Coal rock fabric characterization and micron-centimeter level mechanical property upscaling method and system

Abstract

The invention belongs to the technical field of deep coal bed gas and petroleum development, and discloses a coal rock structure characterization and micron-centimeter level mechanical property upscaling method and system. The method comprises the following steps: determining mineral component positions by using a microscopic component and structure quantitative analysis method; a micro-structure micron-order mechanical property analysis method is used for obtaining micro-structure micron-order mechanical properties; and obtaining deep coal rock centimeter-level mechanical parameters under different stress boundary conditions by utilizing a micrometer-centimeter-level mechanical scale upgrading method based on a finite element model. According to the method, the requirement for a large rock sample is avoided, the operability and economical efficiency of an experiment are improved, and an innovative and efficient solution is provided for mechanical property evaluation of deep coal rock.

Description

Technical Field

[0001] This invention belongs to the field of deep coalbed methane and oil development technology, and particularly relates to a method and system for characterizing coal and rock texture and scaling up the mechanical properties at the micron-centimeter level. Background Technology

[0002] In the development of coal and gas resources at depths of 2000 meters and above, accurately characterizing the rock mechanical properties of reservoirs at centimeter-level precision is a crucial prerequisite for ensuring drilling safety and optimizing fracturing design. However, due to limitations in the engineering geological environment and sampling equipment conditions of deep coal seams, obtaining sufficient standard centimeter-level rock samples suitable for macroscopic mechanical experiments remains extremely difficult. Furthermore, due to the lack of calibration based on measured data, indirect prediction methods for wellbore geomechanical parameters based on logging data cannot guarantee accuracy, leading to frequent engineering problems such as wellbore instability, difficulty in fracture network formation, and poor support, becoming a "bottleneck" problem restricting the efficient development of deep coal and gas. The root cause lies in the fundamental scientific issues behind this technical bottleneck: the microstructural characteristics of deep coal and rock and their multi-scale mechanical properties under their influence. Deep coal and rock are mostly characterized by primary structures, with relatively low fracture development but high filling degree. The types and quantities of diagenetic minerals and the distribution of coal body structure vary greatly, exhibiting strong heterogeneity both longitudinally and laterally, and possessing significant multi-scale mechanical characteristics. Conventional centimeter-level rock mechanics experimental methods, based on classical continuum theory, are insufficient to characterize the influence of the heterogeneous mineral composition and microstructure of rock samples. In recent years, the equipment and technology for characterizing rock microstructure and testing mechanical properties have continued to advance, leading to the development of many upscaling analysis methods, such as homogenization methods like the Mori-Tanaka model and numerical simulation methods based on scanning electron microscopy (SEM) image modeling. However, there are still four limitations to overcome: (1) Existing research has initially established a database of mechanical parameters for organic components such as vitrinite, but SEM is difficult to identify inorganic minerals in coal and rocks, and research on the micromechanical properties of inorganic components and their interfaces in coal and rocks is still very lacking; (2) Homogenization methods cannot characterize the influence of mineral particle shape, spatial distribution, and microcrack development on macroscopic mechanical response; (3) Two-dimensional numerical modeling methods cannot characterize the influence of three-dimensional spatial structure and triaxial stress environment, and it is difficult to conduct experimental verification; (4) Existing research mainly targets the problem of coal mining, with burial depth generally not exceeding 1200m, while the evolution law of coal composition and structural anisotropy of reservoirs deeper than 2000m are significantly different, and a targeted mechanical property characterization system needs to be constructed. Summary of the Invention

[0003] To overcome the problems existing in related technologies, the present invention discloses a method and system for characterizing coal and rock texture and upscaling micron- to centimeter-level mechanical properties, specifically involving a method for characterizing deep coal and rock texture and upscaling micron- to centimeter-level mechanical properties. This invention enables upscaling characterization of deep coal and rock from micron-level mechanical properties to centimeter-level mechanical properties, providing verification data for the interpretation of wellbore geomechanical parameters.

[0004] The technical solution is as follows: A method for characterizing coal and rock texture and scaling up micron-centimeter level mechanical properties, including the following steps: S1. Using quantitative analysis methods of micro-components and structure, based on X-ray diffraction experiments, an automatic mineral analysis system was used to measure the specific mineral composition and proportion of the rock sample, determine the intergrowth relationship between clay minerals and other minerals, and draw morphological color maps to determine the location of mineral components; among them, other minerals include: quartz, pyrite, calcite, siderite, and dolomite. S2, using a method for valid identification of lattice nanoindentation experiments and test results based on morphology color images, a method for inverting the inter-mineral bonding strength by nano-scratch experiments and combining discrete element numerical models, and a method for analyzing the micron-level mechanical properties of microstructures, micron-level mechanical properties of microstructures are obtained. S3, based on the analysis results, uses a finite element model method to characterize the porosity of rock samples based on the CT pore model, a method to convert experimental data into finite element simulation parameters, a method to divide the mesh according to the mineral grain size, a method to assign mineral mechanical properties to the finite element model mesh based on the mineral composition ratio and morphology color map, a method to set the boundary conditions of the finite element model, and a method to scale up the micron-centimeter mechanical properties based on the finite element model, to obtain centimeter-level mechanical parameters of deep coal and rock under different stress boundary conditions.

[0005] Furthermore, based on X-ray diffraction experiments, an automated mineral analysis system was used to measure the specific mineral composition and proportion of the rock samples, determine the intergrowth relationship between clay minerals and other minerals, and draw morphological color maps to determine the location of mineral components, including: S101, grind the rock sample into powder, scan it with an X-ray diffractometer to obtain an X-ray diffraction angle and diffraction signal intensity curve, find the peak value of the curve and the corresponding X-ray diffraction angle based on the curve spectrum and the original curve data, each X-ray diffraction angle corresponds to a mineral, and complete the qualitative analysis of the mineral composition of the rock sample by X-ray diffraction experiment. S102, prepare the rock sample into an experimental sample, import the qualitative analysis results of the X-ray diffraction experiment into the automatic mineral analysis system, measure the specific proportion of each mineral in the experimental sample, and provide proportional parameters for finite element modeling; S103, other minerals have different mineral compositions in deep coal and rock under different geological conditions. The automatic mineral analysis system is used to quantitatively measure the intergrowth relationship between clay minerals and other minerals, so as to provide the contact relationship between clay minerals and other minerals for the establishment of a finite element basic model. S104, the rock sample is made into powder, and the size distribution ratio of single mineral grains in micrometers is measured using an automatic mineral analysis system to provide data for mesh generation of the finite element model; S105 uses the scanning results of an automated mineral analysis system to draw morphological color images, which characterize the location of each mineral and provide the location of indentation points for nanoindentation experiments.

[0006] In step S2, the lattice nanoindentation experiment based on the morphology color image includes: A lattice map of the indentation points from the nanoindentation experiment was plotted on a morphology color image. By adjusting the number and spacing of the indentation points, the points in the lattice map were made to cover all mineral components. Each point on the lattice map was numbered, and the mineral component corresponding to each point was determined based on the morphology color image. The elastic modulus of the material was obtained by analyzing the rebound portion of the indentation using the load-depth curves during loading and unloading in the nanoindentation process, either using the Oliver-Pharr method or the Hartmann regression model. The hardness was calculated by measuring the maximum indentation depth and the maximum applied load, using the following formula: ; In the formula, For the maximum applied load, This corresponds to the indentation contact area. Hardening modulus; The mechanical parameters measured at each indentation point in nanoindentation correspond to the mechanical parameters of the mineral composition. The method for determining the validity of the test results includes: determining the position of each measuring point in the nanoindentation lattice of the morphology color image; if only a single component is indented at the measuring point, then the measuring point is a valid measuring point; otherwise, it is an invalid measuring point, and the corresponding load-depth test data is not used; obtaining the mechanical property parameters of the component through statistical analysis; wherein, the mechanical property parameters include the hardness and elastic modulus of the micro-component. The nano-scratching experiment includes: ultrasonic cleaning of rock samples to remove surface oil and dust; using a Berkovich indenter for hard rock samples and a spherical indenter for soft rock samples; for brittle materials, deriving the required fracture energy and stress intensity factor in the finite element model from crack propagation length, crack depth, and maximum load data measured by the nano-scratching experiment; for tough materials, deriving plastic hardening parameters and damage evolution parameters in the finite element model using plastic deformation, crack propagation, and hardening behavior observed in the nano-scratching experiment.

[0007] In step S2, the nano-scratching experiment and the method for inverting the intermineral bonding strength by combining discrete element numerical models include: Identifying the critical load as the starting point of interface debonding by abrupt change: Scratch test is performed on mineral samples using a nano-scratch test device, and the tangential force and normal force under different loads are recorded. Based on the ratio of tangential force to normal force measured in the nano-scratch test, the friction coefficient is plotted as a function of load. The friction coefficient curve is analyzed to identify abrupt change points. The load corresponding to the abrupt change point is the critical load, which represents the start of debonding or crack propagation at the mineral interface. The scratched area was located by in-situ SEM observation, and the crack was identified as either interfacial or transgranular fracture. After the nano-scratching experiment was completed, the scratched area was observed in-situ using a scanning electron microscope. The crack propagation morphology was determined by SEM image analysis, and the crack was identified as either interfacial or transgranular fracture. Quantifying scratch depth, width, and elevation using a 3D profilometer: The scratch area is scanned using a 3D profilometer to obtain the scratch depth, width, and elevation; the scratch depth, width, and elevation are then quantified using the scanned data. Combining DEM simulation to invert plastic deformation energy: Based on experimental data obtained from nano-scratching experiments, a discrete element numerical model (DEM) containing mineral particles and interfaces is established to simulate the plastic deformation of minerals during the scratching process. Through the DEM simulation, the plastic deformation energy borne by the mineral interface during the scratching process is inverted. The Hertz contact force model was used to calculate the interfacial bonding energy: the Hertz contact force model was used to describe the contact force and contact displacement of the mineral interface during the scratching process; according to the Hertz contact force model, the relationship between contact force and contact displacement is as follows: ; In the formula, For contact force, For the equivalent elastic modulus, For the contact radius, This refers to the contact displacement. The contact force at the mineral interface was calculated using the Hertz model, and the interfacial bonding energy, i.e. the bonding strength between minerals, was derived.

[0008] In step S3, the method for establishing a finite element model for characterizing the porosity of rock samples based on the CT porosity model includes: importing the CT two-dimensional scan results into data processing software and generating a three-dimensional porosity structure model using image reconstruction technology; importing CT scan data and extracting porosity regions through denoising, cropping, and threshold segmentation, and filling pores and removing small volume regions using morphological processing; performing three-dimensional reconstruction and generating a three-dimensional porosity structure model of the rock sample using volume rendering; calculating porosity and exporting the model for analysis or visualization. Write a Python program to automatically import the 3D pore structure model into the finite element simulation software, converting the geometry of the 3D pore structure model into a finite element mesh format; execute the written Python program to seamlessly import the generated 3D pore structure model into the finite element simulation software.

[0009] In step S3, the method for converting experimental data into finite element simulation parameters includes: calculating the elastic modulus and hardness of each mineral component using the load depth curve of the nanoindentation experiment; Derivation of fracture energy of brittle materials using nano-scratch test results and stress intensity factor Yield strength of tough materials Hardening modulus and damage evolution parameters; The results of the nano-scratching experiment include: crack propagation length, crack depth, and maximum load; fracture energy. This describes the energy absorbed by a material during crack propagation, used to quantify crack propagation. The crack propagation area is measured using nano-scratching experiments, and the fracture energy is calculated based on the energy absorbed during crack propagation. The formula is as follows: ; In the formula, The elastic modulus of a material represents its ability to deform elastically when subjected to stress. The area change required for crack propagation; This represents the actual length of the crack propagation. The crack area was measured by scanning the crack propagation length and crack depth in the nano-scratching experiment using scanning electron microscopy. This represents the actual length of the crack propagation. The stress intensity factor This is an important parameter used to describe the stress field intensity at the crack tip and to predict the failure behavior of materials in the presence of cracks. The formula is as follows: ; In the formula, It is a geometric factor, which depends on the shape of the crack and the loading conditions; for nano-scratching experiments, the geometric factor is determined by the shape of the sample and the loading method. This is the stress under maximum load, representing the effect of the applied external load on the material; in nano-scratching experiments, the stress is determined by the maximum load. and contact area To calculate, the expression is: ; In the formula, The contact area between the indenter and the material surface is calculated using the indenter geometry and contact depth. The yield strength of tough materials was derived from the maximum load, plastic deformation zone, and crack propagation behavior in nano-scratching experiments. Hardening modulus and damage evolution parameters; the yield strength Describes the stress in a material during plastic deformation; in nanoscratching experiments, the yield strength is determined by the maximum load. and contact area calculate: ; The hardening modulus This represents the rate of stress increase after yielding, derived from the stress-strain relationship of plastic deformation during the scratching process; the following relationship is used for derivation: ; In the formula, For stress, For yield strength, In response to the situation; The hardening modulus was obtained by fitting the stress-strain curves of the experimental data. ; Damage evolution models are used to describe the accumulation of damage in materials after they are subjected to stress, eventually leading to crack propagation. The formula for damage energy is: ; In the formula, It is the strain energy during the damage process, representing the energy absorbed by the material during crack propagation; It is the damage initiation strain; It is stress.

[0010] In step S3, the method for dividing the mineral grain size into grids includes: grinding the rock sample into powder, using scanning electron microscopy and energy dispersive spectroscopy of an automatic mineral analysis system to obtain high-resolution images of the sample, automatically identifying mineral particles through image processing software, calculating the geometric characteristics of each grain, including diameter and aspect ratio, and generating a grain size distribution table, and dividing the grid size and proportion according to the mineral grain size distribution table; Image processing software is used to automatically identify particles in high-resolution images. Thresholding segmentation, edge detection, and morphological processing techniques are employed to extract the contours and shapes of mineral particles from complex images. During the identification process, the software separates each individual grain based on its grayscale value, shape, and other features, and marks its position and boundaries. The software calculates the geometric features of each grain, including its maximum diameter and aspect ratio. The diameter represents the maximum size of the grain, while the aspect ratio reflects its morphology; a larger aspect ratio indicates a longer grain. Finally, the software summarizes the calculated diameter and aspect ratio data to generate a grain size distribution table.

[0011] In step S3, the method for assigning mineral mechanical properties to the finite element model mesh based on the mineral composition ratio and morphological color map includes: a method of writing a Python program to allocate mineral components to the finite element model mesh according to the ratio, and a method of writing a Python program to allocate different mineral components to the corresponding positions in the color map according to the morphological color map and the intergrowth relationship between minerals; The mineral components are proportionally distributed to the finite element model mesh. Mechanical properties are added to each mineral component. The finite element model mesh is divided and numbered so that each mesh has a corresponding mineral component. The shape and position of each mineral are determined according to the morphological color map and the intergrowth relationship between minerals. According to the shape and position of each mineral, the mesh with the corresponding component is arranged to complete the establishment of the centimeter-level finite element model.

[0012] In step S3, the method for setting boundary conditions for the finite element model includes: For uniaxial compression experiments, boundary conditions and loads are first set. The fixed end is set using Displacement / Rotation boundary conditions, setting all displacements at one end of the sample to zero to simulate the sample being fixed to one end of the compressor. The loaded end is set by applying pressure or displacement, selecting to apply a compressive load to the other end of the sample to simulate the compression head contacting the sample in the experiment. The compressive load is achieved by applying uniform pressure or by controlling the displacement of the sample. For the triaxial compression experiment, the bottom of the finite element model is fixed, and Displacement / Rotation boundary conditions are applied, setting the displacement in all directions at the bottom to zero to simulate the fixed condition of the base in the actual experiment. A compressive load is applied to the top end face, and displacement can be applied to simulate axial compression deformation, or pressure can be applied to simulate the applied compressive force. A confining pressure is applied to the side of the sample, and uniform lateral pressure is set through Pressure to simulate the lateral pressure on the sample in the experiment. All operations are completed through the Load module, combining bottom fixation, top compression, and lateral confining pressure to simulate the actual triaxial stress state. In the Step module, a static analysis step is defined, and the loading time or rate is adjusted to complete the entire triaxial compression simulation setup. Finally, the macroscopic mechanical finite element uniaxial compression or triaxial compression model is run to obtain macroscopic mechanical data.

[0013] Another object of the present invention is to provide a system for characterizing coal and rock texture and upscaling micron- to centimeter-level mechanical properties. This system implements the aforementioned method for characterizing coal and rock texture and upscaling micron- to centimeter-level mechanical properties. The system includes: The microscopic composition and structure quantitative analysis module, based on X-ray diffraction experiments, uses an automatic mineral analysis system to measure the specific mineral composition and proportion of rock samples, determine the intergrowth relationship between clay minerals and other minerals, and draw morphological color maps to determine the location of mineral components. The microstructure micron-level mechanical property analysis module is used to obtain the microstructure micron-level mechanical properties by utilizing a method for valid identification of lattice nanoindentation experiments and test results based on morphology color images, a method for inverting the inter-mineral bonding strength by nano-scratch experiments and combining discrete element numerical models, and a microstructure micron-level mechanical property analysis method. The module for upgrading micrometer- to centimeter-level mechanics based on the finite element model includes: a method for establishing a finite element model for characterizing rock sample porosity based on the CT pore model; a method for converting experimental data into finite element simulation parameters; a method for meshing based on mineral grain size; a method for assigning mineral mechanical properties to the finite element model mesh based on mineral composition ratio and morphology color images; a method for setting boundary conditions for the finite element model; and a method for upgrading the micrometer- to centimeter-level mechanics based on the finite element model to obtain centimeter-level mechanical parameters of deep coal and rock under different stress boundary conditions.

[0014] Combining all the above technical solutions, the beneficial effects of this invention are as follows: First, this invention provides a method for characterizing the texture of deep coal and rock formations and scaling up their mechanical properties to the micrometer-centimeter level, including: a quantitative analysis method for microscopic components and structures, a method for analyzing the micrometer-level mechanical properties of microscopic textures, and a scaling up method for micrometer-centimeter-level mechanics based on a finite element model. The quantitative analysis method for microscopic components and structures is used to analyze the mineral composition, proportions, and pore structure of rock samples, providing basic parameters for the finite element model; morphological images of mineral particles are obtained using a scanning electron microscope (SEM) with an automated mineral analysis system; further, energy dispersive spectroscopy (EDS) is used to analyze the mineral composition, thereby generating color images of the mineral component morphology and spatial distribution, further providing location information for lattice nanoindentation experiments; the automated mineral analysis system is used to measure the intergrowth relationship between clay minerals and other minerals, further determining the contact form between clay minerals and other minerals in the model; the pore structure of the rock sample is scanned using CT, and a three-dimensional pore structure model of the rock sample is established using a Python program.

[0015] Secondly, the micro-scale mechanical property analysis method for microstructure includes lattice nanoindentation experiments and nano-scratch experiments. The nanoindentation experiment combines the morphology color images to measure the micro-mechanical properties of each mineral component, providing a basis for setting the mechanical properties of the finite element model. In addition, this invention constructs an inversion method for the inter-mineral bonding strength that combines nano-scratch experiments and discrete element models. The nano-scratch experiment evaluates the brittleness and toughness of rock samples, determines the crack propagation mode and failure characteristics of brittle materials, and determines the plastic deformation and crack propagation capacity of tough materials, providing brittleness and toughness properties for the finite element model. Furthermore, this invention constructs an inversion method for the inter-mineral bonding strength that combines nano-scratch experiments and discrete element models.

[0016] Third, the scale-up method for micrometer-centimeter-level mechanics based on the finite element model includes: a finite element model method for characterizing rock sample porosity based on the CT pore model; a method for converting experimental data into finite element simulation parameters; a method for meshing based on mineral grain size; a method for assigning mineral mechanical properties to the finite element model mesh based on mineral component ratios, morphological color images, and the intergrowth relationship between clay minerals and other minerals; and a method for setting boundary conditions for the finite element model. It should be noted that the conversion of experimental data into finite element model parameters includes: converting the original experimental data from lattice nanoindentation experiments into the elastic modulus and hardness of each mineral component; converting the crack propagation length, crack depth, and maximum load from nano-scratch experiments into the fracture energy and stress intensity factor of brittle materials, and the yield strength, hardening modulus, and damage evolution parameters of ductile materials; the method for assigning mineral mechanical properties to the finite element model mesh based on mineral component ratios, morphological color images, and the intergrowth relationship between clay minerals and other minerals includes: a method for writing a Python program to proportionally allocate mineral components to the finite element model mesh; and a method for writing a Python program to allocate different mineral components to corresponding positions in the color images based on the morphological color images and the intergrowth relationship between minerals.

[0017] Fourth, this invention evaluates the mechanical properties of small, fragmented deep coal and rock samples, avoiding the need for larger, intact rock samples. This significantly reduces the sampling difficulty and cost of deep coal and rock mechanical experiments, while improving the operability of the experiments. This technical solution can provide accurate mechanical parameters for the development of coalbed methane and oil, optimize the resource extraction process, improve production efficiency and safety, and has significant economic benefits. The expected commercial value is reflected in the optimization of coalbed methane, oil development, and related mineral resource extraction, promoting technological upgrading and efficiency improvement in the industry. In current mechanical experiments, centimeter-sized rock samples are generally required for testing. However, deep coal and rock samples are difficult to obtain and are often small and incomplete, making existing mechanical experimental methods ineffective for evaluation. This invention innovatively solves the problem of difficult acquisition of deep coal and rock samples through a scale upgrade method from micrometer to centimeter, providing a new technical solution for processing small coal and rock samples. Compared with traditional methods that rely on large rock samples, this invention fills the technical gap in the mechanical property analysis of small coal and rock samples and overcomes the limitations of existing technologies.

[0018] Fifth, traditional coal and rock mechanics experiments rely on obtaining large rock samples for mechanical analysis. However, obtaining samples from deep coal and rock formations is extremely difficult, and the samples obtained are often small or fragmented. This problem makes mechanical performance evaluation complex and inefficient. The method of this invention enables effective analysis and evaluation using limited, small samples through micrometer-centimeter-scale mechanical property enhancement technology, solving the long-standing problem of testing the mechanical properties of deep coal and rock. This technical solution makes the use of small samples possible and can accurately predict the mechanical properties of deep coal and rock. Traditional mechanical analysis methods generally consider large rock samples as a prerequisite for accurate evaluation, neglecting the feasibility of mechanical performance testing in small samples. This invention overcomes this bias by innovatively introducing a micrometer-centimeter-scale mechanical property enhancement method. Through detailed analysis of small samples, this invention breaks through the limitations of traditional techniques, providing an efficient mechanical evaluation method suitable for deep coal and rock, effectively utilizing existing small rock samples for high-precision mechanical analysis. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure; Figure 1 This is a flowchart of the method for characterizing coal and rock texture and scaling up micron-centimeter mechanical properties provided in the embodiments of the present invention; Figure 2 This is a schematic diagram of the principle of the method for characterizing coal and rock texture and scaling up micron-centimeter mechanical properties provided in the embodiments of the present invention; Figure 3This is a schematic diagram of the indentation points and their effectiveness identification on the mineral distribution map provided in this embodiment of the invention; Figure 4 This is a schematic diagram of the interface bonding strength based on discrete element numerical model and nano-scratching inversion provided by an embodiment of the present invention. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] The innovation of this invention lies in its proposed method for upscaling mechanical properties from the micrometer to the centimeter scale. This method innovatively integrates quantitative analysis of microscopic components and structure with micrometer-level mechanical property analysis results, utilizing a finite element model to effectively upscale mechanical parameters to the centimeter level. This breaks through the traditional single-scale mechanical evaluation mode, achieving integrated analysis of mechanical properties at different scales. This method effectively solves the problem of obtaining deep coal and rock samples, and is particularly suitable for fragmented coal and rock samples. It can accurately assess mechanical properties without relying on large, intact rock samples, significantly improving experimental operability and economy. Simultaneously, the mechanical parameter upscaling technology based on the finite element model can obtain accurate mechanical properties of deep coal and rock under different stress boundary conditions, avoiding the dependence of traditional methods on large rock samples. It provides accurate mechanical parameters at multiple scales, improving evaluation accuracy and efficiency. Furthermore, by combining quantitative analysis of microscopic components and structure with micrometer-level mechanical property analysis, it comprehensively characterizes the microstructure and mechanical properties of coal and rock, providing scientific and accurate mechanical parameter support for deep coal and rock development. Example 1, as Figure 1 As shown, the coal and rock texture characterization and micron-centimeter-scale mechanical property upscaling method provided in this embodiment of the invention includes: a quantitative analysis method for micro-components and structures, a micron-scale mechanical property analysis method for micro-structure, and a micron-centimeter-scale mechanical upscaling method based on a finite element model.

[0022] S1. Using quantitative analysis methods of micro-components and structure, based on X-ray diffraction experiments, an automatic mineral analysis system was used to measure the specific mineral composition and proportion of the rock sample, determine the intergrowth relationship between clay minerals and other minerals, and draw morphological color maps to determine the location of mineral components; among them, other minerals include: quartz, pyrite, calcite, siderite, and dolomite. For example, specifically including: S101. X-ray diffraction experiments were conducted. The rock sample was ground into powder with a particle size between 10 and 50 micrometers and placed in a dedicated X-ray diffraction experimental carriage. Then, it was placed in an X-ray diffractometer for scanning analysis. After scanning, an X-ray diffraction angle (2θ) versus diffraction signal intensity (Intensity) curve was obtained. Based on the curve spectrum and the original curve data, the peak value and the corresponding X-ray diffraction angle were found. Each X-ray diffraction angle corresponds to a mineral, thereby completing the qualitative analysis of the mineral composition of the rock sample.

[0023] S102, the rock sample is made into a cube-shaped experimental sample with a side length of 3-25mm. Then, the qualitative analysis results of the X-ray diffraction experiment are imported into the automatic mineral analysis system to measure the specific proportion of each mineral, providing proportional parameters for subsequent finite element modeling.

[0024] S103 utilizes an automated mineral analysis system to quantitatively measure the intergrowth relationship between clay minerals and other minerals, providing contact relationships between clay minerals and other minerals for the subsequent establishment of a finite element model.

[0025] S104: 15g of rock sample was made into 200-mesh powder. The size distribution ratio of single mineral grains in the range of 0-100 micrometers was measured using an automatic mineral analysis system, providing data support for subsequent finite element model mesh generation.

[0026] S105 utilizes the scanning results of an automated mineral analysis system to generate morphological color images, which visually represent the location of each mineral. Dot matrix diagrams are generated as needed to provide indentation point locations for subsequent nanoindentation experiments.

[0027] S2, using a method for valid identification of lattice nanoindentation experiments and test results based on morphology color images, a method for inverting the inter-mineral bonding strength by nano-scratch experiments and combining discrete element numerical models, and a method for analyzing the micron-level mechanical properties of microstructures, micron-level mechanical properties of microstructures are obtained. For example, the lattice nanoindentation experiment based on morphology color images first involves drawing a lattice map of the morphology color image. It should be noted that by adjusting the number and spacing of the dots, it is ensured that the dots in the lattice map completely cover all mineral components. Each dot in the lattice is numbered, and each dot is associated with its corresponding mineral component according to the morphology color image. The elastic modulus of the material is obtained by analyzing the rebound portion of the indentation using the load-depth curves obtained during loading and unloading in the nanoindentation process, employing the Oliver-Pharr method or Hartmann regression model. Hardness is calculated by measuring the maximum depth of the indentation and the maximum applied load, using the following formula: ; In the formula, For the maximum applied load, This corresponds to the indentation contact area. Hardening modulus; In summary, the mechanical parameters measured at each indentation point in nanoindentation correspond to the mechanical parameters of the mineral composition.

[0028] The method for determining the validity of test results involves identifying the locations of each measurement point in the nanoindentation lattice of the morphology color image. If a measurement point is indented by only a single component, it is considered a valid measurement point; otherwise, it is considered invalid, and the corresponding load-depth test data will not be used. Multiple sets of lattice tests are conducted to ensure that each major component has at least 10 valid measurement points to mitigate the influence of microscopic heterogeneity. Finally, the mechanical property parameters of the component are obtained through subsequent statistical analysis. The indentation locations on the mineral distribution map and their validity determination are as follows: Figure 3 As shown.

[0029] For example, in the nano-scratch experiment, the rock sample is ultrasonically cleaned to remove surface oil and dust; further, the rock sample surface is polished to reduce the impact of roughness on the results; further, a Berkovich indenter is selected for harder rock samples and a spherical indenter is selected for softer rock samples; further, for brittle materials, the crack propagation length, crack depth, and maximum load measured by the nano-scratch experiment are used to derive the required fracture energy and stress intensity factor in the finite element model; for tough materials, the plastic deformation, crack propagation, and hardening behavior in the nano-scratch experiment are used to derive the plastic hardening parameters and damage evolution parameters in the finite element model.

[0030] For example, the nano-scratch experiment and the method for inverting the inter-mineral bonding strength using discrete element method (DEM) simulation utilize the ratio of tangential force to normal force recorded in the nano-scratch experiment as the friction coefficient. A curve showing the change of friction coefficient with scratch load is plotted. The critical load is identified as the initiation point of interfacial debonding through abrupt change points. The scratched area is located by in-situ SEM observation, and the crack morphology (interfacial fracture or transgranular fracture) is determined. The scratch depth, width, and bulge height are quantified using a 3D profilometer. Plastic deformation energy is inverted using DEM simulation, and the interfacial bonding energy is calculated using the Hertz contact force model. Specifically: The critical load is identified as the initiation point of interfacial debonding through abrupt change points: Scratch tests are performed on mineral samples using nano-scratch experimental equipment, and the tangential and normal forces under different loads are recorded. Based on the ratio of tangential force to normal force measured in the nano-scratch experiment, a curve showing the change of friction coefficient with load is plotted. The friction coefficient curve is analyzed to identify abrupt change points. The load corresponding to this point is the critical load, representing the start of debonding or crack propagation at the mineral interface. In-situ SEM observation was used to locate the scratched area and identify whether the crack was interfacial or transgranular: After the nano-scratching experiment, scanning electron microscopy (SEM) was used to observe the scratched area in situ. SEM image analysis was used to determine the crack propagation morphology and identify whether the crack was interfacial or transgranular. Interfacial fracture: the crack propagates along the contact interface between mineral grains, indicating weak bonding between minerals; transgranular fracture: the crack penetrates the mineral crystal itself, indicating strong bonding between minerals. 3D profilometry was used to quantify the scratch depth, width, and elevation height: The scratched area was scanned using a 3D profilometry instrument to obtain the scratch depth, width, and elevation height. The data obtained from the scan were used to quantify the scratch depth (vertical displacement), width (lateral range), and elevation height (the height difference between the two surfaces and the scratched area). Plastic deformation energy was inverted using DEM simulation: Based on experimental data obtained from nano-scraping experiments, a discrete element numerical model (DEM) incorporating mineral particles and interfaces was established. This model simulates the plastic deformation of minerals during the scratching process. Through DEM simulation, the plastic deformation energy borne by the mineral interface during the scratching process was inverted. This energy data provides the foundation for subsequent binding energy calculations. The Hertz contact force model was used to calculate the interfacial binding energy: The Hertz contact force model was used to describe the contact force and contact displacement of the mineral interface during the scratching process. According to the Hertz model, the relationship between contact force and contact displacement is as follows: ; In the formula, For contact force, For the equivalent elastic modulus, For the contact radius, This refers to the contact displacement. The contact force at the mineral interface was calculated using the Hertz model, and the interfacial bonding energy, i.e. the bonding strength between minerals, was derived.

[0031] The contact force at the mineral interface was calculated using the Hertz model, thereby deriving the interfacial bonding energy, i.e., the bonding strength between minerals. Specifically, the interfacial bonding strength was derived based on discrete element modeling and nano-scratching inversion, as shown below. Figure 4 As shown.

[0032] S3, based on the analysis results, uses a finite element model method to characterize the porosity of rock samples based on the CT pore model, a method to convert experimental data into finite element simulation parameters, a method to divide the mesh according to the mineral grain size, a method to assign mineral mechanical properties to the finite element model mesh based on the mineral composition ratio and morphology color map, a method to set the boundary conditions of the finite element model, and a method to scale up the micron-centimeter mechanical properties based on the finite element model, to obtain centimeter-level mechanical parameters of deep coal and rock under different stress boundary conditions.

[0033] For example, the method for establishing a finite element model characterizing the pores of a rock sample based on a CT pore model first imports the CT two-dimensional scan results into a dedicated data processing software and uses image reconstruction technology to generate an accurate three-dimensional pore structure model; furthermore, a Python program is written to automatically import the three-dimensional pore structure model into the finite element simulation software, ensuring that the geometry of the pore model can be correctly converted into the finite element mesh format. For example, a written Python program is executed to seamlessly import the generated three-dimensional porous structure model into finite element simulation software. The method for converting experimental data into finite element simulation parameters first uses the load-depth curves of nanoindentation experiments to calculate the elastic modulus and hardness of each mineral component.

[0034] For example, the fracture energy of brittle materials can be derived using the results of nano-scratch experiments. ) and stress intensity factor ( ), yield strength of tough materials ( ), hardening modulus ( ) and damage evolution parameters. In the nanoscraping experiment, the results include crack propagation length, crack depth, and maximum load. The fracture energy ( The crack energy (FEE) describes the energy absorbed by a material during crack propagation and is used to quantify the ease or difficulty of crack propagation. Through nano-scratching experiments, we can measure the crack propagation area and, combined with the energy absorbed during crack propagation, estimate the fracture energy. The formula is as follows: ; In the formula, The elastic modulus of a material represents its ability to deform elastically when subjected to stress. The area change required for crack propagation; This represents the actual length of the crack propagation. The results of the nano-scratching experiment include: crack propagation length, crack depth, and maximum load; fracture energy. This describes the energy absorbed by a material during crack propagation, used to quantify crack propagation. The crack propagation area is measured using nano-scratching experiments, and the fracture energy is calculated based on the energy absorbed during crack propagation. The formula is as follows: ; In the formula, The elastic modulus of a material represents its ability to deform elastically when subjected to stress. The area change required for crack propagation; This represents the actual length of the crack propagation. The crack area was measured by scanning the crack propagation length and crack depth in the nano-scratching experiment using scanning electron microscopy. This represents the actual length of the crack propagation. The stress intensity factor This is an important parameter used to describe the stress field intensity at the crack tip and to predict the failure behavior of materials in the presence of cracks. The formula is as follows: ; In the formula, It is a geometric factor, which depends on the shape of the crack and the loading conditions; for nano-scratching experiments, the geometric factor is determined by the shape of the sample and the loading method. This is the stress under maximum load, representing the effect of the applied external load on the material; in nano-scratching experiments, the stress is determined by the maximum load. and contact area To calculate, the expression is: ; In the formula, The contact area between the indenter and the material surface is calculated using the indenter geometry and contact depth. The yield strength of tough materials was derived from the maximum load, plastic deformation zone, and crack propagation behavior in nano-scratching experiments. Hardening modulus and damage evolution parameters; the yield strength Describes the stress in a material during plastic deformation; in nanoscratching experiments, the yield strength is determined by the maximum load. and contact area calculate:

[0035] The hardening modulus This represents the rate of stress increase after yielding, derived from the stress-strain relationship of plastic deformation during the scratching process; the following relationship is used for derivation: ; In the formula, For stress, For yield strength, In response to the situation; The hardening modulus was obtained by fitting the stress-strain curves of the experimental data. ; Damage evolution models are used to describe the accumulation of damage in materials after they are subjected to stress, eventually leading to crack propagation. The formula for damage energy is: ; In the formula, It is the strain energy during the damage process, representing the energy absorbed by the material during crack propagation; It is the damage initiation strain; It is stress.

[0036] In summary, based on the yield strength obtained from the nano-scratch experiment ( ), hardening modulus ( The input file configuration and damage evolution parameters can be set in the finite element simulation software using an elasto-plastic material model and a DuctileDamage model. Below is an example input file configuration: *Material,name=Metal *Elastic 210000, 0.3 *Plastic 450,0.0,500,0.05#ExampleplasticityparametersforVonMisesmodel *DamageInitiation,criterion=MAXIMUMSTRESS 0.8, 0.05, 0.3, 1.0 *DamageEvolution,type=DISPLACEMENT 0.1,0.0,0.5#Energyorstrain-baseddamageevolution The method for dividing the grid based on the mineral grain size distribution involves grinding the rock sample into 200-mesh powder, acquiring high-resolution images of the sample using scanning electron microscopy and energy dispersive spectroscopy of an automated mineral analysis system, automatically identifying mineral particles using image processing software, calculating the geometric characteristics of each grain, such as diameter and aspect ratio, and generating a grain size distribution table. Based on the mineral grain size distribution table, the grid size and proportion are determined.

[0037] The method for assigning mineral mechanical properties to the finite element model mesh based on mineral component ratios, morphological color images, and the intergrowth relationship between clay minerals and other minerals includes: a method for writing a Python program to proportionally allocate mineral components to the finite element model mesh, and a method for writing a Python program to allocate different mineral components to corresponding positions in the color images based on the morphological color images and the intergrowth relationship between minerals. Specifically: The Python program is written to first proportionally allocate mineral components to the finite element model mesh, then assign mechanical properties to each mineral component, number the finite element model mesh so that each mesh corresponds to a mineral component, determine the shape and position of each mineral based on the morphological color images and the intergrowth relationship between minerals, and arrange the meshes with corresponding components according to the shape and position of each mineral to complete the establishment of a centimeter-level finite element model.

[0038] For example, the specific method for writing a Python program is as follows: (1) Read the morphology color image and analyze the mineral distribution: The morphology image is a grayscale image, and the grayscale value of each mineral represents a different mineral category; (2) Assign minerals to the grid according to mineral composition: Use the random.choices() method to assign different minerals to the grid according to their proportions; (3) Analysis of the inter-mineral relationships: The skimage.measure.label method is used to analyze the inter-mineral relationships and identify the connected regions of each mineral particle; (4) Determine the shape and location of minerals based on their intergrowth relationships: Determine the shape and location of each mineral based on the bounding box of the intergrowth region. Then apply this information to the grid arrangement to ensure that the grid cells are correctly arranged according to the shape and location of the minerals; (5) Mesh arrangement and mineral mechanical property assignment: Based on the mesh location and mineral type, assign corresponding mineral mechanical properties to each mesh element. These properties include elastic modulus, Poisson's ratio, fracture energy, stress intensity factor, yield strength, hardening modulus, and damage evolution parameters. Note that the names in the Python program must be consistent with the names in the finite element model, and the parameters should be set according to the experimental results described above.

[0039] For example, the finite element model boundary condition setting method for a uniaxial compression experiment involves: first, setting the boundary conditions and loads. The fixed end is set using Displacement / Rotation boundary conditions, where the displacement in all directions at one end of the sample is set to zero to simulate the sample being fixed to one end of the compressor. The loaded end is set by applying pressure or displacement; a compressive load is applied to the other end of the sample, simulating the compression head contacting the sample in the experiment. The compressive load is achieved by applying a uniform pressure (e.g., 10 MPa) or by controlling the displacement of the sample (e.g., -1 mm), the choice depending on whether the simulation requires control of force or deformation. For the triaxial compression experiment: First, fix the bottom of the finite element model and apply Displacement / Rotation boundary conditions, setting the displacement in all directions at the bottom to zero to simulate the fixed condition of the base in the actual experiment. Next, apply a compressive load to the top face, choosing to apply displacement (e.g., -1mm) to simulate axial compression deformation, or apply pressure (e.g., 10MPa) to simulate the applied compressive force. Apply confining pressure to the sides of the sample, using Pressure to set a uniform lateral pressure (e.g., 5MPa) to simulate the lateral pressure experienced by the sample in the experiment. All operations are completed through the Load module to ensure accurate reproduction of the physical meaning of the compression process in the simulation. The combination of bottom fixation, top compression, and lateral confining pressure simulates the actual triaxial stress state. Define the static analysis step in the Step module and adjust the loading time or rate as needed (e.g., set to 1 second) to complete the entire triaxial compression simulation setup.

[0040] Finally, the macroscopic mechanical finite element uniaxial compression or triaxial compression model is run to obtain macroscopic mechanical data.

[0041] Another example provides a system for characterizing coal and rock texture and scaling up its mechanical properties to the micrometer-centimeter level, the system comprising: The microscopic composition and structure quantitative analysis module is used to measure the specific mineral composition and proportion of rock samples based on X-ray diffraction experiments, determine the intergrowth relationship between clay minerals and other minerals, and draw morphological color maps to determine the location of mineral components. The microstructure micron-level mechanical property analysis module is used to obtain the microstructure micron-level mechanical properties by using lattice nanoindentation experiments and test result validity identification methods based on morphology color images, nano-scratch experiments and mineral bonding strength inversion methods combined with discrete element numerical models. The micrometer-centimeter scale upgrade module based on the finite element model is used to obtain centimeter-level mechanical parameters of deep coal and rock under different stress boundary conditions by establishing a finite element model for characterizing rock sample porosity based on the CT pore model, converting experimental data into finite element simulation parameters, dividing the mesh according to the mineral grain size, assigning mineral mechanical properties to the finite element model mesh based on the mineral composition ratio and morphology color map, and setting the boundary conditions of the finite element model.

[0042] Example 2, as another embodiment of the present invention, a method for characterizing coal and rock texture and scaling up micron-centimeter-level mechanical properties, includes the following steps: (1) X-ray diffraction experiment: The rock sample was made into powder with a particle size between 10 and 50 micrometers and scanned in an X-ray diffractometer to obtain the diffraction angle versus signal intensity curve. The qualitative analysis of mineral composition was completed by analyzing the diffraction angle corresponding to the peak value of the curve.

[0043] (2) Quantitative analysis of mineral composition: The rock sample is made into a cube sample with a side length of 3-25mm. The proportion of each mineral is quantitatively analyzed by an automatic mineral analysis system to provide proportional parameters for subsequent finite element modeling.

[0044] (3) Automatic analysis of the intergrowth relationship between clay minerals and other minerals: The automatic mineral analysis system measures the intergrowth relationship between clay minerals and other minerals, and provides connection interface information for the subsequent contact relationship setting of the finite element model.

[0045] (4) Grain size distribution measurement: 15g of rock sample was made into 200-mesh powder, and the size distribution ratio of single mineral grains in 0-100 micrometers was measured using an automatic mineral analysis system to provide data support for mesh generation of finite element model.

[0046] (5) Morphological color drawing: Using the scanning results of the automatic mineral analysis system, morphological color drawings of rock samples are drawn to intuitively show the distribution and location of minerals.

[0047] (6) Indentation dot matrix diagram preparation: Prepare a dot matrix diagram based on the morphology color image to ensure that the position of the dots can cover all mineral components and provide accurate indentation point positions for subsequent nanoindentation experiments.

[0048] (7) Lattice nanoindentation experiment: Based on the lattice pattern of the morphology color image, select an indenter (such as a Berkovich indenter) to conduct a nanoindentation experiment. Set the loading rate to 0.1 mN / s to 1 mN / s, the unloading rate to 20%-50% of the loading rate, and the indentation depth to no more than a few micrometers above the material surface. Obtain the indentation force-depth curve, which is used to calculate the hardness and elastic modulus. Use the method for valid identification of nanoindentation test results to select the effective hardness and elastic modulus of each mineral.

[0049] (8) Nano-scratch test: The crack propagation length, crack depth and maximum load of the material are measured by the nano-scratch test, and the fracture energy of the brittle material is derived. ) and stress intensity factor ( ), and the yield strength of tough materials ( ), hardening modulus ( The intermineral bond strength of the rock samples was calculated based on nano-scratch experiments and the inversion method of intermineral bond strength from discrete element numerical modeling.

[0050] (9) Pore model conversion: A three-dimensional model of the pores of the rock sample is obtained by CT scanning, and then imported into the finite element simulation software using a Python program or plugin.

[0051] (10) Experimental data conversion: The results of nanoindentation experiments (such as hardness and elastic modulus) are converted into parameters of the finite element model. Based on the results of nano-scratch experiments, the mechanical parameters of brittle and tough materials are derived to form complete input data for the finite element model.

[0052] (11) Mesh generation: Based on the mineral grain size distribution, divide the finite element model into meshes to ensure that the mesh matches the mineral grains.

[0053] (12) Assignment of mineral mechanical properties: Using Python program, based on experimental data from the automatic mineral analysis system, assign corresponding mineral mechanical properties (such as elastic modulus, hardness, etc.) to each grid cell according to the position and proportion of the morphology color map.

[0054] (13) Uniaxial compression model setting: To simulate the uniaxial compression experiment, set boundary conditions and apply loads, fix the bottom end of the sample, and apply compressive force or displacement to the top end face.

[0055] (14) Triaxial compression model setup: To simulate the triaxial compression experiment, fix the bottom end of the sample, apply an axial compression load or displacement to the top end face, and apply uniform lateral confining pressure.

[0056] (15) Finite element calculation: Run the finite element model to calculate and output the mechanical response of the sample (such as stress, strain, displacement, etc.).

[0057] As demonstrated by the above embodiments, this invention provides a method for characterizing the texture of deep coal and rock formations and scaling up their mechanical properties from micrometer to centimeter scale. Traditional mechanical experiments typically require intact rock samples at least centimeter in size, but obtaining standard rock samples is difficult due to limitations in practical working conditions and experimental equipment. The method of this invention can accurately derive microscopic mechanical properties to the centimeter scale, thereby enabling the prediction of the mechanical properties of small-sized rock samples. Specific technical innovations are as follows: A three-dimensional finite element model characterizing the porosity of the rock sample is established using CT scan results. The shape and location of minerals are characterized and the mineral composition and proportion are measured using X-ray diffraction experiments and automated quantitative mineral analysis experiments. The mineral shape, location, composition, and proportion are then characterized into the finite element model using a Python program. A method for inverting the inter-mineral bonding strength by combining nano-scratch experiments and discrete element modeling is proposed. The brittle and tough mechanical properties of the overall model are defined using nano-scratch experiments. A method for validating the results of nano-indentation experiments is proposed. This method avoids the need for large rock samples, improves the operability and economy of the experiment, and provides an innovative and efficient solution for evaluating the mechanical properties of deep coal and rock formations.

[0058] For example, the principle of the coal and rock texture characterization and micron- to centimeter-level mechanical property upscaling method provided in the embodiments of the present invention is as follows: Figure 2 As shown.

[0059] Example 3, as another embodiment of the present invention, the method for characterizing coal and rock texture and scaling up micron-centimeter-level mechanical properties provided in this embodiment of the present invention includes: Rock sample powder preparation: Grind the sample into powder with a particle size of 10 to 50 micrometers to ensure the rock sample is homogeneous and has sufficient surface area to improve measurement accuracy. Use a ball mill to grind the sample to ensure the powder particle size meets the standard.

[0060] X-ray diffraction experiment: The ground rock sample powder was placed in the X-ray diffractometer carrier, and the scanning range was set to 5° to 80° with a scanning step size of 0.02°. After scanning, the diffraction angle (2θ) and diffraction signal intensity curves were obtained. By analyzing the spectra and comparing them with standard mineral spectra, the mineral composition of the sample was determined. The results showed that the coal rock sample mainly contained quartz, feldspar, clay minerals, and organic matter.

[0061] Quantitative analysis of mineral composition: The results of X-ray diffraction experiments were imported into an automated mineral analysis system, which automatically measured the proportion of each mineral. The analysis results showed that quartz accounted for 18%, feldspar for 22%, clay minerals for 30%, and organic matter for 30%.

[0062] Analysis of the association relationships between clay minerals and other minerals: An automated mineral analysis system was used to analyze the association relationships between clay minerals and other minerals. The results show that clay minerals not only form relatively simple binary symbiotic relationships with minerals such as quartz and feldspar, but also exhibit ternary or higher inclusion relationships with other minerals. Model parameters were set based on the proportions.

[0063] Powder preparation and measurement: The rock sample powder was made into a 200-mesh fine powder, and the size distribution ratio of mineral grains between 0 and 100 micrometers was measured using an automated mineral analysis system.

[0064] Mesh generation support: Provides support for mesh generation of finite element models based on grain size distribution data. Appropriate mesh sizes are selected based on the proportions and grain size distribution of minerals such as clay and quartz to ensure simulation accuracy.

[0065] Morphological color mapping: Using the scanning results from the automated mineral analysis system, morphological color maps of the rock samples are generated. The morphological color maps show the spatial distribution of different minerals.

[0066] Dot plot generation: Based on the morphology color image, a dot plot is drawn, ensuring that each dot in the plot covers all mineral components in the rock sample. The number and spacing of the dots are adjusted to ultimately generate a dot plot covering all minerals, providing accurate indentation locations for subsequent nanoindentation experiments.

[0067] Nanoindentation Experiment: Based on the generated lattice pattern, lattice points were selected for Berkovich indenter nanoindentation experiments. The loading rate was set to 0.5 mN / s, the unloading rate to 30% of the loading rate, and the indentation depth not exceeding 200 nm. The elastic modulus was calculated by analyzing the springback portion of the force-depth curve using the Oliver-Pharr method; the hardness was calculated using the maximum indentation depth and the maximum applied load. The effective hardness and elastic modulus of each mineral were selected using the nanoindentation experiment test result validity assessment method.

[0068] Nano-scratch test: After polishing the rock sample surface, a nano-scratch test was performed. An indentation load was applied to the sample using a Berkovich indenter, and the crack propagation length, crack depth, and maximum load were recorded. The fracture energy of the brittle material was calculated. ) and stress intensity factor ( ), and calculate the yield strength of the tough material ( ) and hardening modulus ( The intermineral bond strength of the rock sample was calculated based on the nano-scratch experiment and the inversion method of intermineral bond strength from discrete element numerical modeling.

[0069] 3D CT Pore Model: The pore structure of the rock sample is obtained using CT scans, generating a 3D pore structure model. A Python program is written to import this model into finite element simulation software for further finite element simulation.

[0070] Mesh generation and mineral mechanical property assignment: Based on the mineral grain size distribution, the finite element model mesh is generated. Using a Python program, the mechanical properties of each mesh element are assigned according to its position in the morphology color image, scale, and the intergrowth relationship of the mineral components.

[0071] Uniaxial compression model boundary conditions and load settings: Using the Displacement / Rotation boundary condition, the displacement in all directions at the bottom of the sample is set to zero to ensure no displacement occurs at the bottom, simulating the fixed contact between the sample and the compressor base in the experiment. Then, a compressive load or displacement is applied to the top end face. Specifically, a displacement load is selected, setting the compressive displacement to -1 mm to simulate the deformation of the sample during compression; or a pressure load is selected, setting the axial pressure to 10 MPa to simulate the compressive force borne by the upper end of the sample.

[0072] Triaxial compression model boundary conditions and load settings: First, fix the bottom end of the sample using Displacement / Rotation boundary conditions to ensure no displacement occurs during the simulation. Apply axial compressive load or displacement to the top end face. Specifically, apply displacement (-1mm) to simulate compressive deformation, or apply pressure (10MPa) to simulate compressive force. Apply uniform confining pressure (5MPa) to the sides of the sample to simulate the lateral pressure experienced by the sample during the experiment.

[0073] Finite element calculation: Run the finite element model to calculate and output the mechanical response of the sample (such as stress, strain, displacement, etc.).

[0074] To verify the effectiveness of the micrometer-to-centimeter scale-up method for mechanical properties of this invention, experiments were conducted on coal and rock at depths of 2713 meters or more underground. The elastic modulus test results were compared with the measured values ​​of triaxial compression tests and the test results of the Dilute method, Self-consistent method, and Mori-Tanaka method, as shown in Table 1. The experimental data show that the elastic moduli measured by each method are: 8.6 GPa for triaxial compression tests, 19.2 GPa for the Dilute method, 24.1 GPa for the Self-consistent method, and 26.6 GPa for the Mori-Tanaka method, while the elastic modulus measured by the experimental method of this invention is 12.3 GPa. The comparison results show that the elastic modulus value measured by the experimental method of this invention is closest to the measured value of triaxial compression tests and is significantly better than the other three traditional methods. This fully demonstrates that the method of this invention has higher accuracy and reliability in the assessment of the mechanical properties of deep underground coal and rock, and can more accurately reflect the actual mechanical state of deep coal and rock.

[0075] Table 1. Comparison of the experimental method of this invention with the measured values ​​of triaxial compression tests and the elastic modulus test results of the Dilute method, Self-consistent method, and Mori-Tanaka method.

[0076] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention and within the spirit and principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for characterizing coal and rock texture and scaling up micron-centimeter-level mechanical properties, characterized in that, The method includes the following steps: S1. Using quantitative analysis methods of micro-components and structure, based on X-ray diffraction experiments, an automatic mineral analysis system was used to measure the specific mineral composition and proportion of the rock sample, determine the intergrowth relationship between clay minerals and other minerals, and draw morphological color maps to determine the location of mineral components; among them, other minerals include: quartz, pyrite, calcite, siderite, and dolomite. S2, using a method for valid identification of lattice nanoindentation experiments and test results based on morphology color images, a method for inverting the inter-mineral bonding strength by nano-scratch experiments and combining discrete element numerical models, and a method for analyzing the micron-level mechanical properties of microstructures, micron-level mechanical properties of microstructures are obtained. S3, based on the analysis results, uses a finite element model method to characterize the porosity of rock samples based on the CT pore model, a method to convert experimental data into finite element simulation parameters, a method to divide the mesh according to the mineral grain size, a method to assign mineral mechanical properties to the finite element model mesh based on the mineral composition ratio and morphology color map, a method to set the boundary conditions of the finite element model, and a method to scale up the micron-centimeter mechanical properties based on the finite element model, to obtain centimeter-level mechanical parameters of deep coal and rock under different stress boundary conditions.

2. The method for characterizing coal and rock texture and scaling up micron-centimeter-level mechanical properties according to claim 1, characterized in that, Based on X-ray diffraction experiments, an automated mineral analysis system was used to measure the specific mineral composition and proportion of rock samples, determine the intergrowth relationship between clay minerals and other minerals, and draw morphological color maps to determine the location of mineral components, including: S101, grind the rock sample into powder, scan it with an X-ray diffractometer to obtain an X-ray diffraction angle and diffraction signal intensity curve, find the peak value of the curve and the corresponding X-ray diffraction angle based on the curve spectrum and the original curve data, each X-ray diffraction angle corresponds to a mineral, and complete the qualitative analysis of the mineral composition of the rock sample by X-ray diffraction experiment. S102, prepare the rock sample into an experimental sample, import the qualitative analysis results of the X-ray diffraction experiment into the automatic mineral analysis system, measure the specific proportion of each mineral in the experimental sample, and provide proportional parameters for finite element modeling; S103, other minerals have different mineral compositions in deep coal and rock under different geological conditions. The automatic mineral analysis system is used to quantitatively measure the intergrowth relationship between clay minerals and other minerals, so as to provide the contact relationship between clay minerals and other minerals for the establishment of a finite element basic model. S104, the rock sample is made into powder, and the size distribution ratio of single mineral grains in micrometers is measured using an automatic mineral analysis system to provide data for mesh generation of the finite element model; S105 uses the scanning results of an automated mineral analysis system to draw morphological color images, which characterize the location of each mineral and provide the location of indentation points for nanoindentation experiments.

3. The method for characterizing coal and rock texture and scaling up micron-centimeter-level mechanical properties according to claim 1, characterized in that, In step S2, the lattice nanoindentation experiment based on the morphology color image includes: A lattice map of the indentation points from the nanoindentation experiment was plotted on a morphology color image. By adjusting the number and spacing of the indentation points, the points in the lattice map were made to cover all mineral components. Each point on the lattice map was numbered, and the mineral component corresponding to each point was determined based on the morphology color image. The elastic modulus of the material was obtained by analyzing the rebound portion of the indentation using the load-depth curves during loading and unloading in the nanoindentation process, either using the Oliver-Pharr method or the Hartmann regression model. The hardness was calculated by measuring the maximum indentation depth and the maximum applied load, using the following formula: ； In the formula, For the maximum applied load, This corresponds to the indentation contact area. Hardening modulus; The mechanical parameters measured at each indentation point in nanoindentation correspond to the mechanical parameters of the mineral composition. The method for determining the validity of the test results includes: determining the position of each measuring point in the nanoindentation lattice of the morphology color image; if only a single component is indented at the measuring point, then the measuring point is a valid measuring point; otherwise, it is an invalid measuring point, and the corresponding load-depth test data is not used; obtaining the mechanical property parameters of the component through statistical analysis; wherein, the mechanical property parameters include the hardness and elastic modulus of the micro-component. The nano-scratching experiment includes: ultrasonic cleaning of rock samples to remove surface oil and dust; using a Berkovich indenter for hard rock samples and a spherical indenter for soft rock samples; for brittle materials, deriving the required fracture energy and stress intensity factor in the finite element model from crack propagation length, crack depth, and maximum load data measured by the nano-scratching experiment; for tough materials, deriving plastic hardening parameters and damage evolution parameters in the finite element model using plastic deformation, crack propagation, and hardening behavior observed in the nano-scratching experiment.

4. The method for characterizing coal and rock texture and scaling up micron-centimeter-level mechanical properties according to claim 1, characterized in that, In step S2, the nano-scratching experiment and the method for inverting the intermineral bonding strength by combining discrete element numerical models include: Identifying the critical load as the starting point of interface debonding by abrupt change: Scratch test is performed on mineral samples using a nano-scratch test device, and the tangential force and normal force under different loads are recorded. Based on the ratio of tangential force to normal force measured in the nano-scratch test, the friction coefficient is plotted as a function of load. The friction coefficient curve is analyzed to identify abrupt change points. The load corresponding to the abrupt change point is the critical load, which represents the start of debonding or crack propagation at the mineral interface. The scratched area was located by in-situ SEM observation, and the crack was identified as either interfacial or transgranular fracture. After the nano-scratching experiment was completed, the scratched area was observed in-situ using a scanning electron microscope. The crack propagation morphology was determined by SEM image analysis, and the crack was identified as either interfacial or transgranular fracture. Quantifying scratch depth, width, and elevation using a 3D profilometer: The scratch area is scanned using a 3D profilometer to obtain the scratch depth, width, and elevation; the scratch depth, width, and elevation are then quantified using the scanned data. Combining DEM simulation to invert plastic deformation energy: Based on experimental data obtained from nano-scratching experiments, a discrete element numerical model (DEM) containing mineral particles and interfaces is established to simulate the plastic deformation of minerals during the scratching process. Through the DEM simulation, the plastic deformation energy borne by the mineral interface during the scratching process is inverted. The Hertz contact force model was used to calculate the interfacial bonding energy: the Hertz contact force model was used to describe the contact force and contact displacement of the mineral interface during the scratching process; according to the Hertz contact force model, the relationship between contact force and contact displacement is as follows: ； In the formula, For contact force, For the equivalent elastic modulus, For the contact radius, This refers to the contact displacement. The contact force at the mineral interface was calculated using the Hertz model, and the interfacial bonding energy, i.e. the bonding strength between minerals, was derived.

5. The method for characterizing coal and rock texture and scaling up micron-centimeter-level mechanical properties according to claim 1, characterized in that, In step S3, the method for establishing a finite element model for characterizing the porosity of rock samples based on the CT porosity model includes: importing the CT two-dimensional scan results into data processing software and generating a three-dimensional porosity structure model using image reconstruction technology; importing CT scan data and extracting porosity regions through denoising, cropping, and threshold segmentation, and filling pores and removing small volume regions using morphological processing; performing three-dimensional reconstruction and generating a three-dimensional porosity structure model of the rock sample using volume rendering; calculating porosity and exporting the model for analysis or visualization. Write a Python program to automatically import the 3D pore structure model into the finite element simulation software, converting the geometry of the 3D pore structure model into a finite element mesh format; execute the written Python program to seamlessly import the generated 3D pore structure model into the finite element simulation software.

6. The method for characterizing coal and rock texture and scaling up micron-centimeter-level mechanical properties according to claim 1, characterized in that, In step S3, the method for converting experimental data into finite element simulation parameters includes: calculating the elastic modulus and hardness of each mineral component using the load depth curve of the nanoindentation experiment; Derivation of fracture energy of brittle materials using nano-scratch test results and stress intensity factor Yield strength of tough materials Hardening modulus and damage evolution parameters; The results of the nano-scratching experiment include: crack propagation length, crack depth, and maximum load; fracture energy. This describes the energy absorbed by a material during crack propagation, used to quantify crack propagation. The crack propagation area is measured using nano-scratching experiments, and the fracture energy is calculated based on the energy absorbed during crack propagation. The formula is as follows: ； In the formula, The elastic modulus of a material represents its ability to deform elastically when subjected to stress. The area change required for crack propagation; This represents the actual length of the crack propagation. The crack area was measured by scanning the crack propagation length and crack depth in the nano-scratching experiment using scanning electron microscopy. This represents the actual length of the crack propagation. The stress intensity factor This is an important parameter used to describe the stress field intensity at the crack tip and to predict the failure behavior of materials in the presence of cracks. The formula is as follows: ； In the formula, It is a geometric factor, which depends on the shape of the crack and the loading conditions; for nano-scratching experiments, the geometric factor is determined by the shape of the sample and the loading method. This is the stress under maximum load, representing the effect of the applied external load on the material; in nano-scratching experiments, the stress is determined by the maximum load. and contact area To calculate, the expression is: ； In the formula, The contact area between the indenter and the material surface is calculated using the indenter geometry and contact depth. The yield strength of tough materials was derived from the maximum load, plastic deformation zone, and crack propagation behavior in nano-scratching experiments. Hardening modulus and damage evolution parameters; the yield strength Describes the stress in a material during plastic deformation; in nanoscratching experiments, the yield strength is determined by the maximum load. and contact area calculate: ； The hardening modulus This represents the rate of stress increase after yielding, derived from the stress-strain relationship of plastic deformation during the scratching process; the following relationship is used for derivation: ； In the formula, For stress, For yield strength, In response to the situation; The hardening modulus was obtained by fitting the stress-strain curves of the experimental data. ; Damage evolution models are used to describe the accumulation of damage in materials after they are subjected to stress, eventually leading to crack propagation. The formula for damage energy is: ； In the formula, It is the strain energy during the damage process, representing the energy absorbed by the material during crack propagation; It is the damage initiation strain; It is stress.

7. The method for characterizing coal and rock texture and scaling up micron-centimeter-level mechanical properties according to claim 1, characterized in that, In step S3, the method for dividing the mineral grain size into grids includes: grinding the rock sample into powder, using scanning electron microscopy and energy dispersive spectroscopy of an automatic mineral analysis system to obtain high-resolution images of the sample, automatically identifying mineral particles through image processing software, calculating the geometric characteristics of each grain, including diameter and aspect ratio, and generating a grain size distribution table, and dividing the grid size and proportion according to the mineral grain size distribution table; Image processing software is used to automatically identify particles in high-resolution images. Thresholding segmentation, edge detection, and morphological processing techniques are employed to extract the contours and shapes of mineral particles from complex images. During the identification process, the software separates each individual grain based on its grayscale value, shape, and other features, and marks its position and boundaries. The software calculates the geometric features of each grain, including its maximum diameter and aspect ratio. The diameter represents the maximum size of the grain, while the aspect ratio reflects its morphology; a larger aspect ratio indicates a longer grain. Finally, the software summarizes the calculated diameter and aspect ratio data to generate a grain size distribution table.

8. The method for characterizing coal and rock texture and scaling up micron-centimeter-level mechanical properties according to claim 1, characterized in that, In step S3, the method for assigning mineral mechanical properties to the finite element model mesh based on the mineral composition ratio and morphological color map includes: a method of writing a Python program to allocate mineral components to the finite element model mesh according to the ratio, and a method of writing a Python program to allocate different mineral components to the corresponding positions in the color map according to the morphological color map and the intergrowth relationship between minerals; The mineral components are proportionally distributed to the finite element model mesh. Mechanical properties are added to each mineral component. The finite element model mesh is divided and numbered so that each mesh has a corresponding mineral component. The shape and position of each mineral are determined according to the morphological color map and the intergrowth relationship between minerals. According to the shape and position of each mineral, the mesh with the corresponding component is arranged to complete the establishment of the centimeter-level finite element model.

9. The method for characterizing coal and rock texture and scaling up micron-centimeter-level mechanical properties according to claim 1, characterized in that, In step S3, the method for setting boundary conditions for the finite element model includes: For uniaxial compression experiments, boundary conditions and loads are first set. The fixed end is set using Displacement / Rotation boundary conditions, setting all displacements at one end of the sample to zero to simulate the sample being fixed to one end of the compressor. The loaded end is set by applying pressure or displacement, selecting to apply a compressive load to the other end of the sample to simulate the compression head contacting the sample in the experiment. The compressive load is achieved by applying uniform pressure or by controlling the displacement of the sample. For the triaxial compression experiment, the bottom of the finite element model is fixed, and Displacement / Rotation boundary conditions are applied, setting the displacement in all directions at the bottom to zero to simulate the fixed condition of the base in the actual experiment. A compressive load is applied to the top end face, and displacement can be applied to simulate axial compression deformation, or pressure can be applied to simulate the applied compressive force. A confining pressure is applied to the side of the sample, and uniform lateral pressure is set through Pressure to simulate the lateral pressure on the sample in the experiment. All operations are completed through the Load module, combining bottom fixation, top compression, and lateral confining pressure to simulate the actual triaxial stress state. In the Step module, a static analysis step is defined, and the loading time or rate is adjusted to complete the entire triaxial compression simulation setup. Run a macroscopic mechanics finite element uniaxial compression or triaxial compression model to obtain macroscopic mechanical data.

10. A system for characterizing coal and rock texture and scaling up its mechanical properties to the micrometer-centimeter level, characterized in that, This system implements the coal and rock texture characterization and micron- to centimeter-level mechanical property upscaling method as described in any one of claims 1-9, and the system comprises: The microscopic composition and structure quantitative analysis module, based on X-ray diffraction experiments, uses an automatic mineral analysis system to measure the specific mineral composition and proportion of rock samples, determine the intergrowth relationship between clay minerals and other minerals, and draw morphological color maps to determine the location of mineral components. The microstructure micron-level mechanical property analysis module is used to obtain the microstructure micron-level mechanical properties by utilizing a method for valid identification of lattice nanoindentation experiments and test results based on morphology color images, a method for inverting the inter-mineral bonding strength by nano-scratch experiments and combining discrete element numerical models, and a microstructure micron-level mechanical property analysis method. The module for upgrading micrometer- to centimeter-level mechanics based on the finite element model includes: a method for establishing a finite element model for characterizing rock sample porosity based on the CT pore model; a method for converting experimental data into finite element simulation parameters; a method for meshing based on mineral grain size; a method for assigning mineral mechanical properties to the finite element model mesh based on mineral composition ratio and morphology color images; a method for setting boundary conditions for the finite element model; and a method for upgrading the micrometer- to centimeter-level mechanics based on the finite element model to obtain centimeter-level mechanical parameters of deep coal and rock under different stress boundary conditions.

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