阅读说明：本技术 页岩有机质孔隙连通性分类表征的方法 (Method for classifying and characterizing porosity connectivity of shale organic matter ) 是由 吴建国 杨继进 李国梁 于 2021-10-28 设计创作，主要内容包括：本发明属于油气勘探技术领域,旨在研究有机质孔隙的发育特征规律,获得有机质孔隙连通性的可靠表征信息,具体涉及一种页岩有机质孔隙连通性分类表征的方法,扫描电子显微镜对页岩样品进行预设成像面积的扫描,获取页岩样品的二维图像；采用Avizo软件提取二维图像中每个有机质的孔隙参数；根据该孔隙参数获得有机质集合的类型数值；对各类型的有机质集合采用聚焦离子束-氦离子显微镜进行三维重构,获取有机质三维重构体；采用Avizo软件获取孔隙连通性参数；基于该孔隙连通性参数,获取页岩有机质孔隙的整体连通性评价指数；本发明以有机质孔隙形貌学定量分类为基础,可实现亚10nm级别孔隙的三维连通性表征。(The invention belongs to the technical field of oil and gas exploration, aims to research the development characteristic rule of organic matter pores and obtain reliable characterization information of organic matter pore connectivity, and particularly relates to a method for shale organic matter pore connectivity classification characterization, wherein a scanning electron microscope scans a preset imaging area of a shale sample to obtain a two-dimensional image of the shale sample; extracting the pore parameters of each organic matter in the two-dimensional image by adopting Avizo software; obtaining the type value of the organic matter set according to the pore parameter; performing three-dimensional reconstruction on each type of organic matter set by adopting a focused ion beam-helium ion microscope to obtain an organic matter three-dimensional reconstruction body; acquiring pore connectivity parameters by adopting Avizo software; based on the pore connectivity parameters, obtaining an integral connectivity evaluation index of the shale organic matter pores; the method is based on the quantitative classification of organic matter pore morphology, and can realize the three-dimensional connectivity characterization of the sub-10 nm level pores.)

1. A method for shale organic matter pore connectivity classification characterization is characterized by comprising the following steps:

s100, scanning a preset imaging area of the shale sample by using a scanning electron microscope to obtain a two-dimensional image of the shale sample;

s200, extracting the pore parameters of each organic matter in the two-dimensional image by adopting Avizo software; the pore parameters include porosity, pore median radius, pore sorting coefficient, and pore form factor of the organic matter;

step S300, obtaining a type value of the organic matter set according to the pore parameters;

s400, performing three-dimensional reconstruction on each type of organic matter set by adopting a focused ion beam-helium ion microscope to obtain an organic matter three-dimensional reconstruction body;

step S500, based on the organic matter three-dimensional reconstruction body, acquiring pore connectivity parameters by adopting Avizo software;

and S600, acquiring an integral connectivity evaluation index of the shale organic matter pores based on the pore connectivity parameters.

2. The method for shale organic matter pore connectivity classification characterization according to claim 1, wherein the method for acquiring the two-dimensional image comprises:

step S110, performing argon ion polishing treatment on the shale sample to obtain a pretreated shale sample;

step S120, setting the accelerating voltage of a scanning electron microscope to be 1.2kV, and collecting secondary electron signals of the surface of the pretreated shale sample at a preset imaging resolution;

s130, a scanning electron microscope carries out continuous two-dimensional splicing imaging on the pretreated shale sample according to a preset imaging area; the preset imaging area is，。

3. The method for shale organic matter pore connectivity classification characterization according to claim 2, wherein the preset imaging resolution is；

The preset imaging area is。

4. The method for shale organic matter pore connectivity classification characterization according to claim 2, wherein the pore parameter extraction method comprises:

step S210, preprocessing the two-dimensional image to obtain a preprocessed image; the preprocessing comprises gray level correction and filtering processing;

step S220, based on the gray level difference, extracting all organic matters in the preprocessed image by adopting a threshold segmentation method, and storing the organic matters in a binarization form to obtain a binarization image;

step S230, obtaining a first organic matter area image according to the binary image, wherein the first organic matter area image comprises N first organic matter areas, and the first organic matter areas are of a closed loop structure formed by the same gray level;

step S240, assimilating the difference gray levels in the N first organic matter areas by adopting a hole filling command to obtain a filled second organic matter area image; the second organic matter area image includes N second organic matter areas, the N second organic matter areas respectively corresponding to the N first organic matter areas;

step S250, sequentially assigning values to the N second organic matter regions by adopting MATLAB software to obtain third organic matter region images with different assignment regions; the third organic matter area image comprises N third organic matter areas, and assignment numbers of the N third organic matter areas are sequentially set by adopting a first group of Arabic numerals according to a first sequence;

step S260, sequentially assigning values to the N first organic matter regions by adopting MATLAB software to obtain a fourth organic matter region image with different assignment regions; the fourth organic matter area image comprises N fourth organic matter areas, and assignment numbers of the N fourth organic matter areas are sequentially set by adopting a second group of Arabic numbers according to a second sequence; the second order is arranged in correspondence with the first order and the first set of arabic numerals are arranged in correspondence with the second set of arabic numerals;

step S270, subtracting the fourth organic matter area image from the third organic matter area image to obtain a fifth organic matter area image; the fifth organic matter area image comprises N fifth organic matter areas, and the N fifth organic matter areas are labeled sequentially assigned pore images;

step S280, obtaining porosity based on the fifth organic matter area imageMedian pore radiusPore size fractionAnd a pore shape factor, SF;

wherein, in the step (A),are the areas of the corresponding individual organic matter,the area of the corresponding single organic matter internal pore;

median radius of the poresThe pore radius value corresponding to the position with 50 percent of content on the pore size distribution cumulative curve; the pore size distribution cumulative curve is a distribution curve of pore radius and pore area cumulative percentage content;

(ii) a Wherein the content of the first and second substances,andrespectively representing pore radius values corresponding to the cumulative percentage content of pore area of 25 percent and 75 percent on the pore size distribution cumulative curve;

calculating corresponding N pore shape factors according to N fifth organic matter regions；；Is as followsThe pore shape factor of each pore is,is as followsThe pore area of each pore is the same as the pore size,is as followsThe perimeter of each of the apertures is,；

selecting pore shape factors with the same value to form a group of same factor sets, forming M group of same factor sets by N pore shape factors,；

the same factor set comprises n aperture shape factors,；

acquiring the area sum of corresponding pores as a group of pore area sets based on the corresponding same factor sets; m groups of the same factor sets form M groups of pore area sets;

and acquiring a pore shape factor corresponding to the pore area set with the largest area and the largest area as a pore shape factor SF based on the M groups of pore area sets.

5. The method for shale organic matter pore connectivity classification characterization according to claim 4, wherein the method for obtaining the type value of the organic matter set specifically comprises:

step S310, based on the porosityThe median pore radiusThe pore size fractionAnd the pore form factorBy usingA clustering algorithm for initially classifying the organic matter set; wherein the initial classification value of the organic matter set is K which is more than or equal to 2; the organic matter set comprises N organic matters;

step S320, combiningA clustering algorithm for calculating the variation coefficient of each type of organic matter set，(ii) a Wherein the content of the first and second substances,is composed of、、Or；

Step S330, if any coefficient of variation in the K-type organic matter setAll satisfyTaking the initial classification value K of the organic matter set as a type value of the organic matter set; otherwise, go to step S340;

step S340, let the initial classification value K = K +1 of the organic matter set, and repeat steps S320 to S330.

6. The method for shale organic matter pore connectivity classification characterization according to claim 5,；the standard deviation of the porosity corresponding to the N organic matters in each type of organic matter set is shown;

；the average value of the porosity corresponding to the N organic matters in each type of organic matter set is obtained;

；for each corresponding type of organic matter setThe standard deviation of the median radius of the pores corresponding to the N organic matters is combined;

；the average value of the pore median radius corresponding to the N organic matters in each corresponding organic matter set is obtained;

；the standard deviation of the pore sorting coefficients corresponding to the N organic matters in each type of organic matter set is obtained;

；the average value of the pore sorting coefficients corresponding to the N organic matters in each type of organic matter set is obtained;

；the standard deviation of the pore shape factor corresponding to the N organic matters in each corresponding type of organic matter set;

；the average value of the pore shape factors corresponding to the N organic matters in each corresponding organic matter set is obtained.

7. The method for shale organic matter pore connectivity classification characterization according to claim 6, wherein the method for obtaining the organic matter three-dimensional reconstruction specifically comprises: screening the K-type organic matter set by adopting a focused ion beam-helium ion microscope based on a preset area threshold value to obtain an organic matter three-dimensional reconstruction body; the preset area threshold value is，。

8. The method for shale organic matter pore connectivity classification characterization according to claim 7, wherein the pore connectivity parameter is，(ii) a Wherein the content of the first and second substances,for pore connectivity parameters in the m-th class organic matter set,representing the volume of interconnected pores in the m-th class organic matter collection,represents the volume of the total pores in the m-th class organic matter collection;and K is the type value of the organic matter set.

9. The method for shale organic matter pore connectivity classification characterization according to claim 8, wherein the overall connectivity evaluation index is，；The total area porosity of the m-type organic matter set;representing the total surface porosity of pores in a K-type organic matter set in the shale sample;

；for the first in each organic matter groupThe surface porosity of the organic matter;；

；

；for the first in each organic matter groupThe area of all pores in the organic matter;

。

10. the method for shale organic matter pore connectivity classification characterization according to claim 4,。

Technical Field

The invention belongs to the technical field of oil and gas exploration, and particularly relates to a shale organic matter pore connectivity classification characterization method.

Background

The organic pores are the main matrix pores in the shale, and the connectivity of the organic pores radically influences the desorption-diffusion flow process of shale gas. Therefore, the quantitative characterization of organic matter pore connectivity has important significance for evaluating shale gas development potential, optimizing shale gas development scheme and improving shale gas recovery ratio.

At present, the shale connectivity is mainly characterized by mercury intrusion method, nano CT, focused ion beam-scanning electron microscope (FIB-SEM) and the like. Among them, the fluid property and gas property of mercury used in the mercury intrusion method are too different, the interaction between the fluid and the surface is different, and the high pressure can destroy the shale pore structure, so that it is very doubtful whether the connectivity measured is suitable for shale gas flow. The nano CT and FIB-SEM three-dimensional reconstruction technology is not influenced by the interaction between fluid and the surface and the shielding effect, and can reconstruct a three-dimensional image of pores and show the communication relation among the pores; and the method can be matched with a digital core technology to further quantitatively analyze rich information such as porosity, pore size distribution, pore volume, specific surface area, pore throat coordination number, tortuosity and the like of the nano pores. However, researchers have shown that nanoct accuracy is typically above 50nm, whereas FIB-SEM accuracy is typically above 10nm, both of which make it difficult to reconstruct pores below 10 nm. From the aspect of pore size distribution, a large number of pores below 10nm exist in organic pores, and the organic pores can be used as larger pores communicated by a throat, so that if the pores cannot be accurately reconstructed, serious misjudgment of connectivity evaluation is caused. A Focused ion beam-Helium ion microscope (FIB-HIM) is a new three-dimensional reconstruction technology, the resolution of the FIB-HIM can reach 0.5nm, and the three-dimensional reconstruction problem of organic matter pores below 10nm can be effectively solved.

The three-dimensional reconstructed areas of nano-CT, FIB-SEM and FIB-HIM are generally in the micron level, and how the small volume can represent the connectivity of organic matter pores in the massive shale is also a problem which always troubles researchers. Preliminary studies show that the development condition of organic matter pores has heterogeneity of morphology, and obvious differences exist in the aspects of pore size, pore density, pore shape, pore distribution and the like. The morphological differences reflect that the connectivity of pores in three-dimensional space also has differences; therefore, according to the concept of cluster analysis, the morphological characteristics of the organic matter pores are quantitatively classified, and then the connectivity of each type of organic matter pores is quantitatively represented, so that the workload of representation can be greatly simplified, and the representation result is more representative. At present, research on morphological difference of organic pores is mostly limited to qualitative image description, and no scheme for carrying out quantitative distinguishing according to parameters such as density, size, sorting and shape is available, so that the method is unfavorable for fully knowing organic pore development characteristics and quantitatively characterizing organic pore connectivity.

Disclosure of Invention

In order to solve the problems in the prior art, namely to research the development characteristic rule of organic matter pores and obtain reliable characterization information of organic matter pore connectivity, the invention provides a method for shale organic matter pore connectivity classification characterization, which comprises the following steps: and S100, scanning the shale sample by a scanning electron microscope in a preset imaging area to obtain a two-dimensional image of the shale sample.

S200, extracting the pore parameters of each organic matter in the two-dimensional image by adopting Avizo software; the pore parameters include porosity, pore median radius, pore sorting coefficient, and pore form factor of the organic matter.

And step S300, obtaining the type value of the organic matter set according to the pore parameters.

And S400, performing three-dimensional reconstruction on each type of organic matter set by adopting a focused ion beam-helium ion microscope to obtain an organic matter three-dimensional reconstruction body.

And step S500, acquiring pore connectivity parameters by adopting Avizo software based on the organic matter three-dimensional reconstruction body.

And S600, acquiring an integral connectivity evaluation index of the shale organic matter pores based on the pore connectivity parameters.

In some preferred embodiments, the method of acquiring a two-dimensional image includes: and step S110, performing argon ion polishing treatment on the shale sample to obtain a pretreated shale sample.

And S120, setting the accelerating voltage of the scanning electron microscope to be 1.2kV, and collecting secondary electron signals of the surface of the pretreated shale sample at a preset imaging resolution.

S130, a scanning electron microscope carries out continuous two-dimensional splicing imaging on the pretreated shale sample according to a preset imaging area; the preset imaging area is，。

In some preferred embodiments, the preset imaging resolution is。

The preset imaging area is。

In some preferred embodiments, the method for extracting the pore parameters comprises: step S210, preprocessing the two-dimensional image to obtain a preprocessed image; the preprocessing includes a gamma correction and a filtering process.

And S220, extracting all organic matters in the preprocessed image by adopting a threshold segmentation method based on the gray level difference, and storing the organic matters in a binarization form to obtain a binarization image.

Step S230, obtaining a first organic matter area image according to the binarized image, where the first organic matter area image includes N first organic matter areas, and the first organic matter areas are in a closed loop structure formed by the same gray scale.

Step S240, assimilating the difference gray levels in the N first organic matter areas by adopting a hole filling command to obtain a filled second organic matter area image; the second organic matter region image includes N second organic matter regions, and the N second organic matter regions correspond to the N first organic matter regions, respectively.

Step S250, sequentially assigning values to the N second organic matter regions by adopting MATLAB software to obtain third organic matter region images with different assignment regions; the third organic matter area image comprises N third organic matter areas, and assignment numbers of the N third organic matter areas are sequentially set by adopting a first group of Arabic numerals according to a first sequence.

Step S260, sequentially assigning values to the N first organic matter regions by adopting MATLAB software to obtain a fourth organic matter region image with different assignment regions; the fourth organic matter area image comprises N fourth organic matter areas, and assignment numbers of the N fourth organic matter areas are sequentially set by adopting a second group of Arabic numbers according to a second sequence; the second order is arranged in correspondence with the first order and the first set of arabic numerals is arranged in correspondence with the second set of arabic numerals.

Step S270, subtracting the fourth organic matter area image from the third organic matter area image to obtain a fifth organic matter area image; the fifth organic matter area image comprises N fifth organic matter areas, and the N fifth organic matter areas are labeled and sequentially assigned pore images.

Step S280, obtaining porosity based on the fifth organic matter area imageMedian pore radiusPore size fractionAnd a pore shape factor SF.

Wherein, in the step (A),are the areas of the corresponding individual organic matter,is the area of the corresponding single organic matter internal pore.

Median radius of the poresThe pore radius value corresponding to the position with 50 percent of content on the pore size distribution cumulative curve; the pore size distribution cumulative curve is a distribution curve of pore radius and pore area cumulative percentage content.

(ii) a Wherein the content of the first and second substances,andrespectively represent pore radius values corresponding to 25 percent and 75 percent of the cumulative percentage of pore area on the cumulative curve of the pore size distribution.

Calculating corresponding N pore shape factors according to N fifth organic matter regions；；Is as followsThe pore shape factor of each pore is,is as followsThe pore area of each pore is the same as the pore size,is as followsThe perimeter of each of the apertures is,。

selecting pore shape factors with the same value to form a group of same factor sets, forming M group of same factor sets by N pore shape factors,。

the same factor set comprises n aperture shape factors,。

acquiring the area sum of corresponding pores as a group of pore area sets based on the corresponding same factor sets; m sets of the same factors form M sets of pore area sets.

And acquiring a pore shape factor corresponding to the pore area set with the largest area and the largest area as a pore shape factor SF based on the M groups of pore area sets.

In some preferred embodiments, the method for obtaining the type value of the organic matter set specifically includes: step S310, based on the porosityThe above-mentionedMedian pore radiusThe pore size fractionAnd the pore form factorBy usingA clustering algorithm for initially classifying the organic matter set; wherein the initial classification value of the organic matter set is K which is more than or equal to 2; the organic matter collection includes N organic matters.

Step S320, combiningA clustering algorithm for calculating the variation coefficient of each type of organic matter set，(ii) a Wherein the content of the first and second substances,is composed of、、Or。

In the step S330, the process is executed,any coefficient of variation in the organic matter set if K is of the typeAll satisfyTaking the initial classification value K of the organic matter set as a type value of the organic matter set; otherwise, step S340 is performed.

Step S340, let the initial classification value K = K +1 of the organic matter set, and repeat steps S320 to S330.

In some of the preferred embodiments, the first and second,；and the standard deviation of the porosity corresponding to the N organic matters in each corresponding organic matter set.

；The average value of the porosity corresponding to the N organic matters in each corresponding organic matter set is obtained.

；And the standard deviation of the pore median radius corresponding to the N organic matters in each corresponding organic matter set is adopted.

；The average value of the pore median radius corresponding to the N organic matters in each corresponding organic matter set is obtained.

；And the standard deviation of the pore sorting coefficients corresponding to the N organic matters in each type of organic matter set is obtained.

；And averaging the pore sorting coefficients corresponding to the N organic matters in each corresponding organic matter set.

；The standard deviation of the pore shape factor for the corresponding N organic matters in each type of organic matter set.

；The average value of the pore shape factors corresponding to the N organic matters in each corresponding organic matter set is obtained.

In some preferred embodiments, the method for obtaining the organic three-dimensional reconstruction specifically includes: screening the K-type organic matter set by adopting a focused ion beam-helium ion microscope based on a preset area threshold value to obtain an organic matter three-dimensional reconstruction body; what is needed isThe preset area threshold value is，。

In some preferred embodiments, the pore connectivity parameter is，(ii) a Wherein the content of the first and second substances,for pore connectivity parameters in the m-th class organic matter set,representing the volume of interconnected pores in the m-th class organic matter collection,represents the volume of the total pores in the m-th class organic matter collection;and K is the type value of the organic matter set.

In some preferred embodiments, the overall connectivity evaluation index is，；The total area porosity of the m-type organic matter set;and the total surface porosity of pores in the K-type organic matter aggregate in the shale sample is shown.

；For the first in each organic matter groupThe surface porosity of the organic matter;。

。

；for the first in each organic matter groupThe area of all pores in the organic mass.

。

In some of the preferred embodiments, the first and second,。

the invention has the beneficial effects that: 1) at present, the research on the morphological difference of organic matter pores is mostly limited to qualitative image description, and no scheme for carrying out quantitative distinguishing according to parameters such as density, size, sorting and shape is available.

2) The shale organic matter pore connectivity classification characterization method provided by the invention is based on quantitative classification of organic matter pore morphology, can realize three-dimensional connectivity characterization of sub-10 nm level pores, and overcomes the defect of poor organic matter pore connectivity characterization representativeness in the existing method.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings.

FIG. 1 is a schematic flow chart of an embodiment of the present invention.

FIG. 2 is an SEM two-dimensional image in one embodiment of the invention.

Fig. 3 is a schematic diagram illustrating the principle of distinguishing the internal pores of different organic materials according to an embodiment of the present invention.

Fig. 4 is a flow chart of organic matter set type division in an embodiment of the present invention.

Fig. 5 is a schematic diagram of a representative organic matter image and its pore parameters selected in an embodiment of the present invention.

FIG. 6 is an FIB-HIM three-dimensional reconstruction image of a class I and class II organic mass collection in one embodiment of the invention.

Detailed Description

Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and are not intended to limit the scope of the present invention.

The invention provides a method for shale organic matter pore connectivity classification characterization, which comprises the following steps: s100, scanning a preset imaging area of the shale sample by using a scanning electron microscope to obtain a two-dimensional image of the shale sample; s200, extracting the pore parameters of each organic matter in the two-dimensional image by adopting Avizo software; wherein the pore parameters comprise porosity, pore median radius, pore sorting coefficient and pore form factor of the organic matter; step S300, obtaining a type value of the organic matter set according to the pore parameters; s400, performing three-dimensional reconstruction on each type of organic matter set by adopting a focused ion beam-helium ion microscope to obtain an organic matter three-dimensional reconstruction body; s500, acquiring pore connectivity parameters by adopting Avizo software based on the organic matter three-dimensional reconstruction body; and S600, acquiring an integral connectivity evaluation index of the shale organic matter pores based on the pore connectivity parameters.

The invention is further illustrated by the following examples with reference to the accompanying drawings.

Referring to the attached drawings 1 and 2, the invention provides a method for shale organic matter pore connectivity classification characterization, which comprises the following steps: and S100, scanning the shale sample by a scanning electron microscope in a preset imaging area to obtain a two-dimensional image of the shale sample.

Specifically, the two-dimensional image acquisition method includes: step S110, performing argon ion polishing treatment on the shale sample to obtain a pretreated shale sample; step S120, setting the accelerating voltage of a Scanning Electron Microscope (SEM) to be 1.2kV, and collecting secondary electron signals of the surface of the pretreated shale sample at a preset imaging resolution; step S130, the scanning electron microscope performs continuous two-dimensional stitching imaging on the pretreated shale sample according to a preset imaging area, and the obtained image is shown in fig. 2.

In this embodiment, the predetermined imaging area is，And the result obtained by scanning can meet the test requirement.

Further, the preset imaging resolution is better thanAnd the scanning result of the shale sample is ensured to be consistent with the expectation.

Preferably, the preset imaging resolution is。

Preferably, the preset imaging area is。

S200, extracting the pore parameters of each organic matter in the two-dimensional image by adopting Avizo software; wherein the pore parameters include porosity, pore median radius, pore sorting coefficient, and pore form factor of the organic matter.

Specifically, the extraction method of the pore parameters comprises the following steps: step S210, preprocessing the two-dimensional image to obtain a preprocessed image; the preprocessing includes gradation correction and filter processing. The gray level image obtained by scanning of the SEM has noise points with different degrees, the filtering processing is the processing process of noise reduction on the gray level image, and a further accurate image is obtained through gray level correction and filtering processing. Common image filtering methods include mean filtering, median filtering, gaussian filtering, etc., and in this embodiment, a Non-Local mean (Non Local Means) method is preferably used to filter the acquired two-dimensional image.

Step S220, organic matter is extracted. Specifically, based on gray level difference, all organic matters in the preprocessed image are extracted by a threshold segmentation method and stored in a binarization form to obtain a binarization image; referring to fig. 3, in this example, the gray color of the filtered image is organic matter, the black color is pores, and the white color is inorganic mineral. Different substances have different gray values in the gray image, and the threshold segmentation method is a method for segmenting the substances according to the gray value range. In this embodiment, the gray value of the organic matter is 70 to 145, all the organic matter can be extracted by setting the gray threshold of the organic matter, and the extraction result is stored in the form of a binarized image.

Step S230, obtaining a first organic matter area image according to the binary image, wherein the first organic matter area image comprises N first organic matter areas, and the first organic matter areas are of a closed loop structure formed by the same gray level; in the present embodiment, 0 represents background and 1 represents organic matter.

Step S240, filling organic matter. Specifically, a hole filling command is adopted to assimilate the difference gray levels in the N first organic matter areas to obtain a filled second organic matter area image; the second organic matter area image comprises N second organic matter areas, and the N second organic matter areas correspond to the N first organic matter areas respectively; here, the assimilation is to fill the difference inside the organic material constituting the closed-loop structure or the closed-loop shape with the same gray scale as the organic material, and in this embodiment, the assimilation is to fill the value inside the organic material constituting the closed-loop structure or the closed-loop shape with 1.

And step S250, labeling each filled second organic matter area. Specifically, MATLAB software is adopted to sequentially assign values to the N second organic matter regions, and a third organic matter region image (in this embodiment, denoted as image 1) with different assignment regions is obtained; the third organic matter area image comprises N third organic matter areas, and assignment numbers of the N third organic matter areas are sequentially set according to a first sequence by adopting a first group of Arabic numbers.

Step S260, using MATLAB software to sequentially assign values to the N first organic matter regions, and obtaining a fourth organic matter region image (in this embodiment, denoted as image 2) having different assigned regions; the fourth organic matter area image comprises N fourth organic matter areas, and assignment numbers of the N fourth organic matter areas are sequentially set by adopting a second group of Arabic numbers according to a second sequence; the second order is arranged in correspondence with the first order and the first set of arabic numerals are arranged in correspondence with the second set of arabic numerals.

Step S270, image calculation. Specifically, a fifth organic matter area image (in this embodiment, denoted as image 3) is obtained by subtracting the third organic matter area image from the fourth organic matter area image; the fifth organic matter area image includes N fifth organic matter areas, and the N fifth organic matter areas are labeled sequentially assigned pore images. In the image 3, the pores in the same organic matter have the same gray value, and the pore gray values in different organic matters are different, so that the sources of the pores can be determined. It should be noted that, in the aperture image, there is only one marked 1 and only one marked 2, the number of the obtained marks in this embodiment does not limit the protection scope of the present invention, and this embodiment is only one of the embodiments shown in the drawings.

Step S280, obtaining the porosity based on the obtained fifth organic matter area imageMedian pore radiusPore size fractionAnd a pore shape factor SF.

Wherein the porosity of each organic matterWherein, in the step (A),are the areas of the corresponding individual organic matter,is the area of the corresponding single organic matter internal pore.

Wherein the median pore radiusThe pore radius value corresponding to the position with 50 percent of content on the pore size distribution cumulative curve; the pore size distribution cumulative curve is a distribution curve of pore radius and cumulative percentage of pore area. The area of each organic matter in image 2 can be obtained by using Avizo softwareAnd the area of each aperture in image 3Circumference length, ofAnd radius. In each organic matter, the radius isWhen the cumulative percentage of the corresponding pore area is(ii) a Wherein the content of the first and second substances,representing the largest pore radius in a single organic mass,representing a radius ofThe area of the corresponding aperture of (a),representing the total area of all pores within the corresponding single organic mass. In the cumulative curve of pore size distribution, the inverse order of pore radius (left side numerical value is greater than right side) is abscissa, the cumulative percentage of pore area is ordinate, and the point (a, b) on the curve represents radius not less thanHas a total percentage of pore area of。

Wherein the pore size fraction(ii) a Wherein the content of the first and second substances,andrespectively representing pore radius values corresponding to the cumulative percentage content of pore area of 25 percent and 75 percent on the pore size distribution cumulative curve; the pore sorting coefficient indicates the degree of uniformity of pore size.

In this embodiment, whenWhen it is, it means that the pore uniformity is good; when in useWhen, it means the pore uniformity is moderate; when in useWhen used, the term "poor uniformity" means poor uniformity of porosity.

The determination method of the pore shape factor SF is as follows: first, based on the N fifth organic matter regions, corresponding N pore shape factors are calculated；；Is as followsThe pore shape factor of each pore is,is as followsThe pore area of each pore is the same as the pore size,is as followsThe perimeter of each of the apertures is,。

secondly, selecting pore shape factors with the same value to form a group of same factor sets, forming M group of same factor sets by N pore shape factors,。

wherein the same factor set comprises n pore shape factors,。

thirdly, acquiring the area sum of corresponding pores as a group of pore area sets based on the corresponding same factor sets; the M sets of identical factor sets constitute M sets of pore area sets.

Fourthly, based on the M groups of pore area sets, a pore shape factor corresponding to the area and the largest pore area set is obtained as the pore shape factor SF.

In the present embodiment, it is preferred that,a one-bit decimal value between 0 and 1, including 1.

Step S300, obtaining a type value of the organic matter set according to the pore parameters; wherein the organic matter set comprises N organic matters.

With particular reference to figures 4 and 5 of the drawings,the method for acquiring the type value of the organic matter set specifically comprises the following steps: step S310, based on porosityMedian pore radiusPore size fractionAnd pore form factorBy usingA clustering algorithm, which is used for initially classifying the organic matter set; wherein the initial classification value of the organic matter set is K, and K is more than or equal to 2.

Step S320, combiningA clustering algorithm for calculating the variation coefficient of each organic matter set，(ii) a Wherein the content of the first and second substances,is composed of、、Or。

Step S330, if any variation coefficient in the K-type organic matter setAll satisfyTaking the initial classification value K of the organic matter set as the type value of the organic matter set; otherwise, step S340 is performed.

Step S340, let the initial classification value K = K +1 of the organic matter set, and repeat steps S320 to S330.

Wherein the content of the first and second substances,；and the standard deviation of the porosity corresponding to the N organic matters in each corresponding organic matter set.

；The average value of the porosity corresponding to the N organic matters in each corresponding organic matter set is obtained.

；And the standard deviation of the pore median radius corresponding to the N organic matters in each corresponding organic matter set is adopted.

；The average value of the pore median radius corresponding to the N organic matters in each corresponding organic matter set is obtained.

；And the standard deviation of the pore sorting coefficients corresponding to the N organic matters in each type of organic matter set is obtained.

；And averaging the pore sorting coefficients corresponding to the N organic matters in each corresponding organic matter set.

；The standard deviation of the pore shape factor for the corresponding N organic matters in each type of organic matter set.

；The average value of the pore shape factors corresponding to the N organic matters in each corresponding organic matter set is obtained.

In the embodiment, a K-means algorithm is adopted, and for a given sample set, the sample set is divided into K classes according to the distance between samples; k is a designated numerical value, and the best value is researched and judged by increasing. Setting an initial K value to be 2, namely initially dividing all organic matter sets into two types, performing iterative loop, and for the representative organic matter image and the pore parameter schematic diagram thereof selected in the attached figure 5, when the K value is 4, satisfying the coefficient of variation judgment standard, finishing the classification, thereby finally dividing the organic matter sets into four types.

And S400, performing three-dimensional reconstruction on each type of organic matter set by adopting a focused ion beam-helium ion microscope to obtain an organic matter three-dimensional reconstruction body. Wherein, FIB-HIM (focused ion beam-helium ion microscope) is required to keep consistent three-dimensional reconstruction parameters of each type of organic matter set; the final volume of the FIB-HIM three-dimensional reconstruction needs to be kept consistent; FIB-HIM three-dimensional reconstruction at least up to。

Further, the method for obtaining the organic three-dimensional reconstruction body specifically comprises the following steps: screening the K-type organic matter set by adopting a focused ion beam-helium ion microscope based on a preset area threshold value to obtain an organic matter three-dimensional reconstruction body; wherein the predetermined area threshold is，。

Referring to fig. 5 and fig. 6, a representative of four organic matter groups is selected, and the basic requirement is that the transverse and longitudinal dimensions of the organic matter group are larger than those of the organic matter group. The image and pore parameters of the four selected representative organic matter sets are shown in fig. 5.

Specifically, in the process of adopting a focused ion beam-helium ion microscope, one organic matter meeting the requirement is screened out from each type of organic matter set based on a preset area threshold value, namely K type organic mattersScreening K organic matters meeting the requirements from the matter set, then carrying out three-dimensional reconstruction on the K organic matters, specifically, moving a sample stage, and moving a helium ion microscope imaging (HIM) position to an organic matter area to be detected; rotating the sample table to incline to 54 degrees, and adjusting the position of the sample table to ensure that a scanning area of the helium ion microscope and a scanning area of a Focused Ion Beam (FIB) are both positioned on an organic matter to be measured; setting the accelerating voltage of HIM to be 30kV, the beam current to be 0.5pA and the imaging resolution to beThe imaging area is(ii) a The thickness of FIB slice is 3 nm/slice, and the total thickness is(ii) a And starting the program to further obtain the organic three-dimensional reconstruction body. In this example, fig. 6 shows three-dimensional FIB-HIM reconstructors of class I and class II among four organic materials.

S500, acquiring pore connectivity parameters by adopting Avizo software based on the organic matter three-dimensional reconstruction body; further, the pore connectivity parameter is，(ii) a Wherein the content of the first and second substances,for pore connectivity parameters in the m-th class organic matter set,representing the volume of interconnected pores in the m-th class organic matter collection,representing total porosity in a set of m-th type organic matterVolume;and K is the type value of the organic matter set.

Further, in Avizo, slices are filtered using a Non Local Means (Non Local Means) method; aligning the Slices using Align Slices commands; cutting the three-dimensional body to formThe cube of (1); extracting three-dimensional pores by using a Threshold (Threshold) segmentation method; statistics of total pore volume using Material Statistics commandsCalculating the volume of the interconnected pores using Connected Components commandsAnd calculating the connected pore ratio (i.e. the pore connectivity parameter). In this example, the interconnected pore volume of the type I organic matterIs composed ofTotal pore volumeIs composed ofPore connectivity parameterIs 0.98; interconnected pore volume of class II organic matterIs composed ofTotal pore volumeIs composed ofRatio of interconnected poresIs 0.58; class III organic materials have a connected pore volume ofIs composed ofTotal pore volumeIs composed ofRatio of interconnected poresIs 0.39; the communicated pores of the IV organic matters are extremely small and can be approximately judged to be disconnected.

And S600, acquiring an integral connectivity evaluation index of the shale organic matter pores based on the pore connectivity parameters.

Wherein the overall connectivity evaluation index is，；The total area porosity of the m-type organic matter set;and the total surface porosity of pores in the K-type organic matter aggregate in the shale sample is shown.

Wherein the content of the first and second substances,；for the first in each organic matter groupThe surface porosity of the organic matter;。

。

；for the first in each organic matter groupThe area of all pores in the organic mass.

。

。

In this example, the total face porosity of the type I organic matter1.07%, total face porosity of type II organic matter0.49% of the total face porosity of the group III organic matter0.31%, total face porosity of type IV organic matter0.01 percent, and the total porosity of the pores of all organic matter aggregates of the shale sampleComprises the following steps: 1.07% +0.49% +0.31% +0.01% = 1.88%.

；

77.32%。

In the present invention, according toQuantitatively evaluating the integral connectivity of organic matter pores in the tested shale sample;the larger the value, the better the connectivity of the organic pores in the sample.

While the invention has been described with reference to a preferred embodiment, various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention, and particularly, features shown in the various embodiments may be combined in any suitable manner without departing from the scope of the invention. It is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

In the description of the present invention, the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like, which indicate directions or positional relationships, are based on the directions or positional relationships shown in the drawings, which are for convenience of description only, and do not indicate or imply that the devices or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.

The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, article, or apparatus.

So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.