Three-dimensional finite element simulation method based on a high-precision digital core reconstruction model

Abstract

The present application belongs to the technical field of digital core, and relates to a three-dimensional finite element simulation method based on a high-precision digital core reconstruction model. The method first observes and labels the target region of interest on the surface of the rock sample through an electron microscope, performs a focused ion beam scanning electron microscope (FIB-SEM) scanning experiment, and converts the processed digital image into a calculation grid unit by using a grid mapping method. The mechanical parameters of each component of shale and the corresponding statistical distribution characteristics thereof are obtained by using electron microscopy technology in cooperation with a nanoindentation test method. Finally, the gray interval determined based on a segmentation threshold is used to assign the corresponding physical and mechanical parameters to the unit grid of each gray interval. The digital core established by the present application quantitatively incorporates the spatial distribution and structure of different mineral materials and micro-defects in the rock into the numerical model, the precision reaches 5-10 nm, and the real representation of geometry, boundary / interface and material heterogeneity is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of digital core technology and relates to a three-dimensional finite element simulation method based on a high-precision digital core reconstruction model. Background Art

[0002] Reservoir shale, a widely distributed fine-grained sedimentary rock, is an unconventional reservoir rock found worldwide and is considered a source rock for fossil fuels. It is particularly important as a source rock and seal for hydrocarbon production. Reservoir shale is a naturally occurring composite material composed of various minerals, including organic matter, illite, quartz, calcite, and feldspar, as well as defects such as gaps and cracks. It exhibits extreme heterogeneity. The spatial distribution of these components within this multiphase system is a characteristic of the shale, formed during its sedimentary evolution. The complex microstructure within shale significantly influences its macroscopic mechanical behavior and fracturing characteristics, such as crack initiation and propagation. Therefore, the influence of microstructure must be considered when studying the mechanical properties and crack propagation processes of reservoir shale. Studying the microscopic effects of shale mineral composition and the spatial distribution of defects on the mechanical behavior of the medium, the propagation of microcracks, and the failure modes is of great theoretical significance for oil and gas reservoir development planning.

[0003] Reservoir rocks are highly dense, especially tight shales. Existing methods make it difficult to study fracturing mechanisms directly through physical experiments. With the advancement of computer technology, numerical methods have become an effective alternative and are widely used to study the mechanical behavior and failure processes of rocks, helping to reveal and explain the laws governing crack propagation. Numerical methods not only allow for the replication of experimental procedures but also capture the evolution of cracks. Heterogeneity significantly influences the mechanical response of rock. To ensure that numerical models more realistically reflect the heterogeneous nature of rock materials, heterogeneity is incorporated into numerical models, assuming that the mechanical properties of rock materials follow statistical distributions. However, due to the simplification of the complex spatial structure of rock and the introduction of random parameters that are uncertain, subjective, and highly dependent on statistically distributed parameters, the distribution of material mechanical parameter assignments is random, leading to the possibility that crack propagation paths may also be random. This means that these numerical models reduce the accuracy of numerical simulation results, making it difficult to accurately replicate rock physics experimental results. The experimental results may not reflect the phenomena and experimental effects that researchers expected when designing the experiments.

[0004] It can be seen that the correctness and adaptability of numerical simulation depend largely on the established micro model and the physical and mechanical parameters of the model. Only when the spatial distribution of the model's mineral and pore structure reflects the real structural characteristics of the rock sample and the corresponding mechanical parameters are given with sufficient scientific basis, rather than just relying on subjective imagination, can the simulation results have theoretical and application value. Due to the highly heterogeneous microstructure and complex mechanical properties of the rock matrix minerals, the characterization of rock microstructure and theoretical modeling are limited. "Parameter given inaccurate" and "model given inaccurate" are the two technical bottlenecks of rock mechanics numerical simulation.

[0005] Therefore, the establishment of an accurate model of three-dimensional digital core that can truly reflect the complex lithology and micro-pore development characteristics will play a crucial role in numerical simulation. With the development of observation methods such as scanning and the significant improvement of computer computing power, high-resolution imaging has become a feasible method to describe the microstructure characteristics of heterogeneous materials on the nanometer to micrometer scale, making it possible to establish accurate models and obtain accurate mechanical parameters. With the help of digital image processing technology, a corresponding numerical simulation grid that can truly reflect the microstructure of the rock is established. The common methods for constructing three-dimensional digital cores are divided into two categories: numerical reconstruction methods, including random methods and process methods, the central idea of which is to reconstruct three-dimensional digital cores based on two-dimensional digital images using reconstruction algorithms. Although the cost is low, two-dimensional images contain less microstructure information, and the reconstructed three-dimensional digital core has a large difference from the real core. Physical experimental methods: the current mature method is based on CT scanning technology and FIB-SEM scanning technology to reconstruct three-dimensional digital cores. CT scanning technology can accurately identify the micro-pore structure of the rock, with a resolution of several hundred nanometers, but micro-pores smaller than the resolution of the CT scanner cannot be identified. And also limited by the resolution, the rock matrix of the digital core is a single mineral lithology component, which cannot describe the reservoir core with complex lithology, and cannot accurately characterize the real spatial distribution of mineral and pore structure. This greatly affects the range of application of three-dimensional digital cores in unconventional reservoirs and the accuracy of rock physical and mechanical properties and pressure numerical simulation. In other words, the reconstruction of digital cores based on CT scanning technology is less applicable to high-density and highly heterogeneous rocks, especially shale. FIB-SEM scanning technology makes up for the shortcomings of CT scanning technology, and pores larger than 5-10 nm can be identified (for the identification of organic matter pores in dense reservoir shale, this resolution is also sufficient to meet the research needs), and different lithology mineral particles can be accurately and clearly identified.

[0006] Currently, the selection of mechanical parameters for different mineral particles is somewhat subjective. The development of nanoindentation and scratching techniques, combined with high-resolution imaging technology, has made it possible to objectively determine mechanical parameters, thereby obtaining more reliable and accurate mechanical parameters and establishing corresponding material mechanical parameter data. Summary of the Invention

[0007] In view of the problems existing in the existing background technology, the purpose of the present invention is to provide a three-dimensional finite element simulation method based on a high-precision digital core reconstruction model to solve the defects of the finite element simulation method based on digital core reconstruction in the existing technology.

[0008] The technical solutions of the present invention are as follows:

[0009] Based on a high-precision three-dimensional finite element simulation method for digital core reconstruction model, the steps are as follows:

[0010] Step S101: Observe the surface of the rock sample using an electron microscope and mark the target area of ​​interest;

[0011] Step S102: Processing the target area into the size required for the experiment;

[0012] Step S103: performing a focused ion beam scanning electron microscope (FIB-SEM) scanning experiment to obtain a three-dimensional digital core image reflecting the true composition and structural heterogeneity characteristics of the rock at the nanometer level;

[0013] Step S104: Using improved digital image processing technology to segment, reduce noise, enhance, and merge the image obtained in step S103, and using a multi-threshold segmentation method to determine the segmentation threshold, clarify the grayscale intervals corresponding to each mineral occurrence, organic matter, interface, pore, and crack defect, and convert the grayscale image into a grayscale image with multiple grayscale values;

[0014] Then stitch the segmented images together;

[0015] The merged multi-grayscale image is smoothed to remove isolated pixels.

[0016] Step S105: using a grid mapping method to convert the processed digital image into calculation grid units;

[0017] Step S106: Using electron microscopy combined with nanoindentation testing to obtain the mechanical parameters of each shale component and their corresponding statistical distribution characteristics, and using electron microscopy combined with nanoscratch testing to obtain the mechanical parameters of mineral grain boundaries / interfaces and structural defects;

[0018] Step S107: Based on the grayscale interval determined by the segmentation threshold, the corresponding physical and mechanical parameters are assigned to the unit grid of each grayscale interval. The mechanical parameters of the same grayscale interval (the same mineral) are assigned using the Monte Carlo method, that is, the mechanical parameters obtained in S106 are assigned to the reconstruction model in S105 to complete the reconstruction of the numerical core;

[0019] Step S108: Incorporate the digital core into the numerical software to complete the finite element numerical calculation.

[0020] In step S104, the specific operation steps of the improved digital image processing technology are as follows: extracting part of the pixels of the entire FIB-SEM image as processing units through rectangles, and then using traditional segmentation methods to process each part separately. Let g(x, y) be the grayscale value of the point (x, y) in the original FIB-SEM image, f i (x, y) is the grayscale value of the extracted rectangular image from the original FIB-SEM image. The image is divided into n parts, each with a height of h△. The extraction process can be expressed as follows:

[0021]

[0022] When extracting the image height and If the height h of the original FIB-SEM image is greater than that of the original FIB-SEM image, the extraction operation is stopped.

[0023] In step S101, the specific operation method is as follows: Since the model range of the FIB-SEM scan is limited, generally 30×30×30μm. Therefore, in order to ensure that the intended research target is within the FIB-SEM scanning range, it is necessary to pre-scan the rock sample using a scanning electron microscope. The scanning range needs to be large enough to include typical features such as organic matter inside the rock, pores inside organic matter, inorganic minerals, pores inside inorganic minerals, and cracks. At the same time, the resolution of the scanning electron microscope must also be high enough to observe the smallest nanopore distribution and typical features such as obvious mineral grain boundaries. The location of the research area can be determined and then marked.

[0024] In the step S103, the specific operation is as follows: the FIB-SEM scanning technology is used to continuously cut the sample by FIB (ion beam) and image the cut surface by SEM (high-energy electron beam) to obtain the three-dimensional spatial distribution morphology of the sample. In order to facilitate milling and imaging, the angle between the ion beam and the electron microscope beam is between 30° and 60°, and the specific value depends on the etching depth required. The principle of SEM is to distinguish minerals based on the conductivity of the elements. Different materials have different conductivity elements. Materials with high conductivity emit more energy and appear brighter in the SEM image. Therefore, different minerals can be used to present different brightness in the SEM grayscale image to distinguish minerals, pores, etc. in the sample, while the same minerals have similar grayscale values.

[0025] In step S104, the segmented digital image is processed using improved digital image processing techniques. This step, crucial for ensuring the accuracy of the digital core numerical model, includes enhancement, filtering, and noise reduction. Enhancement enhances image contrast to facilitate identification and differentiation of mineral components; filtering and noise reduction remove noise from the image. Based on the image's color characteristics and the multiphase system of the rock image (mineral components, pores, and other defects), an appropriate multi-threshold segmentation method is selected to determine the segmentation threshold. Based on the segmentation threshold, the corresponding grayscale intervals are determined, converting the grayscale image into a multi-value grayscale image.

[0026] In step S104, the images that have been segmented and processed by digital image processing technology are spliced ​​together, which is the inverse operation of formula (1).

[0027] In step S105, the specific operations are as follows: assuming that a single-layer digital image has a certain thickness t, the rock is homogeneous within the thickness t, and the thickness of the unit can be the same as the milling thickness, or can be defined according to calculation requirements and actual conditions; the digital image is converted into a finite element calculation grid through a grid mapping method, so the number of calculation grids of a single layer depends on the number of pixels of a single digital image, and the length and width of the calculation grid depend on the size of the image pixels; and a number of digital image slices are superimposed at a certain interval to realize three-dimensional modeling.

[0028] The specific operations of step S106 are as follows: the mineral composition analysis and specific mineral location of the sample surface are performed through an electron microscope, and the positions of different minerals and their distribution characteristics are calibrated; a representative area with high flatness is selected from the predicted mineral composition for nanoindentation testing to obtain mechanical parameters (elastic modulus, Poisson's ratio, strength); with the help of electron microscopy technology, a suitable area is selected for scratch testing to obtain the mechanical parameters of the mineral grain boundaries / interfaces.

[0029] Beneficial effects of the present invention:

[0030] The digital rock core constructed by this invention truly and quantitatively incorporates the spatial distribution and structure of different mineral materials and microscopic defects within the rock into the numerical model, achieving a realistic representation of geometric, boundary / interface, and material heterogeneity. Specifically, geometric heterogeneity: The spatial structure of defects such as mineral particles and pores (including position, size, shape, and orientation) is obtained through FIB-SEM scanning experiments; boundary / interface heterogeneity: This is extracted through edge detection technology during image processing and mechanical parameters are assigned to it using nanoscratch experiments; material heterogeneity: The mechanical parameters (elastic modulus, Poisson's ratio, strength) and statistical distribution characteristics of different mineral particles are obtained through nanoindentation technology and characterized and implemented in the numerical model. In particular, the statistical distribution characteristics of the mechanical parameters of the same mineral particles are implemented in the digital rock core, making the constructed digital rock core more realistic. In other words, through the above technologies, the rock core is finely characterized and micromechanically assigned, comprehensively representing the core's true state, and completing the reconstruction of the digital rock core finite element simulation method. This can then enable various sophisticated numerical studies such as the dynamic expansion of cracks between mineral particles and the disturbance of cracks on pores under external forces. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The figure is a flow chart of a three-dimensional finite element simulation method based on a high-precision digital core reconstruction model according to the present invention.

[0032] Figure 2 This is a schematic diagram of the present invention by selecting and marking a representative area for study under an electron microscope.

[0033] Figure 3 Schematic diagram of the principle of the FIB-SEM experimental equipment of the present invention.

[0034] Figure 4 Schematic diagram of the non-uniform color characteristics and image segmentation of the FIB-SEM image of the present invention.

[0035] Figure 5 This is a schematic diagram of the present invention for locating specific minerals on the sample surface under an electron microscope, calibrating the positions of different minerals, and performing nanoindentation experiments on the calibrated positions to obtain the mechanical parameters of each mineral.

[0036] Figure 6 Schematic diagram of converting digital images into numerical calculation grids based on the grid mapping method of the present invention, (a) Schematic diagram of a single FIB-SEM digital image, (b) Schematic diagram of converting digital images into finite element calculation grids using the grid mapping method, Figure 6 (c) is a single-layer finite element mesh diagram.

[0037] Figure 7 Schematic diagram of the FIB-SEM scanning three-dimensional image and the reconstructed finite element numerical digital core model of the present invention, (a) Schematic diagram of the FIB-SEM scanning three-dimensional image, (b) reconstruction of the digital core three-dimensional finite element model by the superposition principle, (c) Schematic diagram of the reconstructed three-dimensional finite element numerical digital core model, (d) Schematic diagram of the crack morphology of the digital core three-dimensional finite element model after the uniaxial compression numerical test. DETAILED DESCRIPTION

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0039] The purpose of the present invention is to provide a three-dimensional finite element simulation method for digital core reconstruction based on fine core characterization and micromechanical assignment, so as to solve the two major bottleneck problems of "inaccurate model" and "inaccurate parameters" in existing rock mechanics numerical simulation.

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. The embodiments do not constitute a limitation of the embodiments of the present invention.

[0041] This embodiment provides a three-dimensional finite element simulation method based on a high-precision digital core reconstruction model. Figure 1 This is a flow chart of a three-dimensional finite element simulation method for the dynamic evolution of micro-fractures based on a high-precision digital core reconstruction model provided by an embodiment of the present invention. Figure 1 The construction of a digital core three-dimensional finite element simulation method based on core fine characterization and micromechanical assignment specifically includes:

[0042] Step S101: Observe the surface of the rock sample through an electron microscope and mark the target area of ​​interest.

[0043] In this embodiment, a scanning electron microscope is used to scan the rock sample. The scanning range needs to be large enough to include typical features such as organic matter inside the rock, pores inside organic matter, inorganic minerals, pores inside inorganic minerals, and cracks. At the same time, the resolution of the scanning electron microscope must be high enough to observe the smallest nanopore distribution and typical features such as obvious mineral grain boundaries. Figure 2 , under the scanning electron microscope, mark the research area of ​​interest.

[0044] Step S102: The target area is processed into a suitable size, which is approximately a 10 mm × 10 mm × 5 mm square sheet.

[0045] Step S103: Perform a focused ion beam scanning electron microscope (FIB-SEM) scanning experiment to obtain a three-dimensional digital core image that reflects the true composition and structural heterogeneity characteristics of the rock with nanometer-level precision.

[0046] Reference Figure 3 , shows a schematic diagram of the FIB-SEM scanning technology principle. The FIB-SEM scanning technology principle is to continuously cut the sample through the FIB (ion beam) and image the cut surface with the SEM (high-energy electron beam) to obtain the three-dimensional spatial distribution morphology of the sample. To facilitate milling and imaging, the angle between the ion beam and the electron microscope beam is between 30° and 60°, the specific value depending on the required etching depth. The principle of SEM is to distinguish minerals based on the conductivity of elements. Different materials have different elemental conductivity. Materials with high conductivity emit more energy and appear brighter in SEM images. Therefore, the different brightness of different minerals in the SEM grayscale image can be used to distinguish minerals, pores, etc. within the sample, while minerals of the same type have similar grayscale values. For reservoir rock research, the resolution can generally be set to 5-20nm (but is not limited to this and should be adjusted according to actual conditions). This resolution is sufficient to identify nanoscale pores in organic and inorganic matter. Things to note about FIB-SEM technology: the sample should be smaller than 5×5×1cm. If the sample is too large, it needs to be cut and sampled. The sample surface must be able to be sprayed with gold to increase conductivity. The cutting depth must be less than 10μm.

[0047] For example 3D grayscale images obtained using FIB-SEM technology, please refer to Figure 7 (a) The mineral components are clearly distinguishable. (Resolution 10nm, model size 16×8×4μm)

[0048] Step S104: Use improved digital image processing technology to perform segmentation, noise reduction, enhancement, merging and other processing, and determine the segmentation threshold through a multi-threshold segmentation method, and clarify the grayscale intervals corresponding to defects such as mineral occurrence, organic matter, interface, pores, cracks, etc.

[0049] Reference Figure 4 Figure 2 shows a schematic diagram of the non-uniform color characteristics of an acquired grayscale image (bright on top and dark on the bottom). Due to the image's color non-uniformity, an improved digital image processing technique is used to address this non-uniform color characteristic. The main idea behind this method is to extract pixels from the entire FIB-SEM image using rectangles as processing units, then process each segment individually using traditional segmentation methods.

[0050] Assume that g(x,y) is the grayscale value of point (x,y) in the original FIB-SEM image, f i (x,y) is the grayscale value of the rectangular image extracted from the original FIB-SEM image. The image is divided into n parts, and the height of each part is h△. Figure 4 , the segmentation and extraction process can be expressed by the following formula:

[0051]

[0052] If the height of the image is extracted by segmentation and If the height h of the original FIB-SEM image is greater than that of the original FIB-SEM image, the extraction operation is stopped.

[0053] The segmented digital images are processed separately using digital image processing technology. This step is a key step to ensure the accuracy of the digital core numerical model. Specifically, it includes:

[0054] The purpose of enhancement processing is to enhance the contrast of the image so as to better identify and distinguish the various mineral components.

[0055] Filtering and noise reduction processing aims to remove noise points in graphics.

[0056] The determination of the segmentation threshold is based on the color characteristics of the image and the multiphase system of the rock image (various mineral components, pores and other defects). An appropriate multi-threshold segmentation method is selected to determine the segmentation threshold. In this example, an automatic method, the Otsu multi-threshold segmentation method, is used. Assuming that the grayscale level of the image is L, the grayscale range is [0, 1, ..., L -1]. There are n-1 thresholds T1, T2, ... T n-1 , the image is divided into n categories, expressed as C0 = {0,1,…,T1},…,C i ={T i +1,T i +2,…,T i+1},…,C n-1 ={T n-1 +1,T n-1 +2,…,L-1}, the probabilities of each category are P0, P1,…P n-1 , the mean values ​​are ω0,μ1,…μ n-1 , the multi-threshold segmentation formula is as follows:

[0057]

[0058] According to the segmentation threshold value in the above formula, the corresponding grayscale intervals are determined, and the grayscale image is converted into a grayscale image with multiple grayscale values.

[0059] The segmented images are then spliced ​​together, which is the inverse operation of the segmentation formula.

[0060] The combined multi-gray value image is smoothed to eliminate isolated pixel points.

[0061] Step S105: converting the digital image into a calculation grid cell using a grid mapping method;

[0062] Referring to Figure 6 , a schematic diagram of converting a digital image into a numerical calculation grid based on a grid mapping method according to an embodiment is shown. It is assumed that a single-layer digital image has a certain thickness t, and the rock is homogeneous within the thickness t. The thickness of the cell can be the same as the milled thickness, or can be defined by the calculation requirements and actual conditions. The digital image is converted into a finite element calculation grid through the grid mapping method. Therefore, the number of calculation grids of a single layer depends on the number of pixels of a single digital image, and the length and width of the calculation grid depend on the size of the image pixels. Referring to Figure 7 (b) stacking a plurality of digital image slices according to a certain interval to realize three-dimensional modeling.

[0063] Step S106: obtaining the mechanical parameters of each component of shale and the corresponding statistical distribution characteristics by electron microscopy combined with nanoindentation testing method, and obtaining the mechanical parameters of mineral particle boundaries / interfaces and structural defects by electron microscopy combined with nanoscratch testing method.

[0064] Referring to Figure 5 , a schematic diagram of locating specific minerals on the surface of a sample under an electron microscope, calibrating the positions of different minerals, and performing nanoindentation experiments on the calibrated positions to obtain the mechanical parameters of each mineral according to an embodiment is shown.

[0065] For obtaining the mechanical parameters of minerals, the surface of the sample should be observed under an electron microscope, the mineral components should be predicted, and the areas with higher flatness and representativeness should be selected for nanoindentation experiments to obtain the mechanical parameters (elastic modulus, Poisson's ratio, and strength) of single minerals.

[0066] For obtaining the mechanical parameters of interfaces, electron microscopy technology should be used to select appropriate areas for scratch experiments to obtain the mechanical parameters of mineral particle boundaries / interfaces.

[0067] The results of indentation experiments and scratch experiments have a certain degree of dispersion. Therefore, for single minerals, multiple indentation experiments should be performed, and for each interface, multiple scratch experiments should be performed. The mechanical parameters obtained from multiple experiments are statistically analyzed to obtain the average values of the corresponding material mechanical parameters and their statistical distribution characteristics. The statistical distribution characteristics of material mechanical parameters are the basis for reconstructing the heterogeneous distribution of single mineral finite element cell mechanical properties based on the Monte Carlo method.

[0068] Step S107: Based on the grayscale interval determined by the segmentation threshold, the corresponding physical and mechanical parameters are assigned to the unit grid of each grayscale interval. The mechanical parameters of the same grayscale interval (the same mineral) are assigned using the Monte Carlo method, that is, the mechanical parameters obtained in S106 are assigned to the reconstructed model in S105 to complete the reconstruction of the three-dimensional digital core numerical model.

[0069] In the embodiment, the image is converted into a multi-gray value image, and the mechanical parameters are assigned according to the gray values ​​corresponding to the gray image. If the image is not converted into a multi-gray value image, the mechanical parameters are assigned according to the gray interval.

[0070] The constructed 3D finite element numerical model of the digital core geometrically represents the true structure of the rock. However, in reality, the material mechanical parameters of a single mineral are not uniform and exhibit certain inhomogeneities. This inhomogeneity is also reflected in the experimental results of step S106. Therefore, when reconstructing the digital core finite element model, the mechanical parameters of the individual minerals are assigned using the Monte Carlo method based on the experimental results of step S106, thus ensuring the characterization of the inhomogeneity of the material mechanical parameters.

[0071] In this embodiment, referring to Figure 7 (c) shows a schematic diagram of the reconstructed three-dimensional finite element numerical model of the digital core. The three-dimensional digital core takes into account the spatial structural characteristics of the actual mineral phases. The uneven color of the enlarged image on the right side of the single-phase mineral represents the heterogeneity of its mechanical parameters.

[0072] Step S108: Incorporate the digital core into the numerical software to complete the finite element numerical calculation.

[0073] In this embodiment, referring to Figure 7 (d) shows a schematic diagram of crack morphology after uniaxial compression numerical testing on a three-dimensional finite element model of a digital core. The digital core constructed in this invention accurately and quantitatively incorporates the spatial distribution and structure of different mineral materials and mesoscopic defects within the rock into the numerical model, achieving a realistic representation of geometry, boundaries / interfaces, and material heterogeneity. This enables detailed numerical studies of the dynamic expansion of cracks between mineral particles and the perturbation of pores by cracks under external forces.

[0074] In summary, the proposed 3D finite element simulation method for digital core reconstruction based on detailed core characterization and micromechanical assignment breaks through traditional numerical simulation methods, including the problem of distorted representation of the actual core in previous digital core-based simulation methods. The digital core constructed by this method truly and quantitatively incorporates the spatial distribution and structure of different mineral materials and microscopic defects within the rock into the numerical model, achieving a realistic and detailed characterization and micromechanical assignment of geometry, boundaries / interfaces, and material heterogeneity. This method fully replicates the actual state of the core and completes the reconstruction of the digital core finite element simulation method, providing a reference and laying a foundation for the use of 3D digital cores to carry out accurate and detailed numerical simulations of rock mechanical behavior and fracturing characteristics such as crack propagation.

[0075] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. Based on a high-precision digital core reconstruction model three-dimensional finite element simulation method, characterized by: Here are the steps: Step S101: Observe the surface of the rock sample using an electron microscope and mark the target area of ​​interest; Step S102: Processing the target area into the size required for the experiment; Step S103: performing a focused ion beam scanning electron microscope (FIB-SEM) scanning experiment to obtain a three-dimensional digital core image reflecting the true composition and structural heterogeneity characteristics of the rock at the nanometer level; Step S104: Using improved digital image processing technology to segment, reduce noise, enhance, and merge the image obtained in step S103, and using a multi-threshold segmentation method to determine the segmentation threshold, clarify the grayscale intervals corresponding to each mineral occurrence, organic matter, interface, pore, and crack defect, and convert the grayscale image into a grayscale image with multiple grayscale values; Then stitch the segmented images together; Smoothing the merged multi-grayscale image to remove isolated pixels; Step S105: using a grid mapping method to convert the processed digital image into calculation grid units; Step S106: Using electron microscopy combined with nanoindentation testing to obtain the mechanical parameters of each shale component and their corresponding statistical distribution characteristics, and using electron microscopy combined with nanoscratch testing to obtain the mechanical parameters of mineral grain boundaries / interfaces and structural defects; Step S107: Based on the grayscale interval determined by the segmentation threshold, the corresponding physical and mechanical parameters are assigned to the unit grid of each grayscale interval. The mechanical parameters of the same grayscale interval are assigned using the Monte Carlo method, that is, the mechanical parameters obtained in S106 are assigned to the reconstruction model in S105 to complete the reconstruction of the numerical core; Step S108: Incorporate the digital core into the numerical software to complete the finite element numerical calculation.

2. The three-dimensional finite element simulation method based on a high-precision digital core reconstruction model according to claim 1, characterized in that: In step S104, the specific operation steps of the improved digital image processing technology are as follows: extracting part of the pixels of the entire FIB-SEM image as processing units through rectangles, and then using traditional segmentation methods to process each part separately; let g(x, y) be the grayscale value of the point (x, y) in the original FIB-SEM image, f i (x, y) is the grayscale value of the extracted rectangular image in the original FIB-SEM image. The image is divided into n parts, and the height of each part is h△. The extraction process can be expressed by the following formula: When extracting the image height and If the height h of the original FIB-SEM image is greater than that of the original FIB-SEM image, the extraction operation is stopped.

3. The three-dimensional finite element simulation method based on a high-precision digital core reconstruction model according to claim 1 or 2, characterized in that: In step S105, the specific operation is as follows: assuming that the single-layer digital image has a certain thickness t, the rock is homogeneous within the thickness t, and the thickness of the unit is the same as the milling thickness, or is defined according to the calculation requirements and actual conditions; The digital image is converted into a finite element calculation grid through the grid mapping method. Therefore, the number of single-layer calculation grids depends on the number of pixels in a single digital image, and the length and width of the calculation grid depend on the size of the image pixels; several digital image slices are superimposed at a certain interval to achieve three-dimensional modeling.

4. The three-dimensional finite element simulation method based on a high-precision digital core reconstruction model according to claim 1 or 2, characterized in that: In the step S103, the specific operation is: the FIB-SEM scanning technology is used to continuously cut the sample by ion beam FIB and image the cut surface by high-energy electron beam SEM to obtain the three-dimensional spatial distribution morphology of the sample; the angle between the ion beam and the electron microscope beam is between 30° and 60°.

5. The three-dimensional finite element simulation method based on a high-precision digital core reconstruction model according to claim 3, characterized in that: In the step S103, the specific operation is: the FIB-SEM scanning technology is used to continuously cut the sample by ion beam FIB and image the cut surface by high-energy electron beam SEM to obtain the three-dimensional spatial distribution morphology of the sample; the angle between the ion beam and the electron microscope beam is between 30° and 60°.

6. The three-dimensional finite element simulation method based on a high-precision digital core reconstruction model according to claim 1, 2 or 5, characterized in that: In step S104, the segmented digital images are processed using improved digital image processing technology, specifically including enhancement, filtering, and noise reduction.

7. The three-dimensional finite element simulation method based on a high-precision digital core reconstruction model according to claim 3, characterized in that: In step S104, the segmented digital images are processed using improved digital image processing technology, specifically including enhancement, filtering, and noise reduction.

8. The three-dimensional finite element simulation method based on a high-precision digital core reconstruction model according to claim 4, characterized in that: In step S104, the segmented digital images are processed using improved digital image processing technology, specifically including enhancement, filtering, and noise reduction.

9. The three-dimensional finite element simulation method based on a high-precision digital core reconstruction model according to claim 1, 2, 5, 7 or 8, characterized in that: In step S104, the images that have been segmented and processed using digital image processing techniques are spliced ​​together. The splicing process is the inverse operation of formula (1).

10. The three-dimensional finite element simulation method based on a high-precision digital core reconstruction model according to claim 3, characterized in that: In step S104, the images that have been segmented and processed using digital image processing techniques are spliced ​​together. The splicing process is the inverse operation of formula (1).

Images