CN113870200B - Method for quantitatively analyzing pore coordination number in sandstone and carbonate reservoir - Google Patents

Method for quantitatively analyzing pore coordination number in sandstone and carbonate reservoir Download PDF

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CN113870200B
CN113870200B CN202111087382.2A CN202111087382A CN113870200B CN 113870200 B CN113870200 B CN 113870200B CN 202111087382 A CN202111087382 A CN 202111087382A CN 113870200 B CN113870200 B CN 113870200B
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sandstone
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久博
黄文辉
穆娜娜
雷涵
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China University of Geosciences Beijing
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Abstract

The invention discloses a method for quantitatively analyzing the coordination number of pores in sandstone and carbonate reservoirs, which comprises the following steps: and S1, preparing a plurality of groups of casting body slices based on the same sandstone sample or carbonate rock sample, and establishing a pore key portrait chain for identifying the pore complete units of the same sandstone sample or carbonate rock sample. The invention utilizes a plurality of groups of casting body slices with high difference degree to fuse and master the global pore characteristics in the sandstone sample or the carbonate rock sample, the precision of the pore coordination number obtained based on the quantitative analysis of the global pore characteristics is high, and the invention can output the pore coordination number in the sandstone reservoir sample and the carbonate rock reservoir sample.

Description

Method for quantitatively analyzing pore coordination number in sandstone and carbonate reservoir
Technical Field
The invention relates to the technical field of pore analysis, in particular to a method for quantitatively analyzing the coordination number of pores in sandstone and carbonate reservoirs.
Background
The pore structure refers to the space structure of pores and throats in the rock, comprises the type, size, distribution, mutual communication relation, the geometric shape of the pores, microscopic heterogeneous characteristics and the like, and is an important parameter for reflecting the microscopic heterogeneous of the reservoir. The coordination number of pores in a reservoir refers to the number of throats connecting each pore, usually expressed as an average of statistical results. The pore coordination number reflects an important parameter for pore connectivity. The method not only controls the migration and storage of oil, but also has great influence on the oil production capacity, the oil displacement efficiency and the ultimate recovery ratio of a reservoir.
Currently, the quantitative research on the coordination number of the pores mainly focuses on the 3D-CT image to calculate the pore-throat distribution and the pore-throat radius in the core sample. The 3D-CT image method has the advantages of accurate test result, time consumption, material consumption and high use cost. Pores in cast body slices for conventional sandstone and carbonate reservoir samples are not applicable. In addition, the 3D-CT image has great difficulty in the coordination number of the pores of the compact sandstone and the carbonate rock sample of the erosion pore type, and in the prior art, the casting body slice is used for carrying out quantitative analysis on the coordination number of the pores, only a single casting body slice is selected for carrying out quantitative analysis, and the single casting body slice only contains local pore characteristics, so that the coordination number analysis of the pores established on the local pore characteristics has the defect of low precision.
Disclosure of Invention
The invention aims to provide a method for quantitatively analyzing the coordination numbers of pores in sandstone and carbonate reservoirs, which aims to solve the technical problems of time consumption, high cost, limited application range and low precision in the prior art for calculating the coordination numbers of the pores in the reservoirs.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a method for quantitatively analyzing the coordination number of pores in sandstone and carbonate reservoirs comprises the following steps:
s1, preparing a plurality of groups of casting body slices based on the same sandstone sample or carbonate rock sample, and establishing a key pore portrait chain for identifying the whole pore units of the same sandstone sample or carbonate rock sample;
step S2, identifying the pore whole units of the same sandstone sample or carbonate rock sample based on the pore key sketch chain, and quantizing the pore whole units to obtain a group of pore coordination data chains;
and S3, fusing all the pore coordination data chains to obtain a pore full coordination data array, and quantitatively obtaining the pore coordination number of the same sandstone sample or carbonate rock sample based on the pore full coordination data array.
As a preferred embodiment of the present invention, in step S1, the multiple sets of casting slices are continuously extracted from the same sandstone sample or carbonate rock sample along the same direction and have the same calibration baseline, and the specific method for establishing the pore key profile chain includes:
s101, scanning a plurality of groups of casting slices by an electron microscope to obtain a plurality of groups of SEM images of the casting slices, and performing differential classification on the SEM images of the plurality of groups of casting slices to obtain a plurality of low-viscosity SEM image clusters;
and S102, selecting SEM images of cast body slices at the centers of the clusters from the plurality of SEM image clusters to form a pore key portrait chain.
As a preferable aspect of the present invention, a specific method for performing differential classification on a plurality of sets of SEM images of cast sheets and selecting the SEM image of cast sheet at the cluster center includes:
sequentially calculating the histogram difference of the SEM images of the cast body slices of each group, and classifying all the SEM images of which the histogram difference is lower than a specified threshold value into a cluster so as to ensure that the difference between the clusters of the SEM images is high, wherein the calculation formula of the histogram difference is as follows:
Figure GDA0003613882390000021
wherein X is SEM image of each set of casting sheet, and X isi，xjHistogram of SEM image of i, j group cast sheet, p (x)i,xj) Is xiAnd xjIs given by the joint probability distribution function of p (x)i) And p (x)j) Are each xiAnd xjThe edge probability distribution function of (1);
and sequentially calculating the histogram mean value of each SEM image cluster, and selecting the SEM image of the cast body slice with the Euclidean minimum of the histogram mean value as the cluster center of the SEM image cluster.
As a preferred embodiment of the present invention, in step S2, the specific method for identifying the pore complete unit includes:
s201, selectively converting the SEM images of all casting slices on the pore key image chain into binary images of the casting slices to generate binary images of the casting slices;
step S202, carrying out pore edge identification on the binary images of the casting sheets, and sequentially carrying out pore unit division on each binary image of the casting sheets on the basis of the pore edges;
and step S203, summarizing the pore units in the binary image of each casting sheet to form a pore whole unit.
As a preferable aspect of the present invention, the selective binary image conversion includes binary conversion of each type of color and pixel in the SEM image of the cast body sheet, and the step S201 further includes performing noise reduction processing on the binary image of the cast body sheet, where the specific method of the noise reduction processing includes:
the method comprises the following steps: deleting the pixel units with the connected pixel points lower than a specified threshold value, and reassigning the deleted pixel units;
the second method comprises the following steps: the pixel cells having the connection pixel points higher than a predetermined threshold are expanded, and the expanded pixel cells are reconnected.
As a preferred embodiment of the present invention, in step S2, the specific method for quantifying the pore complete unit to obtain a group of pore coordination data chains includes:
quantitatively quantifying an array of pore coordination data for each of said binary images of cast slab based on said pore cells;
and linking all the pore matching data arrays by the radial depth of the pore key portrait chain to form a pore matching data chain.
In a preferred embodiment of the present invention, in step S3, the specific method for fusing all the pore coordination data chains to obtain a pore full coordination data array includes:
fusing the pore coordination data chains layer by layer along the radial depth to obtain a pore full coordination data array;
and generating a pore full coordination data array according to the pore full coordination data array, and generating a pore coordination data histogram based on the pore full coordination data array.
As a preferred embodiment of the present invention, the pore coordination number of the same sandstone sample or carbonate sample is quantitatively calculated based on the histogram of pore coordination numbers.
As a preferred aspect of the present invention, a specific method for obtaining a pore full coordination data array by performing layer-by-layer fusion on the pore coordination data chain along a radial depth includes:
adjusting all pore coordination data arrays on the pore coordination data chain along the direction of coincidence of the calibration base lines so that all pore coordination data arrays are vertically arranged;
and carrying out successive vertical superposition on all the pore coordination data arrays to generate a pore full coordination data array.
As a preferable aspect of the present invention, the method for drawing the calibration baseline includes:
and taking the projection point of the intersection line of the cross section and the longitudinal section of the same sandstone sample or carbonate rock sample as the calibration midpoint of the calibration baseline, and respectively taking the projection line of the cross section and the longitudinal section as the calibration transverse baseline and the calibration longitudinal baseline of the calibration baseline.
Compared with the prior art, the invention has the following beneficial effects:
the invention utilizes a plurality of groups of casting body slices with high difference degree to fuse and master the global pore characteristics in the sandstone sample or the carbonate rock sample, the precision of the pore coordination number obtained based on the quantitative analysis of the global pore characteristics is high, and the invention can output the pore coordination number in the sandstone reservoir sample and the carbonate rock reservoir sample.
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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 flow chart of a quantitative analysis method provided by an embodiment of the present invention;
FIG. 2 is a block diagram illustrating a quantitative analysis of the coordination numbers of pores in sandstone and carbonate reservoirs in accordance with an embodiment of the present invention;
fig. 3 is a schematic flow chart of a quantitative analysis method for a single cast slice of a sandstone sample and a carbonate sample according to an embodiment of the present invention;
fig. 4 is a flow chart of a quantitative analysis method for a plurality of sets of casting slices of a sandstone sample, which is provided by the embodiment of the invention;
fig. 5 is a schematic flow chart of a quantitative analysis method for multiple sets of cast body slices of a tight sandstone sample, and a carbonate rock sample with primary intergranular pores and intergranular pores as reservoir spaces, according to 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 and 2, the present invention provides a method for quantitatively analyzing the coordination number of pores in sandstone and carbonate reservoirs, comprising the following steps:
s1, preparing a plurality of groups of casting body slices based on the same sandstone sample or carbonate rock sample, and establishing a pore key sketch chain for identifying pore complete units of the same sandstone sample or carbonate rock sample;
in the step S1, the multiple groups of casting slices are continuously extracted from the same sandstone sample or carbonate sample along the same direction and have the same calibration baseline, and the specific method for establishing the pore key sketch chain includes:
the multiple groups of casting body slices are subjected to differential division, casting body slices with large differences are selected to respectively quantitatively analyze the pore coordination data, the pore coordination data with large differences are fused to obtain the complete pore structure characteristics of the same sandstone sample or carbonate rock sample to the maximum extent, meanwhile, the complete pore coordination data can be mastered to the maximum extent based on the complete pore structure characteristics, the quantitative precision is improved, and the casting body slices with high similarities exist in the multiple groups of casting body slices continuously extracted from the same sandstone sample or carbonate rock sample in the same direction, so the casting body slices with high similarities have similar pore structure characteristics, the mutual fusion cannot play a role in supplementing the pore structure characteristics, on the contrary, the excessive similar pore structure characteristics cause a large amount of redundant calculation, and therefore, the casting body slices with large differences in the multiple groups of casting body slices need to be selected for fusion, so as to fully supplement the pore structure characteristics, the concrete steps are as follows:
s101, scanning a plurality of groups of casting slices by an electron microscope to obtain a plurality of groups of SEM images of the casting slices, and performing differential classification on the SEM images of the plurality of groups of casting slices to obtain a plurality of low-viscosity SEM image clusters;
and S102, selecting SEM images of cast body slices at the centers of the clusters from the plurality of SEM image clusters to form a pore key portrait chain.
The specific method for carrying out differential classification on a plurality of groups of SEM images of the casting body slices and selecting the SEM images of the casting body slices at the cluster center comprises the following steps:
sequentially calculating the histogram difference of the SEM images of the cast body slices of each group, and classifying all the SEM images of which the histogram difference is lower than a specified threshold value into a cluster so as to ensure that the difference between the clusters of the SEM images is high, wherein the calculation formula of the histogram difference is as follows:
Figure GDA0003613882390000061
wherein X is SEM image of each set of casting sheeti，xjHistogram of SEM image of i, j group cast sheet, p (x)i,xj) Is xiAnd xjIs given by the joint probability distribution function of p (x)i) And p (x)j) Are each xiAnd xjThe edge probability distribution function of (1);
and sequentially calculating the histogram mean value of each SEM image cluster, and selecting the SEM image of the cast body slice with the Euclidean minimum of the histogram mean value as the cluster center of the SEM image cluster.
Step S1 may select casting slices containing complementary pore features from the sets of casting slices for fusion, which may reduce redundant calculations and increase efficiency while improving accuracy of quantitative analysis.
Step S2, identifying the pore whole units of the same sandstone sample or carbonate rock sample based on the pore key sketch chain, and quantizing the pore whole units to obtain a group of pore coordination data chains;
in step S2, the specific method for identifying the pore complete unit includes:
s201, selectively converting the SEM images of all casting slices on the pore key image chain into binary images of the casting slices to generate binary images of the casting slices;
in this embodiment, an Image Segmenter of an application program built in MATLAB is selected to convert the reservoir casting Image. Unlike relying on the unobservability of code translation and relying on the imprecision of drawing software. The application program Image Segmenter can be used for identifying various colors and pixels in the Image for conversion, so that the accuracy is realized.
Step S202, carrying out pore edge identification on the binary images of the casting sheets, and sequentially carrying out pore unit division on each binary image of the casting sheets on the basis of the pore edges;
identifying pore edges of binary images of cast body slices by applying functions bwporium and bwearea, and identifying pore edges of binary images of cast body slices by applying functions bwporium and bwearea
P＝bwperim(A)；
[X,Y]＝bwboundaries(BW,'P')；
The function bwporim is to search the edge of the whole matrix and calculate after the edge detection is finished. bwbuildings is a function that can be used to obtain the contours, including the outer contour and the inner edge, of an object in a binary image. [ X, Y ] represents the pore coordinate value.
After the aperture edge is obtained, each connected pixel unit or matrix identified by the above system is identified and calculated.
Figure GDA0003613882390000071
The function bwleabel is used to find a matrix function of the x, y coordinate values. Firstly, an E matrix with the same size as the image is returned, wherein the E matrix comprises a class label marking each connected area in BW, and X and Y are coordinate axes of the pore edge respectively.
And step S203, summarizing the pore units in the binary image of each casting sheet to form a pore whole unit.
The selective binary image conversion includes binary conversion of various colors and pixels in the SEM image of the cast body sheet, and the step S201 further includes performing noise reduction on the binary image of the cast body sheet, where the specific method of the noise reduction includes:
the method comprises the following steps: deleting the pixel units with the connected pixel points lower than a specified threshold value, and reassigning the deleted pixel units;
the second method comprises the following steps: the pixel cells having the connection pixel points higher than a predetermined threshold are expanded, and the expanded pixel cells are reconnected.
Specifically, after obtaining a binary image of a cast sheet containing a pore structure feature, the influence of clay minerals, inclusions, oil and gas in the core and the shooting of dark corners can cause the generation of noise in the image and the unrecognizable space inside some pores. In order to obtain the coordination bonds of the pores more accurately, the function imeriode is used for eliminating the existing noise points, and the function imdillate is used for connecting the pores which should be communicated.
B＝imerode(B,ones(2))；
C＝B；C(:,1:end-1)＝C(:,2:end)；
End；
The function imode is a technology for realizing image corrosion by Matlab. The usage is that B is the binary image of the casting slice to be processed, and ones (2) is the pixel unit. 2 represents a pixel unit with a connection pixel point less than 2, and 2 is a prescribed threshold. That is, pixels with connected pixels less than 2 are eliminated. And C represents reassigning the eliminated pixel points.
B’＝imdilate(B’,ones(5))；
C’＝B’；C’(:,1:end-1)＝C’(:,2:end)；
D＝B’；D(1:end-1,:)＝D(2’:end,:)；
End；
The function, imdilate, is a technique for the Matlab to perform a dilation operation on the image. Similar to the imode theory, B' is a binary image of a cast sheet to be processed, and ones (5) is a pixel unit. 5 represents a pixel unit with a connection pixel point less than 5, and 5 is a specified threshold. That is, when the pixel units with the connected pixel points less than 5 are expanded and reconnected. It is noted that pixel noise reduction must be performed before image reconstruction is performed. Otherwise it will be counterproductive and increase the amount of noise in the image.
In step S2, the specific method for quantifying the pore whole unit to obtain a group of pore coordination data chains includes:
quantitatively quantifying an array of pore coordination data for each of said binary images of cast slab based on said pore cells;
after acquiring the pore units of each cast sheet binary image, performing coordination number calculation on the divided pore units by applying a Network function which is a function for connecting pixel nodes in the image, and outputting a pore coordination data array of each cast sheet binary image.
Network＝zeros(max(B(:)))；
for I＝1:size(E,1)
Network(E(I,1),E(I,2))＝1；
Network(E(I,2),E(I,1))＝1；
End；
And linking all the pore matching data arrays by the radial depth of the pore key portrait chain to form a pore matching data chain.
And S3, fusing all the pore coordination data chains to obtain a pore full coordination data array, and quantitatively obtaining the pore coordination number of the same sandstone sample or carbonate rock sample based on the pore full coordination data array.
In step S3, the specific method for fusing all the pore coordination data chains to obtain the pore full coordination data array includes:
fusing the pore coordination data chains layer by layer along the radial depth to obtain a pore full coordination data array;
and generating a pore full coordination image according to the pore full coordination data array, and generating a pore coordination number histogram based on the pore full coordination image.
And quantitatively calculating the pore coordination number of the same sandstone sample or carbonate rock sample based on the pore coordination number histogram.
The histogram of pore coordination numbers and the mean pore throat coordination number are output using the function imhist (i.e., the function that outputs the histogram) and the function mean (the function that calculates the mean).
The specific method for obtaining the pore full coordination data array by fusing the pore coordination data chains layer by layer along the radial depth comprises the following steps:
adjusting all pore coordination data arrays on the pore coordination data chain along the direction of coincidence of the calibration baselines so that all pore coordination data arrays are vertically arranged;
and carrying out successive vertical superposition on all the pore coordination data arrays to generate a pore full coordination data array.
The calibration baseline drawing method comprises the following steps:
and taking the projection point of the intersection line of the cross section and the longitudinal section of the same sandstone sample or carbonate rock sample as the calibration midpoint of the calibration baseline, and respectively taking the projection line of the cross section and the longitudinal section as the calibration transverse baseline and the calibration longitudinal baseline of the calibration baseline.
And drawing a calibration baseline for keeping the position correspondence of data in the fusion process so as to accord with the original homodromous real structure of the same sandstone sample or carbonate rock sample, and avoiding the sudden increase or reduction of the pore structure characteristic quantity caused by dislocation fusion to cause errors of quantitative analysis results.
As shown in fig. 3, the specific procedure of quantitatively calculating the number of pore coordinates of the sample (B) of the ultramarine carbonate reservoir of the talimupelland from the paleontological sandstone reservoir (a) of the deldos basin using a single-pack cast sheet is as follows. Observing under a microscope, firstly obtaining casting slice images of various reservoir core samples, firstly, using an application program Image Segmenter in MATLAB software to respectively perform binary transformation on pore structure characteristics in sandstone and carbonate reservoir samples according to a quantitative analysis flow chart, using a function imeriode to eliminate existing noise points, and using the function imedilate to connect the holes which are supposed to be communicated to generate three groups of casting slice binary images which are (C)/(C') in figure 3.
Further, the functions bwporium and bweraa are applied to identify the pore edge, and the identified cells in the binary image are divided into (E)/(E') in fig. 3. Meanwhile, the pores in the graph E are analyzed by the functions bwleabel and Network ═ zeros (), and the coordination assignment of each unit pore is obtained as (F)/(F ') in fig. 3, and the connected image is (G)/(G').
Finally, the histogram of pore coordination numbers and the mean of average pore throat coordination numbers were output as (H)/(H') in fig. 3 using the function imhist (i.e., the function that outputs the histogram) and the function mean (the function that calculates the average). The average pore coordination number for the sandstone sample was 1.2067, and the average pore coordination number for the carbonate sample was 0.6205.
As shown in fig. 4, the concrete procedure of the present example of the quantitative calculation of the sandstone (a) in the ancient world sandstone reservoir in the deldos basin using the plurality of sets of cast slabs was as follows. Observing under a microscope, firstly obtaining a plurality of groups of casting slice SEM images of a sandstone reservoir core sample, selecting three groups of subject slice SEM images with large difference, namely A1, A2 and A3 in the Image of the sandstone reservoir core sample, firstly, applying an application program Image Segmenter in MATLAB software to respectively perform binary transformation on pore structure characteristics in the sandstone sample, applying a function imode to eliminate existing noise points, and applying the function imode to connect the pores which should be communicated to generate three groups of casting slice binary images which are B1, B2 and B3 in the Image of FIG. 4.
Further, the functions bwporium and bwearea are applied to identify the pore edges and divide the identified cells in the binary images of the three sets of cast slab slices into C1, C2 and C3 in fig. 4. Meanwhile, the pore full coordination data arrays of the pore units of the binary image of the cast body flake are obtained by analyzing the pore by using the functions of bwleabel and Network ═ zeros (), the data arrays of the pore full coordination data of the pore units of the binary image of the cast body flake are D1, D2 and D3 in FIG. 4, D1, D2 and D3 jointly form a pore coordination data chain, the data fusion is carried out to obtain the data array of the pore full coordination data of E in FIG. 4, and the image of the pore full coordination data of F in FIG. 4.
Finally, the histogram of pore coordination numbers and the mean of average pore throat coordination numbers were output as G in FIG. 4 using the function imhist (i.e., the function that outputs the histogram) and the function mean (the function that calculates the average). The sandstone samples had an average pore coordination number of 1.1105, and the data was more accurate than that of a single cast flake.
As shown in fig. 5, in addition to the conventional sandstone reservoir, the carbonate sample with the casting film pores as the main reservoir space is. The algorithm can also be used for tight sandstone reservoirs with low porosity and pore radius squadrons, and carbonate rock samples with primary intergranular pores or intergranular pores as reservoir spaces.
The measurement results are shown in fig. 5, A, B, C are carbonate rock samples of compact sandstone reservoirs, and the protogranular pores and the intergranular pores are used as reservoir spaces respectively. The final measured pore coordination number distribution plot is M, N, O in FIG. 5. The corresponding average pore coordination numbers were 0.7382, 0.6667, 1.1105.
The invention utilizes a plurality of groups of casting body slices with high difference degree to fuse and master the global pore characteristics in the sandstone sample or the carbonate rock sample, the precision of the pore coordination number obtained based on the quantitative analysis of the global pore characteristics is high, and the invention can output the pore coordination number in the sandstone reservoir sample and the carbonate rock reservoir sample.
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 (8)

1. A method for quantitatively analyzing the coordination numbers of pores in sandstone and carbonate reservoirs is characterized by comprising the following steps:
step S1, preparing a plurality of groups of casting body slices based on the same sandstone sample or carbonate rock sample, establishing a pore key portrait chain for identifying the pore whole unit of the same sandstone sample or carbonate rock sample, continuously extracting the plurality of groups of casting body slices from the same sandstone sample or carbonate rock sample along the same direction and having the same calibration base line, and establishing the pore key portrait chain by the specific method, which comprises the following steps:
s101, scanning a plurality of groups of casting slices by an electron microscope to obtain a plurality of groups of SEM images of the casting slices, and performing differential classification on the SEM images of the plurality of groups of casting slices to obtain a plurality of low-viscosity SEM image clusters;
s102, selecting SEM images of cast body slices at the center of each cluster from the plurality of SEM image clusters to form a pore key portrait chain;
wherein:
the specific method for carrying out differential classification on a plurality of groups of SEM images of the casting sheets and selecting the SEM images of the casting sheets at the center of a cluster comprises the following steps:
sequentially calculating the histogram difference of the SEM images of the cast body slices of each group, and classifying all the SEM images of which the histogram difference is lower than a specified threshold value into a cluster so as to ensure that the difference between the clusters of the SEM images is high, wherein the calculation formula of the histogram difference is as follows:
Figure FDA0003613882380000011
wherein X is SEM image of each set of casting sheeti，xjHistogram of SEM image of i, j group cast sheet, p (x)i,xj) Is xiAnd xjIs given by the joint probability distribution function of p (x)i) And p (x)j) Are each xiAnd xjThe edge probability distribution function of (1);
sequentially calculating the histogram mean value of each SEM image cluster, and selecting the cast body slice SEM image with the Euclidean minimum of the histogram mean value as the cluster center of the SEM image cluster;
step S2, identifying the pore whole units of the same sandstone sample or carbonate rock sample based on the pore key sketch chain, and quantizing the pore whole units to obtain a group of pore coordination data chains;
and S3, fusing all the pore coordination data chains to obtain a pore full coordination data array, and quantitatively obtaining the pore coordination number of the same sandstone sample or carbonate rock sample based on the pore full coordination data array.
2. The method of claim 1 for quantitatively analyzing the coordination number of pores in sandstone and carbonate reservoirs, wherein the method comprises the following steps: in step S2, the specific method for identifying the pore complete unit includes:
s201, selectively converting the SEM images of all casting slices on the pore key image chain into binary images of the casting slices to generate binary images of the casting slices;
step S202, carrying out pore edge identification on the binary images of the casting sheets, and sequentially carrying out pore unit division on each binary image of the casting sheets on the basis of the pore edges;
and step S203, summarizing the pore units in the binary image of each casting sheet to form a pore whole unit.
3. The method for quantitatively analyzing the coordination number of the pores in the sandstone and carbonate reservoirs according to claim 2, wherein the coordination number of the pores in the sandstone and carbonate reservoirs is determined by the following steps: the selective binary image conversion includes binary conversion of various colors and pixels in the SEM image of the cast body sheet, and the step S201 further includes performing noise reduction on the binary image of the cast body sheet, where the specific method of the noise reduction includes:
the method comprises the following steps: deleting the pixel units with the connected pixel points lower than a specified threshold value, and reassigning the deleted pixel units;
the second method comprises the following steps: the pixel cells having the connection pixel points higher than a predetermined threshold are expanded, and the expanded pixel cells are reconnected.
4. A method of quantitatively analyzing the coordination number of the pores in sandstone and carbonate reservoirs according to claim 3, wherein: in the step S2, a specific method for quantifying the pore complete unit to obtain a group of pore configuration data chains includes:
quantitatively quantifying an array of pore coordination data for each of said binary images of cast slab based on said pore cells;
and linking all the pore matching data arrays by the radial depth of the pore key portrait chain to form a pore matching data chain.
5. The method for quantitatively analyzing the coordination numbers of the pores in the sandstone and carbonate reservoirs according to claim 4, wherein the step S3 is characterized in that the specific method for fusing all the coordination data chains of the pores to obtain the data array of the full coordination numbers of the pores comprises the following steps:
fusing the pore coordination data chains layer by layer along the radial depth to obtain a pore full coordination data array;
and generating a pore full coordination image according to the pore full coordination data array, and generating a pore coordination number histogram based on the pore full coordination image.
6. The method of claim 5, wherein the pore coordination numbers of the same sandstone or carbonate sample are quantitatively calculated based on the histogram of pore coordination numbers.
7. The method for quantitatively analyzing the coordination numbers of the pores in the sandstone and carbonate reservoirs according to claim 6, wherein the specific method for obtaining the pore full coordination data array by performing layer-by-layer fusion on the pore coordination data chains along the radial depth comprises the following steps of:
adjusting all pore coordination data arrays on the pore coordination data chain along the direction of coincidence of the calibration base lines so that all pore coordination data arrays are vertically arranged;
and carrying out successive vertical superposition on all the pore coordination data arrays to generate a pore full coordination data array.
8. The method of claim 7, wherein the method of plotting the calibration baseline comprises:
and taking the projection point of the intersection line of the cross section and the longitudinal section of the same sandstone sample or carbonate rock sample as the calibration midpoint of the calibration baseline, and respectively taking the projection line of the cross section and the longitudinal section as the calibration transverse baseline and the calibration longitudinal baseline of the calibration baseline.
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