Method, device and equipment for determining microscopic mechanical property of rock

Abstract

The embodiment of the invention relates to the technical field of rock mechanics, in particular to a rock micromechanical property determination method, device and equipment, and the method comprises the following steps: carrying out indentation test on a target area on the surface of a rock sample to obtain micromechanical response data of the rock sample; the micro-mechanical response data represents the change relationship between the micro-mechanical response of the rock sample and time; scanning a target area on the surface of the rock sample in the indentation test process to obtain microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents a change relationship between the microstructure of the rock sample and time; and determining the micromechanical properties of the rock sample according to the micromechanical response data of the rock sample and the corresponding microstructure evolution data. According to the embodiment of the invention, the heterogeneous micro-mechanical property of the rock can be accurately represented based on collaborative analysis of the micro-mechanical response and the microstructure of the rock sample at multiple time points, and dynamic, in-situ and quantitative representation of the micro-mechanical property of the rock is realized.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of rock mechanics technology, and specifically to a method, device, and apparatus for determining the micromechanical properties of rocks. Background Art

[0002] As the primary constituent of the Earth's crust, rocks possess highly complex microscopic pore structures and components. Taking sedimentary rocks as an example, shales are rich in organic matter and clay minerals, with developed nanoscale bedding pores; coal rocks have distinct cleats and matrix pores; and carbonate rocks are commonly found with dissolution pores and microcracks. Among igneous rocks, granite is composed of mosaics of mineral grains such as quartz, feldspar, and mica, while basalt exhibits a typical cryptocrystalline-vitreous structure. This microscopic characteristic of multiple mineral components and multi-scale pores results in significant mechanical heterogeneity in rocks at the microscale, manifested as spatial heterogeneity in parameters such as elastic modulus, hardness, and fracture toughness at the micrometer and even nanometer scales.

[0003] Traditional triaxial stress experiments simulate the three-dimensional stress state in formations by applying confining pressure and axial stress, thereby obtaining macroscopic parameters such as stress-strain curves and peak strength. However, these experimental samples are typically at the centimeter scale and cannot reveal micromechanical behavior. Existing nanoindentation techniques can obtain statistical distributions of parameters such as hardness and modulus through gridded testing, but these techniques are essentially static representations of rock micromechanical properties using statistical principles and cannot accurately characterize rock micromechanical properties based on dynamic changes.

[0004] Therefore, how to overcome the problem in existing methods that can only achieve static representation of rock micromechanical properties using statistical principles, and how to accurately characterize rock micromechanical properties based on the dynamic changes of rocks are key issues that need to be solved urgently. Summary of the Invention

[0005] The purpose of the embodiments of this specification is to provide a method, device and equipment for determining the micromechanical properties of rocks, so as to overcome the problem that the existing methods for determining the micromechanical properties of rocks can only use statistical principles to achieve static representation of the micromechanical properties of rocks.

[0006] In order to solve the above technical problems, the specific technical solutions of the embodiments of this specification are as follows:

[0007] In one aspect, an embodiment of this specification provides a method for determining the micromechanical properties of rock, the method comprising:

[0008] Performing an indentation test on a target area on the surface of the rock sample to obtain micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time;

[0009] Scanning a target area on the surface of the rock sample during the indentation test to obtain microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents a relationship between the microstructure of the rock sample and time;

[0010] The micromechanical properties of the rock sample are determined based on the micromechanical response data of the rock sample and the corresponding microstructural evolution data.

[0011] Furthermore, the method further comprises:

[0012] Based on energy spectrum analysis, a scanning electron microscope is used to obtain chemical composition data and physical structure data of the target area on the surface of the rock sample;

[0013] Determining a target indenter and a tilt angle of the indenter of the scanning electron microscope cavity according to the chemical composition data and the physical structure data;

[0014] The indentation test on the target area of the rock sample surface includes:

[0015] At the tilt angle, an indentation test is performed on a target area on the surface of the rock sample using a target indenter of a scanning electron microscope cavity.

[0016] Furthermore, the method further comprises:

[0017] Determining the temperature field and electric field corresponding to the target area according to the chemical composition data and physical structure data of the target area;

[0018] Simulating the geological environment of the rock sample according to the temperature field and electric field corresponding to the target area;

[0019] The indentation test on the target area of the rock sample surface includes:

[0020] In the simulated geological environment of the rock sample, an indentation test is performed on the target area on the surface of the rock sample.

[0021] Furthermore, the indentation test is performed on the target area of the rock sample surface to obtain the micromechanical response data of the rock sample, including:

[0022] determining a load mode for the indentation test based on geological data of the rock sample;

[0023] An indentation test is performed on a target area on the surface of the rock sample under the load mode to obtain load and displacement variation data at multiple time points.

[0024] Furthermore, the indentation test includes a loading phase, a constant load phase and an unloading phase;

[0025] Determining the load mode of the indentation test based on the geological data of the rock sample includes:

[0026] The loading mode, constant load mode and unloading mode corresponding to the loading stage, constant load stage and unloading stage are determined according to the geological data of the rock sample.

[0027] Furthermore, scanning the target area during the indentation test to obtain microstructure evolution data corresponding to the micromechanical response data includes:

[0028] During the indentation test, a scanning electron microscope is used to scan the target area to obtain rock sample crack expansion data and rock sample particle migration data corresponding to the micromechanical response data; the rock sample crack expansion data represents the relationship between the change of rock sample crack morphology and time; the rock sample particle migration data represents the relationship between the change of rock sample particle coordinates and time.

[0029] Furthermore, determining the micromechanical properties of the rock sample based on the micromechanical response data and the corresponding microstructural evolution data of the rock sample includes:

[0030] spatiotemporally coupling the micromechanical response and microstructure at multiple time points during the indentation test;

[0031] The micromechanical properties of the rock sample are determined based on the micromechanical responses and microstructures at multiple time points after the spatiotemporal coupling.

[0032] Furthermore, the method further comprises:

[0033] The micromechanical responses at multiple time points are modified according to the microstructures at multiple time points.

[0034] On the other hand, an embodiment of this specification further provides a device for determining the micromechanical properties of rock, the device comprising:

[0035] A testing module is used to perform an indentation test on a target area on the surface of a rock sample to obtain micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time;

[0036] a scanning module, configured to scan a target area on the surface of the rock sample during the indentation test to obtain microstructural evolution data corresponding to the micromechanical response data; the microstructural evolution data representing a relationship between the microstructure of the rock sample and time;

[0037] The determination module is used to determine the micromechanical properties of the rock sample based on the micromechanical response data and the corresponding microstructural evolution data of the rock sample.

[0038] On the other hand, embodiments of this specification further provide a computer device, including:

[0039] memory for storing computer programs;

[0040] A processor is used to execute the computer program to implement any one of the above methods for determining rock micromechanical properties.

[0041] On the other hand, an embodiment of this specification further provides a computer program product, which, when executed by a processor of a computer device, executes instructions of any one of the above methods.

[0042] As can be seen from the technical solutions provided by the embodiments of this specification, the embodiments of this specification can perform indentation testing on a target area on the surface of a rock sample to obtain micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time; during the indentation test, the target area on the surface of the rock sample is scanned to obtain microstructural evolution data corresponding to the micromechanical response data; the microstructural evolution data represents the relationship between the microstructure of the rock sample and time; and the micromechanical properties of the rock sample are determined based on the micromechanical response data and the corresponding microstructural evolution data. Compared with existing methods, the embodiments of this specification can simultaneously perform indentation testing on the target area on the surface of the rock sample while scanning the target area on the surface of the rock sample, thereby obtaining the micromechanical response and microstructure of the rock sample at multiple time points. Based on the coordinated analysis of the micromechanical response and microstructure of the rock sample at multiple time points, the inherent relationship between micromechanical behavior and microstructural evolution can be revealed, thereby more accurately characterizing the heterogeneous micromechanical properties of the rock, and achieving dynamic, in situ, and quantitative characterization of the rock micromechanical properties.

[0043] The above description is only an overview of the technical solutions of some embodiments of this specification. In order to more clearly understand the technical means of some embodiments of this specification, they can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiments of this specification more obvious and easy to understand, the following specifically cites preferred embodiments and provides detailed descriptions in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art.

[0045] Figure 1 This is a flow chart of a method for determining rock micromechanical properties provided in an embodiment of this specification;

[0046] Figure 2This is a schematic diagram of the overall process logic of a method for determining rock micromechanical properties provided in an embodiment of this specification;

[0047] Figure 3 This is a microstructure image of the rock sample surface in Area A before the indentation test provided in the examples of this specification;

[0048] Figure 4 This is a microstructure image of a target area magnified locally before the indentation test on the surface of a rock sample in region A provided in the examples of this specification;

[0049] Figure 5 This is a microstructure image of the rock sample surface in Area A after indentation testing provided in the examples of this specification;

[0050] Figure 6 This is a microstructure image of a target area magnified locally after an indentation test on the surface of a rock sample in region A provided in the examples of this specification;

[0051] Figure 7 This is a schematic diagram of a pressure-load curve recorded during an indentation test of a target area of a rock sample in Region A provided in an embodiment of this specification;

[0052] Figure 8 Schematic diagram of the curves of the change of the indenter load, the indenter displacement, and the indentation depth of the target area of the rock sample in area A over time during the indentation test of the target area of the rock sample in area A provided in the embodiments of this specification;

[0053] Figure 9 This is a schematic diagram of the structure of a device for determining the micromechanical properties of rock provided in an embodiment of this specification;

[0054] Figure 10 It is a schematic diagram of the structural composition of the computer device provided in the embodiment of this specification. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this specification.

[0056] Figure 1 This is a flow chart of a method for determining the micromechanical properties of rock provided in the embodiments of this specification. Figure 2 This is an overall logic flow chart of a method for determining rock micromechanical properties provided in an embodiment of this specification. When implemented, the method includes the following steps:

[0057] S101: performing an indentation test on a target area on the surface of a rock sample to obtain micromechanical response data of the rock sample; the micromechanical response data represents a relationship between the micromechanical response of the rock sample and time.

[0058] In some embodiments, the load mode of the indentation test can be determined based on the geological data of the rock sample; an indentation test can be performed on a target area on the surface of the rock sample under the load mode to obtain load and displacement change data at multiple time points.

[0059] Determining the load pattern for indentation testing using geological data helps accurately characterize the micromechanical properties of rock. The time-series load-displacement data obtained from indentation testing not only contains static mechanical parameters but also records the dynamic response process, providing an excellent data foundation for studying the dynamic rheological properties of rock and the mechanisms of damage accumulation.

[0060] Indentation testing uses a nanoindenter to apply a mechanical load to a material's surface under controlled conditions, while simultaneously recording the load-displacement relationship to invert the mechanical properties of the material or rock. For heterogeneous, multiphase composite materials like rock, the interaction between the indenter and the mineral particles, cement, and pore structure during testing exhibits significant temporal and spatial heterogeneity. A nanoindenter consists of a precision loading unit, a mechanical sensing module, a three-dimensional displacement sensing module, and a data acquisition system. Load resolution can be set to 0.1 mN, and displacement measurement accuracy can be set to 1 nm.

[0061] The loading mode corresponding to a rock sample can be determined based on geological data such as its mineral composition, microstructural characteristics, porosity distribution, and bedding structure. Loading modes can include step loading, cyclic loading, and monotonic loading. In the monotonic loading mode, the load increases steadily and continuously, visually demonstrating the entire process of rock deformation from its initial state to failure. This allows for investigation of the mechanical response of rock when first subjected to load. In the cyclic loading mode, the load increases and decreases periodically, simulating the repeated stresses experienced by rock in actual engineering projects. This facilitates the study of rock fatigue properties and the observation of changes in the rock's internal structure and damage accumulation during multiple loading and unloading cycles. The step loading mode gradually increases the load in specific steps, enabling a clear analysis of the changes in the mechanical properties of rock at different stress stages. The loading rate can be adjusted within a range of 0.01 mN / s–10 mN / s. Low-speed loading can more accurately capture the mechanical behavior of rock during slow deformation, while high-speed loading can simulate rapid stress scenarios such as earthquakes and study the mechanical response of rock under impact. For example, in shale, when the clay mineral content exceeds 40%, a stepped loading strategy is required to prevent indenter damage. For granite containing brittle quartz particles, a quasi-static continuous loading mode is appropriate. Specific parameters for this loading mode include peak load (typically 50-500 mN), loading rate (0.05-5 mN / s), dwell time (10-600 s), and number of cycles. These parameters can be dynamically adjusted based on the estimated Young's modulus and hardness range of the rock.

[0062] Before the experiment, the rock sample surface can be calibrated and pre-processed. A scanning electron microscope can be used to perform three-dimensional topographic scanning of the rock sample surface, identifying mineral phase boundaries and microcrack distribution, and selecting representative target areas. The surface roughness of the rock sample can be controlled to Ra < 50nm, and atomically flat surfaces can be achieved through ion beam polishing. The indenter tip type can be selected based on the test objectives. For example, a Vickers pyramid indenter is suitable for anisotropy analysis, a Bosch indenter facilitates elastic recovery measurement, and a spherical indenter is more suitable for studying creep properties. During the indentation test, the indenter can follow a preset trajectory to indent the target area.

[0063] The data acquisition system can simultaneously record time-series data of loads and micromechanical response data, including time-series data of displacements and contact stiffness. The sampling frequency must meet the Nyquist criterion and must be at least 1kHz. Multiple preprocessing steps can be performed on the load, displacement, contact stiffness, and other time-series data acquired by the data acquisition system: baseline correction can be performed to eliminate the effects of device compliance. A digital filter (Butterworth low-pass filter, 1kHz cutoff frequency) can then be applied to suppress high-frequency noise. Finally, a thermal drift compensation algorithm can be used to eliminate displacement drift caused by ambient temperature fluctuations.

[0064] Based on the time series data of load, displacement, contact stiffness, etc. acquired by the data acquisition system, a load-displacement curve corresponding to the indentation test process can be constructed. The load-displacement curve contains the variation data of the indenter load and the rock sample displacement over time. The load-displacement curve can be divided into five characteristic segments: the nonlinear response in the initial contact stage can reflect the surface adsorption layer effect; the slope of the elastic deformation segment can correspond to the contact stiffness; the load mutation at the plastic yield point can indicate dislocation initiation; the creep curve in the load-holding stage can reflect the time-dependent deformation mechanism; and the elastic recovery rate of the unloading curve can characterize the energy dissipation characteristics. For layered rocks, the curve can show multi-stage yield characteristics, which is closely related to interlayer slip and mineral phase transformation.

[0065] S102: Scanning a target area on the surface of the rock sample during the indentation test to obtain microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents a relationship between the microstructure of the rock sample and time.

[0066] In some embodiments, the target area can be scanned during the indentation test to obtain rock sample crack expansion data and rock sample particle migration data corresponding to the micromechanical response data; the rock sample crack expansion data represents the changing relationship between the rock sample crack morphology and time; the rock sample particle migration data represents the changing relationship between the rock sample particle coordinates and time.

[0067] By simultaneously applying mechanical loads and analyzing the spatiotemporal trajectory of fracture network development and particle motion in the target area, this method transitions from static parameter testing to dynamic process monitoring, providing a new experimental paradigm and precise experimental data for studying brittle-ductile transitions, localized failure, and multiphase coupling effects in rock. Furthermore, the simultaneous acquisition of micromechanical responses and microstructural evolution helps reveal transient processes and nonlinear effects that are unobservable using traditional experimental methods. This provides an experimental basis for analyzing the coevolutionary laws of fracture propagation and particle migration.

[0068] During the indentation test, a scanning electron microscope (SEM) can be used to scan the target area of the rock sample surface in real time, obtaining a microstructural image of the target area at each time point during the indentation test. Based on the microstructural image of the target area at each time point during the indentation test, the relationship between the change in the rock sample crack morphology and time, as well as the relationship between the change in the rock sample particle coordinates and time, can be obtained.

[0069] Specifically, for the relationship between rock sample crack morphology and time in the target area, a dynamic digital image correlation (DIC) algorithm can be developed to calculate the sub-pixel displacement field of continuously acquired microstructural image sequences. Combined with the crack tip stress intensity factor model, the crack propagation rate and path deflection law can be quantitatively characterized. For example, phase consistency-based edge detection technology can be used to automatically identify the crack outline in each frame of the image, and the main crack and secondary crack network can be segmented using a watershed algorithm to calculate the temporal evolution of parameters such as crack length, aperture, and bifurcation angle. For complex three-dimensional crack systems, stereo imaging technology can be combined with a dual-probe secondary electron detector to obtain parallax images, and a three-dimensional reconstruction algorithm can be used to establish a model of the spatial topological structure of the crack over time.

[0070] Regarding the relationship between the coordinates and time of rock sample particles in the target area, SEM observations can enhance atomic number contrast using backscattered electron (BSE) mode. Combined with energy spectrum distribution analysis, the chemical composition of different mineral particles can be distinguished. An improved scale-invariant feature transformation algorithm can be used to extract particle feature points, and the displacement vector between adjacent frames can be calculated using the optical flow method to automatically track the motion trajectories of multiple particles. Furthermore, to improve recognition accuracy in complex backgrounds, a deep learning framework can be introduced. For example, a U-Net neural network can be trained in a deep learning framework to perform pixel-level segmentation of particle boundaries. This, combined with a Kalman filter algorithm, can predict motion trajectories, effectively overcoming the interference of electron beam scanning distortion and image noise. The resulting particle migration database contains a time series of parameters such as the three-dimensional coordinates (X, Y, Z), velocity vector (Vx, Vy, Vz), and rotation angle of each particle, providing experimental validation data for the development of discrete element models.

[0071] After obtaining the temporal relationship between rock sample crack morphology and the coordinates of rock sample particles in the target area, time series analysis algorithms can be used to analyze the continuously acquired microstructural image sequences. For example, the Hurst exponent can be used to determine the long-range correlation of the crack growth process, and the Lévy flight theory can be used to describe the jump-like propagation behavior of the crack tip. In particle transport analysis, complex network theory can be applied to construct a particle contact force chain evolution model, revealing the force chain reorganization mechanism through the time-varying characteristics of the node degree distribution and clustering coefficient.

[0072] Furthermore, machine learning algorithms can be introduced to efficiently process massive microstructural image sequences. For example, convolutional neural networks (CNNs) can be used to automatically classify crack propagation modes (e.g., transgranular, intergranular, or bifurcation propagation). Long short-term memory (LSTM) networks can be used to predict particle migration trajectories and, combined with SHAP value analysis, reveal key factors controlling migration direction (e.g., local stress gradients, particle shape anisotropy, etc.).

[0073] In some embodiments, pulsed electron beam technology can be used to increase imaging temporal resolution to nanoseconds, enabling the capture of transient fracture processes induced by stress wave propagation. Combining SEM with focused ion beam (FIB) milling technology enables in-situ cross-sectional observation and reconstruction of three-dimensional crack networks. Furthermore, integrating cathodoluminescence (CL) spectroscopy with electron backscatter diffraction (EBSD) technology allows for the simultaneous acquisition of mineral phase transformation information and crystal orientation evolution data during mechanical testing, enabling the establishment of multi-field coupled mechanical-chemical-structural constitutive relationships.

[0074] S103: Determine the micromechanical properties of the rock sample according to the micromechanical response data of the rock sample and the corresponding microstructural evolution data.

[0075] In some embodiments, the micromechanical response and microstructure at multiple time points during the indentation test can be spatiotemporally coupled; and the micromechanical properties of the rock sample can be determined based on the micromechanical response and microstructure at multiple time points after the spatiotemporal coupling.

[0076] By establishing a spatiotemporal coupling model that dynamically correlates micromechanical responses with structural evolution, the team breaks through the limitations of traditional methods of fragmented analysis in the temporal and spatial dimensions. By integrating high-resolution spatiotemporal mechanical test data with multimodal microscopic observations, they construct a complete analytical chain from transient mechanical behavior to long-term structural evolution, revealing transient processes and nonlinear effects that are unobservable with traditional experimental methods. This helps to deeply analyze the co-evolutionary laws of crack propagation and particle migration, and thus accurately determine the micromechanical properties of rocks.

[0077] For the temporal coupling of micromechanical responses and microstructures at multiple time points during the indentation test, an FPGA-based hardware-level synchronization controller can be used to precisely align the nanoindenter's load signal (e.g., a sampling rate of 100kHz), the SEM electron beam scanning signal (e.g., a frame synchronization accuracy of ±10ns), and the Raman spectrum acquisition trigger signal (timing jitter <1μs), ensuring that each mechanical data point (load P, displacement h) and the corresponding structural image (crack length L, particle coordinates (x, y) etc.) have a defined timestamp.

[0078] To address the spatial coupling of micromechanical responses and microstructures at multiple time points during indentation testing, a micron-scale positioning marker array (such as a cross-mark grid produced by photolithography) can be used, combined with an affine transformation algorithm, to eliminate coordinate system deviations between different observation devices (SEM, AFM, X-ray CT). This allows for sub-pixel (<50nm) spatial registration accuracy in three dimensions. For coordinate system drift during dynamic deformation, a real-time correction algorithm based on feature point tracking can be used to achieve dynamic registration by optimizing the maximum mutual information (MI) between adjacent frames.

[0079] After completing the spatiotemporal coupling of the micromechanical response and microstructure at multiple time points during the indentation test, a unified data model can be established to integrate the mechanical time series data (P(t)-h(t) curve), structural evolution data (such as crack density D(x,y,t)), and composition distribution data (mineral phase diagram M(x,y)). The spatiotemporal voxelization method can be used to interpolate discrete sampling points into a continuous four-dimensional data field, forming a spatiotemporal hypercube data structure that can perform tensor operations.

[0080] According to the micromechanical response and microstructure at multiple time points after spatiotemporal coupling, the micromechanical properties of the rock sample can be determined, including:

[0081] The transient characteristics of the load-displacement curve can be extracted by wavelet transform (such as the sudden drop point corresponding to the crack nucleation moment), and time-varying parameters such as creep compliance J(t) and relaxation modulus E(t) can be calculated;

[0082] Computer vision algorithms (such as the image segmentation model SAM) can be used to quantify spatiotemporal evolution parameters such as the crack network fractal dimension DF(t), particle migration diffusion coefficient D(t), and pore topology connectivity index C(t);

[0083] Based on spatiotemporal cross-correlation analysis, the hysteresis correlation between mechanical and structural parameters can be calculated, for example, the time-lag relationship between crack density D(t+Δt) and local stress concentration factor K(t) can be determined;

[0084] Dynamic modal decomposition method can be used to identify the key modes that dominate the spatiotemporal evolution during the indentation test and extract the key characteristic frequencies and spatial patterns that control the damage evolution;

[0085] A data-driven constitutive neural network can be established, with the spatiotemporal structural parameters corresponding to the indentation test process as input and the mechanical response as output. The implicit constitutive relationship between the rock sample structure and its mechanical parameters can be obtained through deep reinforcement learning training.

[0086] A micromechanical model of multiphase media can be developed to simulate the dynamic interaction of the particle-matrix-pore system based on the discrete element-finite element coupling (DEM-FEM) method, and the micromechanical parameters of the rock sample, such as the interface strength and friction coefficient, can be determined through parameter inversion.

[0087] In some embodiments, based on energy spectrum analysis, a scanning electron microscope is used to obtain chemical composition data and physical structure data of a target area on the surface of a rock sample; based on the chemical composition data and physical structure data, a target indenter of an indenter in a scanning electron microscope cavity and an inclination angle of the indenter in a scanning electron microscope cavity are determined; and at the inclination angle, an indentation test is performed on the target area on the surface of the rock sample using the target indenter of the indenter in a scanning electron microscope cavity.

[0088] The collaborative application of scanning electron microscopy and energy spectrum analysis provides multi-physics coupled characterization capabilities for rock micromechanics testing. Specifically, based on high-spatial-resolution microscopic observations, combined with elemental distribution and mineral phase identification results, the indenter type, loading direction, and contact mode can be dynamically adjusted, significantly improving the spatial targeting and data reliability of micromechanics testing.

[0089] During the chemical composition data acquisition phase, the energy spectrum analysis system can achieve qualitative and quantitative elemental analysis of the target area by collecting and interpreting characteristic X-ray spectra. Characteristic X-rays excited by the interaction between the electron beam and the sample are received by a silicon drift detector and converted into energy spectrum signals by a pulse processor. By identifying energy spectrum peaks (e.g., the Si-Kα peak corresponds to quartz, and the Ca-Kα peak corresponds to calcite) and integrating the peak areas, the weight percentage and atomic percentage of each element can be obtained. For regions with complex mineral compositions, a mapping mode can be used to obtain a two-dimensional element distribution map at a pixel level. This, combined with an automatic phase recognition algorithm (e.g., based on a random forest classifier), can be used to delineate mineral phase boundaries. For example, in shale samples, kerogen-rich areas (>70% C), clay mineral regions (combination of Al, Si, and O), and pyrite grains (characteristic peaks of Fe and S) can be precisely distinguished, providing a chemical basis for targeted positioning during subsequent mechanical testing.

[0090] Physical structural data acquisition can leverage the high-resolution imaging capabilities of a scanning electron microscope (SEM). Through the coordinated analysis of secondary electron and backscattered electron signals, three-dimensional topography and density contrast information of the target area can be constructed. Secondary electron imaging is sensitive to surface topography, clearly revealing topological features such as micron-scale pores and nanoscale bedding. Backscattered electron imaging, on the other hand, reflects differences in atomic number and can distinguish grain boundaries and inclusions between different mineral phases. Stereo imaging techniques can be combined with digital elevation model reconstruction algorithms to quantitatively calculate surface roughness parameters, pore geometry (pore size distribution, aspect ratio), and mineral grain orientation distribution. For layered rocks (such as shales and gneisses), electron backscatter diffraction can be used to further obtain crystal orientation data, enabling the development of models linking preferred mineral grain orientation with mechanical anisotropy.

[0091] After obtaining chemical composition and physical structure data for the target area on the rock sample surface, the target indenter tip can be selected based on the dual principles of material matching and geometric adaptation. Specifically, in terms of material matching, the indenter material can be selected based on the hardness range of the mineral being tested. For example, for superhard minerals (such as diamond indenters for quartz testing, with Vickers hardness >10 GPa), a single-crystal diamond indenter can be used to avoid deformation. For medium- to low-hardness minerals (such as calcite and clay minerals), sapphire or tungsten carbide indenters can be used to reduce testing costs. Regarding geometric adaptation, the indenter tip shape can be selected based on the size and morphology of the mineral phase. A Berkovich triangular pyramid indenter (tip curvature radius of 100 nm) is suitable for hardness and modulus testing of homogeneous micron-scale areas; a spherical indenter (radius 5-50 μm) is more suitable for analyzing elastic-plastic transition behavior and creep properties. For layered structures or pore edge areas, a flat punch is used to avoid stress concentration-induced abnormal fractures. During the indenter selection process, the chemical composition data (to avoid chemical reactions between the indenter material and the sample) and physical structure data (such as the impact of pore distribution density on the effective contact area) can be comprehensively evaluated. The stress field distribution under different indenters can be previewed through finite element simulation to ultimately determine the optimal indenter configuration scheme.

[0092] After obtaining the chemical composition data and physical structure data of the target area on the surface of the rock sample, the tilt angle of the indenter in the SEM cavity can be optimized by comprehensively considering the SEM observation angle, sample surface morphology, mineral crystal orientation, and test target. Specifically, for rough surfaces with significant undulations (Ra>1μm), the local normal direction can be calculated using DEM data, and the sample stage tilt angle can be dynamically adjusted (in the range of 10°-45°) to make the indenter axis perpendicular to the test point surface, ensuring that the load application direction is consistent with theoretical calculations. For anisotropic minerals (such as the layered structure of mica and the columnar cleavage of pyroxene), a specific loading direction is set in combination with EBSD lattice orientation data to study the effect of crystallographic orientation on mechanical response: for example, load is applied perpendicular to the mica (001) plane to test the interlayer bonding strength, or along the c-axis direction of quartz to study the anisotropic elastic modulus. In the multi-layer composite structure area, a multi-angle continuous testing strategy is adopted to obtain the mechanical parameter matrix under different loading directions by changing the tilt angle at 5° intervals (0°, 5°, 10°), and a direction-dependent model of the mineral interface strength is established.

[0093] In some embodiments, the determination of the target indenter and the indenter's tilt angle for an in-cavity SEM indenter can be implemented using a machine learning algorithm. Specifically, a mineral feature database can be constructed, containing energy spectrum fingerprints, grayscale threshold ranges, and surface morphological features of typical rock minerals. During testing, real-time chemical-physical data can be used to perform mineral phase classification using a convolutional neural network, and a pre-defined indenter selection rule library (if-then rules) can be used to generate initial parameter recommendations. Furthermore, a reinforcement learning algorithm can be employed to dynamically optimize indenter type and tilt parameters based on historical test data, including the success rate of different parameter combinations and data stability. For example, when a high-porosity region is detected, the algorithm automatically increases the weight of a spherical indenter to avoid stress concentration; when a layered structure is identified, a tilt compensation strategy is prioritized. This data-driven parameter optimization approach transforms manual experience into a quantifiable decision-making model, significantly improving testing efficiency and repeatability.

[0094] In some embodiments, the tilt angle of the indenter in the scanning electron microscope cavity can be determined based on the chemical composition data, the physical structure data, and the observation angle of the scanning electron microscope.

[0095] By precisely adjusting the geometric parameters, the interaction between the electron beam and the sample surface is ensured to be in the optimal state, thereby obtaining real-time observation images with high signal-to-noise ratio and low distortion, while maintaining the mechanical loading accuracy of the indentation test.

[0096] The imaging quality of a scanning electron microscope is affected by the synergistic effect of the electron beam incident angle and the detector acceptance angle. By adjusting the inclination angle of the sample stage so that the electron beam axis coincides with the surface normal direction of the test point, the deviation in the electron scattering direction caused by the local morphology can be minimized. For rough surfaces (Ra>0.5μm), real-time surface morphology reconstruction technology can be used to dynamically calculate the local normal direction based on DEM data, and drive the sample stage to compensate for the tilt angle with an accuracy of 0.001°. The optimal acceptance angle of the secondary electron detector is usually 35°-45°. The indenter and the sample stage can be jointly tilted to form the optimal acceptance angle between the surface normal direction of the test area and the detector axis. For multi-detector systems (such as In-lens SE and BSE combinations), an angle optimization matrix can be established to balance the acquisition efficiency of different signal sources.

[0097] During loading, the indenter geometry may obstruct the electron beam path or the detector field of view. Finite element ray tracing simulations can be used to simulate the shadow region of the indenter at various tilt angles, selecting configurations with no or minimal obstruction. For Berkovich indenters, the indenter's long axis can be set at a 45° angle to the detector axis to avoid the primary shadow region. To ensure that the indenter-sample contact point remains centered within the SEM field of view (covering at least 30% of the field of view), a pre-defined kinematic model is used to calculate the indenter's motion trajectory. This model compensates for sample stage translation during tilt adjustment, maintaining spatial stability of the contact point within the imaging field of view (drift <0.1 μm / min). Furthermore, tilted loading can introduce asymmetric contact stress distributions. Using Hertz contact theory corrections, the stress field offset caused by the tilt angle can be calculated. When the angle exceeds 5°, a contact mechanics compensation algorithm is activated to correct for anisotropic deviations in the load-displacement curve.

[0098] Furthermore, during continuous loading, surface deformation of the sample may alter local geometric conditions, necessitating the establishment of a real-time feedback adjustment mechanism: 1) Analyze real-time SEM image sequences based on the DIC algorithm to calculate the surface displacement gradient tensor. When local deformation causes the normal direction to change by more than 0.5°, micro-angle compensation of the sample stage (in steps of 0.01°) is triggered to maintain orthogonal electron beam incidence. 2) Surface height changes (such as the formation of indentations) can alter the optimal focus position. An autofocus algorithm can be used to adjust the objective lens current in real time through edge sharpness analysis, combined with dynamic astigmatism correction (with a 30ms refresh rate per frame) to maintain image clarity. 3) A closed-loop control system for tilt angle, load, displacement, and focal length can be constructed, employing a PID algorithm to coordinate the response speeds of the various actuators to ensure that mechanical vibrations caused by angle adjustment decay to nanometer-scale amplitudes (<2nm) before resuming loading.

[0099] Through the above-mentioned multi-dimensional optimization strategy, the tilt angle configuration of the indenter in the scanning electron microscope cavity can maintain the optimal observation angle throughout the entire mechanical testing cycle, providing high-fidelity visual data support for the in situ dynamic study of the micromechanical behavior of rocks.

[0100] In some embodiments, the temperature field and electric field corresponding to the target area are determined based on the chemical composition data and physical structure data of the target area; the geological environment of the rock sample is simulated based on the temperature field and electric field corresponding to the target area; and an indentation test is performed on the target area on the surface of the rock sample under the simulated geological environment of the rock sample.

[0101] Through the deep integration of chemical composition and physical structure data, we can construct temperature field and electric field distribution models that reflect actual geological conditions, thereby achieving accurate reproduction of complex geological environments, and providing an accurate environment for studying the long-term evolutionary behavior of rocks under thermal-electrical-mechanical coupling.

[0102] During the chemical composition data analysis phase, a thermophysical parameter model can be established based on the quantitative elemental distribution and mineral phase information obtained from energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD). Specifically, mineral thermal databases (such as Thermocalc and HPx) can be used to match parameters such as thermal conductivity, specific heat capacity, and thermal expansion coefficient of each mineral phase, and the law of mixtures can be used to calculate the effective thermal conductivity of the composite rock. Electrical parameters can be determined based on mineral resistivity databases (such as the CIPW standard). For example, the Archie formula can be used to calculate the resistivity of water-bearing rock. For dry rock, the effective medium theory (EMT) can be used to simulate the conductive behavior of the mineral-pore composite system.

[0103] Furthermore, the spatiotemporal evolution of geological environment simulations can be achieved through time-stepping. Specifically, initial conditions can be set by current geological data, and boundary conditions can be dynamically adjusted based on the tectonic history. Within each time step, the temperature field (affecting rock thermal cracking and mineral phase transitions), electric field (controlling ion migration and alteration reactions), mechanical field (inducing stress redistribution), and chemical field (changing mineral composition and pore structure) are sequentially updated, forming a positive feedback loop. For simulations on a million-year scale, a strategy combining explicit time integration with adaptive mesh refinement (AMR) is employed to improve efficiency while maintaining computational accuracy.

[0104] In some embodiments, the indentation test includes a loading stage, a constant load stage, and an unloading stage; and the loading mode, constant load mode, and unloading mode corresponding to the loading stage, constant load stage, and unloading stage are determined based on the geological data of the rock sample.

[0105] Staged control of rock indentation testing is a core technical component of micromechanical characterization. The precise design of loading, dead load, and unloading modes directly determines the reliability and engineering applicability of test data. This technical system, based on multi-dimensional analysis of rock and geological data, establishes differentiated loading strategies that precisely match testing requirements under varying lithologies, structures, and environmental conditions, providing a methodological basis for revealing the spatiotemporal evolution of rock micromechanical behavior.

[0106] Loading modes can include monotonic loading mode, cyclic loading mode, and high-speed loading mode. For example, in the monotonic loading mode, the load increases continuously and steadily, which can intuitively show the entire process of rock from its initial state to gradual deformation and finally failure. It can be used to explore the mechanical response of rock when it is first subjected to load. In the cyclic loading mode, the load increases and decreases periodically, which can simulate the repeated stress conditions of rock in actual engineering. It helps to study the fatigue properties of rock and observe the changes in the internal structure of rock and the accumulation of damage under multiple loading and unloading cycles. The step loading mode gradually increases the load according to specific steps, which can clearly analyze the changes in the mechanical properties of rock at different stress stages. The loading rate is adjusted within the range of 0.01 mN / s to 10 mN / s. Low-speed loading can more carefully capture the mechanical behavior of rock during slow deformation; high-speed loading can simulate rapid stress scenarios such as earthquakes and study the mechanical response of rock under impact.

[0107] The loading pattern during the loading phase can be configured by comprehensively considering the rock's brittle-ductile characteristics, mineral hardness distribution, and microstructural characteristics. For layered shales or gneisses, differentiated loading rates can be set based on the angle θ between the bedding direction and the loading direction: when θ = 0° (parallel to the bedding), step loading (0.5 mN per step, 10 s hold) is used to prevent sudden fracture caused by interlaminar slip; when θ = 90° (perpendicular to the bedding), cyclic loading (1 mN / s rate) is used to fully stimulate interlaminar bonding strength. For porous sandstones or carbonate rocks, a porosity-loading rate correlation model can be introduced to dynamically adjust the loading rate based on the porosity obtained from CT scans. For composite rocks containing weak minerals (such as clay and gypsum), a preload-relaxation cyclic strategy can be adopted: after an initial load of 0.5 mN, hold for 30 s to allow the weak phase to plastically adjust, before continuing the main loading process to achieve a stable mechanical response.

[0108] The constant load mode can be set based on the quantification of time-dependent deformation (creep, stress relaxation). Specifically, based on the mineral composition data, rheological models of different phase components can be established. Based on the rheological model, different constant load times can be set. For example, for elastic phases such as quartz, the constant load time can be set to 10-30 seconds to monitor the elastic recovery rate; for viscoelastic phases such as clay, the constant load time can be set to 300-600 seconds to record the creep displacement curve; for viscoplastic phases such as organic matter, the constant load time can be set to greater than 1800 seconds to obtain the steady-state creep rate.

[0109] The choice of unloading mode directly affects the measurement accuracy of residual deformation and elastic parameters. Therefore, for brittle rocks (such as granite and basalt), instantaneous unloading (at a rate of 10 mN / s) can be used to capture the transient response of elastic rebound; for ductile rocks (such as salt rock and organic shale), stepped unloading (0.2 mN per step, 5 seconds apart) can be used to isolate the viscoelastic recovery component.

[0110] Furthermore, based on the mineral hardness ratio, differential unloading paths can be set. For example, when the hardness ratio is >3, a spherical indenter with a logarithmic unloading curve can be used; when the hardness ratio is 1-3, a Berkovich indenter with linear unloading can be used; and when the hardness ratio is <1, a flat punch indenter with exponential unloading can be used.

[0111] In some embodiments, the micromechanical responses at the multiple time points may be modified according to the microstructures at the multiple time points.

[0112] By using the time series of microstructural observation data as constraints to correct the micromechanical responses at multiple time points, we can achieve an approximation from the original mechanical response to the true intrinsic behavior based on the spatiotemporal causal relationship between microstructural evolution and mechanical behavior, which helps to reveal key mechanisms such as rock damage accumulation, energy dissipation and instability precursors.

[0113] From a microstructural perspective, changes in grain size have a significant impact on the micromechanical response. According to the Hall-Petch relationship, grain refinement generally increases the material's strength because grain boundaries hinder dislocation motion, causing dislocations to accumulate at the boundaries, thereby increasing the material's resistance to deformation. Therefore, if an abnormal increase in grain size is observed at a particular point in time, while the yield strength of the rock sample particles in the micromechanical response does not decrease accordingly, then the micromechanical response at that point in time is abnormal. Grain boundary characteristics also play a significant role in micromechanical behavior. High-angle grain boundaries have higher energy and lower slip transfer efficiency, effectively hindering dislocation motion; low-angle grain boundaries, on the other hand, have a relatively weaker effect on deformation. When grain boundary type and distribution undergo abnormal changes, such as a shift from a structure dominated by high-angle grain boundaries to one dominated by low-angle grain boundaries, the plastic deformation mechanism of the rock sample / particles may change, thereby affecting the normal micromechanical response. Phase distribution and phase transformation processes are also crucial for determining the micromechanical response. The mechanical properties of different phases in a rock sample vary significantly, and the precipitation, dissolution, or growth of second-phase particles can alter the material's load-bearing capacity and deformation behavior. For example, in aluminum alloys, the fine reinforcing phases that precipitate during aging can pin dislocations, significantly increasing the material's strength. However, if, at a certain point in time, the reinforcing phase exhibits abnormal coarsening while the hardness observed in the micromechanical response does not decrease significantly, this clearly contradicts normal mechanical behavior. Furthermore, the morphology and evolution of defects (such as pores, cracks, and dislocations) can have a decisive influence on the micromechanical response. The growth and aggregation of pores can reduce the material's toughness, while the initiation and propagation of cracks can directly threaten the material's structural integrity. The proliferation, movement, and interaction of dislocations are the core mechanisms of plastic deformation. Therefore, by comparing and analyzing the defect characteristics in the microstructure at different time points, it is possible to determine whether the micromechanical response conforms to the expected defect evolution pattern.

[0114] To accurately assess the micromechanical response, quantitative criteria and standards can be established. Specifically, based on the rock sample's constitutive model and micromechanical theory, combined with extensive experimental data and simulation results, the range of correspondence between microstructural parameters and micromechanical response parameters under normal conditions can be determined. For example, by conducting microstructural and mechanical property tests on a large number of samples of the same material, empirical formulas relating grain size to yield strength can be statistically derived, and their reasonable fluctuation ranges determined. Finite element simulations can be used to calculate stress-strain curves for materials with varying phase distributions and defect morphologies, establishing a standardized library of mechanical response curves. To correct the micromechanical response based on the microstructure at each time point, the acquired microstructural data at multiple time points can be substituted into the relevant models and formulas to calculate the corresponding predicted micromechanical response values. These values can then be compared with the actual measured micromechanical response data. If the actual value falls outside the reasonable fluctuation range of the predicted value, it can be preliminarily determined that the micromechanical response at that time point is abnormal. During this comparative analysis, statistical methods can be used to process the data, taking into account measurement errors and minor variations in experimental conditions, to improve the accuracy and reliability of the assessment results.

[0115] When anomalies are detected in the micromechanical response at multiple time points, corrections can be made based on the microstructure at those points. For example, if microstructural analysis indicates that the anomalous micromechanical response is due to unusual changes in grain size (the rock sample particles in the target area of the rock sample surface), mechanical properties such as yield strength and hardness corresponding to the current grain size can be recalculated based on the Hall-Petch relationship, and the original micromechanical response data can be adjusted. The impact of grain size changes on other mechanical properties (such as plasticity and toughness) can also be considered to comprehensively correct various aspects of the micromechanical response. If the anomaly is caused by abnormal phase distribution or phase transformation, thermodynamic and kinetic theories can be used to analyze the driving force, resistance, and transformation rate during the phase transformation process. Combined with information on the morphology and content of the phases in the microstructure, a more accurate phase transformation model can be established to modify phase transformation-related mechanical properties in the micromechanical response, such as elastic modulus and Poisson's ratio. For micromechanical anomalies caused by abnormal defect morphology and evolution, fracture mechanics and damage mechanics theories can be used to model and analyze the defect propagation behavior and damage accumulation process. By calculating parameters such as the stress intensity factor and damage variable at the defect tip, the degree of influence of the defect on the mechanical properties of the material can be predicted, and key indicators such as strength and toughness in the micromechanical response can be corrected accordingly. I will not go into details here.

[0116] In some embodiments, the microstructure data can be input into a numerical simulation model to simulate the micromechanical response under normal conditions, and compared with the actual abnormal mechanical response to verify the rationality and effectiveness of the correction strategy. The numerical simulation model can be a finite element method or a molecular dynamics simulation. The finite element method can discretize the complex microstructure model, and by solving the mechanical equilibrium equation, calculate the stress and strain distribution of the material under different loads and boundary conditions, thereby simulating the change process of the micromechanical response. Molecular dynamics simulation can study the mechanical behavior of materials at the atomic scale, revealing the influence of microscopic mechanisms such as dislocation motion and atomic diffusion on mechanical properties. During the simulation process, sensitivity analysis can also be performed on different correction parameters to determine the key influencing factors, optimize the correction scheme, and improve the accuracy of the correction results.

[0117] A specific embodiment of this specification is provided below:

[0118] 1. A block rock sample from site A is selected and processed into a rectangular thin slice of target size by wire cutting. The upper surface of the rectangular thin slice is subjected to argon ion polishing to obtain a sample-prepared rock sample.

[0119] 1.1 First, accurately identify the target formation based on the research objectives and requirements, and carefully select representative block rock samples from the Site A formation. During the selection process, full consideration should be given to the integrity of the rock, avoiding sections with obvious cracks, fractures, or other defects to ensure that the selected samples truly reflect the characteristics of the rock in the formation. During this process, a preliminary inspection of the selected block rock samples can be performed to record their appearance characteristics, such as color and texture.

[0120] 1.2 Use specialized wire cutting equipment to cut the block rock sample into rectangular slices of the target size according to precise dimensional requirements. During the wire cutting process, strictly control the cutting speed and wire tension to ensure the smoothness and accuracy of the cut surface. Excessive cutting speed may result in a rough cut surface, while too slow a cutting speed may affect work efficiency and cause unnecessary thermal effects on the sample. After cutting, measure the resulting rectangular slice to ensure that its length, width, and height match the target dimensions, such as 3mm × 3mm × 2mm.

[0121] 1.3 Place the cut rectangular thin slices in a special argon ion polishing device. During the polishing process, the upper surface of the slice is finely polished by controlling parameters such as the energy, flow rate and polishing time of the argon ions. Argon ions impact the sample surface under a certain energy, removing tiny bumps and uneven parts on the surface, thereby obtaining an ideal smooth surface. The polishing process needs to be carried out in a high vacuum environment to reduce the interference of impurities and ensure the polishing effect. At the same time, the polishing process is monitored in real time to avoid excessive polishing that may cause damage to the sample surface or loss of its original microstructural characteristics. After polishing is completed, the sample surface is inspected again. High-precision detection equipment such as atomic force microscopes can be used to measure the surface roughness to ensure that the surface reaches the ideal smoothness and meets the surface quality requirements of subsequent experiments. Finally, the rock sample after sample preparation is obtained.

[0122] 2. Using scanning electron microscopy-energy spectrum analysis technology, identify the chemical composition and physical structure of the target area on the surface of the rock sample after the sample preparation treatment.

[0123] 2.1 Carry out comprehensive debugging and calibration of the scanning electron microscope to ensure that its various performance indicators are in the best condition. Check the emission current, acceleration voltage and other parameters of the electron gun to enable it to stably generate a high-energy electron beam. Adjust the current of the scanning coil to ensure that the electron beam performs accurate raster scanning on the sample surface. Then clean the sample chamber to remove possible impurities and contaminants to prevent them from interfering with the rock sample or test results. Install the sample stage firmly and ensure that it can be accurately moved and positioned in three dimensions to observe and analyze different areas of the rock sample surface. At the same time, select suitable detectors, such as secondary electron detectors and backscattered electron detectors, and calibrate their sensitivity. Secondary electron detectors are mainly used to obtain morphological information on the sample surface, while backscattered electron detectors are more sensitive to compositional differences in the sample and can provide preliminary information about the elemental composition.

[0124] 2.2 Carefully place the prepared rock sample on the sample stage, ensuring it is securely fixed to prevent displacement during observation. Use the sample stage's motion control device to precisely align the target area on the rock sample surface with the electron beam's scanning range.

[0125] 2.3 Record the sample's initial position so that you can accurately return to the same area for comparative observation or further analysis during subsequent analysis. Then, start the scanning electron microscope (SEM) and set the appropriate accelerating voltage and working distance. The accelerating voltage is generally selected between 5 kV and 30 kV, depending on the rock sample's properties and research objectives. The working distance is controlled between a few millimeters and more than ten millimeters to obtain a clear image. Electron beam scanning then begins. The electron beam scans the surface of the sample, row by row and column by column, interacting with atoms on the sample surface and generating signals such as secondary electrons and backscattered electrons. These signals are collected by a detector and converted into electrical signals. After amplification and processing, an image of the rock sample surface is displayed on the monitor.

[0126] 3. Vertically load the prepared rock sample onto the nanoindenter, equip the nanoindenter with a target-type indenter, place the loaded nanoindenter inside the scanning electron microscope chamber, and adjust it to the preset tilt angle.

[0127] 3.1 First, prepare the sample stage of the nanoindenter and ensure that its surface is clean and free of impurities to avoid affecting the placement of the sample and the test results. Carefully pick up the pre-treated rock sample and use specialized tools (such as tweezers or sample clamps) to avoid damaging the sample surface.

[0128] 3.2 Slowly place the prepared rock sample vertically on the sample stage of the nanoindenter, ensuring that the bottom surface of the sample is in close contact with the sample stage and that the long axis of the sample is aligned with the loading direction of the nanoindenter. After placing the sample, use the sample stage's fixture (such as a clamp or magnetic device) to firmly secure the rock sample to the sample stage to prevent it from moving or shaking during the test.

[0129] 3.3 Select the appropriate target type of indenter. Select an indenter based on the mechanical properties to be measured and carefully install the selected indenter on the indenter loading device of the nanoindenter, ensuring that the indenter is securely mounted and concentric with the loading axis.

[0130] 3.4 Place the nanoindenter inside the SEM chamber and tilt it. First, move the nanoindenter, loaded with the prepared rock sample and indenter, to the vicinity of the SEM chamber. During the movement, keep the nanoindenter level and stable to avoid collisions or violent vibrations to prevent damage to the loaded sample and indenter. Open the sealed door of the SEM chamber and slowly place the nanoindenter in a pre-designed position inside the chamber. This position should ensure the stability of the nanoindenter inside the chamber and facilitate connection and coordination with the temperature control system, pressure control system, and voltage control system inside the chamber. Use a specialized adjustment device (such as a tilt bracket or rotating base) to tilt the nanoindenter at a certain angle within the chamber to ensure that the electron beam of the SEM can clearly and in real time monitor the morphological changes of the target area on the rock surface during the nanoindentation test.

[0131] 3.5 After tilting the nanoindenter, recheck its position and stability within the chamber to ensure that it does not move during the entire test. Also, check the connections between the nanoindenter and the various systems within the SEM chamber, including signal transmission lines, power lines, and interfaces with temperature, pressure, and voltage control systems, to ensure that all equipment can work together and be fully prepared for subsequent in-situ nanoindentation testing.

[0132] 4. The temperature field is controlled by the SEM temperature control module, and the voltage field is controlled by the integrated electric field control module. The temperature control range is from room temperature to 1000°C, and the voltage control range is from open circuit voltage to several thousand volts.

[0133] 4.1 Temperature control is achieved using a specialized temperature control system within the scanning electron microscope. This temperature control system primarily consists of heating elements and temperature sensors. The heating elements utilize resistive heaters, evenly distributed within the microscope cavity to ensure uniform heating of the interior. The temperature sensor monitors the cavity temperature in real time. During experiments, a target temperature is set based on research needs, such as simulating temperature environments at different depths underground. The temperature control system regulates the temperature by controlling the power of the heating elements. When the sensor detects that the current temperature is below the target, the power is increased to raise the temperature; otherwise, the power is reduced. During the regulation process, a closed-loop feedback mechanism is used to continuously adjust the heating power until a stable target temperature is reached. This allows for precise control of the temperature field, with a control range from room temperature to 1000°C, ensuring that temperature conditions in a variety of practical scenarios can be simulated.

[0134] 4.2 For electric field control, an appropriate electrode layout can be set up within the SEM chamber based on experimental requirements. Electrodes of specific materials and shapes can be selected to ensure a uniform and controllable electric field across the sample area. The power supply provides an adjustable voltage output, and the target voltage is set before the experiment. After initiating the electric field control simulation, the power supply applies voltage to the electrodes, generating an electric field. An electric field intensity monitoring device monitors the electric field intensity across the sample area in real time and feeds this data back to the control system. If the actual electric field intensity deviates from the target value, the control system adjusts the power supply output voltage to achieve the set value. Simultaneously, a current monitoring device (such as an ammeter) monitors current changes, further optimizing voltage regulation based on these changes. This allows for precise control of both electric field intensity and current, meeting the requirements for studying electric field factors under multi-field coupling conditions. This allows the rock sample to be subjected to an accurate multi-field coupling environment, providing experimental conditions for subsequent in situ nanoindentation testing.

[0135] 5. Inside the scanning electron microscope, a loading-constant load-unloading mode is set up for in-situ nanoindentation testing under different temperature field and electric field coupling conditions. The dynamic evolution process of nano-micro cracks and particle migration behavior in the target area of the rock sample are simultaneously observed through real-time imaging technology of the electron microscope, and the load-displacement relationship curve is recorded, realizing the visual dynamic quantitative characterization of the micromechanical properties of rocks under multi-field coupling.

[0136] During the 5.1 loading phase, the indenter gradually applies pressure to the target area on the rock sample surface at a rate of 0.01 mN / s–10 mN / s, allowing observation of the rock's initial deformation. During the constant load phase, the pressure is maintained constant, allowing investigation of changes in rock properties under sustained stress. During the unloading phase, analysis of the rock's elastic recovery and residual deformation is possible. Different loading modes, such as monotonic, cyclic, and step loading, can simulate the stress state of rock in various engineering scenarios, providing a comprehensive understanding of its mechanical response.

[0137] 5.2 During the nanoindentation test, the real-time imaging technology of the electron microscope can be used to shoot the target area of the rock sample in real time with high resolution and high acquisition frame rate, and the dynamic evolution process of nano-micro cracks can be clearly and synchronously observed, including the initiation position, expansion direction, expansion rate of the cracks, and the changes in the crack morphology during the loading, constant load and unloading processes; the particle migration behavior can also be accurately observed, that is, the movement, rotation and rearrangement of mineral particles relative to the surrounding materials under the action of load. The records of these dynamic processes can provide an intuitive basis for studying the micromechanical behavior of rocks. Figure 3 As shown, from Figure 1 The microscopic surface structure of the rock sample in area A can be seen, as well as an indenter approaching or contacting the sample surface. The changes in the rock surface during the indentation process, such as the initiation and expansion of cracks, can be observed in real time through a scanning electron microscope.

[0138] 5.3 Under different multi-field coupling conditions, carry out in-situ nanoindentation tests with various applied load modes, and simultaneously record the load-displacement relationship and the dynamic process of the target rock area. Specifically, Figure 3 and Figure 4 The microstructure image of the target area of the rock sample in area A before the indentation test is shown. Figure 5 and Figure 6 Shows the microstructural image of the target area of the rock sample in area A after indentation testing. Figure 7 A schematic diagram of the load-displacement curve recorded during the indentation test of the target area of the rock sample in area A is shown. Figure 8 A schematic diagram shows the curves of the nanoindenter indenter load, indenter displacement, and indentation depth of the target area of the rock sample in area A changing with time during the indentation test of the target area of the rock sample in area A.

[0139] 5.4 From the perspective of time, Figure 7 Each data point of the load-displacement curve corresponds to a SEM image frame at the same moment, forming a time series of "mechanical response-structural evolution". From a spatial dimension, the indentation position is strictly aligned with the SEM observation area, achieving a precise "point-to-point" correlation. In the elastic stage, the slope of the curve corresponds to the elastic modulus, and the SEM image shows no visible cracks, only lattice elastic deformation. In the yield stage, the curve grows nonlinearly, corresponding to the initiation of microcracks ( Figures 4 to 6 The transition of mineral grain boundaries or initial cracks can be seen in the SEM images. In the failure stage, the load drops suddenly accompanied by a sudden increase in displacement. The SEM images capture the main crack propagation path and particle shedding ( Figure 6 During the unloading phase, the rebound slope of the curve reflects the residual deformation, and the SEM image shows the irreversible solidification of the crack network. By linking the curve and image, we can quantify the differences in mineral grain strength.

[0140] The method for determining rock micromechanical properties provided in the embodiments of this specification can perform an indentation test on a target area on the surface of a rock sample to obtain micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time; during the indentation test, the target area on the surface of the rock sample is scanned to obtain microstructural evolution data corresponding to the micromechanical response data; the microstructural evolution data represents the relationship between the microstructure of the rock sample and time; and the micromechanical properties of the rock sample are determined based on the micromechanical response data and the corresponding microstructural evolution data. Compared to existing methods, the embodiments of this specification can simultaneously perform an indentation test on the target area on the surface of the rock sample while scanning the target area on the surface of the rock sample, thereby obtaining the micromechanical response and microstructure of the rock sample at multiple time points. Based on the coordinated analysis of the micromechanical response and microstructure of the rock sample at multiple time points, the inherent relationship between micromechanical behavior and microstructural evolution can be revealed, thereby more accurately characterizing the heterogeneous micromechanical properties of the rock, and achieving dynamic, in situ, and quantitative characterization of the rock micromechanical properties.

[0141] Based on the above-mentioned method for determining the micromechanical properties of rocks, this specification also proposes an embodiment of a device for determining the micromechanical properties of rocks. Figure 9 As shown, the rock micromechanical property determination device 900 may specifically include the following modules:

[0142] The testing module 901 can be used to perform an indentation test on a target area on the surface of a rock sample to obtain micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time;

[0143] The scanning module 902 may be used to scan a target area on the surface of the rock sample during the indentation test to obtain microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents a relationship between the microstructure of the rock sample and time;

[0144] The determination module 903 may be configured to determine the micromechanical properties of the rock sample based on the micromechanical response data and the corresponding microstructural evolution data of the rock sample.

[0145] In some embodiments, the above-mentioned test module 901 can be specifically used to obtain chemical composition data and physical structure data of the target area on the surface of the rock sample using a scanning electron microscope based on energy spectrum analysis; determine the target indenter of the scanning electron microscope cavity indenter and the inclination angle of the scanning electron microscope cavity indenter according to the chemical composition data and physical structure data; and perform an indentation test on the target area on the surface of the rock sample using the target indenter of the scanning electron microscope cavity indenter at the inclination angle.

[0146] In some embodiments, the above-mentioned test module 901 can also be specifically used to determine the temperature field and electric field corresponding to the target area based on the chemical composition data and physical structure data of the target area; simulate the geological environment of the rock sample based on the temperature field and electric field corresponding to the target area; and perform indentation testing on the target area on the surface of the rock sample under the simulated geological environment of the rock sample.

[0147] In some embodiments, the above-mentioned testing module 901 can also be specifically used to determine the load mode of the indentation test based on the geological data of the rock sample; perform an indentation test on the target area on the surface of the rock sample under the load mode to obtain the load and displacement change data at multiple time points.

[0148] In some embodiments, the test module 901 may be further configured to determine the loading mode, constant load mode, and unloading mode corresponding to the loading phase, constant load phase, and unloading phase according to the geological data of the rock sample.

[0149] In some embodiments, the scanning module 902 can be specifically used to scan the target area using a scanning electron microscope during the indentation test to obtain rock sample crack expansion data and rock sample particle migration data corresponding to the micromechanical response data; the rock sample crack expansion data represents the relationship between the change of rock sample crack morphology and time; the rock sample particle migration data represents the relationship between the change of rock sample particle coordinates and time.

[0150] In some embodiments, the above-mentioned determination module 903 can be specifically used to perform spatiotemporal coupling of the micromechanical responses and microstructures at multiple time points during the indentation test; and determine the micromechanical properties of the rock sample based on the micromechanical responses and microstructures at multiple time points after the spatiotemporal coupling.

[0151] In some embodiments, the determination module 903 may be further configured to modify the micromechanical responses at multiple time points according to the microstructures at the multiple time points.

[0152] The rock micromechanical properties determination device provided in the embodiments of this specification can perform an indentation test on a target area on the surface of a rock sample to obtain micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time; during the indentation test, the target area on the surface of the rock sample is scanned to obtain microstructural evolution data corresponding to the micromechanical response data; the microstructural evolution data represents the relationship between the microstructure of the rock sample and time; and the micromechanical properties of the rock sample are determined based on the micromechanical response data and the corresponding microstructural evolution data. Compared to existing methods, the embodiments of this specification can simultaneously perform an indentation test on the target area on the surface of the rock sample while scanning the target area on the surface of the rock sample, thereby obtaining the micromechanical response and microstructure of the rock sample at multiple time points. Based on the coordinated analysis of the micromechanical response and microstructure of the rock sample at multiple time points, the inherent relationship between micromechanical behavior and microstructural evolution can be revealed, thereby more accurately characterizing the heterogeneous micromechanical properties of the rock, and achieving dynamic, in situ, and quantitative characterization of the rock micromechanical properties.

[0153] It should be noted that the units, devices or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described in terms of functions and are divided into various modules and described separately. Of course, when implementing this specification, the functions of each module can be implemented in the same or multiple software and / or hardware, or the module that implements the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0154] The embodiments of this specification also provide a computer device for a method of determining the micromechanical properties of rocks, including a processor and a memory for storing instructions executable by the processor. When the processor is implemented, it can perform the following steps according to the instructions: perform an indentation test on a target area on the surface of a rock sample to obtain micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and the change over time; scan the target area on the surface of the rock sample during the indentation test to obtain microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents the relationship between the microstructure of the rock sample and the change over time; and determine the micromechanical properties of the rock sample based on the micromechanical response data of the rock sample and the corresponding microstructure evolution data.

[0155] In order to complete the above instructions more accurately, refer to Figure 10 As shown, the embodiment of this specification also provides another specific computer device 1000, wherein the computer device 1000 includes a network communication port 1001, a processor 1002 and a memory 1003, and the above structures are connected through internal cables so that each structure can perform specific data interaction.

[0156] The processor 1002 can be specifically used to perform an indentation test on a target area on the surface of a rock sample to obtain micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time; during the indentation test, the target area on the surface of the rock sample is scanned to obtain microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents the relationship between the microstructure of the rock sample and time; and the micromechanical properties of the rock sample are determined based on the micromechanical response data of the rock sample and the corresponding microstructure evolution data.

[0157] The memory 1003 may be specifically used to store corresponding instruction programs.

[0158] In this embodiment, the network communication port 1001 can be a virtual port that is bound to different communication protocols, thereby being capable of sending or receiving different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0159] In this embodiment, the processor 1002 can be implemented in any appropriate manner. For example, the processor can take the form of a microprocessor or a processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, an application-specific integrated circuit (ASIC), a programmable logic controller, an embedded microcontroller, etc. This specification is not intended to limit this.

[0160] In this embodiment, the memory 1003 includes volatile memory and non-volatile memory. The memory 1003 can include multiple levels. In digital systems, anything that can store binary data can be considered a memory. In integrated circuits, a circuit with a storage function that does not have a physical form is also called a memory, such as RAM and FIFO. In systems, a physical storage device is also called a memory, such as a memory stick or TF card.

[0161] The embodiment of this specification also provides a computer program product, including at least one instruction or at least one program, which is loaded and executed by a processor to implement the following Figure 1 The method shown.

[0162] Those skilled in the art should understand that in the various embodiments of this specification, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.

[0163] Those skilled in the art will also understand that in the embodiments of this specification, the term "and / or" is merely a description of the association relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent three situations: A exists alone, A and B exists simultaneously, and B exists alone. Furthermore, the character " / " in this specification generally indicates that the associated objects are in an "or" relationship.

[0164] Those skilled in the art will also appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0165] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0166] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0167] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0168] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for determining the micromechanical properties of rock, characterized in that: The method comprises: Performing an indentation test on a target area on the surface of the rock sample to obtain micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time; Scanning a target area on the surface of the rock sample during the indentation test to obtain microstructure evolution data corresponding to the micromechanical response data; the microstructure evolution data represents a relationship between the microstructure of the rock sample and time; The micromechanical properties of the rock sample are determined based on the micromechanical response data of the rock sample and the corresponding microstructural evolution data.

2. The method according to claim 1, characterized in that The method further comprises: Based on energy spectrum analysis, a scanning electron microscope is used to obtain chemical composition data and physical structure data of the target area on the surface of the rock sample; Determining a target indenter and a tilt angle of the indenter of the scanning electron microscope cavity according to the chemical composition data and the physical structure data; The indentation test on the target area of the rock sample surface includes: At the tilt angle, an indentation test is performed on a target area on the surface of the rock sample using a target indenter of a scanning electron microscope cavity.

3. The method according to claim 2, characterized in that The method further comprises: Determining the temperature field and electric field corresponding to the target area according to the chemical composition data and physical structure data of the target area; Simulating the geological environment of the rock sample according to the temperature field and electric field corresponding to the target area; The indentation test on the target area of the rock sample surface includes: In the simulated geological environment of the rock sample, an indentation test is performed on the target area on the surface of the rock sample.

4. The method according to claim 1, characterized in that The indentation test is performed on the target area of the rock sample surface to obtain the micromechanical response data of the rock sample, including: determining a load mode for the indentation test based on geological data of the rock sample; An indentation test is performed on a target area on the surface of the rock sample under the load mode to obtain load and displacement variation data at multiple time points.

5. The method according to claim 4, characterized in that: The indentation test includes a loading stage, a constant load stage and an unloading stage; Determining the load mode of the indentation test based on the geological data of the rock sample includes: The loading mode, constant load mode and unloading mode corresponding to the loading stage, constant load stage and unloading stage are determined according to the geological data of the rock sample.

6. The method according to claim 1, characterized in that Scanning the target area during the indentation test to obtain microstructure evolution data corresponding to the micromechanical response data includes: During the indentation test, a scanning electron microscope is used to scan the target area to obtain rock sample crack expansion data and rock sample particle migration data corresponding to the micromechanical response data; the rock sample crack expansion data represents the relationship between the change of rock sample crack morphology and time; the rock sample particle migration data represents the relationship between the change of rock sample particle coordinates and time.

7. The method according to claim 1, characterized in that: Determining the micromechanical properties of the rock sample based on the micromechanical response data and the corresponding microstructural evolution data of the rock sample includes: spatiotemporally coupling the micromechanical response and microstructure at multiple time points during the indentation test; The micromechanical properties of the rock sample are determined based on the micromechanical responses and microstructures at multiple time points after the spatiotemporal coupling.

8. The method according to claim 1, characterized in that: The method further comprises: The micromechanical responses at multiple time points are modified according to the microstructures at multiple time points.

9. A device for determining the micromechanical properties of rock, characterized in that: The device comprises: A testing module is used to perform an indentation test on a target area on the surface of a rock sample to obtain micromechanical response data of the rock sample; the micromechanical response data represents the relationship between the micromechanical response of the rock sample and time; a scanning module, configured to scan a target area on the surface of the rock sample during the indentation test to obtain microstructural evolution data corresponding to the micromechanical response data; the microstructural evolution data representing a relationship between the microstructure of the rock sample and time; The determination module is used to determine the micromechanical properties of the rock sample based on the micromechanical response data and the corresponding microstructural evolution data of the rock sample.

10. A computer device, characterized in that: include: memory for storing computer programs; A processor, configured to execute the computer program to implement the method according to any one of claims 1 to 8.

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