within the detection area.

[0124] In step S4032, a cut-off value for organic matter identification (organic matter identification threshold) is set. The image of the contiguous components of the organic matter and the transition zone whose effectiveness ratio is below this cut-off value is recorded as an image of the contiguous components of the transition zone, while the image with an effectiveness ratio greater than or equal to this cut-off value is recorded as an image of the contiguous components of the organic matter. Specifically, α0 denotes the cut-off value for organic matter identification to distinguish between organic matter and the transition zone. If α ≥ α0, the image of the contiguous components of the organic matter and the transition zone E is recorded. ias the image of the connected components of the organic substance; if α < α0, it is identified as the image of the connected components of the transition zone.

[0125] Whether the image of the interconnected components of organic matter and transition zone E i which is organic substance, is indicated by q i specified, i.e.: qi={1 if egg is an organic substance, 0 if egg is a transition zone}

[0126] All images of the related components of the organic matter and the transition zone are identified to complete the process of organic matter identification.

[0127] In the present invention, the organic substance and the transition zone are identified based on the effectiveness ratio of the image of the associated components of the organic substance and the transition zone. This method offers high identification accuracy, simple implementation, and high feasibility, thereby ensuring rapid identification of the image of the associated components of the organic substance and the transition zone.

[0128] As in Fig. As shown in Figure 1B, in step S103 the images of the organic pores and the images of the inorganic pores are identified. Specifically, pore images are extracted from the standard grayscale image, and pore identification is performed based on the image of the contiguous components of the organic matter to determine the images of the organic pores and the images of the inorganic pores. After identifying the organic matter and the transition zone, the organic and inorganic pores can be identified by evaluating the contact between the pores, the organic matter, and the transition zone.

[0129] In one embodiment, the image of the organic pores and the image of the inorganic pores are combined by the Fig. 4 steps shown S404-S406 identified.

[0130] As in Fig. As shown in Figure 4, the pore image is obtained in step S404. Specifically, based on the grayscale distribution of the standard grayscale image, the lower and upper grayscale limits for pores are determined to obtain the pore image. More precisely, the grayscale distribution of the standard grayscale image shows that the pores have the lowest grayscale values. The threshold segmentation method can be used to determine this. Fig. The pores shown in image 9 are extracted.

[0131] As in Fig. As shown in Figure 4, the image of the connected components of the pores (image F) is obtained in step S405. Specifically, a connected component algorithm is used to obtain the image of the connected components of the pores. Furthermore, individual pores are calculated using the connected component algorithm, with the results displayed in Figure 4. Fig. Figure 10 shows the image of the connected components of the i-th pore. This image is called F. i This is referred to as... In one embodiment, the algorithm of the related components can be, for example, the seed-fill algorithm or the two-pass algorithm.

[0132] As in Fig. As shown in section 4, in step S406 the image of the organic pores and the image of the inorganic pores are determined. In particular, based on the images of the contiguous components of the pores and the organic matter, an identification of the pores is carried out in order to determine the image of the organic pores and the image of the inorganic pores.

[0133] In one embodiment, the identification of the pores is carried out by steps S4061-S4064.

[0134] In step S4061, the image of the dilated connected components of the pores (image G) is obtained. Specifically, based on a dilation template, the image of the connected components of the pores is subjected to pore dilation to obtain the image of the dilated connected components of the pores and the image of their boundaries. More precisely, for the i-th image of the pores F i The dilation was performed according to a pore template T, thereby obtaining the image of the dilated, contiguous components of the pores, as shown in Fig. 12 shown. In one embodiment, the pore template T is in Fig. 11 shown.

[0135] The image after dilation is described as an image of the dilated, connected components of the pores G. i designated, Gi=Fi⊕T where ⊕ denotes a pore dilation operator.

[0136] The image of the boundaries of the dilated connected components of the pores is called H i designated: Hi=Gi\Fi where \ denotes a set difference operation.

[0137] In step S4062, the perimeter of the dilated connected component of the pores within the image of the dilated connected components of the pores is calculated. Specifically, the perimeter of the image of the dilated connected component of the pores G is determined. i calculated after dilation, which is defined as the circumference of the dilated contiguous component of the pores L li is referred to as such.

[0138] In step S4063, the intersection of the boundary image and the image of the connected components of the organic matter is taken as the intersection image to calculate its area. Specifically, the intersection of the boundary image H iof the image of the dilated connected component of the pores and the image of the connected component of the organic substance E j as an image of the overlap P ij denoted, and its area is calculated and used as the area of ​​the image of the intersection S. ij designated: Pij=Ej|Hi where | denotes an overlap operator.

[0139] In step S4064, the identification of organic pores is carried out based on the area of ​​the image of the intersection and the extent of the dilated connected component of the pores in order to obtain the image of organic pores and the image of inorganic pores.

[0140] In one embodiment, the identification of the organic pores is carried out by steps S40641-S40643.

[0141] In step S40641, the area of ​​the intersection image is taken as the dividend and the perimeter of the dilated connected component of the pores as the divisor to calculate a quotient that is taken as the proportionality coefficient of the image of the connected components of the pores. Specifically, for the identification of the i-th image of the connected components of pores F i a proportion of the overlap between the image of the dilated connected components of pores G i and the image of the interconnected components of organic matter in relation to the total pore circumference as β i designated: βi=∑jSijLli

[0142] If β i If the value is equal to 0, this means that the overlap of the image of the connected component of the pores F iWith each image of the organic substance, there is an empty set, and the image of the connected component of the pores F i is referred to as a non-organic pore; otherwise, it is an organic pore.

[0143] In step S40642, the image of the connected components of pores with a proportionality coefficient of zero is referred to as the image of inorganic pores, and the image with a proportionality coefficient greater than zero and less than or equal to one is referred to as the image of organic pores. As in Fig. As shown in Figure 13, the white area within the frame represents the intersection of the image of the dilated, connected components of the pores and the image of the connected components of the organic matter. β denotes the proportion of the pores overlapping with organic matter relative to the total pore area, i.e., the proportionality coefficient, where 0 ≤ β ≤ 1. For pores composed of organic matter, β > 0; for pores composed of inorganic matter (non-organic pores), β = 0; for pores composed of organic matter located at the periphery, 0 < β < 1; and for pores composed of organic matter located within the periphery, β = 1.

[0144] In step S40643, a cut-off value for edge organic pore images (edge ​​organic pore threshold) is set. The organic pore image whose proportionality coefficient is greater than zero and less than or equal to the cut-off value is designated as an edge organic pore image, and the image with a proportionality coefficient greater than the cut-off value and less than or equal to one is designated as an inset organic pore image. Specifically, identifying edge organic pores requires setting the edge organic pore cut-off value β0. If 0 < β ≤ β0, the pore is identified as an edge organic pore; and if β0 < β ≤ 1, the pore is identified as an inset organic pore.

[0145] If 0 < β i ≤ β0, the image of the connected components of the pores F i described as an image of the pores located at the edge of the organic substance, and when β0 < β i If the value is ≤ 1, it is referred to as an image of the internal pores of the organic substance. As in Fig. Figure 13 shows the left image showing the inner pores of the organic substance, the middle image showing the pores of the organic substance at the edge, and the right image showing the non-organic pores.

[0146] If x i indicates whether the image of the connected component of the pores is F i it concerns the organic pore, and y i If it indicates whether it is the inner pore of the organic substance, then the following applies: xi={1,Fi is an organic pore; 0,Fi is a non-organic pore yi={1, Fi is an internal organic pore; 0, Fi is an external organic pore

[0147] All related components of the pores F i are identified to complete the identification of the organic pores.

[0148] The present invention proposes a novel method for identifying organic pores in digital images of slate. Images of inorganic pores and organic pores within images of contiguous pore components can be identified using the proportionality coefficient. For the organic pore image, the image of the periphery pores of the organic substance and the image of the intrinsic pores of the organic substance can be identified using the cut-off value for the periphery pores of the organic substance. This method offers high identification accuracy, ease of implementation, and high feasibility, thus ensuring rapid identification of the images of the periphery pores of the organic substance and the images of the intrinsic pores of the organic substance.

[0149] As in Fig. As shown in Figure 1B, in step S104 the microfracture image and the inorganic pore image are identified. In particular, the microfractures are identified based on the inorganic pore image in order to determine the microfracture image and the inorganic pore image.

[0150] In one embodiment, the identification of microfractures is carried out by steps S1041-S1043.

[0151] In step S1041, the maximum and minimum Feret diameters are calculated for the image of the inorganic pores. The difference between pores and microfractures lies particularly in their aspect ratio. Within the scope of the present invention, the maximum Feret diameter is used as an approximation for the length of a pore, and the minimum Feret diameter as an approximation for the width of a pore. The Feret diameter is used to distinguish between inorganic pores and microfractures in the slate. As in Fig. As shown in 14, microfracture (right image in Fig. 14) the difference between the maximum and minimum Feret diameter is large, whereas in the case of the pore (left image in Fig. 14) the difference between the maximum and minimum feret diameter is small.

[0152] In step S1042, the minimum Feret diameter is used as the dividend and the maximum Feret diameter as the divisor, whereupon a quotient is calculated as the diameter ratio of the image of the inorganic pores. In particular, the present invention employs two indicators for the identification of microfractures: firstly, the maximum Feret diameter, and secondly, the ratio between the minimum Feret diameter and the maximum Feret diameter. RF max denotes the maximum feret diameter, RF min denotes the minimum Feret diameter, and γ denotes the ratio of the two: γ=RFminRFmax where γ denotes the diameter ratio of the image of the inorganic pores.

[0153] In step S1043, a diameter cut-off value and a ratio cut-off value are defined. Images of inorganic pores that meet the criteria for microfracture identification are referred to as microfracture images, while those that do not meet the criteria are referred to as inorganic pore images. The criterion for microfracture identification is that the maximum Feret diameter is greater than the diameter cut-off value and the diameter ratio is less than the ratio cut-off value.

[0154] The diameter cut-off value is called RF. max0 denoted by and the ratio cut-off value is denoted as γ0. If the i-th image of the inorganic pores meets the conditions RF maxi RF max0 and if γ < γ0 is fulfilled, it is identified as a microfracture and as F i' otherwise it is identified as an inorganic pore and referred to as F i '' designated.

[0155] With z i It is indicated whether the image of the connected components of the pores F i If it is a microfracture, then the following applies: zi={1, Fi is a microfracture0, Fi is an inorganic pore

[0156] All images of the inorganic pores are identified to complete the identification of all microfractures.

[0157] The present invention proposes a novel method for identifying microfractures in digital images of slate. Two indicators are used for microfracture identification: firstly, the maximum Feret diameter, and secondly, the ratio of the minimum Feret diameter to the maximum Feret diameter. This method offers high identification accuracy, simple implementation, and high feasibility, thereby ensuring rapid identification of microfractures and inorganic pores in images.

[0158] It should be noted that in practice, parameters such as the lower and upper limits of the gray levels for organic substances and the transition zone, the threshold for identifying the organic substance, the lower and upper limits of the gray levels for pores, the threshold for pores located at the edge of the organic substance, the diameter threshold, the ratio threshold, the first gray level value, the second gray level value, the tolerance coefficient, the preset standard deviation, and the denoising window can be selected or determined according to the actual requirements and circumstances. The range of values ​​or the determination of these parameters is not limited within the scope of the present invention.

[0159] According to a further aspect of the present invention, a device for detecting the shale pore type based on a digital drill core is provided, configured to perform a method for detecting the shale pore type based on a digital drill core. The device comprises a preprocessing module, an organic matter identification module, a pore identification module, and a microfracture identification module.

[0160] The preprocessing module is configured to perform grayscale adjustment and image denoising on the original grayscale image of the slate sample to be identified, resulting in a standard grayscale image. The organic matter identification module is configured to extract the image of the organic matter and the transition zone from the standard grayscale image, thus identifying the organic matter and determining the image of the contiguous organic matter components. The pore identification module is configured to extract the pore image from the standard grayscale image and identify the pore based on the image of the contiguous organic matter components, thereby identifying the organic pore image and the inorganic pore image.The microfracture identification module is configured to perform microfracture identification on the inorganic pore image in order to determine the microfracture image and the inorganic pore image.

[0161] The present invention provides a method and a device for identifying the pore type of schist based on a digital drill core. Digital images of rock samples are obtained through imaging scans and processed to identify organic pores, inorganic pores, and microfractures in schist (as shown in [reference to relevant document]). Fig. 15). Furthermore, the present invention can also perform quantitative calculations of pores and microfractures to obtain pore structure parameters for different types of pore spaces.

[0162] According to a further aspect of the present invention, a method for the quantitative characterization of the shale pore structure based on a digital drill core is provided. Based on the total number of pixels in the grayscale image and the actual length of a pixel, and in combination with the result for the identification of organic matter, the image of the organic pores, and the image of the inorganic pores, which were determined by the method for recognizing the shale pore type based on a digital drill core, the parameters of the pore structure of the shale sample to be identified can be determined.The pore structure parameters include at least one of the following parameters: parameters for characterizing porosity, parameters for characterizing pore volume, parameters for the distribution of the equivalent pore radius, parameters for characterizing the form factor of the pores, and parameters for characterizing the microfracture structure.

[0163] As in Fig. As shown in Figure 16, the parameters for characterizing the porosity, the parameters for characterizing the pore volume, the parameters for the distribution of the equivalent pore radius, the parameters for characterizing the form factor of the pores, and the parameters for characterizing the microfracture structure of the slate sample to be identified are calculated based on the total number N of pixels in the original grayscale image of the slate sample to be identified and the actual length p of a pixel (image resolution), as well as in combination with the image of the organic pores, the image of the microfractures, and the image of the inorganic pores, which were determined by the procedure for detecting the slate pore type based on a digital drill core.

[0164] In one embodiment, the parameters for characterizing porosity include: the porosity within the organic matter, the porosity of the organic pores, the porosity of the inorganic pores, the porosity of the microfractures, and the total porosity. Specifically, the parameters for characterizing the porosity of the slate sample to be identified are determined using the following equations: Porosity within organic matter ϕr' (Volume fraction of the pores within the organic matter): ϕr'=∑xiSi∑qiEi Porosity of organic pores ϕ r : ϕr=∑xiSiN Porosity of inorganic pores ϕ i : ϕi=∑(1−xi)(1−zi)SiN Porosity of microfractures ϕ f : ϕf=∑(1−xi)ziSiN Total porosity ϕ: ϕ=∑SiN where: x iwith a value of 1 or 0, indicating whether the i-th image of the connected components of the pores is an image of the organic pores; S i indicates the number of pixels in the i-th image of the organic pores / inorganic pores / microfractures; q i with a value of 1 or 0, indicating whether the i-th image of the connected components of the organic matter and the transition zone is an image of the organic matter; E i the i-th image of the contiguous components of the organic matter and the transition zone; N is the total number of pixels in the original grayscale image; and z i with a value of 1 or 0 indicates whether the i-th image of the connected components of the pores is an image of the microfractures.

[0165] In one embodiment, the parameters for characterizing the pore volume include the total pore volume, the volume of organic pores, the volume of inorganic pores, and the volume of microfractures. Specifically, the parameters for characterizing the pore volume of the slate sample to be identified are determined using the following equations: Total pore volume V i corresponding to the two-dimensional original grayscale image: Vi=p2⋅Si Total pore volume Vi corresponding to the three-dimensional original grayscale image: Vri=p3⋅Si

[0166] After calculating the total pore volume, the distribution of the pore volume of the organic pores, the inorganic pores and the microfractures can be determined separately.

[0167] Volume of organic pores V ri corresponding to the two-dimensional original grayscale image: Vri=xi⋅p2⋅Si

[0168] Volume of organic pores V ri corresponding to the three-dimensional original grayscale image: Vri=xi⋅p3⋅Si

[0169] Volume of inorganic pores V ii corresponding to the two-dimensional original grayscale image: Vii=(1−xi)⋅p2⋅Si

[0170] Volume of inorganic pores V ii corresponding to the three-dimensional original grayscale image: Vii=(1−xi)⋅p3⋅Si

[0171] Volume of microfractures V fi corresponding to the two-dimensional original grayscale image: Vfi=(1−xi)⋅zi⋅p2⋅Si

[0172] Volume of the microfracture corresponding to the three-dimensional original grayscale image: Vfi=(1−xi)⋅zi⋅p3⋅Si where p represents the actual length corresponding to one pixel; S iindicates the number of pixels in the i-th image of the organic pores / inorganic pores / microfractures; x i with a value of 1 or 0, indicating whether the i-th image of the connected components of the pores is an image of the organic pores; and z i with a value of 1 or 0 indicates whether the i-th image of the connected components of the pores is an image of the microfractures.

[0173] In one embodiment, the parameters of the equivalent pore radius distribution include the equivalent pore radius itself. The equivalent pore radius can be calculated, in particular, based on the pore area (volume) and the surface area (perimeter). The parameters of the equivalent pore radius distribution of the slate sample to be identified are determined using the following equations: Equivalent pore radius r i corresponding to the two-dimensional original grayscale image: ri=Viπ Equivalent pore radius r i corresponding to the three-dimensional original grayscale image: ri=3Vi4π3 where V i indicates the total pore volume.

[0174] In one embodiment, the parameters for characterizing the pore shape factor include the pore shape factor itself. Specifically, the shape factor describes how closely a pore resembles a circle (or a sphere); that is, the shape factor of a circle (or sphere) is 1. The shape factor can be calculated from the pore area (volume) and circumference (surface area). The parameters for characterizing the pore shape factor of the slate sample to be identified are determined using the following equations: Form factor f i corresponding to the two-dimensional original grayscale image: fi=4πSiPi2 Form factor f i corresponding to the three-dimensional original grayscale image: fi=Pi336πSi2 where S i indicates the number of pixels in the i-th image of the organic pores / inorganic pores / microfractures; and P i is the number of pixels on the peripheral surface of the i-th organic pore / inorganic pore.

[0175] In one embodiment, the parameters for characterizing the microfracture structure include the microfracture length, microfracture width, microfracture inclination angle, and microfracture density. In particular, the parameters for characterizing the microfracture structure of the slate sample to be identified are determined using the following equations and steps.

[0176] As in Fig. As shown in Figure 17, the shape of a microfracture is approximated as a deformed rectangle (rectangular cuboid). Therefore, the fracture length and width can be calculated from the fracture area and the fracture perimeter: Area of ​​microfractures S ssicorresponding to the two-dimensional original grayscale image: Sssi=Li×Wi Extent of microfractures P pi corresponding to the two-dimensional original grayscale image: Ppi=2(Li+Wi)

[0177] By simultaneously solving the equation for the microfracture area (equation 34) and the equation for the microfracture perimeter (equation 35), which correspond to the two-dimensional original grayscale image, the length and width of the microfracture, which correspond to the two-dimensional original grayscale image, can be determined.

[0178] Microfracture length corresponding to the two-dimensional original grayscale image: Li=Pi4+Pi24−4Si2

[0179] Microfracture width corresponding to the two-dimensional original grayscale image: Wi=Pi4+Pi24−4Si2

[0180] Volume of microfractures S ssicorresponding to the three-dimensional original grayscale image: Sssi=Li2×Wi

[0181] Area of ​​the microfracture P pi corresponding to the three-dimensional original grayscale image: Ppi=2Li2+4Li×Wi

[0182] By simultaneously solving the expression for the volume of the microfractures (38) and the expression for the area of ​​the microfractures (39) corresponding to the original three-dimensional grayscale image, the lengths and widths of the microfractures corresponding to the original three-dimensional grayscale image can be determined. Furthermore, the length and width of the microfractures corresponding to the original three-dimensional grayscale image can be determined by Newton iteration (using a Newton dynamics plugin).

[0183] The origin and major axis of an equivalent ellipse result from the second-order moment of the microfracture, which yields a tilt angle θ of the microfracture. In particular, as in Fig. Figure 18 shows the second-order moments of the microfracture being calculated to obtain the origin and principal axis of the ellipse from which the angle of inclination of the fracture θ is determined.

[0184] Assume that the number of microfractures counted in the original grayscale image is M: the microfracture density ρ L , which corresponds to the two-dimensional original grayscale image, is calculated as follows: ρL=MN×p2 the density of microfractures ρ L , which corresponds to the three-dimensional original grayscale image, is calculated: ρL=MN×p3

[0185] Where L iW specifies the length of the i-th microfracture; W specifies the width of the i-th microfracture; p specifies the actual length of a pixel; and N specifies the total number of pixels in the original grayscale image.

[0186] According to a further aspect of the present invention, a device for the quantitative characterization of the schist pore structure based on a digital drill core is also provided, which performs the method for the quantitative characterization of the schist pore structure based on a digital drill core. The device for quantitative characterization comprises a module for the quantitative characterization of parameters.

[0187] The module for the quantitative characterization of parameters is configured to calculate the parameters for characterizing porosity, the parameters for characterizing pore volume, the parameters for the distribution of the equivalent pore radius, the parameters for characterizing the form factor of the pores, and the parameters for characterizing the microfracture structure of the slate sample to be identified, based on the total number of pixels of the original grayscale image of the slate sample to be identified and the actual length of a pixel in combination with the image of the organic pores, the image of the microfractures, and the image of the inorganic pores.

[0188] The present invention proposes a novel method and a novel device for the quantitative characterization of the slate pore structure for digital images of slate, with which the parameters for characterizing the porosity, the parameters for characterizing the pore volume, the parameters of the distribution of the equivalent pore radius, the parameters for characterizing the form factor of the pores and the parameters for characterizing the microfracture structure of the slate sample to be identified can be quantitatively characterized.

[0189] The method for identifying the shale pore type based on a digital drill core and for quantitative characterization according to the present invention can also be combined with a computer-readable storage medium. The storage medium stores a computer program which, when executed, performs a method for identifying the shale pore type based on a digital drill core and / or a method for quantitatively characterizing the parameters of the shale pore structure based on a digital drill core. The computer program is capable of executing computer instructions, which include computer program code. The computer program code may be in the form of source code, object code, executable files, certain intermediate forms, or the like.

[0190] Computer-readable storage media may include: any unit or device capable of carrying computer program code, recording media, USB flash drives, removable media, magnetic disks, optical media, computer memory, read-only memory (ROM), working memory (RAM), electrical carrier signals, telecommunications signals, software distribution media, etc.

[0191] It should be noted that the content of computer-readable storage media may be appropriately expanded or reduced according to the requirements of legislation and patent practice in different jurisdictions. For example, due to legislation and patent practice in some countries, computer-readable storage media do not include electrical carrier signals and telecommunications signals.

[0192] According to the present invention, scanned images of slate are acquired using SEM / CT. First, an image preprocessing method is proposed that replicates the properties of digital images of slate and improves image clarity. Second, methods for identifying organic matter, organic pores, inorganic pores, and microfractures in the slate using graphical algorithms are proposed. Finally, the structural parameters of the various identified deposit spaces are calculated and statistically analyzed. Therefore, the present invention provides a method for the quantitative characterization of various types of slate deposit structures, which is of great importance for evaluating the quality of slate deposits and optimizing development strategies.

[0193] In one embodiment, the method according to the invention is used to detect and quantitatively characterize the type of slate pores based on digital drill cores, wherein the original image is a two-dimensional SEM map scan image, as shown in Fig. Figure 19 is shown. The image resolution is 5000 x 3000 with an accuracy of 10 nm. From Fig. 19 shows that the rock particles contain inorganic dissolved pores and the organic matter contains organic pores. Fig. 19 is referred to as image A.

[0194] Fig. Figure 20 shows the grayscale distribution of image A, which is typically bimodal. The image indicates that the first peak represents organic substances and the second peak represents rock particles. The proportion of pixels with a grayscale value below 70 is extremely small, while the pixels with a grayscale value above 150 represent rock particles. Therefore, the grayscale adjustment equation (1) can be applied to stretch the grayscale values ​​in the range of 70–150, i.e., I1 = 80 and I2 = 150 in equation (1).

[0195] The image after grayscale adjustment is in Fig. 21 is shown and referred to as image B, where the brightness distribution in Fig. Figure 22 shows that after grayscale adjustment, the contrast between pores, organic matter, and rock particles is enhanced.

[0196] With denoising parameters m = 30, σ0 = 0.5, and a denoising window size of 3, the denoising template is calculated using equations (2) and (3). The denoising algorithm is executed three times, with the results displayed in Fig. 23 are shown. The left image is the denoised image, the middle image is a local section of the denoised image, and the right image is the original image before denoising.

[0197] According to equation (6), when I3 = 11 and I4 = 100, the image D of the organic matter and the transition zone is obtained, as in Fig. 24 shown.

[0198] The contiguous component of the organic matter and the transition zone is calculated. Here, the pixel connectivity condition is set to 8x connectivity. The calculated image of the contiguous component E of the organic matter and the transition zone is in Fig. 25 are shown, with different shades representing different related components.

[0199] To identify the organic matter, the area (volume) efficiency ratio α is calculated using equation (7), the distribution of which in Fig. 26 is shown.

[0200] With a cut-off value for the identification of organic matter of α0 = 0.7, the contiguous component of the organic matter, as in Fig. 27 shown, and transition zone of the edges of the inorganic pores, as in Fig. 28 shown, received.

[0201] The grayscale threshold for pores is set to 10, and grayscale values ​​from 0 to 10 are identified as pores. The extracted image of the pores is in Fig. 29 is shown, and the image of the interconnected components of the pores is in Fig. 30 to see.

[0202] According to the calculation method of the present invention, the image of the dilated pores G in Fig. 31 and the image of the boundaries H of the dilated pores in Fig. 32 shown.

[0203] The image of the overlap of image H and organic substance E is in Fig. Figure 33 shows that, with a threshold value of β0 = 1 for the pores of the organic substance located at the edge, the pores of the organic substance located at the edge and the pores of the inorganic substance are treated as in Fig. Figure 34 shows the calculated results. The left image shows the inner pores of the organic matter, the middle image the pores of the organic matter at the edge, and the right image the pores of the inorganic matter. Furthermore, the diameter threshold RF was calculated. max0 = 100 µm and the ratio threshold γ0=5 for performing the identification of microfractures was set.

[0204] According to the method for quantitative characterization of shale pore structures based on digital drill cores of the present invention, the calculated results determine the porosity of the organic substance. ϕr'=3.56%, the porosity of organic pores ϕ r = -0.39%, the porosity of the inorganic pores ϕ i = 0.22% and the total porosity ϕ=0.61%.

[0205] The distribution of the shape factors of the entire pores is in Fig. Figure 35 shows the volume distribution of the organic pores in Fig. Figure 36 shows the volume distribution of the inorganic pores in Fig. Figure 37 shows the radius distribution of the total pores. Fig. Figure 38 shows the radius distribution of the organic pores. Fig. Figure 39 shows the radius distribution of the inorganic pores. Fig. 40 shows the factor distribution of the total pores in Fig. 41 shows the factor distribution of the organic pores in Fig. 42 is shown, and the factor distribution of the inorganic pores is in Fig. 43 shown.

[0206] The present invention proposes a method for identifying the shale pore type based on a digital drill core and for quantitative characterization. Compared with the prior art, the present invention has the following advantages. 1 The present invention improves the standard deviation of the Gaussian denoising kernel function and proposes a novel image processing method for denoising digital images of slate. According to the image processing method for denoising proposed in the present invention, the local image quality within the pores, the organic matter, and the rock matrix can be improved, ensuring the clarity of the boundaries between pores and organic matter as well as between organic matter and the rock matrix. 2. The present invention proposes a novel method for identifying organic matter in digital images of slate. The organic matter and the transition zone can be identified based on the effectiveness ratios of the images of the contiguous components for the organic matter and the transition zone. This method is easy to implement, highly practical, and can quickly identify images of the contiguous components of the organic matter and the transition zone with high identification accuracy. 3. The present invention proposes a novel method for identifying organic pores in digital images of slate, which can identify images of inorganic pores and images of organic pores within images of the contiguous components of pores using a proportional coefficient. For images of organic pores, images of marginal and inward pores composed of organic matter can be identified based on a threshold value for marginal organic pores. This method is easy to implement, highly practical, and can quickly identify images of marginal and inward pores composed of organic matter with high identification accuracy. 4. The present invention proposes a novel method for identifying and processing microfractures in digital images of slate. Two indicators are used to identify microfractures: the maximum Feret diameter and the ratio of minimum Feret diameter to maximum Feret diameter. This method is easy to implement, highly practical, and enables the rapid identification of microfracture images and images of inorganic pores with high identification accuracy. 5. The present invention proposes a novel method for the quantitative characterization of parameters of the slate pore structure for digital images of slate, with which the quantitative characterization of porosity parameters, pore volume parameters, parameters for the distribution of the equivalent pore radius, pore form factor parameters and microfracture structure parameters for slate samples to be identified is achieved.

[0207] The present invention enables the quantitative identification of organic pores, inorganic pores, and microfractures in shale, as well as the quantitative characterization of shale pore structures. Furthermore, structural parameters of organic pores, inorganic pores, and microfractures can be analyzed, allowing for a statistical distribution of the different types of reservoir spaces in shale rocks. The present invention is of considerable importance for assessing the quality of shale deposits and optimizing effective development strategies for shale oil and gas.

[0208] It is understood that the embodiments of the present invention are not limited to the specific structures, processing steps, or materials disclosed herein, but extend to equivalent substitutions of these features that are known to the person skilled in the art. It should also be noted that the terminology used here serves only to describe a particular embodiment and is not to be understood as a limitation.

[0209] In the description of the present invention, "a plurality of" means two or more unless otherwise specified. It is understood that the terms "top," "bottom," "left," "right," "inside," "outside," "front," "back," "head section," "end section," and the like denote orientations or positions based on those shown in the drawings, which are used only for the simplification and illustration of the present invention and are not intended to specify or imply any particular orientation or the configuration and operation of a device or element in any particular orientation. Therefore, the aforementioned terms are not intended to limit the present invention. The terms "first," "second," "third," and similar terms are used for illustrative purposes only and are not intended to indicate or imply any relative significance.

[0210] In the present invention, the terms "connect," "fasten," and the like, unless otherwise specified or defined, are to be understood in a broad sense and may, for example, be understood as permanent connections, detachable connections, or integral connections; mechanical or electrical connections; direct connections; or indirect connections via an intermediate structure or an internal connection between two elements. The specific meanings of the aforementioned terms in the present invention may be understood by a person skilled in the art taking into account specific conditions.

[0211] Throughout this application, certain terms are used consistently to designate specific system components. As will be known to those skilled in the art, the same component can generally be given different names. Therefore, the present application does not intend to distinguish between components that differ only in name but not in function. In this application, the terms "comprise," "include," and "have" are used in an open manner and are therefore to be understood as "including, but not limited to...". Furthermore, the terms used herein, such as "essentially," "basically," or "approximately," are within the tolerances customary in this field for such terms.For example, the term "coupling" as used herein includes both direct coupling and indirect coupling via additional components, elements, circuits, or modules. In indirect coupling, the intervening components, elements, circuits, or modules do not alter the signal information but can adjust current, voltage, and / or power levels. Derived coupling (for example, the coupling of one element to another by derivation) includes both direct and indirect coupling between two elements in the same way as "coupling."

[0212] The phrase “an embodiment” or “embodiments,” as mentioned in the description, means that the features, structures, or properties described in connection with the respective embodiment are included in at least one embodiment of the present invention. Therefore, the phrases “embodiment” or “embodiments” used in the description do not necessarily refer to the same embodiment.

[0213] The embodiments of the present invention serve for illustration and description purposes, but do not claim to be exhaustive and are not intended to limit the invention disclosed herein. Numerous modifications and variations are obvious to a person skilled in the art. The embodiments have been selected and described to better illustrate the principles and practical applications of the present invention, so that a person skilled in the art can understand the present invention and thus design various embodiments with different modifications suitable for specific purposes.

[0214] Although the embodiments of the present invention are described above, the disclosure is provided to facilitate understanding of the embodiment of the present invention without, however, limiting the present invention. Without departing from the spirit and scope of the present disclosure, a person skilled in the art may make various modifications and improvements to the forms and details of the implementation. The scope of protection of the present invention is set forth in the appended claims. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

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[0000] Gou Qiyang, Xu Shang, Hao Fang, Lu Yangbo, Zhang Aihua, Wang Yuxuan, Cheng Xuan, Qing Jiawei, Characterization method of shale pore structure based on nano-CT: a case study of Well JY-1. Acta Petrolei Sinica (2018, 39(11): 1253-1261

[0078] Huang Jiaguo, Xu Kaiming, Guo Shaobin, Guo Hewei, Comprehensive Study on Pore Structures of Shale Reservoirs Based on SEM, NMR and X-CT. Geoscience, 2015, 29(01):198-205

[0079]

Claims

[1] Method for identifying the shale pore type based on a digital drill core, comprising: Identifying the organic matter and the transition zone based on a grayscale image of a shale sample to be identified, thereby obtaining a result for the identification of the organic matter; and Obtaining an image of the organic pores and an image of the inorganic pores based on the result for identifying the organic matter and the grayscale image, which serve as the result for identifying the shale pore type. [2] Method for identifying the shale pore type according to claim 1, characterized by that the organic matter and the transition zone are identified by the following steps: Preservation of the lower and upper grayscale boundaries corresponding to the organic matter and the transition zone, based on the grayscale distribution of the grayscale image, thereby obtaining an image with contiguous components of the organic matter and the transition zone; and Performing the identification of organic matter based on a total regional area or total regional volume and an effective regional area or effective regional volume in the image of the contiguous components of organic matter and the transition zone, thereby obtaining the result for the identification of organic matter and a result for the identification of the transition zone. [3] Method for identifying the shale pore type according to claim 2, characterized by , that the identification of the organic substance is carried out by the following steps: Taking the effective regional area or effective regional volume as the dividend and the total regional area or total regional volume as the divisor to obtain an area effectiveness ratio or a volume effectiveness ratio, which serves as the effectiveness ratio of the picture of the contiguous components of organic matter and the transition zone; Establishing a threshold for the identification of organic matter and recording the image of the coherent components of the organic matter and the transition zone with an effectiveness ratio that is less than the threshold for the identification of organic matter, as an image of the coherent components of the transition zone, which serves as the result for the identification of the transition zone; and Recording the image of the contiguous components of the organic matter and the transition zone with an effectiveness ratio greater than or equal to the threshold for the identification of the organic matter, as an image of the contiguous components of the organic matter that serves as the result for the identification of the organic matter. [4] Method for identifying the shale pore type according to claim 3, characterized by , that the image of organic pores and the image of inorganic pores are identified by the following steps: Obtaining an image of the contiguous components of pores based on the grayscale distribution of the grayscale image; and Performing the identification of pores based on the image of the related components of pores and the image of the related components of organic matter, in order to obtain the image of organic pores and the image of inorganic pores. [5] Method for identifying the shale pore type according to claim 4, characterized by , that the identification of the pores is carried out through the following steps: Performing a pore dilation on the image of the connected components of pores based on a dilation template to obtain an image of the dilated connected components of pores and an image of their boundaries; Calculating the perimeter of a dilated connected component of pores in the image of the dilated connected component of pores; Taking an intersection of the image of the boundaries and the image of the connected components of the organic substance as the image of the intersection, in order to obtain an area of ​​the image of the intersection; and Performing the identification of organic pores based on the area of ​​the image of the intersection and the extent of the dilated connected component of pores to determine the image of organic pores and the image of inorganic pores. [6] Method for identifying the shale pore type according to claim 5, characterized by , that the identification of organic pores is carried out through the following steps: Taking the area of ​​the image of the intersection as the dividend and the perimeter of the expanded connected component of the pores as the divisor, to obtain a quotient that serves as the proportionality coefficient of the image of the connected components of pores; and Recording the image of the connected components of pores with a proportional coefficient equal to zero as the image of the inorganic pores and the image of the connected components with a proportional coefficient greater than zero and less than or equal to one as the image of the organic pores. [7] Method for identifying the shale pore type according to claim 6, characterized by , that the identification of the pores is carried out through the following steps: Establishing a threshold for the organic matter content of the pores located at the edge for the organic pore image; and Recording the image of the organic pores with a proportional coefficient greater than zero and less than or equal to the threshold for the organic matter of the pores located at the edge as an image of the organic matter of the pores located at the edge and the image of the organic pores with a proportional coefficient greater than the threshold for the organic matter of the pores located at the edge and less than or equal to one as an image of the organic matter of the pores located at the inside. [8] Method for identifying the schist pore type according to any one of claims 1 to 7, characterized by , that the procedure further includes: Calculating the maximum and minimum Feret diameters for the image of inorganic pores; Taking the minimum Feret diameter as the dividend and the maximum Feret diameter as the divisor to obtain a quotient that serves as the diameter ratio of the image of the inorganic pores; and Setting a diameter threshold and a ratio threshold, and recording the image of the inorganic pores that meets the condition for the identification of microfractures as the image of microfractures, and the image of the inorganic pores that does not meet the condition for the identification of microfractures as the image of inorganic pores. [9] Method for identifying the shale pore type according to claim 8, characterized by , that the condition for identifying microfracture is that the maximum Feret diameter is larger than the diameter threshold and the diameter ratio is smaller than the ratio threshold. [10] Method for identifying the slate pore type according to any one of claims 1 to 9, characterized by that the grayscale image is an original 2D or 3D grayscale image of the slate sample to be identified. [11] Method for identifying the shale pore type according to claim 10, characterized by , that the method further includes adjusting the grayscale levels of the original grayscale image, wherein: a 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 matrix; and An original grayscale image is obtained after grayscale adjustment based on the first grayscale value and the second grayscale value. [12] Method for identifying the shale pore type according to claim 11, characterized by , that the procedure further includes the following: Determining a denoising window based on the original grayscale image after grayscale adjustment; Calculating a standard deviation of the denoising based on a maximum grayscale value and a minimum grayscale value within the noise window; and Performing denoising of the original grayscale image after grayscale adjustment based on the standard deviation of the denoising. [13] Computer-readable storage medium comprising a series of instructions for carrying out the method according to any one of claims 1 to 12. [14] Methods for the quantitative characterization of the parameters of the shale pore structure based on a digital drill core, comprising: Obtaining pore structure parameters of the slate sample to be identified based on a total number of pixels in the grayscale image and an actual length of a pixel, and combining with the result of the identification of the organic substance, the image of the organic pores and the image of the inorganic pores obtained by the method according to any one of claims 1 to 12, wherein the pore structure parameters include at least one of the parameters for characterizing the porosity, the parameter for characterizing the pore volume, the parameter for the distribution of the equivalent pore radius, the parameter for characterizing the form factor of the pores and the parameter for characterizing the microfracture structure. [15] Computer-readable storage medium on which a series of instructions for carrying out the method according to claim 14 are stored.

Images