A shale matrix digital core reconstruction method

Abstract

The present application belongs to the technical field of oil and gas exploration and development, and particularly relates to a shale matrix digital core reconstruction method, which comprises: obtaining morphological parameters of different mineral components of shale by scanning electron microscope energy spectrum analysis, and obtaining contents of different mineral components by XRD; obtaining core photos by scanning electron microscope, classifying the core photos according to mineral types to obtain pore types matched with different minerals, extracting pore structures of the core photos, and performing characteristic parameter statistics on pore and throat structures after extraction; reconstructing the matrix according to the morphological parameters of different mineral components and the contents of different mineral components; and carrying out three-dimensional reconstruction of the pore structure of the shale matrix according to the reconstructed matrix, using pore and throat characteristic parameters. The present application reconstructs the types, distribution and pore types in different minerals, and can more accurately understand the three-dimensional pore structure of shale.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas exploration and development, and particularly relates to a shale matrix digital core reconstruction method. BACKGROUND

[0002] With the continuous development of unconventional oil and gas reservoirs, the understanding of reservoir pore structure and micro flow mechanism needs to be more and more accurate. There are various methods for measuring reservoir pore structure, such as mercury injection, nuclear magnetic resonance, nitrogen adsorption, SEM, FIB-SEM and CT measurement technology. Each method has its own measurement range and accuracy. For example, mercury injection, nuclear magnetic resonance and nitrogen adsorption methods cannot directly observe the pore structure and need to calculate the pore size and other parameters from the measurement results. The results are greatly affected by the calculation model. SEM, FIB-SEM and CT methods can directly observe the pore structure, but it is difficult to meet the requirements of accuracy and measurement range at the same time. In recent years, digital core technology has developed rapidly. Digital core reconstruction technology can combine the results of various measurement technologies to comprehensively represent the pore structure. It can also flexibly set parameters to obtain different types of pore structure. The reconstruction size can be adjusted according to the computing power, and the fluid flow rule in the pore structure can be simulated combined with the percolation model. At present, there are three kinds of digital core reconstruction methods, namely random method, process method and random-process combination method. The random method includes four-parameter random growth method, Gaussian method, simulated annealing method, sequential indication method, multiple-point statistics method and Markov chain-Monte Carlo method (MCMC). The process simulation method reconstructs the digital core by simulating the diagenetic process, including sedimentation, compaction and diagenetic process. The reconstructed digital core is more close to the real core, but its disadvantage is that it is only suitable for rocks with relatively simple diagenetic process. The random-process combination method first reconstructs the digital core by using the process method, and then simulates annealing based on this basis to finally obtain the required three-dimensional digital core model, which can overcome the shortcomings of the two methods. However, the calculation process is complex and the programming difficulty is great. Moreover, these methods do not consider the anisotropy of pore structure and the shape of different mineral components and different types of pore structure in each mineral component. SUMMARY

[0003] The present application aims to overcome the above-mentioned deficiencies of the prior art and provide a shale matrix digital core reconstruction method.

[0004] To achieve the above-mentioned application purposes, the technical solutions adopted by the present application are as follows:

[0005] The shape parameters of different mineral components of shale are obtained by scanning electron microscope energy spectrum analysis, and the contents of different mineral components are obtained by XRD;

[0006] The core photos are obtained by using a scanning electron microscope, the core photos are classified according to mineral types, pore types matched with different minerals are obtained, the pore structure of the core photos is extracted, and feature parameter statistics of the pore and throat structure are performed after the extraction;

[0007] The matrix is reconstructed according to the morphological parameters of different mineral components and the content of different mineral components;

[0008] According to the reconstructed matrix, the three-dimensional reconstruction of the shale matrix pore structure is performed by using the pore and throat feature parameters.

[0009] In one embodiment, the morphologies of different mineral components of shale are obtained by using scanning electron microscope energy spectrum analysis, and the content of different mineral components is obtained by using XRD, including:

[0010] A core original section sample with one parallel bedding and two vertical beddings is prepared;

[0011] The core original section sample is analyzed by using scanning electron microscope energy spectrum, element compositions at different positions of shale are obtained, the mineral types at the positions are obtained according to chemical formulas of different mineral components, and the morphological parameters of different mineral components are obtained by measuring mineral particles;

[0012] A shale powder sample is prepared, and the content of different mineral components is obtained by using XRD.

[0013] In one embodiment, the core photos are obtained by using a scanning electron microscope, including:

[0014] The core original section sample with one parallel bedding and two vertical beddings is argon ion polished, and the core photos of 100 points uniformly distributed on the surface of the sample are taken by using a scanning electron microscope after the argon ion polishing, each photo has a view size of 12.4*12.4 μm and an accuracy of 12 nm.

[0015] In one embodiment, the core photos are classified according to mineral types, pore types matched with different minerals are obtained, the pore structure of the core photos is extracted, and feature parameter statistics of the pore and throat structure are performed after the extraction, including:

[0016] The core photos are classified according to mineral types, and core photos corresponding to different mineral types are obtained;

[0017] The pore structure of the core photo is extracted, the pore structure picture is converted into an 8-bit gray value graph, and then a threshold segmentation algorithm is used for image binaryzation processing to convert the photo into a black and white binary image, with black representing pores and white representing mineral skeletons; the black and white binary image is subjected to noise reduction and smoothing processing; the black and white binary image is segmented into a pore structure image and a throat structure image by using the RemoveOutliers and Image Calculator commands in the ImageJ software; based on the pore structure image and the throat structure image, the pore and throat structures are subjected to feature parameter statistics, and the feature parameters include pore diameter probability distribution, throat diameter probability distribution, throat length probability distribution, tortuosity probability distribution, shape factor probability distribution and coordination number probability distribution;

[0018] The feature parameters of the pore and throat structures corresponding to the same mineral type are combined to obtain the feature parameter statistical results of the pore and throat structures corresponding to each mineral type.

[0019] In one embodiment, the matrix is reconstructed according to the morphological parameters of different mineral components and the contents of different mineral components, including:

[0020] Step one, define a 2000x2000x2000 three-dimensional array, set the parallel bedding direction and the vertical bedding direction, and each array element represents a pixel with a precision of 12 nm; a random position is determined as the starting point of mineral growth within the three-dimensional array;

[0021] Step two, determine the mineral type by using a random number according to the mineral content ratio, and reconstruct the mineral particles by using an inflation algorithm according to the mineral morphology and particle size;

[0022] Step three, take an arbitrary position on the surface of the mineral particle as the growth point of the next mineral particle;

[0023] Step four, repeat steps two and three until the three-dimensional array boundary is reached, and the matrix reconstruction is completed.

[0024] In one embodiment, according to the reconstructed matrix, the three-dimensional reconstruction of the shale matrix pore structure is carried out by using the pore and throat feature parameters, including:

[0025] Step one, randomly determine a position as the starting point of pore growth within each mineral particle; first, determine the pore morphology on the parallel bedding plane according to the shape factor probability distribution and the pore diameter probability distribution of the parallel bedding direction, and then determine the pore morphology on the vertical bedding plane according to the shape factor probability distribution and the pore diameter probability distribution of the vertical bedding direction;

[0026] Step two, determine the number and growth direction of the throat according to the throat coordination number probability distribution of parallel and vertical layering direction; determine the throat morphology according to the throat diameter probability distribution, tortuosity probability distribution and throat length probability distribution;

[0027] Step three, at the other end of the throat, determine the pore morphology of the parallel bedding plane according to the shape factor probability distribution and pore diameter probability distribution of the parallel bedding direction, and then determine the pore morphology of the vertical bedding plane according to the shape factor probability distribution and pore diameter probability distribution of the vertical bedding direction;

[0028] Step four, repeat steps two to three, repeat the construction of the throat and the pore, until the pore reaches the mineral boundary, and the reconstruction is completed.

[0029] The advantages and beneficial effects of the present application relative to the prior art are:

[0030] The type and distribution of the reconstructed mineral and the pore type in different minerals can more accurately understand the three-dimensional pore structure of shale. The anisotropy of shale can be embodied by using the pore structure of samples in different directions, and the matching relationship between the pore and the mineral can be more accurately reconstructed by using the pore type in different mineral types, so that the action relationship of different pores on the fluid can be more conveniently set for subsequent seepage simulation, and the fluid flow relationship can be more accurately simulated. The present application innovatively uses a scanning electron microscope to count the morphology of different minerals and the matching relationship with different pore types; the reconstruction steps are innovatively designed, the three-dimensional distribution model of the mineral in the matrix is established by using the morphology and content of the mineral by using the expansion algorithm, and then the pore structure and connectivity in different minerals are reconstructed, so that the shale digital core containing the mineral component and the pore structure is realized. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0032] Figure 1 Three core original cross-section samples;

[0033] Figure 2 Element composition results of different positions of shale;

[0034] Figure 3 The content results of different mineral components measured by XRD, wherein (a) is the content of different minerals, and (b) is the content of different clay minerals;

[0035] Figure 4Core samples in three directions obtained after argon ion polishing;

[0036] Figure 5 Point drawing for scanning electron microscope;

[0037] Figure 6 Three core photos;

[0038] Figure 7 Different pore types formed by different minerals, wherein (a) is organic matter pore, (b) is intergranular pore, (c) is feldspar intercrystalline pore, (d) is illite intragranular pore, and (e) is illite interlayer slit;

[0039] Figure 8 Black and white binary image;

[0040] Figure 9 Pore structure image;

[0041] Figure 10 Throat structure image;

[0042] Figure 11 Digital core reconstruction flowchart;

[0043] Figure 12 Pore diameter probability distribution schematic diagram;

[0044] Figure 13 Pore throat coordination number probability distribution schematic diagram in vertical bedding direction;

[0045] Figure 14 Pore throat coordination number probability distribution schematic diagram in parallel bedding direction. DETAILED DESCRIPTION

[0046] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clear and explicit, the present application will be further described in detail below in combination with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0047] The present application provides a shale matrix digital core reconstruction method, comprising:

[0048] In step S10, morphological parameters of different mineral components of shale are obtained by using scanning electron microscope energy spectrum analysis, and contents of different mineral components are obtained by using XRD;

[0049] In step S20, core photos are obtained by using a scanning electron microscope, the core photos are classified according to mineral types to obtain pore types matched with different minerals, pore structures of the core photos are extracted, and feature parameter statistics of pore and throat structures are performed after extraction;

[0050] Step S30, reconstructing the matrix according to the morphological parameters of different mineral components and the content of different mineral components;

[0051] Step S40, according to the reconstructed matrix, using the pore and throat characteristic parameters to carry out three-dimensional reconstruction of the shale matrix pore structure.

[0052] The present application forms a shale matrix digital core reconstruction method considering mineral distribution and pore type, reconstructing the type and distribution of minerals and the pore type in different minerals, which can more accurately understand the three-dimensional pore structure of shale.

[0053] Further, in step S10, the morphology of different mineral components of shale is obtained by using scanning electron microscope energy spectrum analysis, and the content of different mineral components is obtained by using XRD, including:

[0054] A core original section sample with one parallel bedding and two vertical beddings is prepared;

[0055] The core original section sample is analyzed by using scanning electron microscope energy spectrum to obtain the element composition at different positions of shale, the mineral type at the position is obtained according to the chemical formula of different mineral components, and the morphological parameters of different mineral components are obtained by measuring the mineral particles;

[0056] The morphological parameters in this step include particle size and aspect ratio;

[0057] A shale powder sample is prepared, and the content of different mineral components is obtained by using XRD.

[0058] Further, in step S20, the core photograph is obtained by using a scanning electron microscope, including:

[0059] The core original section sample with one parallel bedding and two vertical beddings is polished by argon ion, and after argon ion polishing, the core photograph of 100 uniformly distributed points on the sample surface is taken by using a scanning electron microscope, each photograph has a view size of 12.4*12.4 μm and an accuracy of 12 nm.

[0060] Further, in step S20, the core photograph is classified according to the mineral type to obtain the pore type matched with different minerals, the pore structure of the core photograph is extracted, and the characteristic parameters of the pore and throat structure are counted after extraction, including:

[0061] The core photograph is classified according to the mineral type to obtain the core photograph corresponding to different mineral types;

[0062] The pore structure of the core photo is extracted, the pore structure picture is converted into an 8-bit gray value graph, and then a threshold segmentation algorithm is used for image binaryzation processing to convert the photo into a black and white binary image, with black representing pores and white representing mineral skeletons; the black and white binary image is subjected to noise reduction and smoothing processing; the black and white binary image is segmented into a pore structure image and a throat structure image by using the RemoveOutliers and Image Calculator commands in the ImageJ software; based on the pore structure image and the throat structure image, the pore and throat structures are subjected to feature parameter statistics, and the feature parameters include pore diameter probability distribution, throat diameter probability distribution, throat length probability distribution, tortuosity probability distribution, shape factor probability distribution, and coordination number probability distribution;

[0063] In this step, the pore diameter probability distribution is the proportion of the number of pores with different diameters to the total number of pores; the throat diameter probability distribution is the proportion of the number of throats with different diameters to the total number of pores, the throat length probability distribution is the ratio of the actual length of the seepage channel to the apparent length passing through the seepage medium, the tortuosity probability distribution is represented by the ratio of the actual length of the available throat to the straight line length of the throat at both ends, the shape factor probability distribution is the ratio of the cross-sectional area of the pore and the throat to the square of the perimeter, and the coordination number probability distribution is the number of throats connected to a single pore.

[0064] The feature parameters of the pore and throat structures corresponding to the same mineral type are combined to obtain the feature parameter statistical results of the pore and throat structures corresponding to each mineral type.

[0065] Further, in step S30, the matrix is reconstructed according to the morphological parameters of different mineral components and the contents of different mineral components, including:

[0066] Step one, define a 2000x2000x2000 three-dimensional array, set the parallel bedding direction and the vertical bedding direction, and each array element represents a pixel with a precision of 12 nm; a position is randomly determined as the starting point of mineral growth in the three-dimensional array;

[0067] Step two, determine the mineral type by using a random number according to the mineral content ratio, and reconstruct the mineral particles by using an inflation algorithm according to the mineral morphology and particle size;

[0068] In this step, the step of determining the mineral type according to the mineral content ratio using a random number is as follows: the content ratio distribution of the mineral component is converted into a mineral cumulative probability distribution, the cumulative probability distribution range is 0-100, a random number between 0-100 is generated, and if the random number falls within the cumulative probability interval corresponding to a certain mineral component, the mineral component is selected for reconstruction; the expansion algorithm is as follows: the growth point is the center of a certain mineral particle, and each expansion step expands one pixel outward along the surface of the mineral particle, and the expansion direction is determined by the aspect ratio, and the expansion continues until the mineral particle size corresponding to the mineral component is reached;

[0069] Step three, any position on the surface of the mineral particle is taken as the growth point of the next mineral particle;

[0070] In this step, any position on the surface of the mineral particle is taken as the growth point of the next mineral particle corresponding to the mineral component;

[0071] Step four, repeat steps two and three until the three-dimensional array boundary is reached, and the matrix reconstruction is completed.

[0072] Further, in step S40, according to the reconstructed matrix, the three-dimensional reconstruction of the shale matrix pore structure is carried out using the pore and throat characteristic parameters, including:

[0073] Step one, a position inside each mineral particle is randomly determined as the starting point of pore growth, and the pore morphology of the parallel bedding plane is first determined according to the shape factor probability distribution and the pore diameter probability distribution of the parallel bedding direction, and then the pore morphology of the vertical bedding plane is determined according to the shape factor probability distribution and the pore diameter probability distribution of the vertical bedding direction;

[0074] In this step, the pore morphology includes pore shape and size; wherein the step of determining the parameter value (size) according to different probability distributions is as follows: the parameter probability distribution is converted into a cumulative probability curve, a random number between 0-100 is generated using a random number generation function, if the intersection point between the random number and the cumulative probability curve falls within a probability distribution range (excluding the two endpoint values), the upper limit of the probability distribution range is taken as the parameter value, i.e. the parameter value of the pore to be reconstructed; if the intersection point between the random number and the cumulative probability curve falls on the endpoint value corresponding to a probability distribution range, the endpoint value is taken as the parameter value, i.e. the parameter value of the pore to be reconstructed; as shown in Figure 12 Fig. 1, a random number between 0-100 is generated using a random number generation function, for example, a random number of 50 is generated, and the pore diameter corresponding to the intersection point of 50 and the cumulative probability curve is in the interval of 0.04-0.063, and 0.063 is selected as the diameter value of the current reconstructed pore; if the random number and the intersection point of the cumulative probability curve correspond to 0.04, 0.04 is selected as the diameter value of the current reconstructed pore;

[0075] Step two, determine the number and growth direction of the throat according to the coordination number probability distribution of the pore throat in parallel and vertical layering direction; determine the throat morphology according to the throat diameter probability distribution, tortuosity probability distribution and throat length probability distribution;

[0076] In this step, a random number between 0 and 100 is generated by a random number generating function, for example, a random number of 50 is generated, 50 is crossed with Figure 13 The coordination number corresponding to the intersection point of the cumulative probability curve is in the interval of 1-2, 50 is crossed with Figure 14 The coordination number corresponding to the intersection point of the cumulative probability curve is less than 1, that is, there are 2 throats in the vertical layering direction, and 1 throat is connected with the reconstructed pore in the horizontal direction. The method for determining the throat morphology according to the throat diameter probability distribution, tortuosity probability distribution and throat length probability distribution is the same as the method for determining the pore morphology in step one.

[0077] Step three, at the other end of the throat, determine the pore morphology of the parallel layering surface according to the shape factor probability distribution and pore diameter probability distribution in the parallel layering direction, and then determine the pore morphology of the vertical layering surface according to the shape factor probability distribution and pore diameter probability distribution in the vertical layering direction;

[0078] Step four, repeat steps two to three to repeatedly construct throats and pores until the pores reach the mineral boundary, and the reconstruction is completed.

[0079] The present application has undergone many tests, and now some test results are taken as reference to further describe the application in detail, which will be described in detail below with specific examples.

[0080] Example 1

[0081] As shown in Figure 11 , a shale matrix digital core reconstruction method adopts the following steps:

[0082] The morphological parameters of different mineral components of shale are obtained by scanning electron microscope energy spectrum analysis, and the contents of different mineral components are obtained by XRD; wherein, the specific implementation steps are as follows: as shown in Figure 1 , a core original section sample with parallel layering and two vertical layerings is made, and the labels are parallel layering, vertical layering 1 and vertical layering 2; the core original section sample is analyzed by scanning electron microscope energy spectrum to obtain the element composition at different positions of shale, as shown in Figure 2 ; according to the chemical formula of different mineral components, the mineral types at the positions are obtained, and the morphological parameters of different mineral components are obtained by measuring the mineral particles, including particle size and aspect ratio, and table 1 is the chemical formula and morphology of different mineral components; a shale powder sample is made, and the contents of different mineral components are obtained by XRD, as shown in Figure 3 ;

[0083] Table 1 Chemical formulas and forms of different minerals

[0084] Mineral component Chemical formula Monomer morphology Quartz SiO2 Granular, massive Plagioclase [[Na[AlSi3O8] and Ca[AlSi3O8]]] Granular, massive Calcite CaCO3 Granular, massive Dolomite CaMg[CO3]2 Predominantly granular, massive Pyrite FeS2 Granular, massive or nubbly Illite [KAl2[(SiAl)4O 10 ]·(OH)2·nH2O]]> Scaly, feather, fimbriated Kaolinite Al4(Si4O 10 )2 Book, worm, accordion Montmorillonite ((Al, Mg)2(Si4O 10 )(OH) 24 H2O]]> Scaly, honeycombed, cottony

[0085] Core images were obtained using scanning electron microscopy. The specific steps involved argon ion polishing of the original core cross-section sample with one parallel bedding plane and two perpendicular bedding planes. After argon ion polishing, core samples with three orientations were obtained, such as... Figure 4 As shown; core images of 100 points evenly distributed on the surface of the core sample were taken using a scanning electron microscope, as shown. Figure 5 As shown, each core image has a field of view of 12.4 × 12.4 μm and a resolution of 12 nm. Figure 6 As shown;

[0086] Core images are classified according to mineral type to obtain pore types that match different minerals, such as... Figure 7 As shown;

[0087] The pore structure of the core images is extracted, and the feature parameters of the pores and throat structures are statistically analyzed. Specifically, the core images are classified according to mineral type to obtain core images corresponding to different mineral types; the pore structure of the core images is extracted, the pore structure images are converted into 8-bit grayscale images, and then a threshold segmentation algorithm is used for image binarization to convert the images into black and white binary images, such as... Figure 8 As shown, black represents pores and white represents the mineral skeleton; noise reduction and smoothing are performed on the black-and-white binarized image; the Remove Outliers and Image Calculator commands in ImageJ software are used to segment the black-and-white binarized image into pore structure images and throat structure images, as shown. Figure 9 and Figure 10 As shown, based on pore structure images and throat structure images, feature parameters of pore and throat structures are statistically analyzed. The feature parameters include the probability distribution of pore diameter, throat diameter, throat length, tortuosity, shape factor, and coordination number. The feature parameters of pore and throat structures corresponding to the same mineral type are merged to obtain the statistical results of feature parameters of pore and throat structures corresponding to each mineral type.

[0088] The matrix is ​​reconstructed based on the morphological parameters and content of different mineral components; the specific implementation steps are as follows:

[0089] Step 1: Define a 2000×2000×2000 three-dimensional array, set the parallel bedding direction and the perpendicular bedding direction, each array element represents a pixel, with a precision of 12nm, and randomly determine a position inside the three-dimensional array as the mineral growth starting point;

[0090] Step two, according to the mineral content ratio, a random number is used to determine the mineral type, and according to the mineral morphology and particle size, an inflation algorithm is used to reconstruct the mineral particles;

[0091] Step three, an arbitrary position on the surface of the mineral particle is taken as the growth point of the next mineral particle;

[0092] Step four, repeat step two and step three until the three-dimensional array boundary is reached, and the matrix reconstruction is completed.

[0093] According to the reconstructed matrix, the three-dimensional reconstruction of the shale matrix pore structure is carried out by using the pore and throat characteristic parameters; wherein, the specific implementation steps are:

[0094] Step one, a position inside each mineral particle is randomly determined as the starting point of pore growth, first, the pore morphology of the parallel bedding plane is determined according to the shape factor probability distribution and pore diameter probability distribution of the parallel bedding direction, and then the pore morphology of the vertical bedding plane is determined according to the shape factor probability distribution and pore diameter probability distribution of the vertical bedding direction;

[0095] Step two, the number and growth direction of the throat are determined according to the pore throat coordination number probability distribution of the parallel and vertical bedding directions; the throat morphology is determined according to the throat diameter probability distribution, tortuosity probability distribution and throat length probability distribution;

[0096] Step three, at the other end of the throat, the pore morphology of the parallel bedding plane is determined according to the shape factor probability distribution and pore diameter probability distribution of the parallel bedding direction, and then the pore morphology of the vertical bedding plane is determined according to the shape factor probability distribution and pore diameter probability distribution of the vertical bedding direction;

[0097] Step four, repeat steps two to three, repeat the construction of the throat and the pore, until the pore reaches the mineral boundary, and the reconstruction is completed.

[0098] The above only describes the preferred embodiments of the present application and is not intended to limit the present application, any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of shale matrix digital core reconstruction, characterized in that, The method comprises the following steps: Obtaining morphological parameters of different mineral components of shale by using scanning electron microscope energy spectrum analysis, and obtaining contents of different mineral components by using XRD; Obtaining core photos by using a scanning electron microscope, classifying the core photos according to mineral types, obtaining pore types matched with different minerals, and extracting pore structures of the core photos, and then counting characteristic parameters of the pore and throat structures after the extraction; Reconstructing a matrix according to the morphological parameters of different mineral components and the contents of different mineral components; Carrying out three-dimensional reconstruction of shale matrix pore structures by using pore and throat characteristic parameters according to the reconstructed matrix; Classifying the core photos according to mineral types, obtaining pore types matched with different minerals, and extracting pore structures of the core photos, and then counting characteristic parameters of the pore and throat structures after the extraction, which comprises the following steps: Classifying the core photos according to mineral types to obtain core photos corresponding to different mineral types; Extracting pore structures of the core photos, converting the pore structure pictures into 8-bit gray value maps, and then performing image binarization processing on the maps by using a threshold segmentation algorithm to convert the photos into black-and-white binary images, wherein black represents pores and white represents mineral skeletons; performing noise reduction and smoothing processing on the black-and-white binary images; dividing the black-and-white binary images into pore structure images and throat structure images by using Remove Outliers and Image Calculator commands in ImageJ software; and counting characteristic parameters of the pore and throat structures based on the pore structure images and the throat structure images, wherein the characteristic parameters include pore diameter probability distribution, throat diameter probability distribution, throat length probability distribution, tortuosity probability distribution, shape factor probability distribution and coordination number probability distribution; Merging the characteristic parameters of the pore and throat structures corresponding to the same mineral type to obtain characteristic parameter counting results of the pore and throat structures corresponding to each mineral type; Reconstructing a matrix according to the morphological parameters of different mineral components and the contents of different mineral components, which comprises the following steps: Step 1: defining a 2000*2000*2000 three-dimensional array, setting a parallel bedding direction and a vertical bedding direction, and setting each array element to represent a pixel with a precision of 12 nm; and randomly determining a position in the three-dimensional array as a mineral growth starting point; Step 2: determining a mineral type by using a random number according to a mineral content ratio, and reconstructing a mineral particle by using an inflation algorithm according to a mineral morphology and a particle size; Step 3: taking an arbitrary position on a surface of the mineral particle as a growth point of a next mineral particle; Step 4: repeating steps 2 and 3 until the three-dimensional array boundary is reached, and completing the matrix reconstruction; Carrying out three-dimensional reconstruction of shale matrix pore structures by using pore and throat characteristic parameters according to the reconstructed matrix, which comprises the following steps: Step 1: randomly determining a position in each mineral particle as a pore growth starting point, determining a pore morphology on a parallel bedding plane according to a shape factor probability distribution and a pore diameter probability distribution of the parallel bedding direction, and then determining a pore morphology on a vertical bedding plane according to a shape factor probability distribution and a pore diameter probability distribution of the vertical bedding direction; Step two, determine the number and growth direction of the throat according to the probability distribution of the coordination number of the pore throat in parallel and vertical layering direction; determine the throat morphology according to the probability distribution of the throat diameter, tortuosity and throat length; Step three, at the other end of the throat, determine the pore morphology in the parallel layering plane according to the probability distribution of the shape factor and pore diameter in the parallel layering direction, and determine the pore morphology in the vertical layering plane according to the probability distribution of the shape factor and pore diameter in the vertical layering direction; Step four, repeat step two to step three, repeat the construction of the throat and the pore, until the pore reaches the mineral boundary, and the reconstruction is completed.

2. The method of claim 1, wherein, The morphological parameters of different mineral components of shale are obtained by scanning electron microscopy and energy spectrum analysis, and the contents of different mineral components are obtained by XRD, including: A core original section sample with one parallel layering and two vertical layerings is prepared; The core original section sample is analyzed by scanning electron microscopy and energy spectrum to obtain the elemental composition at different positions of the shale, the mineral type at the position is obtained according to the chemical formula of different mineral components, and the morphological parameters of different mineral components are obtained by measuring the mineral particles; A shale powder sample is prepared, and the contents of different mineral components are obtained by XRD.

3. The method of claim 1, wherein, The core photos are obtained by scanning electron microscopy, including: The core original section sample with one parallel layering and two vertical layerings is polished by argon ion, and the core photos of 100 uniformly distributed points on the sample surface are taken by scanning electron microscopy after argon ion polishing, each photo has a view size of 12.4x12.4μm and an accuracy of 12nm.

Images