CN113533158A - Coal reservoir pore structure parameter quantitative analysis method based on SEM image - Google Patents
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Abstract
The invention discloses a coal reservoir pore structure parameter quantitative analysis method based on an SEM image, which comprises the following steps: obtaining an SEM image of each coal reservoir sample under scanning of an electron microscope, and performing binarization processing on the SEM image of each coal reservoir sample to obtain a binary image; establishing a two-dimensional coordinate system related to the binary image, identifying pixel values corresponding to all pixel points in the binary image corresponding to each coal reservoir sample according to a matrix traversal mode, taking the pixel points with sudden change of the pixel values as pore edges and dividing pore space individuals in the binary image; determining the coordinate value of each pixel point in a two-dimensional coordinate system, calculating the pore radius of each pore space individual, and acquiring the pore coordination number of each pore space individual in the binary image; outputting a pore radius distribution histogram, a pore coordination number histogram, an average pore radius and an average pore coordination number of each coal reservoir sample; the invention has high calculation precision on the pore radius and the pore coordination number and low realization cost.
Description
Technical Field
The invention relates to the technical field of separation, in particular to a coal reservoir pore structure parameter quantitative analysis method based on an SEM image.
Background
The pore space of the coal is a storage place and a migration channel of underground water and coal bed gas, the structural characteristics (the number of pores, the size of a single pore, the pore distribution characteristics and the pore connectivity) of the pore space directly influence the enrichment and the osmotic migration of the coal bed gas, the research on the pore characteristics of the coal has important significance on the gas drainage and the coal bed gas development of the coal mine, the pore characteristics of the coal reservoir comprise the pore type, the structure, the size, the quantity and the like which are one of important parameters for measuring the storage and the migration performance of the coal bed gas, the research content of the pore space of the coal comprises the characteristics of the pore size, the shape, the structure, the type, the porosity, the pore volume and the specific surface area, wherein the distribution of coal matrix, pore cracks and minerals is not uniform, so that the porosity and the mineral content of different CT slices of the coal rock are greatly changed, and the porosity and the mineral content of the coal rock obtained by means such as the conventional helium method, the industrial analysis and the like can only reflect the average value of the porosity and the mineral content of the sample, the spatial distribution characteristics of the pore fractures and minerals are not reflected and this can be effectively achieved by X-CT scanning. In the research of CT porosity and mineral content quantitative characterization, the CT porosity is represented by the ratio of the number of pixel units corresponding to each slice pore crack to the total number of pixel units, and the mineral content can be represented by the ratio of the number of pixel units corresponding to each slice mineral content to the total number of pixel units, so that the CT porosity and the mineral content of each section can be obtained, and the porosity and the mineral content of the whole coal rock can be obtained.
At present, quantitative research on coal reservoir pore parameters mainly focuses on the research on porosity, pore radius and pore distribution. Under the current state of the art, people mostly adopt a common microscope, a Scanning Electron Microscope (SEM), a mercury intrusion method and a low-temperature nitrogen adsorption method to study the size, the shape and the structure of pores in coal. The mercury pressing method can quantitatively obtain pore structure information such as pore size, pore distribution, pore type and the like in the range of pore radius more than 3.75 nm. The minimum pore radius measured by the low-temperature nitrogen adsorption method reaches about 0.3nm, but the pore diameter of the maximum pore measured by the low-temperature nitrogen adsorption method can only reach 100-150nm generally. The pore structure characteristics of the sample can be presented from an ultramicro level by means of a Scanning Electron Microscope (SEM) or a transmission electron microscope technology. The X-CT image calculates the pore-to-throat distribution and pore throat radius in the core sample. In addition, the coal seam pore parameter-pore coordination number is difficult to obtain by conventional experimental means at present, and the experimental means for coal seam pore research at present has the following defects:
(1) the calculation accuracy of the pore radius is poor, the analysis is long in time consumption, and the use cost is very high.
(2) The pore coordination number affecting the permeability of the coal reservoir cannot be obtained by conventional CT or nuclear magnetic resonance techniques.
Disclosure of Invention
The invention aims to provide a coal reservoir pore structure parameter quantitative analysis method based on an SEM image, so as to solve the technical problems in the prior art.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a coal reservoir pore structure parameter quantitative analysis method based on SEM images comprises the following steps:
100, preparing coal reservoir samples with different pore types, acquiring an SEM image of each coal reservoir sample under scanning of an electron microscope, performing binarization processing on the SEM image of each coal reservoir sample, and converting the SEM image into a binary image;
200, establishing a two-dimensional coordinate system related to the binary image, identifying pixel values corresponding to all pixel points in the binary image corresponding to each coal reservoir sample according to a matrix traversal mode, taking the pixel points with the mutated pixel values as pore edges, and dividing pore space individuals in the binary image based on the pore edges;
step 300, determining a coordinate value of each pixel point in the two-dimensional coordinate system, calculating the pore radius of each pore space individual in the binary image based on the coordinate value, and acquiring the pore coordination number of each pore space individual in the binary image;
and step 400, outputting a pore radius distribution histogram and a pore coordination number histogram of each coal reservoir sample, and calculating an average pore radius and an average pore coordination number.
As a preferred scheme of the present invention, in step 100, in an SEM image of the coal reservoir sample, pixel values of pixel points in a non-pore range in the SEM image are greater than pixel values of pixel points in a pore range, and pixel values of pixel points in a plurality of non-pore ranges and pixel values of pixel points in a pore range are sampled and extracted, and a minimum pixel value of pixel points in a sampled non-pore range and a maximum pixel value of pixel points in a pore range are determined;
and setting the pixel points exceeding the minimum pixel value in the pore-free range to be 255 and setting the pixel points lower than the maximum pixel value in the pore-free range to be 0 so as to realize the binarization processing of the SEM image.
In a preferred embodiment of the present invention, in step 200, the method for identifying the individual pore edge and pore space in the binary image corresponding to each coal reservoir sample is implemented as follows:
step 201, establishing a two-dimensional coordinate system by taking the vertical crossing edges of the binary image as coordinate axes, traversing the binary image according to a preceding and subsequent mode to determine the pixel value of each pixel point, wherein the traversal intervals during the selected row and column traversal are all one pixel;
step 202, comparing the pixel value of each pixel point with the pixel value of the previous pixel point in the same row, determining the pixel point with the suddenly changed pixel value and redefining the RGB value of the pixel point;
step 203, a curve formed by connecting all the pixel points with redefined RGB values end to end is set as the pore edge, and the area surrounded by all the pixel points with redefined RGB values is set as the pore space individual.
As a preferred scheme of the present invention, the pixel point with a single pixel value mutation obtained by traversing rows and columns is used as a starting point of the pore space individual, and row and column coordinates of the pixel point are stored in a set;
comparing the pixel point with the row-column coordinates of the pixel points in the set, wherein the single pixel value obtained by traversing the next row is suddenly changed, so as to lead the pixel points with the row coordinate difference of 1 between the row coordinates and the pixel points in the set into the same set;
and taking the pixel point with the sudden change of the pixel value obtained by the line traversal as an end point of the pore space individual.
As a preferred scheme of the present invention, in step 202, the pixel point with the first pixel value mutation in each row in the traversal process is determined, the priority of the pixel point with the pixel value mutation in each row is divided according to the traversal order, the priority of the pixel point with the pixel value mutation in different rows is set to be in one-to-one correspondence with the traversal order, and the pixel point with the pixel value mutation in the same row is set to be the same priority;
the pixel points with only one sudden change of pixel values are respectively set as a starting point and an end point of the pore space individual according to the priority sequence, the pixel points are divided into two mapping curves according to the sequence from the starting point to the end point, and the two mapping curves sequentially connect the pixel points with different priorities and sudden changes of pixel values to form the pore space individual.
As a preferable aspect of the present invention, in step 300, the pore radius is half of an average value of a long axis and a short axis of a single pore space individual, the pore radius of each pore space individual in the binary image is obtained by using a maximum X coordinate, a minimum X coordinate, a maximum Y coordinate, and a minimum Y coordinate of a pixel point of each pore space individual in the two-dimensional coordinate system, and the specific implementation steps are as follows:
selecting a pixel point corresponding to the maximum value Xmax of the abscissa of each pore space individual corresponding to the pixel point with the suddenly changed pixel value in the set, a pixel point corresponding to the minimum value Xmin of the abscissa, a pixel point with the maximum value Ymax of the ordinate and a pixel point with the minimum value Ymin of a coordinate table;
the major axis radius of each individual pore space is Max (Xmax-Xmin, Ymax-Ymin), and the minor axis radius of each individual pore space is Min (Xmax-Xmin, Ymax-Ymin (;
the pore radius for each individual of said pore spaces will be calculated by the formula [ (Xmax-Xmin) + (Ymax-Ymin) ]/2.
As a preferred embodiment of the present invention, in step 300, the method for obtaining the coordination number of the pores of each individual pore space in the binary image comprises:
taking the central position of the pore radius of the pore space individual as a mass point of the pore space individual, taking the mass point of the pore space individual as a circle center and acquiring circumferential curves with different radii;
sequentially counting the number of intersections between the pore space individuals and circumferential curves with different radiuses, determining the pore space individuals when the pore space individuals intersect with the circumferential curves each time, and taking the total number of different pore space individuals corresponding to the circumferential curves with different radiuses as the coordination number of pores;
and sequentially calculating corresponding pore space coordination numbers for all pore space individuals in sequence, and counting the number of the pore space individuals corresponding to the same pore space coordination number.
As a preferred embodiment of the present invention, the number of intersections between the pixel points of the pore space individuals and the circumferential curves with different radii is different, the radius selection range of the circumferential curve is set, only the number of intersections between the circumferential curve and the pixel points within the radius selection range is counted, the pore space individuals are identified each time the pore space individuals intersect with the circumferential curve, and the number of different pore space individuals corresponding to the circumferential curves with different radii is superimposed to serve as the pore coordination number.
As a preferred aspect of the present invention, the specific operation manner for determining whether the pore space individuals intersected with different circumferential curves are the same is as follows: and comparing the pixel points crossed with the circumferential curve with the set where the counted pixel points with the sudden change of the pixel values are located, determining the pixel points belonging to the same pore space individual, and taking different pore space individuals as the pore coordination numbers.
As a preferred embodiment of the present invention, in step 400, the pore coordination numbers of each coal reservoir sample are counted and classified, the number of the pore space individuals corresponding to the same pore coordination number is determined, and the average pore-throat coordination number of each coal reservoir sample is calculated;
counting the number of the pore space individuals corresponding to different pore radii of each coal reservoir sample, determining the number of the pore space individuals with the same pore radius, and calculating the average value of the pore radii of each coal reservoir sample.
Compared with the prior art, the invention has the following beneficial effects:
the method can accurately calculate the structural parameters of the pore units with smaller pore radii, has high calculation accuracy of the pore radii, can calculate the pore coordination number of a common sandstone reservoir sample by a circumferential curve intersection algorithm, and adds the calculation influence of the smaller pore radii on the pore coordination number, so that the calculation mode is simple and accurate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
FIG. 1 is a schematic diagram of a framework for quantitative analysis of pore structure parameters according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for quantitatively analyzing parameters of a pore structure according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a sample structure of a coal reservoir sample provided by an embodiment of the invention;
FIG. 4 is a schematic representation of a SEM image of a coal reservoir sample provided by an embodiment of the invention;
FIG. 5 is a schematic diagram of a coal reservoir sample binary image provided by an embodiment of the invention;
FIG. 6 is a histogram of the distribution of pore diameters radius provided by an embodiment of the present invention;
FIG. 7 is a binary image of pore connectivity of a coal reservoir sample provided by an embodiment of the present invention;
FIG. 8 is a schematic representation of the coordination results of pore units provided by an embodiment of the present invention;
FIG. 9 is a histogram of the distribution of pore coordination numbers provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the invention provides a quantitative analysis method for coal reservoir pore structure parameters based on SEM images, and it should be noted that the coal reservoir pore structure parameters quantitatively analyzed in the present embodiment include a pore radius distribution diagram, an average pore radius, a pore coordination number distribution diagram, and an average pore coordination number, and the method can accurately calculate the structure parameters of the pore unit with a smaller pore radius, has high calculation accuracy for the pore radius, and can calculate the pore coordination number of a common sandstone reservoir sample through a circumferential curve crossing algorithm, and adds the calculation influence of the small pore radius on the pore coordination number, so that the calculation method is simple and accurate.
The method specifically comprises the following steps as shown in figure 2:
In step 100, in an SEM image of a coal reservoir sample, pixel values of pixels in a non-pore range in the SEM image are greater than pixel values of pixels in a pore range, and pixel values of pixels in a plurality of non-pore ranges and pixel values of pixels in a pore range are sampled and extracted, and a minimum pixel value of a pixel in a sampled non-pore range and a maximum pixel value of a pixel in a pore range are determined;
and setting the pixel points exceeding the minimum pixel value in the pore-free range to be 255 and setting the pixel points lower than the maximum pixel value in the pore-free range to be 0 so as to realize the binarization processing of the SEM image.
In the embodiment, the pixel value of the pixel point in the non-pore range is 0, the pixel value is used as a white background, and the pixel value of the pixel point in the corresponding pore range is 255, so that the SEM image obtained by the coal reservoir samples of different pore types under a scanning electron microscope is converted into a binary image rich in pore information.
In step 200, identifying the pore edge contour in the binary image corresponding to each coal reservoir sample is to obtain the pore parameters more accurately, in this embodiment, since the pixel point of the binary image only includes two pixel values, which are 0 and 255, the row-column coordinate of each pixel point and the pixel value corresponding to each pixel point can be obtained when traversing each pixel point of the binary image according to the determinant, the pixel value of each pixel point is compared with the pixel value of the previous pixel point, when the pixel value of the traversed pixel point changes from 255 to 0, the edge position of the pore unit corresponding to the pixel point is considered, and when the pixel value of the traversed pixel point changes from 0 to 255, the pixel point with the pixel value of 0 is used as the edge position of the pore unit.
The implementation method for identifying the pore edge and the pore space individual in the binary image corresponding to each coal reservoir sample comprises the following steps:
step 201, establishing a two-dimensional coordinate system by using the vertical crossing edges of the binary image as coordinate axes, traversing the binary image according to a preceding and subsequent mode to determine the pixel value of each pixel point, and selecting the traversal intervals during the row and column traversal to be one pixel.
Step 202, comparing the pixel value of each pixel point with the pixel value of the previous pixel point in the same row, determining the pixel point with the suddenly changed pixel value, and redefining the RGB value of the pixel point.
Step 203, a curve formed by connecting all the pixel points with redefined RGB values end to end is set as a pore edge, and an area surrounded by all the pixel points with redefined RGB values is set as a pore space individual.
Even if the shapes of the pore units of each coal reservoir are different, when the binary image corresponding to each coal reservoir is traversed in a row-column mode, each pore space individual has a pixel point with only single pixel value mutation.
And comparing the pixel point with the single pixel value mutation obtained by traversing the next row with the row-column coordinates of the pixel points in the set, so as to lead the pixel points with the row coordinate difference of 1 between the row coordinates and the pixel points in the set into the same set.
And taking the pixel point with the suddenly changed pixel value obtained by the line traversal as the terminal point of the pore space individual, so that the row-column coordinate of the pixel point corresponding to the pore edge profile corresponding to each pore space individual during the row-column traversal is obtained.
In step 202, the first pixel value mutation pixel point of each line in the traversal process is determined, the priority of the pixel value mutation pixel point of each line is divided according to the traversal order, the priority of the pixel value mutation pixel points of different lines is set to be in one-to-one correspondence with the traversal order, and the pixel value mutation pixel points of the same line are set to be the same priority.
The method comprises the steps of respectively setting pixel points with only one pixel value mutation as a starting point and an end point of a pore space individual according to a priority sequence, dividing the pixel points into two plotting curves from the starting point to the end point, sequentially connecting the pixel points with different priorities and with different pixel values mutation to form the pore space individual through the two plotting curves, wherein a single pore space individual is a pore unit, so that the pore units are divided, the checking and the recognition are convenient, and the pore coordination number of each pore unit can be manually compared with the pore coordination number calculated by program programming by comparing the size of the divided pore unit with the size of the long axis and the short axis of each pore unit, so that the manual verification work of the quantitative analysis of the pores of the coal reservoir is realized.
And 300, determining the coordinate value of each pixel point in the two-dimensional coordinate system, calculating the pore radius of each pore space individual in the binary image based on the coordinate value, and acquiring the pore coordination number of each pore space individual in the binary image.
The pore radius is half of the average value of the long axis and the short axis of a single pore space individual, according to the above, when traversing the binary image according to the row and column, the pixel point of which the pixel value is suddenly changed from 255 to 0 is taken as the pixel point of the pore edge, and the row and column coordinates of the pixel point are stored in the set corresponding to the single pore space individual, so when calculating the pore radius of each pore space individual, the maximum value of the row coordinate, the minimum value of the row coordinate, the maximum value of the column coordinate and the minimum value of the column coordinate in each set are firstly found out, then the maximum value of the row coordinate, the minimum value of the row coordinate, the maximum value of the column coordinate and the minimum value of the column coordinate are converted into the maximum X coordinate, the minimum X coordinate, the maximum Y coordinate and the minimum Y coordinate in the two-dimensional coordinate system, and the long axis and the short axis of the single pore space individual are obtained, and the specific implementation steps are as follows:
selecting a pixel point corresponding to the maximum value of the abscissa of a pixel point with a sudden change of the pixel value in the corresponding set of each pore space individual, a pixel point corresponding to the minimum value of the abscissa, a pixel point with the maximum value of the ordinate and a pixel point with the minimum value of the coordinate table, wherein the radius of the long axis of each pore space individual is Max [ (Xmax-Xmin), (Ymax-Ymin) ], the radius of the short axis of each pore space individual is Min [ (Xmax-Xmin), (Ymax-Ymin) ], and calculating the pore radius of each pore space individual through a formula [ (Xmax-Xmin) + (Ymax-Ymin) ]/2.
The coordination number of a pore is the ratio of the number of a central pore to the number of pores adjacent to the periphery of the central pore, and if there are 5 pores adjacent to the periphery of a central pore, the coordination number of the pore is 5.
In step 300, the method for obtaining the coordination number of the pores of each pore space in the binary image is as follows: taking the central position of the pore radius of the pore space individual as a particle of the pore space individual, taking the particle of the pore space individual as a circle center and acquiring circumferential curves with different radii;
sequentially counting the number of intersections between the pore space individuals and the circumferential curves with different radiuses, determining the pore space individuals when the pore space individuals intersect with the circumferential curves each time, and accumulating the total number of the different pore space individuals corresponding to the circumferential curves with different radiuses as the coordination number of pores;
calculating the corresponding pore coordination numbers of all pore space individuals in sequence, and counting the number of the pore space individuals corresponding to the same pore coordination number.
Because the pixel points of the pore space individuals are different from the intersection numbers of the circumferential curves with different radiuses, and the radius selection range of the circumferential curves is set, only the intersection numbers of the circumferential curves and the pixel points in the radius selection range are counted, the pore space individuals are identified when the circumferential curves are intersected with the circumferential curves each time, and the numbers of the different pore space individuals corresponding to the circumferential curves with different radiuses are superposed to be used as the coordination numbers of pores.
The specific operation mode for determining whether the pore space individuals intersected with different circumferential curves are the same is as follows: and comparing the pixel points crossed with the circumferential curve with the set where the counted pixel points with the sudden change of the pixel values are located, determining the pixel points belonging to the same pore space individual, and taking different pore space individuals as the pore coordination numbers.
According to the method, the radius of the circumferential curve is changed, a plurality of pore coordination numbers corresponding to one pore unit can be obtained, the superposition result of the cross number of the circumferential curve and the pore space individuals is used as the pore coordination number, the pore space individuals with smaller pore radius are also added to the calculation process of the pore distribution number, the calculation precision is improved, in addition, the selected range of the circumferential curve can be set, the problem that the calculation error is caused by the overlarge selected mode of the circumferential curve is avoided, the obtained pore coordination numbers are more accurate, and the method can be regulated and controlled by a program, and is quicker and simpler to realize.
And step 400, outputting a pore radius distribution histogram and a pore coordination number histogram of each coal reservoir sample, and calculating an average pore radius and an average pore coordination number.
In step 400, counting the pore coordination numbers of each coal reservoir sample for classification, determining the number of pore space individuals corresponding to the same pore coordination number, and calculating the average pore throat coordination number of each coal reservoir sample;
and counting the number of pore space individuals corresponding to different pore radii of each coal reservoir sample, determining the number of pore space individuals with the same pore radius, and calculating the average value of the pore radii of each coal reservoir sample.
In order to verify the quantitative analysis method for the pore structure parameters of the coal reservoir, the method takes the coal bed of No. 3 of the Shanxi group of the Qinhui basin as a research object, and quantitatively calculates the pore distribution, the pore radius size and the pore coordination number based on the SEM image as shown in FIG. 3.
As shown in fig. 4, the coal samples were observed and SEM images of the rich pore features were captured by observation under a scanning electron microscope.
According to the quantitative analysis flowchart, firstly, the SEM image is binarized to obtain a corresponding binary image, as shown in fig. 5.
Based on the results of the individual pore space partition, the pore radius of each individual pore space in fig. 4 was calculated, and a histogram of the distribution of pore radius and an average pore size (18.0697 μm) were obtained, as shown in fig. 6.
After obtaining the parameters related to the pore radius and the filled binary image, based on the analysis of the pores in fig. 6, each color unit space in the coordination distribution of each unit pore is obtained for calculation, the communicated image is shown in fig. 7, and the coordination condition distribution of each pore space individual and its vicinity is shown in fig. 8.
Finally, as shown in FIG. 9, the histogram of pore coordination numbers and the average pore-throat coordination number were output, and the average pore coordination number of the coal-rock sample was 1.1009.
Therefore, the method can accurately calculate the structural parameters of the pore units with smaller pore radii, has high calculation accuracy of the pore radii, can calculate the pore coordination number of a common sandstone reservoir sample through a circumferential curve intersection algorithm, eliminates the calculation influence of the smaller pore radii on the pore coordination number, enables the calculation mode to be simple and accurate, is very convenient and fast to process compared with a CT method, consumes lower manpower and material resources, and simultaneously realizes quantitative output of the pore coordination number in an image and histogram intuitive mode.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (10)
1. A coal reservoir pore structure parameter quantitative analysis method based on SEM images is characterized by comprising the following steps:
100, preparing coal reservoir samples with different pore types, acquiring an SEM image of each coal reservoir sample under scanning of an electron microscope, performing binarization processing on the SEM image of each coal reservoir sample, and converting the SEM image into a binary image;
200, establishing a two-dimensional coordinate system related to the binary image, identifying pixel values corresponding to all pixel points in the binary image corresponding to each coal reservoir sample according to a matrix traversal mode, taking the pixel points with the mutated pixel values as pore edges, and dividing pore space individuals in the binary image based on the pore edges;
step 300, determining a coordinate value of each pixel point in the two-dimensional coordinate system, calculating the pore radius of each pore space individual in the binary image based on the coordinate value, and acquiring the pore coordination number of each pore space individual in the binary image;
and step 400, outputting a pore radius distribution histogram and a pore coordination number histogram of each coal reservoir sample, and calculating an average pore radius and an average pore coordination number.
2. The SEM-image-based quantitative analysis method for coal reservoir pore structure parameters according to claim 1, characterized in that: in step 100, in an SEM image of the coal reservoir sample, pixel values of pixel points in a non-pore range in the SEM image are greater than pixel values of pixel points in a pore range, and pixel values of pixel points in a plurality of non-pore ranges and pixel values of pixel points in a pore range are sampled and extracted, and a minimum pixel value of pixel points in a sampled non-pore range and a maximum pixel value of pixel points in a pore range are determined;
and setting the pixel points exceeding the minimum pixel value in the pore-free range to be 255 and setting the pixel points lower than the maximum pixel value in the pore-free range to be 0 so as to realize the binarization processing of the SEM image.
3. The SEM-image-based quantitative analysis method for coal reservoir pore structure parameters according to claim 2, characterized in that: in step 200, the method for identifying the pore edge and pore space individual in the binary image corresponding to each coal reservoir sample is realized by:
step 201, establishing a two-dimensional coordinate system by taking the vertical crossing edges of the binary image as coordinate axes, traversing the binary image according to a preceding and subsequent mode to determine the pixel value of each pixel point, wherein the traversal intervals during the selected row and column traversal are all one pixel;
step 202, comparing the pixel value of each pixel point with the pixel value of the previous pixel point in the same row, determining the pixel point with the suddenly changed pixel value and redefining the RGB value of the pixel point;
step 203, a curve formed by connecting all the pixel points with redefined RGB values end to end is set as the pore edge, and the area surrounded by all the pixel points with redefined RGB values is set as the pore space individual.
4. The SEM-image-based quantitative analysis method for coal reservoir pore structure parameters according to claim 3, characterized in that: taking the pixel point with the single pixel value mutation obtained by traversing the rows and the columns as a starting point of the pore space individual, and storing the row and column coordinates of the pixel point in a set;
comparing the pixel point with the row-column coordinates of the pixel points in the set, wherein the single pixel value obtained by traversing the next row is suddenly changed, so as to lead the pixel points with the row coordinate difference of 1 between the row coordinates and the pixel points in the set into the same set;
and taking the pixel point with the sudden change of the pixel value obtained by the line traversal as an end point of the pore space individual.
5. The SEM-image-based quantitative analysis method for coal reservoir pore structure parameters according to claim 3, characterized in that: in step 202, determining the pixel point with the first pixel value mutation in each line in the traversal process, dividing the priority of the pixel point with the pixel value mutation in each line according to the traversal order, setting the priority of the pixel point with the pixel value mutation in different lines to be in one-to-one correspondence with the traversal order, and setting the pixel point with the pixel value mutation in the same line to be the same priority;
the pixel points with only one sudden change of pixel values are respectively set as a starting point and an end point of the pore space individual according to the priority sequence, the pixel points are divided into two mapping curves according to the sequence from the starting point to the end point, and the two mapping curves sequentially connect the pixel points with different priorities and sudden changes of pixel values to form the pore space individual.
6. The SEM-image-based quantitative analysis method for coal reservoir pore structure parameters according to claim 4, characterized in that: in step 300, the pore radius is half of an average value of a long axis and a short axis of a single pore space individual, the pore radius of each pore space individual in the binary image is obtained by using a maximum X coordinate, a minimum X coordinate, a maximum Y coordinate, and a minimum Y coordinate of a pixel point of each pore space individual in the two-dimensional coordinate system, and the specific implementation steps are as follows:
selecting a pixel point corresponding to the maximum value Xmax of the abscissa of each pore space individual corresponding to the pixel point with the suddenly changed pixel value in the set, a pixel point corresponding to the minimum value Xmin of the abscissa, a pixel point with the maximum value Ymax of the ordinate and a pixel point with the minimum value Ymin of a coordinate table;
the major axis radius of each individual pore space is Max (Xmax-Xmin, Ymax-Ymin), and the minor axis radius of each individual pore space is Min (Xmax-Xmin, Ymax-Ymin);
the pore radius for each individual of said pore spaces will be calculated by the formula [ (Xmax-Xmin) + (Ymax-Ymin) ]/2.
7. The SEM-image-based quantitative analysis method for coal reservoir pore structure parameters according to claim 6, characterized in that: in step 300, the method for obtaining the coordination number of the pores of each individual pore space in the binary image is as follows:
taking the central position of the pore radius of the pore space individual as a mass point of the pore space individual, taking the mass point of the pore space individual as a circle center and acquiring circumferential curves with different radii;
sequentially counting the number of intersections between the pore space individuals and circumferential curves with different radiuses, determining the pore space individuals when the pore space individuals intersect with the circumferential curves each time, and taking the total number of different pore space individuals corresponding to the circumferential curves with different radiuses as the coordination number of pores;
and sequentially calculating corresponding pore space coordination numbers for all pore space individuals in sequence, and counting the number of the pore space individuals corresponding to the same pore space coordination number.
8. The SEM-image-based quantitative analysis method for coal reservoir pore structure parameters according to claim 7, characterized in that: the number of the pixel points of the pore space individuals is different from the number of the intersections of the circumferential curves with different radiuses, the radius selection range of the circumferential curves is set, only the number of the intersections of the circumferential curves and the pixel points within the radius selection range is counted, the pore space individuals are identified when the pore space individuals intersect with the circumferential curves every time, and the number of the different pore space individuals corresponding to the circumferential curves with different radiuses is superposed to be used as the coordination number of the pores.
9. The method for quantitatively analyzing the pore structure parameters of the coal reservoir based on the SEM image as claimed in claim 8, wherein the specific operation manner of determining whether the pore space individuals intersected with different circumferential curves are the same is as follows: and comparing the pixel points crossed with the circumferential curve with the set where the counted pixel points with the sudden change of the pixel values are located, determining the pixel points belonging to the same pore space individual, and taking different pore space individuals as the pore coordination numbers.
10. The SEM-image-based quantitative analysis method for coal reservoir pore structure parameters according to claim 9, characterized in that: in step 400, counting the pore coordination numbers of each coal reservoir sample for classification, determining the number of the pore space individuals corresponding to the same pore coordination number, and calculating the average pore throat coordination number of each coal reservoir sample;
counting the number of the pore space individuals corresponding to different pore radii of each coal reservoir sample, determining the number of the pore space individuals with the same pore radius, and calculating the average value of the pore radii of each coal reservoir sample.
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