Shale matrix digital core reconstruction method

Abstract

The invention belongs to the technical field of petroleum and natural gas exploration and development, and particularly relates to a shale matrix digital core reconstruction method which comprises the following steps: obtaining morphological parameters of different mineral components of shale by utilizing scanning electron microscope energy spectrum analysis, and obtaining the contents of the different mineral components by utilizing XRD (X-Ray Diffraction); obtaining core photos by using a 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 a matrix according to the morphological parameters of the different mineral components and the contents of the different mineral components; and according to the reconstructed matrix, carrying out three-dimensional reconstruction of the pore structure of the shale matrix by utilizing the pore and throat characteristic parameters. According to the method, the types and distribution of the minerals and the pore types in different minerals are reconstructed, and the three-dimensional pore structure of the shale can be more accurately known.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas exploration and development, and in particular to a shale matrix digital core reconstruction method. Background Art

[0002] With the continuous development of unconventional oil and gas reservoirs, it is necessary to have a more refined understanding of the reservoir pore structure and microscopic seepage mechanism. There are many methods for measuring reservoir pore structure, such as mercury injection, nuclear magnetic resonance, nitrogen adsorption, SEM, FIB-SEM and CT. Each method has its own measurement range and accuracy. For example, mercury injection, nuclear magnetic resonance, nitrogen adsorption and other methods cannot directly observe the pore structure. It is necessary to reversely 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 can directly observe the pore structure, but it is difficult to meet the requirements of accuracy and measurement range. In recent years, digital core technology has developed rapidly. Digital core reconstruction technology can combine the results of multiple measurement technologies to fully represent the pore structure. It can also flexibly set parameters to obtain different types of pore structures. The reconstruction size can be adjusted according to the computing power, and it can be combined with the seepage model to simulate the flow law of fluid in the pore structure. At present, there are three methods for digital core reconstruction: random method, process method and random-process combination method. Random methods include four-parameter random growth method, Gaussian method, simulated annealing method, sequential indicator method, multi-point statistical method and Markov chain-Monte Carlo method (MCMC). The process simulation method reconstructs the digital core to simulate the diagenesis process, including sedimentation, compaction and diagenesis. The reconstructed digital core is closer to the real core, but its disadvantage is that it is only suitable for rocks with relatively simple diagenesis. The random-process combination method is to first reconstruct the digital core using the process method, and then simulate annealing based on this basis to finally obtain the required three-dimensional digital core model, which can overcome the shortcomings of these two methods, but the calculation process is complicated and programming is difficult. In addition, these methods currently do not consider the anisotropy of pore structure and the morphology of different mineral components and different types of pore structures in each mineral component. Summary of the invention

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

[0004] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is as follows:

[0005] The morphological parameters of different mineral components of shale were obtained by scanning electron microscope energy spectrum analysis, and the contents of different mineral components were obtained by XRD.

[0006] The core photos were obtained by scanning electron microscopy, and the core photos were classified according to the mineral types to obtain the pore types matching different minerals. The pore structure of the core photos was extracted, and the characteristic parameters of the pore and throat structure were statistically analyzed after extraction.

[0007] Reconstructing the matrix based on the morphological parameters of different mineral components and the contents of different mineral components;

[0008] Based on the reconstructed matrix, the three-dimensional reconstruction of the shale matrix pore structure is carried out using the pore and throat characteristic parameters.

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

[0010] Prepare one bedding-parallel and two bedding-perpendicular core original cross-section samples;

[0011] The original cross-section samples of the core were analyzed using scanning electron microscope energy spectrum to obtain the elemental composition of different positions of the shale. The mineral type at that position was obtained based on the chemical formula of different mineral components, and the morphological parameters of different mineral components were obtained by measuring the mineral particles.

[0012] Shale powder samples were prepared and the contents of different mineral components were obtained using XRD.

[0013] In one embodiment, a core photograph is obtained using a scanning electron microscope, including:

[0014] Argon ion polishing was performed on the original cross-section samples of the core with one parallel bedding and two perpendicular beddings. After argon ion polishing, core photos of 100 points evenly distributed on the sample surface were taken using a scanning electron microscope. The field of view of each photo was 12.4×12.4μm with an accuracy of 12nm.

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

[0016] Classifying the core photos according to mineral types to obtain core photos corresponding to different mineral types;

[0017] The pore structure of the core photo is extracted, the pore structure image is converted into an 8-bit grayscale value image, and then the image is binarized using a threshold segmentation algorithm to convert the photo into a black-and-white binary image, where black represents pores and white represents 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 using the RemoveOutliers and Image Calculator commands in ImageJ software; based on the pore structure image and the throat structure image, characteristic parameter statistics of the pore and throat structures are performed, and 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;

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

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

[0020] Step 1: Define a 2000×2000×2000 three-dimensional array, set the parallel bedding direction and the vertical bedding direction, each array element represents a pixel, the accuracy is 12nm, and randomly determine a position inside the three-dimensional array as the starting point of mineral growth;

[0021] Step 2: Determine the mineral type using random numbers according to the mineral content ratio, and reconstruct the mineral particles using an expansion algorithm according to the mineral morphology and particle size;

[0022] Step 3: Use any position on the surface of the mineral particle as the growth point of the next mineral particle;

[0023] Step 4: Repeat steps 2 and 3 until the three-dimensional array boundary is reached, and the matrix reconstruction is completed.

[0024] In one embodiment, based on the reconstructed matrix, three-dimensional reconstruction of the pore structure of the shale matrix is ​​performed using pore and throat characteristic parameters, including:

[0025] Step 1: randomly determine a position inside each mineral particle as the starting point of pore growth, first determine the pore morphology parallel to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the bedding direction, and then determine the pore morphology perpendicular to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the bedding direction;

[0026] Step 2: Determine the number and growth direction of throats according to the probability distribution of pore throat coordination numbers parallel to the bedding and perpendicular to the bedding directions; determine the throat morphology according to the probability distribution of throat diameter, tortuosity and throat length;

[0027] Step 3: at the other end of the throat, determine the pore morphology parallel to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the direction parallel to the bedding plane, and then determine the pore morphology perpendicular to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the direction perpendicular to the bedding plane;

[0028] Step 4: Repeat steps 2 to 3 to repeatedly construct throats and pores until the pores reach the mineral boundary and the reconstruction is completed.

[0029] The advantages and beneficial effects of the present invention over the prior art are as follows:

[0030] Reconstruct the types and distribution of minerals and the pore types in different minerals to more accurately understand the three-dimensional pore structure of shale. The pore structure of samples in different directions can reflect the anisotropy of shale, and the pore types in different mineral types can more accurately reconstruct the matching relationship between pores and minerals, so that it is more convenient to set the relationship between different pores and fluids for subsequent seepage simulation, and more accurately simulate the fluid flow relationship. The present invention innovatively uses a scanning electron microscope to count the morphology of different minerals and their matching relationship with different pore types; innovatively designs the reconstruction step, first using the morphology and content of the minerals to use the expansion algorithm to establish a three-dimensional distribution model of minerals in the matrix, and then reconstructs the pore structure and connectivity in different minerals, thus realizing a digital shale core containing mineral components and pore structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 These are the original cross-section samples of three cores;

[0033] Figure 2 The results of element composition at different locations of shale are shown;

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

[0035] Figure 4These are the core samples in three directions obtained after argon ion polishing;

[0036] Figure 5 Schematic diagram of the scanning electron microscope point selection;

[0037] Figure 6 are three core photos;

[0038] Figure 7 The pore types formed by different minerals, among which (a) is organic pore, (b) is intergranular pore, (c) is feldspar intercrystalline pore, (d) is illite intragranular pore, and (e) is illite interlayer crack;

[0039] Figure 8 It is a black and white binary image;

[0040] Fig. 9 is the pore structure image;

[0041] Fig.10 This is an image of the laryngeal structure;

[0042] Fig.11 Reconstruct flow charts for digital cores;

[0043] Fig.12 Schematic diagram of pore diameter probability distribution;

[0044] Fig.13 Schematic diagram of the probability distribution of pore throat coordination numbers in the vertical bedding direction;

[0045] Fig.14 Schematic diagram of the probability distribution of pore throat coordination numbers parallel to the bedding direction. DETAILED DESCRIPTION

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

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

[0048] Step S10, using scanning electron microscope energy spectrum analysis to obtain the morphological parameters of different mineral components of shale, and using XRD to obtain the content of different mineral components;

[0049] Step S20, obtaining a core photograph using a scanning electron microscope, classifying the core photograph according to the mineral type, obtaining pore types matching different minerals, extracting the pore structure of the core photograph, and performing characteristic parameter statistics on the pore and throat structure after extraction;

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

[0051] Step S40: Based on the reconstructed matrix, three-dimensional reconstruction of the pore structure of the shale matrix is ​​performed using pore and throat characteristic parameters.

[0052] The present invention forms a shale matrix digital core reconstruction method that takes into account mineral distribution and pore types, reconstructs the types and distribution of minerals and the pore types in different minerals, and can more accurately understand the three-dimensional pore structure of shale.

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

[0054] Prepare one bedding-parallel and two bedding-perpendicular core original cross-section samples;

[0055] The original cross-section samples of the core were analyzed using scanning electron microscope energy spectrum to obtain the elemental composition of different positions of the shale. The mineral type at that position was obtained based on the chemical formula of different mineral components, and the morphological parameters of different mineral components were obtained by measuring the mineral particles.

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

[0057] Shale powder samples were prepared and the contents of different mineral components were obtained using XRD.

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

[0059] Argon ion polishing was performed on the original cross-section samples of the core with one parallel bedding and two perpendicular beddings. After argon ion polishing, core photos of 100 points evenly distributed on the sample surface were taken using a scanning electron microscope. The field of view of each photo was 12.4×12.4μm with an accuracy of 12nm.

[0060] Furthermore, in step S20, the core photos are classified according to the mineral types to obtain pore types matching different minerals, the pore structure of the core photos is extracted, and after extraction, characteristic parameter statistics of the pore and throat structure are performed, including:

[0061] Classifying the core photos according to mineral types to obtain core photos corresponding to different mineral types;

[0062] The pore structure of the core photo is extracted, the pore structure image is converted into an 8-bit grayscale value image, and then the image is binarized using a threshold segmentation algorithm to convert the photo into a black-and-white binary image, where black represents pores and white represents 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 using the RemoveOutliers and Image Calculator commands in ImageJ software; based on the pore structure image and the throat structure image, characteristic parameter statistics of the pore and throat structures are performed, and 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;

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

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

[0065] Furthermore, 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 1: Define a 2000×2000×2000 three-dimensional array, set the parallel bedding direction and the vertical bedding direction, each array element represents a pixel, the accuracy is 12nm, and randomly determine a position inside the three-dimensional array as the starting point of mineral growth;

[0067] Step 2: Determine the mineral type using random numbers according to the mineral content ratio, and reconstruct the mineral particles using an expansion algorithm according to the mineral morphology and particle size;

[0068] In this step, the steps of using random numbers to determine the mineral type according to the mineral content ratio are as follows: converting the content ratio distribution of the mineral component into the mineral cumulative probability distribution, the cumulative probability distribution range is 0-100, generating a random number between 0-100, and if the random number falls within the cumulative probability interval corresponding to a certain mineral component, then this mineral component is selected for reconstruction; the expansion algorithm is to take the growth point as 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 until it expands to the mineral particle size corresponding to the mineral component;

[0069] Step 3: Use any position on the surface of the mineral particle as the growth point of the next mineral particle;

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

[0071] Step 4: Repeat steps 2 and 3 until the three-dimensional array boundary is reached, and the matrix reconstruction is completed.

[0072] Furthermore, in step S40, based on the reconstructed matrix, three-dimensional reconstruction of the pore structure of the shale matrix is ​​performed using pore and throat characteristic parameters, including:

[0073] Step 1: randomly determine a position inside each mineral particle as the starting point of pore growth, first determine the pore morphology parallel to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the bedding direction, and then determine the pore morphology perpendicular to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the bedding direction;

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

[0075] Step 2: Determine the number and growth direction of throats according to the probability distribution of pore throat coordination numbers parallel to the bedding and perpendicular to the bedding directions; determine the throat morphology according to the probability distribution of throat diameter, tortuosity and throat length;

[0076] In this step, a random number between 0 and 100 is generated using a random generation function. For example, the random number generated is 50. Fig.13 The coordination number corresponding to the intersection of the cumulative probability curve is in the range of 1-2, 50 and Fig.14 The coordination number corresponding to the intersection of the cumulative probability curve is less than 1, that is, there are two throats in the vertical bedding direction and one throat in the horizontal direction connected to the pore reconstructed in step 1. The method for determining the throat morphology based on the probability distribution of throat diameter, tortuosity and throat length is the same as the method for determining the pore morphology in step 1.

[0077] Step 3: at the other end of the throat, determine the pore morphology parallel to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the direction parallel to the bedding plane, and then determine the pore morphology perpendicular to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the direction perpendicular to the bedding plane;

[0078] Step 4: Repeat steps 2 to 3 to repeatedly construct throats and pores until the pores reach the mineral boundary and the reconstruction is completed.

[0079] The present invention has been tested for many times, and some test results are now cited as references to further describe the invention in detail, and the following is a detailed description in conjunction with specific embodiments.

[0080] Example 1

[0081] like Fig.11 As shown, 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 content of different mineral components is obtained by XRD; wherein the specific implementation steps are as follows: Figure 1 As shown in the figure, a core original cross-section sample with one parallel bedding and two perpendicular beddings was prepared, which were labeled as parallel bedding, perpendicular bedding 1 and perpendicular bedding 2. The original cross-section sample of the core was analyzed by scanning electron microscope energy spectrum to obtain the element composition of shale at different positions, such as Figure 2 As shown; According to the chemical formula of different mineral components, the mineral type at that location is obtained, and the morphological parameters of different mineral components are obtained by measuring the mineral particles. The morphological parameters include particle size and aspect ratio. Table 1 shows the chemical formula and morphology of different mineral components; Shale powder samples are made, and the content of different mineral components is obtained by XRD, as shown Figure 3 As shown;

[0083] Table 1 Chemical formula and morphology of different minerals

[0084] Mineral components Chemical formula Monomer morphology quartz <![CDATA[SiO2]]> Granular, blocky Plagioclase <![CDATA[Na[AlSi3O8] and Ca[AlSi3O8]]]> Granular, blocky Calcite <![CDATA[CaCO3]]> Granular, blocky dolomite <![CDATA[CaMg[CO3]2]]> Mostly granular or blocky Pyrite <![CDATA[FeS2]]> Granular, lumpy or nodular Illite <![CDATA[KAl2[(SiAl)4O 10 ]·(OH)2·nH2O]]> Scaly, feathery, or wispy Kaolinite <![CDATA[Al4(Si4O 10 )(OH)2]]> In the shape of a book page, worm, or accordion Montmorillonite <![CDATA[(AlMg)2(Si4O 10 )(OH) 24 H2O]]> Scaly, honeycomb, cotton-like

[0085] The core photographs were obtained by scanning electron microscopy, wherein the specific implementation steps are as follows: argon ion polishing was performed on the original cross-section samples of the core parallel to the bedding and two perpendicular to the bedding, and the core samples in three directions were obtained after argon ion polishing, such as Figure 4 As shown; the core photos 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, the field of view of each core photo is 12.4×12.4μm, with an accuracy of 12nm. Figure 6 As shown;

[0086] The core photos are classified according to the mineral type to obtain the pore types matching different minerals, such as Figure 7 As shown;

[0087] The pore structure of the core photo is extracted, and after the extraction, the characteristic parameters of the pore and throat structure are counted; wherein the specific implementation steps are: the core photo is classified according to the mineral type to obtain the core photos corresponding to the different mineral types; the pore structure of the core photo is extracted, the pore structure picture is converted into an 8-bit gray value image, and then the threshold segmentation algorithm is used to perform image binarization processing to convert the photo into a black and white binary image, such as Figure 8 As shown in the figure, black represents pores and white represents 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 pore structure image and throat structure image using the Remove Outliers and Image Calculator commands in ImageJ software, as shown in the figure. Fig. 9 and Fig.10 As shown; based on the pore structure image and throat structure image, the characteristic parameters of the pore and throat structure are statistically analyzed, and the characteristic parameters include the pore diameter probability distribution, the throat diameter probability distribution, the throat length probability distribution, the tortuosity probability distribution, the shape factor probability distribution, and the coordination number probability distribution; the characteristic parameters of the pore and throat structure corresponding to the same mineral type are merged to obtain the statistical results of the characteristic parameters of the pore and throat structure corresponding to each mineral type.

[0088] The matrix is ​​reconstructed according to the morphological parameters of different mineral components and the contents of different mineral components; wherein the specific implementation steps are:

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

[0090] Step 2: Determine the mineral type using random numbers according to the mineral content ratio, and reconstruct the mineral particles using an expansion algorithm according to the mineral morphology and particle size;

[0091] Step 3: Use any position on the surface of the mineral particle as the growth point of the next mineral particle;

[0092] Step 4: Repeat steps 2 and 3 until the three-dimensional array boundary is reached, and the matrix reconstruction is completed.

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

[0094] Step 1: randomly determine a position inside each mineral particle as the starting point of pore growth, first determine the pore morphology parallel to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the bedding direction, and then determine the pore morphology perpendicular to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the bedding direction;

[0095] Step 2: Determine the number and growth direction of throats according to the probability distribution of pore throat coordination numbers parallel to the bedding and perpendicular to the bedding directions; determine the throat morphology according to the probability distribution of throat diameter, tortuosity and throat length;

[0096] Step 3: at the other end of the throat, determine the pore morphology parallel to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the direction parallel to the bedding plane, and then determine the pore morphology perpendicular to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the direction perpendicular to the bedding plane;

[0097] Step 4: Repeat steps 2 to 3 to repeatedly construct throats and pores until the pores reach the mineral boundary and the reconstruction is completed.

[0098] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A shale matrix digital core reconstruction method, characterized in that: include: The morphological parameters of different mineral components of shale were obtained by scanning electron microscope energy spectrum analysis, and the contents of different mineral components were obtained by XRD. The core photos were obtained by scanning electron microscopy, and the core photos were classified according to the mineral types to obtain the pore types matching different minerals. The pore structure of the core photos was extracted, and the characteristic parameters of the pore and throat structure were statistically analyzed after extraction. Reconstructing the matrix based on the morphological parameters of different mineral components and the contents of different mineral components; Based on the reconstructed matrix, the three-dimensional reconstruction of the shale matrix pore structure is carried out using the pore and throat characteristic parameters.

2. The shale matrix digital core reconstruction method according to claim 1, characterized in that: The morphological parameters of different mineral components of shale are obtained by scanning electron microscope energy spectrum analysis, and the content of different mineral components is obtained by XRD, including: Prepare one bedding-parallel and two bedding-perpendicular core original cross-section samples; The original cross-section samples of the core were analyzed using scanning electron microscope energy spectrum to obtain the elemental composition of different positions of the shale. The mineral type at that position was obtained based on the chemical formula of different mineral components, and the morphological parameters of different mineral components were obtained by measuring the mineral particles. Shale powder samples were prepared and the contents of different mineral components were obtained using XRD.

3. The shale matrix digital core reconstruction method according to claim 1, characterized in that: The core photos were obtained by scanning electron microscopy, including: Argon ion polishing was performed on the original cross-section samples of the core with one parallel bedding and two perpendicular beddings. After argon ion polishing, core photos of 100 points evenly distributed on the sample surface were taken using a scanning electron microscope. The field of view of each photo was 12.4×12.4μm with an accuracy of 12nm.

4. The shale matrix digital core reconstruction method according to claim 1, characterized in that: The core photos are classified according to the mineral type to obtain the pore types matching different minerals. The pore structure of the core photos is extracted. After extraction, the characteristic parameters of the pore and throat structure are statistically analyzed, including: Classifying the core photos according to mineral types to obtain core photos corresponding to different mineral types; The pore structure of the core photo is extracted, the pore structure picture is converted into an 8-bit grayscale value picture, and then the image is binarized using a threshold segmentation algorithm to convert the photo into a black-and-white binary image, where black represents pores and white represents 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 using the Remove Outliers and Image Calculator commands in ImageJ software; based on the pore structure image and the throat structure image, characteristic parameters of the pore and throat structures are statistically analyzed, and 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; The characteristic parameters of the pore and throat structures corresponding to the same mineral type are combined to obtain the statistical results of the characteristic parameters of the pore and throat structures corresponding to each mineral type.

5. The shale matrix digital core reconstruction method according to claim 1, characterized in that: Reconstructing the matrix based on the morphological parameters of different mineral components and the content of different mineral components, including: Step 1: Define a 2000×2000×2000 three-dimensional array, set the parallel bedding direction and the vertical bedding direction, each array element represents a pixel, the accuracy is 12nm, and randomly determine a position inside the three-dimensional array as the starting point of mineral growth; Step 2: Determine the mineral type using random numbers according to the mineral content ratio, and reconstruct the mineral particles using an expansion algorithm according to the mineral morphology and particle size; Step 3: Use any position on the surface of the mineral particle as the growth point of the next mineral particle; Step 4: Repeat steps 2 and 3 until the three-dimensional array boundary is reached, and the matrix reconstruction is completed.

6. The shale matrix digital core reconstruction method according to claim 1, characterized in that: Based on the reconstructed matrix, the pore and throat characteristic parameters are used to carry out the three-dimensional reconstruction of the shale matrix pore structure, including: Step 1: randomly determine a position inside each mineral particle as the starting point of pore growth, first determine the pore morphology parallel to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the bedding direction, and then determine the pore morphology perpendicular to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the bedding direction; Step 2: Determine the number and growth direction of throats according to the probability distribution of pore throat coordination numbers parallel to the bedding and perpendicular to the bedding directions; determine the throat morphology according to the probability distribution of throat diameter, tortuosity and throat length; Step 3: at the other end of the throat, determine the pore morphology parallel to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the direction parallel to the bedding plane, and then determine the pore morphology perpendicular to the bedding plane according to the shape factor probability distribution and pore diameter probability distribution in the direction perpendicular to the bedding plane; Step 4: Repeat steps 2 to 3 to repeatedly construct throats and pores until the pores reach the mineral boundary and the reconstruction is completed.

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