CN115239600A - Shale scanning electron microscope image multi-component division method - Google Patents
Shale scanning electron microscope image multi-component division method Download PDFInfo
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Abstract
The invention discloses a shale scanning electron microscope image multi-component division method, which is characterized in that based on the difference of gray values of pores, organic matters and matrix minerals in a shale scanning electron microscope image, a multi-threshold segmentation method is utilized to perform primary multi-component calibration on the image, and an area screening method and manual interactive image processing are selected according to the development characteristics of the organic matters to perform fine processing on the organic matters; and (3) adopting a boundary expansion algorithm to enable the organic matter to coincide with the organic hole, so as to screen out the organic hole, calibrating the inorganic hole by utilizing subtraction operation, and finally dividing the shale matrix minerals by adopting Boolean operation, thereby completing the multi-component division of the shale scanning electron microscope image. The method is simple and convenient to operate, has high division result precision, can be used for qualitatively dividing organic holes and inorganic holes, can also be used for quantitatively calculating different pore parameters, and has wide application value in shale gas reservoir evaluation and exploration and development.
Description
Technical Field
The invention relates to the technical field of nano CT scanning and digital core image processing, in particular to a shale scanning electron microscope image multi-component division method.
Background
The micro-nano pore space characterization and characterization of the shale gas reservoir is the basic work of shale gas exploration and development, and has important significance on shale gas reservoir evaluation and fluid transmission mechanism research. In the shale gas exploration and development process, the pore structure can be depicted and characterized by coring and adopting an experimental method, but the pore structure is influenced by the scale difference of different components, and the complex component structures such as organic pores, inorganic pores and organic matters are difficult to quantitatively evaluate and divide. With the development of the nano-scale scanning electron microscope imaging technology, the visual characterization of the shale nano-scale multi-component structure through the high-resolution scanning electron microscope imaging becomes an important research means.
The shale reservoir has complex pore structure, large morphological difference among the components and strong reservoir heterogeneity. The division of organic matters can be greatly influenced by the existence of aperture back bottom and inclined plane shadow in the scanning gray level image, so that the traditional image division method is difficult to finely divide the scanning image; meanwhile, the gray values of the organic pores and the inorganic pores in a scanned image are similar, and the organic pores and the inorganic pores are difficult to effectively distinguish by adopting a conventional method. Therefore, it is necessary to combine the special relationship among the shale components to research a corresponding screening method to qualitatively divide organic pores, inorganic pores and microcracks, improve the accuracy of division results, and provide technical support for quantitative characterization of pore form structures.
Disclosure of Invention
The invention mainly overcomes the defects in the prior art, and aims to provide a shale scanning electron microscope image multi-component partitioning method.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
the shale scanning electron microscope image multi-component division method is characterized by comprising the following steps:
s1: scanning the shale sample polished surface through a nanoscale scanning electron microscope imaging device to obtain a two-dimensional core gray image;
s2: preprocessing the two-dimensional core gray image obtained in the step S1, improving the image contrast and eliminating image artifacts and noise points;
s3: performing primary division on the organic matters in the image preprocessed in the step S2 by adopting a threshold segmentation method;
s4: superposing the image preprocessed in the step S2 with the organic matter image primarily divided in the step S3, changing the transparency of any image to check the excessively divided area, and refining and correcting the excessively divided area through image editing to obtain a secondarily divided image of the organic matter;
s5: dividing all pores and cracks with low gray values by combining a threshold segmentation method and a brightness difference segmentation method to obtain images containing all pores and cracks, and screening the cracks by combining a connectivity test and an aspect ratio screening standard to obtain images only containing all cracks;
s6: removing all the cracks marked out in the step S5 from the images of all the pores and cracks in the step S5 through subtraction operation, wherein the removed images are images containing organic holes and inorganic holes, and performing connectivity test and numbering marking on all the pores;
s7: expanding the outer boundaries of all pores in the step S6 by 1 pixel outwards by using a boundary expansion algorithm, superposing the organic matters refined and corrected in the step S4 and the pores subjected to boundary expansion in the same base map, constructing the organic hole boundaries of the overlapped parts by adopting an intersection method, and screening the pores of the overlapped parts to obtain all numbers corresponding to the organic holes;
s8: taking the image marked based on the number in the step S6 as a sample space, and only screening the pores corresponding to the organic pore numbers in the step S7 to obtain an image only containing organic pores; subtracting the organic holes from the sample space through subtraction operation to obtain an image only containing the inorganic holes;
s9: taking a union set of the organic matter image refined and corrected in the step S4 and the image containing all the holes in the step S5, and taking a complementary set of the image after the union set to obtain a shale matrix mineral image;
s10: and (3) respectively superposing the organic matter refined and corrected in the step S4, the cracks screened in the step S5, the organic holes and the inorganic holes screened in the step S8 and the shale matrix minerals screened in the step S9 on the same picture, and marking the 5 components with 0, 1, 2, 3 and 4 respectively by using image numbers, so that multi-component refined qualitative segmentation of the scanning gray level image is realized.
Further, the preparation process of the polished surface of the shale sample in the step S1 comprises: firstly, carrying out rock grinding on the surface of a shale sample by adopting ultrathin carborundum paper until the surface is smooth; and then, polishing the surface of the sample by adopting argon ion polishing equipment under a vacuum condition to obtain a smooth polished surface.
Further, the method for improving the contrast of the image in step S2 is contrast enhancement. The gray value of the shale image scanned and imaged by the scanning electron microscope is usually dark, the contrast of the image is poor, the subsequent image segmentation is difficult due to narrow gray gradient, and the contrast enhancement algorithm can amplify the gray gradient in equal proportion under the condition of not changing the information of the original image.
The method for eliminating noise in step S2 is a non-local mean filtering method. Due to the existence of electronic signal interference, the scanning electron microscope image generally needs filtering. For multi-component division of shale images, determination of boundaries of all mineral components of the images is particularly important, and noise can be eliminated by filtering the images by a non-local mean filtering method under the condition that image boundary information is kept as much as possible.
Furthermore, the organic matter in the step S3 is mainly a carbonaceous component, and often shows a light gray characteristic with a medium gray level in the scanning electron microscope image, and the lower limit and the upper limit of the gray level threshold of the organic matter are respectively selected to realize the positioning and marking of the organic matter component.
Further, in step S4, in order to check the accuracy of the division result, the organic matter primarily divided in step S3 and the grayscale image preprocessed in step S2 are superimposed together, and an area with inaccurate division is dynamically observed and confirmed by repeatedly changing the transparency of the organic matter image. The excessive division area of the organic matter mainly comprises a polishing surface inclined plane shadow and a hole seam back bottom, and the area pixel value which is wrongly marked as the organic matter is changed from 1 to 0 through an image editor, so that the fine correction of the organic matter can be completed.
Further, in the step S5, a threshold segmentation method is adopted to divide pores with larger pore diameters and cracks with larger opening degrees, a brightness difference segmentation method is adopted to divide pores with smaller pore diameters and cracks with smaller opening degrees, and finally, the results of the two segmentation methods are merged, so that the qualitative division of all pores and cracks is realized.
Further, the connectivity testing method in step S5 specifically includes: the same pore or crack is marked with the same number, and the marked numbers of the pores or cracks which are not mutually communicated are different.
At present, the method for dividing the cracks and the pores is mainly based on a morphological screening method, the pores and the cracks are divided by calculating the aspect ratio of a minimum circumscribed rectangle of each of the pores and the cracks and combining a threshold screening standard of the aspect ratio, and the lower limit of the aspect ratio of the cracks determined by the method is 4, that is, the aspect ratio is greater than 4, and is the crack, and the aspect ratio is less than 4, and is the pore.
Further, the evaluation criteria of connectivity in step S6 are as follows: for a single pixel point of a two-dimensional plane, the two-dimensional plane has 4 adjacent pixel points and 4 diagonal pixel points, and when the adjacent pixel value or the diagonal pixel value is consistent with the pixel value of the pixel point, the two pixel points are considered to be in a connected relation. The communicated pores are marked with the same number, and the numbers of the non-communicated pores are different.
Further, in step S7, the organic pores are screened by using a specific relationship between the organic pores and the organic matters. Because the organic hole is mainly formed by the thermal evolution of organic matters, the space where the organic hole is located is always located in the organic matters, and therefore the outer boundary of the organic hole and the inner boundary of the organic matters are in contact with each other on the image. The outer boundary of the organic hole is expanded outwards and then is overlapped with the organic matter, the numbers of all the organic holes can be obtained by screening the pore numbers of the overlapped part, and the outer boundary of the inorganic hole is not related to the organic matter generally.
Further, in the step S9, boolean operation is mainly used for the partition of the shale matrix minerals. Because the organic matters and all the pore gaps are divided and corrected in the step S4 and the step S5, the shale matrix minerals can be quickly divided by adopting Boolean operation, and the division error caused by the back bottom of the pore gaps and the shadow of the inclined plane is avoided.
The shale scanning electron microscope image multi-component division method provided by the invention takes the forms and distribution characteristics of different types of pores as main division basis, takes a digital core technology and Boolean operation as main mathematical methods, respectively adopts specific division means for different components, and theoretically explains a specific method for dividing a shale scanning electron microscope gray scale image into 5 component images. The method is simple and convenient to operate, high in division result precision, capable of being used for qualitatively dividing organic holes and inorganic holes, also capable of being used for quantitatively calculating different pore parameters, and wide in application value in shale gas reservoir evaluation and exploration and development.
Has the beneficial effects that:
compared with the prior art, the invention has the following beneficial effects:
the method mainly comprises the steps of taking the forms and distribution characteristics of different types of pores as main division basis, taking a digital core technology and Boolean operation as main mathematical methods, respectively adopting specific division means for different components, and theoretically explaining a specific method for dividing the shale scanning electron microscope gray scale image into 5 component images. The method is simple and convenient to operate, high in division result precision, capable of being used for qualitatively dividing organic holes and inorganic holes, also capable of being used for quantitatively calculating different pore parameters, and wide in application value in shale gas reservoir evaluation and exploration and development.
Drawings
FIG. 1 is a shale scanning electron microscope gray scale image;
FIG. 2 is a primary division and secondary division diagram of an organic matter;
FIG. 3 is a graph showing the result of dividing organic pores and inorganic pores;
FIG. 4 is a graph comparing results of multi-component partitioning;
FIG. 5 is a schematic view of a multi-component division process of a shale scanning electron microscope image.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
Example (b):
a shale scanning electron microscope image multi-component dividing method comprises the following steps:
s1: scanning the shale sample polished surface through a nanoscale scanning electron microscope imaging device to obtain a two-dimensional core gray image, and the specific process comprises the following steps: firstly, carrying out rock grinding on the surface of a shale sample by adopting ultrathin carborundum paper until the surface is smooth; and then, polishing the surface of the sample by adopting argon ion polishing equipment under a vacuum condition to obtain a smooth polished surface.
S2: preprocessing the two-dimensional core gray image obtained in the step S1, improving image contrast, and eliminating image artifacts and noise points:
one method of improving the contrast of an image is contrast enhancement. The gray value of the shale image scanned and imaged by the scanning electron microscope is usually dark, the contrast of the image is poor, the subsequent image segmentation is difficult due to narrow gray gradient, and the contrast enhancement algorithm can amplify the gray gradient in equal proportion under the condition of not changing original image information.
The method for eliminating the noise point is a non-local mean filtering method. Due to the existence of electronic signal interference, the scanning electron microscope image generally needs filtering. For multi-component division of shale images, determination of boundaries of all mineral components of the images is particularly important, and noise can be eliminated by filtering the images by a non-local mean filtering method under the condition that image boundary information is kept as much as possible.
S3: performing primary division on the organic matters in the image preprocessed in the step S2 by adopting a threshold segmentation method: the organic matter is mainly carbonaceous, often shows a light gray characteristic with medium gray in a scanning electron microscope image, and the positioning mark of the organic matter component can be realized by respectively selecting the lower limit and the upper limit of the gray threshold of the organic matter.
S4: superposing the image preprocessed in the step S2 with the organic matter image primarily divided in the step S3, changing the transparency of any image to check the excessively divided area, and refining and correcting the excessively divided area through image editing to obtain a secondarily divided image of the organic matter; in order to check the accuracy of the division result, the organic matter primarily divided in step S3 and the gray level image preprocessed in step S2 are superimposed together, and the area with inaccurate division is dynamically observed and confirmed by repeatedly changing the transparency of the organic matter image. The excessive division areas of the organic matters are mainly polishing surface inclined plane shadow and aperture back bottom, and the area pixel values which are wrongly marked as the organic matters are changed from 1 to 0 through an image editor, so that the fine correction of the organic matters can be completed.
S5: dividing all pores and cracks with low gray values by combining a threshold segmentation method and a brightness difference segmentation method to obtain images containing all pores and cracks, and screening the cracks by combining a connectivity test and an aspect ratio screening standard to obtain images only containing all cracks;
and finally, merging the results of the two dividing methods, namely realizing the qualitative division of all pores and cracks.
The connectivity test method specifically comprises the following steps: the same pores or cracks are marked with the same number, and the marked numbers of the pores or cracks which are not communicated with each other are different.
At present, the method for dividing the cracks and the pores is mainly based on a morphological screening method, the pores and the cracks are divided by calculating the aspect ratio of the minimum circumscribed rectangle of each pore and crack and combining a threshold screening standard of the aspect ratio, and the lower limit of the aspect ratio of the cracks determined by the method is 4, namely, the aspect ratio is greater than 4, the cracks are selected, and the aspect ratio is less than 4, the pores are selected.
S6: removing all cracks marked out in the step S5 from the images of all pores and cracks in the step S5 through subtraction operation, wherein the removed images are images containing organic holes and inorganic holes, and performing connectivity test and numbering marking on all pores;
the evaluation criteria for connectivity are as follows: for a single pixel point of a two-dimensional plane, the two-dimensional plane has 4 adjacent pixel points and 4 diagonal pixel points, and when the adjacent pixel value or the diagonal pixel value is consistent with the pixel value of the pixel point, the two pixel points are considered to be in a connected relation. The communicated pores are marked with the same number, and the numbers of the non-communicated pores are different.
S7: expanding the outer boundaries of all the pores in the step S6 by 1 pixel outwards by using a boundary expansion algorithm, superposing the organic matters refined and corrected in the step S4 and the pores subjected to boundary expansion in the same base map, constructing the organic hole boundaries of the overlapped parts by adopting an intersection manner, and screening the pores of the overlapped parts to obtain all numbers corresponding to the organic holes;
this step utilizes a specific relationship between the organic pores and the organic matter to screen the organic pores. Because the organic hole mainly evolves by organic matter heat, the space that the organic hole belongs to is in organic matter inside all the time, therefore the outer boundary of organic hole and the interior boundary of organic matter contact each other on the image. The outer boundary of the organic hole is expanded outwards and then is overlapped with the organic matter, the numbers of all the organic holes can be obtained by screening the pore numbers of the overlapped part, and the outer boundary of the inorganic hole is not related to the organic matter generally.
S8: taking the image based on the number mark in the step S6 as a sample space, and only screening the pores corresponding to the organic pore number in the step S7 to obtain an image only containing organic pores; subtracting the organic holes from the sample space through subtraction operation to obtain an image only containing the inorganic holes;
s9: taking a union set of the organic matter image refined and corrected in the step S4 and the image containing all the holes in the step S5, and taking a complementary set of the image after taking the union set to obtain a shale matrix mineral image;
boolean operation is mainly adopted for the partition of shale matrix minerals. Because the organic matters and all the pore gaps are divided and corrected in the step S4 and the step S5, the shale matrix minerals can be quickly divided by adopting Boolean operation, and the division error caused by the back bottom of the pore gaps and the shadow of the inclined plane is avoided.
S10: and (3) respectively superposing the organic matter refined and corrected in the step S4, the cracks screened in the step S5, the organic holes and the inorganic holes screened in the step S8 and the shale matrix minerals screened in the step S9 on the same picture, and marking the 5 components with 0, 1, 2, 3 and 4 respectively by using image numbers, so that multi-component refined qualitative segmentation of the scanning gray level image is realized.
Example 1:
the shale scanning electron microscope gray scale image used in the embodiment is from an organic matter-rich core of a deep marine shale gas reservoir in a certain block of the Sichuan basin, and the scanning equipment is a Zeiss SIGMA 500 field emission scanning electron microscope. Wherein, the image scanning precision is 10nm, the resolution is 1024 multiplied by 1024, the physical size is 10.24 μm multiplied by 10.24 μm, and the image gray value is between 0 and 255.
The following shows how the shale scanning electron microscope image multi-component division method provided by the invention divides the gray level image, and performs multi-component superposition and verification based on the division result.
The shale scanning electron microscope gray level image used in the embodiment is shown in fig. 1, a large microcrack which is distributed along the edge of an organic matter develops in the center of the gray level image, the surface roughness of the crack is large, the tortuosity of the left side along the organic matter part is large, and a plurality of microcracks with small opening degrees develop at the upper right part and the lower right part of the image; organic matters and organic pores in the image are developed, the left side of the image is taken as a main part, and the connectivity is good; siliceous minerals such as quartz, feldspar and the like are developed on the right side of the image; meanwhile, a small amount of inorganic pores are developed in the matrix rock area, mainly the erosion pores, and the connectivity is poor.
Preprocessing is carried out on the shale scanning electron microscope gray level image, and the adopted preprocessing method is a contrast enhancement method and a non-local homogeneous filtering method. The image after contrast enhancement is used for checking the organic matter division precision in the step S4, and the accuracy of the pore division in the step S5 can be improved by eliminating noise through a non-local homogeneous filtering method.
The initial division of the organic matter adopts a threshold segmentation method, and the division result is shown in fig. 2 (a), and the organic matter is excessively divided under the influence of the aperture back and the inclined plane shadow. The pixel value of the region excessively divided into organic matters is changed from 1 to 0 under the contrast condition of the superimposed base map by using image editing, and the corrected secondary division result of the organic matters is shown in fig. 2 (b).
In the shale scanning electron microscope image, the gray scale range of cracks and pores is the lowest, and the components are easily distinguished and are often black. And finally, merging the results of the two dividing methods, thereby realizing the qualitative division of all pores and cracks. And screening the cracks by combining the connectivity test and the aspect ratio screening standard to obtain an image only containing all the cracks.
The method accurately realizes the qualitative screening of the organic holes by utilizing the special relation of the organic holes and the organic matters on the image through a boundary expansion algorithm and an intersection taking mode, and marks out the inorganic holes through subtraction (as shown in figure 3 (a)). The distribution of the positions of the organic, organic and inorganic pores divided in this example is shown in fig. 3 (b).
And superposing the organic matter marked out in the step S4 and all the pore gaps marked out in the step S5 in the same image, and carrying out binarization processing on the superposed image. The binarization processing mode is to take a union set of organic matters, all pores and cracks, mark an image area after taking the union set as 1, and mark the rest non-image areas as 0 in a unified way. And (3) taking a complementary set for the image area after the union set, marking the complementary set area as 1 in another base map, and marking the rest areas as 0, wherein the marked shale matrix minerals are shown as yellow parts in a figure 4.
And respectively superposing matrix minerals, organic matters, cracks, organic pores and inorganic pores in the same image, wherein the shale matrix minerals are represented by yellow, the organic matters are represented by green, the cracks are represented by red, the organic pores are represented by light blue, and the inorganic pores are represented by dark blue, so that the multi-component qualitative segmentation of the shale scanning electron microscope image is completed (as shown in figure 4). The specific processing flow of the invention is schematically shown in figure 5.
The shale scanning electron microscope image multi-component division method provided by the invention takes the forms and distribution characteristics of different types of pores as main division basis, takes a digital core technology and Boolean operation as main mathematical methods, respectively adopts specific division means for different components, and theoretically explains a specific method for dividing a shale scanning electron microscope gray scale image into 5 component images. The method is simple and convenient to operate, has high division result precision, can be used for qualitatively dividing organic holes and inorganic holes, can also be used for quantitatively calculating different pore parameters, and has wide application value in shale gas reservoir evaluation and exploration and development.
Although the present invention has been described with reference to the above embodiments, it should be understood that the present invention is not limited to the above embodiments, and those skilled in the art can make various changes and modifications without departing from the scope of the present invention.
Claims (10)
1. A shale scanning electron microscope image multi-component division method is characterized by comprising the following steps:
s1: scanning the shale sample polished surface through a nanoscale scanning electron microscope imaging device to obtain a two-dimensional core gray image;
s2: preprocessing the two-dimensional core gray image obtained in the step S1, improving the image contrast and eliminating image artifacts and noise points;
s3: performing primary division on the organic matters in the image preprocessed in the step S2 by adopting a threshold segmentation method;
s4: superposing the image preprocessed in the step S2 with the organic matter image primarily divided in the step S3, changing the transparency of any image to check the excessively divided area, and refining and correcting the excessively divided area through image editing to obtain a secondarily divided image of the organic matter;
s5: dividing all pores and cracks with low gray values by combining a threshold segmentation method and a brightness difference segmentation method to obtain images containing all pores and cracks, and screening the cracks by combining a connectivity test and an aspect ratio screening standard to obtain images only containing all cracks;
s6: removing all cracks marked out in the step S5 from the images of all pores and cracks in the step S5 through subtraction operation, wherein the removed images are images containing organic holes and inorganic holes, and performing connectivity test and numbering marking on all pores;
s7: expanding the outer boundaries of all pores in the step S6 by 1 pixel outwards by using a boundary expansion algorithm, superposing the organic matters refined and corrected in the step S4 and the pores subjected to boundary expansion in the same base map, constructing the organic hole boundaries of the overlapped parts by adopting an intersection method, and screening the pores of the overlapped parts to obtain all numbers corresponding to the organic holes;
s8: taking the image marked based on the number in the step S6 as a sample space, and only screening the pores corresponding to the organic pore numbers in the step S7 to obtain an image only containing organic pores; subtracting the organic holes from the sample space through subtraction operation to obtain an image only containing the inorganic holes;
s9: taking a union set of the organic matter image refined and corrected in the step S4 and the image containing all the holes in the step S5, and taking a complementary set of the image after the union set to obtain a shale matrix mineral image;
s10: and (3) respectively superposing the organic matter refined and corrected in the step S4, the cracks screened in the step S5, the organic holes and the inorganic holes screened in the step S8 and the shale matrix minerals screened in the step S9 on the same picture, and marking the 5 components with 0, 1, 2, 3 and 4 respectively by using image numbers, so that multi-component refined qualitative segmentation of the scanning gray level image is realized.
2. The shale scanning electron microscope image multi-component partitioning method as claimed in claim 1, wherein the preparation process of the polished face of the shale sample in the step S1 is as follows: firstly, carrying out rock grinding on the surface of a shale sample by adopting ultrathin carborundum paper until the surface is smooth; and then, polishing the surface of the sample by adopting argon ion polishing equipment under a vacuum condition to obtain a smooth polished surface.
3. The shale scanning electron microscope image multi-component partitioning method as claimed in claim 1, wherein the method for improving the image contrast in step S2 is contrast enhancement; the gray value of the shale image scanned and imaged by the scanning electron microscope is usually dark, the contrast of the image is poor, the subsequent image segmentation is difficult due to narrow gray gradient, and the contrast enhancement algorithm can amplify the gray gradient in equal proportion under the condition of not changing the information of the original image;
the method for eliminating the noise points in the step S2 is a non-local mean filtering method; due to the existence of electronic signal interference, the scanning electron microscope image generally needs filtering; for multi-component division of shale images, determination of boundaries of all mineral components of the images is particularly important, and noise can be eliminated by filtering the images by a non-local mean filtering method under the condition that image boundary information is kept as much as possible.
4. The shale scanning electron microscope image multi-component division method as claimed in claim 1, characterized in that the organic matter in step S3 is mainly carbonaceous component, a light gray characteristic with medium gray often appears in the scanning electron microscope image, and the positioning marking of the organic matter component can be realized by selecting the lower limit and the upper limit of the gray threshold of the organic matter respectively.
5. The shale scanning electron microscope image multi-component division method as claimed in claim 1, wherein in step S4, in order to check the accuracy of the division result, the organic matter primarily divided in step S3 and the gray scale image preprocessed in step S2 are superimposed together, and the area with inaccurate division is dynamically observed and confirmed by repeatedly changing the transparency of the organic matter image; the excessive division area of the organic matter mainly comprises a polishing surface inclined plane shadow and a hole seam back bottom, and the area pixel value which is wrongly marked as the organic matter is changed from 1 to 0 through an image editor, so that the fine correction of the organic matter can be completed.
6. The shale scanning electron microscope image multi-component partitioning method as claimed in claim 1, wherein in step S5, a threshold segmentation method is adopted to partition pores with larger pore diameters and cracks with larger opening degrees, meanwhile, a brightness difference segmentation method is adopted to partition pores with smaller pore diameters and cracks with smaller opening degrees, and finally, a union set is taken for results of the two segmentation methods, namely, qualitative partitioning of all pores and cracks is realized.
7. The shale scanning electron microscope image multi-component partitioning method as claimed in claim 1, wherein the connectivity testing method in step S5 specifically comprises: marking the same pore or crack with the same number, wherein the marking numbers of the pores or cracks which are not mutually communicated are different;
at present, the method for dividing the cracks and the pores is mainly based on a morphological screening method, the pores and the cracks are divided by calculating the aspect ratio of a minimum circumscribed rectangle of each of the pores and the cracks and combining a threshold screening standard of the aspect ratio, and the lower limit of the aspect ratio of the cracks determined by the method is 4, that is, the aspect ratio is greater than 4, and is the crack, and the aspect ratio is less than 4, and is the pore.
8. The shale scanning electron microscope image multi-component partitioning method as claimed in claim 1, wherein the evaluation criteria of connectivity in step S6 are as follows: for a single pixel point of a two-dimensional plane, the single pixel point has 4 adjacent pixel points and 4 diagonal pixel points, and when the adjacent pixel value or the diagonal pixel value is consistent with the pixel value of the pixel point, the two pixel points are considered to be in a communicating relation; the communicated pores are marked with the same number, and the numbers of the non-communicated pores are different.
9. The shale scanning electron microscope image multi-component partitioning method as claimed in claim 1, wherein in step S7, the organic pores are screened by using a special relationship between the organic pores and the organic matter; because the organic hole is mainly evolved by organic matter heat, the space where the organic hole is located is always positioned in the organic matter, and the outer boundary of the organic hole and the inner boundary of the organic matter are contacted with each other on the image; the outer boundary of the organic hole is expanded outwards and then is overlapped with the organic matter, the numbers of all the organic holes can be obtained by screening the pore numbers of the overlapped part, and the outer boundary of the inorganic hole is not related to the organic matter generally.
10. The shale scanning electron microscope image multicomponent segmentation method as claimed in claim 1, characterized in that, in the step S9, boolean operations are mainly adopted for the segmentation of shale matrix minerals; because the organic matters and all the pore gaps are divided and corrected in the step S4 and the step S5, the shale matrix minerals can be quickly divided by adopting Boolean operation, and the division error caused by the back bottom of the pore gaps and the shadow of the inclined plane is avoided.
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| CN116342541A (en) * | 2023-03-29 | 2023-06-27 | 中国矿业大学 | Rock-soil body permeability calculation method based on adjacent image pore fusion reconstruction |
| CN117404072A (en) * | 2023-12-15 | 2024-01-16 | 山东新云鹏电气有限公司 | Drilling site management system based on artificial intelligence |
| CN117593299A (en) * | 2024-01-18 | 2024-02-23 | 北京大学 | Method, device, equipment and medium for evaluating space effectiveness of lamellar shale reservoir |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116342541A (en) * | 2023-03-29 | 2023-06-27 | 中国矿业大学 | Rock-soil body permeability calculation method based on adjacent image pore fusion reconstruction |
| CN116342541B (en) * | 2023-03-29 | 2024-03-22 | 中国矿业大学 | Rock-soil body permeability calculation method based on adjacent image pore fusion reconstruction |
| CN117404072A (en) * | 2023-12-15 | 2024-01-16 | 山东新云鹏电气有限公司 | Drilling site management system based on artificial intelligence |
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| CN117593299A (en) * | 2024-01-18 | 2024-02-23 | 北京大学 | Method, device, equipment and medium for evaluating space effectiveness of lamellar shale reservoir |
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