Method and system for predicting micro-dynamic stress deformation behavior of rock

Abstract

The application provides a rock microcosmic dynamic stress deformation behavior prediction method and system, relates to the technical field of rock physics research, and comprises the following steps: collecting backscattered electron images corresponding to rock samples in a research area; demarcating a plurality of microzones on the surface of the rock samples based on the backscattered electron images, and acquiring pore structure characterization parameters corresponding to the microzones; for the demarcated same microzone, respectively controlling an in-situ measurement experimental device to measure intrinsic elastic modulus of different rock internal fabrics in the microzone, and controlling a nanoindenter to perform a cyclic load indentation experiment on the microzone to obtain microcosmic deformation mechanical parameters corresponding to the microzone; and predicting microcosmic dynamic stress deformation behavior of the rock samples based on the pore structure characterization parameters, the intrinsic elastic modulus and the microcosmic deformation mechanical parameters corresponding to the microzone. The application establishes a spatial corresponding relationship between the pore structure and the intrinsic elastic modulus and the microcosmic deformation mechanical parameters, and thus significantly improves the accuracy of the rock microcosmic dynamic stress deformation behavior.

Description

Technical Field

[0001] This invention relates to the field of rock physics research technology, and in particular to a method and system for predicting the microscopic dynamic stress deformation behavior of rocks. Background Technology

[0002] Currently, pore structure is typically obtained through scanning electron microscopy, gas adsorption, or mercury intrusion porosimetry to acquire two-dimensional / three-dimensional pore parameters. However, micromechanical parameters rely on atomic force microscopy (AFM) or nanoindentation to independently measure another parallel sample or different locations. Due to the lack of a unified spatial coordinate benchmark, the two types of data cannot be precisely correlated in physical location. Although some studies have attempted to establish correlations through image registration, the spatial matching accuracy is limited to the micrometer level due to differences in the field of view of the equipment, sample transfer deformation, and marker drift. Therefore, it is difficult to reveal the in-situ deformation mechanism of pores under dynamic loads, let alone use it to predict the microscopic dynamic stress deformation behavior of rocks. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a method and system for predicting the microscopic dynamic stress deformation behavior of rocks, which establishes a spatial correspondence between pore structure and intrinsic elastic modulus and microscopic deformation mechanical parameters, thereby significantly improving the accuracy of the microscopic dynamic stress deformation behavior of rocks.

[0004] In a first aspect, the present invention provides a method for predicting the microscopic dynamic stress-deformation behavior of rocks, comprising: Backscattered electron images of rock samples within the study area were collected; Multiple micro-regions were delineated on the surface of the rock sample based on backscattered electron images, and the corresponding pore structure characterization parameters of the micro-regions were obtained. For the same defined micro-region, the intrinsic elastic modulus of different rock internal structures within the micro-region was measured by controlling the in-situ measurement experimental equipment, and the cyclic loading indentation experiment was performed on the micro-region by controlling the nanoindenter to obtain the micro-deformation mechanical parameters of the micro-region. Based on the pore structure characterization parameters, intrinsic elastic modulus, and micro-deformation mechanical parameters corresponding to the micro-regions, the micro-dynamic stress-deformation behavior of rock samples is predicted, and the behavior prediction results are obtained.

[0005] In one implementation, based on the pore structure characterization parameters, intrinsic elastic modulus, and microscopic deformation mechanical parameters corresponding to the micro-regions, the microscopic dynamic stress-deformation behavior of the rock sample is predicted, yielding behavior prediction results, including: The mineral content corresponding to the micro-region is converted into the volume fraction of the various minerals contained in the micro-region; Based on the volume fraction of various minerals, the pore structure characterization parameters of micro-regions, and the intrinsic elastic modulus, the elastic modulus prediction results of micro-regions during micro-deformation process are predicted. Based on the volume fraction of various minerals, the pore structure characterization parameters of micro-regions, and the micro-deformation mechanical parameters, the effective hardness prediction results of micro-regions during the micro-deformation process are predicted. Based on the predicted results of elastic modulus and effective hardness, stress-strain relationship curves of microregions during microdeformation are constructed. The prediction results include one or more of the following: elastic modulus prediction results, effective hardness prediction results, and stress-strain relationship curves.

[0006] In one embodiment, the elastic modulus prediction results of a micro-region during micro-deformation are made based on the volume fractions of various minerals, the pore structure characterization parameters of the micro-region, and the intrinsic elastic modulus, including: The matrix elastic modulus of the micro-region is obtained by weighted summation of the product of the volume fractions of various minerals and the intrinsic elastic moduli of the various minerals contained in the micro-region. Based on the pore structure characterization parameters, the comprehensive pore heterogeneity index corresponding to the micro-region is determined, and the modulus correction coefficient is determined in combination with the porosity corresponding to the micro-level. The product of the matrix elastic modulus and the modulus correction factor is used as the predicted result of the elastic modulus of the micro-region during the micro-deformation process.

[0007] In one embodiment, the pore structure characterization parameters include: porosity data under multiple view planes, pore volume data for multiple pore size intervals, specific surface area data for multiple pore size intervals, and pore volume percentage for multiple pore size intervals; based on the pore structure characterization parameters, a comprehensive pore heterogeneity index corresponding to the micro-region is determined, including: The ratio between the standard deviation of porosity data under multiple view planes and the mean of porosity data under multiple view planes is used as the porosity heterogeneity index. The ratio between the standard deviation of pore volume data for multiple pore size ranges and the mean of pore volume data for multiple pore size ranges is used as the pore volume heterogeneity index. The ratio of the standard deviation of the specific surface area data for multiple pore size ranges to the mean of the specific surface area data for multiple pore size ranges is used as the heterogeneity index of the specific surface area data. The pore size distribution heterogeneity index is determined based on the pore volume ratio of multiple pore size ranges. The porosity heterogeneity index, pore volume heterogeneity index, specific surface area heterogeneity index, and pore size distribution heterogeneity index are weighted and fused to obtain the comprehensive porosity heterogeneity index corresponding to the micro-region.

[0008] In one embodiment, the microscopic deformation mechanical parameters include at least the intrinsic hardness corresponding to multiple minerals; based on the volume fraction corresponding to multiple minerals, the pore structure characterization parameters corresponding to the micro-region, and the microscopic deformation mechanical parameters, the effective hardness prediction result of the micro-region during the microscopic deformation process is predicted, including: The matrix hardness of the micro-region during the micro-deformation process is obtained by weighted summation of the product of the volume fractions corresponding to various minerals and the intrinsic hardness corresponding to various minerals contained in the micro-region. The hardness correction factor is determined based on the microscopic porosity. The product of matrix hardness and hardness correction factor is used as the effective hardness prediction result of the micro-region during the micro-deformation process.

[0009] In one implementation, based on the predicted elastic modulus and effective hardness, a stress-strain relationship curve of the micro-region during the micro-deformation process is constructed, including: In the elastic stage, the strain prediction results are determined based on the elastic modulus prediction results and the applied stress. During the plastic stage, the yield strength of the micro-region is determined based on the comprehensive porosity heterogeneity index and effective hardness prediction results corresponding to the micro-region, so as to determine the strain prediction results of different strain levels based on the yield strength. Based on the strain prediction results of the elastic stage and the strain prediction results of the plastic stage, the stress-strain relationship curve of the micro-region during the micro-deformation process is constructed.

[0010] In one embodiment, the micro-deformation mechanical parameters further include measured values ​​of elastic modulus and effective hardness; the method further includes: The validity of the predicted elastic modulus and the predicted effective hardness were verified by using the measured values ​​of elastic modulus and effective hardness, respectively. Save the predicted elastic modulus, predicted effective hardness, and stress-strain curves after the validity verification is passed.

[0011] Secondly, the present invention also provides a system for predicting the microscopic dynamic stress-deformation behavior of rocks, comprising: The image acquisition module is used to acquire backscattered electron images of rock samples within the study area; The micro-region delineation module is used to delineate multiple micro-regions on the surface of a rock sample based on backscattered electron images and obtain the corresponding pore structure characterization parameters of the micro-regions. The experimental module is used to control the in-situ measurement experimental equipment to measure the intrinsic elastic modulus of different rock internal structures in the same defined micro-region, and to control the nanoindenter to perform cyclic loading indentation experiments on the micro-region to obtain the corresponding micro-deformation mechanical parameters of the micro-region. The prediction module is used to predict the microscopic dynamic stress-deformation behavior of rock samples based on the pore structure characterization parameters, intrinsic elastic modulus, and microscopic deformation mechanical parameters corresponding to the micro-regions, and obtain the behavior prediction results.

[0012] Thirdly, the present invention also provides an electronic device including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement any of the methods provided in the first aspect.

[0013] Fourthly, the present invention also provides a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement any of the methods provided in the first aspect.

[0014] This invention provides a method and system for predicting the microscopic dynamic stress-deformation behavior of rocks. First, backscattered electron images of rock samples within a study area are acquired. Then, based on the backscattered electron images, multiple micro-regions are delineated on the surface of the rock samples, and the corresponding pore structure characterization parameters are obtained. Next, for the same delineated micro-region, in-situ measurement equipment is used to measure the intrinsic elastic modulus of different rock internal structures within the micro-region, and a nanoindenter is used to perform cyclic loading indentation experiments on the micro-region to obtain the corresponding microscopic deformation mechanical parameters. Finally, based on the corresponding pore structure characterization parameters, intrinsic elastic modulus, and microscopic deformation mechanical parameters, the microscopic dynamic stress-deformation behavior of the rock samples is predicted, yielding the behavior prediction results. This method guides micro-region delineation using backscattered electron imaging and sequentially conducts intrinsic elastic modulus measurements and cyclic loading indentation experiments on the same micro-region. For the first time, it achieves spatial co-location and data coupling of pore structure characterization parameters, fabric-level intrinsic elastic modulus, and micro-deformation mechanical parameters at the same physical location. This establishes a direct mapping relationship between pore structure and intrinsic mechanical response, avoiding structural errors caused by sample separation, positional offset, or scale mismatch in traditional methods. The prediction model built based on this spatial correspondence significantly improves the accuracy and reliability of predicting the micro-dynamic stress-deformation behavior of rocks.

[0015] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for predicting the microscopic dynamic stress deformation behavior of rocks, provided in an embodiment of the present invention; Figure 2 A flowchart illustrating another method for predicting the microscopic dynamic stress deformation behavior of rocks provided in an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a rock micro-dynamic stress deformation behavior prediction system provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Currently, existing technologies have the following problems: (1) Structure-mechanics separation: Pore structure characterization and mechanical performance testing are independent of each other, lacking spatial correspondence, making it difficult to establish accurate structure-performance correlation; (2) Lack of in-situ monitoring: Existing methods cannot observe pore deformation behavior in real time during mechanical loading, making it difficult to reveal the influence mechanism of pores on mechanical response; (3) Lack of prediction models: There is no method that can quantitatively predict rock mechanical properties based on mineral composition and pore structure parameters, which restricts the application of well logging data in mechanical evaluation. Therefore, there is an urgent need for a comprehensive method that can systematically characterize the microscopic pore structure of rocks, monitor pore mechanical deformation in situ, and construct a quantitative structure-performance prediction model.

[0021] Based on this, the present invention provides a method and system for predicting the microscopic dynamic stress deformation behavior of rocks, establishing a spatial correspondence between pore structure and intrinsic elastic modulus and microscopic deformation mechanical parameters, thereby significantly improving the accuracy of the microscopic dynamic stress deformation behavior of rocks.

[0022] To facilitate understanding of this embodiment, a method for predicting the microscopic dynamic stress-deformation behavior of rocks disclosed in this embodiment will first be described in detail. (See [link to relevant documentation]). Figure 1 The diagram shows a flowchart of a method for predicting the microscopic dynamic stress-deformation behavior of rocks. The method mainly includes the following steps S102 to S106: Step S102: Collect backscattered electron images of rock samples within the study area.

[0023] Rock samples refer to solid rock specimens taken from the study area and subjected to standard pretreatment (including cleaning, drying, impregnation, and surface polishing); backscattered electron images refer to two-dimensional intensity images formed by using field emission scanning electron microscopes to excite backscattered electron signals generated on the sample surface.

[0024] In one embodiment, the pre-processed rock sample is mounted onto the scanning electron microscope stage, and a high-magnification two-dimensional large-field-of-view backscattered electron image is acquired by adjusting the electron beam parameters and detector settings in a high-vacuum environment.

[0025] Step S104: Based on the backscattered electron image, multiple micro-regions are delineated on the surface of the rock sample, and the pore structure characterization parameters corresponding to the micro-regions are obtained.

[0026] A micro-region refers to a local area with clear spatial coordinates that is physically marked on the surface of a rock sample under the guidance of the backscattered electron image, serving as a unified positioning benchmark for subsequent multi-source measurements. Pore structure characterization parameters include porosity, pore volume, specific surface area, pore size distribution, and porosity, which are used to quantitatively describe the geometric and topological characteristics of pores within the micro-region.

[0027] In one embodiment, based on the pore distribution characteristics and mineral texture differences shown in the backscattered electron image, multiple micro-region markers are formed on the surface of the rock sample by means of photolithography, laser marking or focused ion beam, etc.; then, combined with gas adsorption, mercury porosimetry and other experiments, the samples at the same micro-region location are tested to obtain their pore structure characterization parameters.

[0028] Step S106: For the same defined micro-region, control the in-situ measurement experimental equipment to measure the intrinsic elastic modulus of different rock internal structures in the micro-region, and control the nanoindenter to perform cyclic load indentation experiments on the micro-region to obtain the micro-deformation mechanical parameters corresponding to the micro-region.

[0029] The internal structure of a rock refers to the distinguishable mineral phases or composite structural units within a micro-region, including single mineral grains, mineral aggregates, and organic matter-clay complexes. The intrinsic elastic modulus is a mechanical parameter characterizing the inherent stiffness of each internal rock structure at the nanoscale, obtained by force curve fitting using atomic force microscopy. Cyclic indentation experiments involve repeatedly applying a loading-holding-unloading process to the micro-region under the control of a nanoindenter. The microscopic deformation mechanical parameters include at least the measured values ​​of intrinsic hardness, elastic modulus, and effective hardness corresponding to multiple minerals.

[0030] In one implementation, based on micro-area identification, the rock sample is first placed under an atomic force microscope to locate the target structure and measure its intrinsic elastic modulus; then it is transferred to a nanoindenter to perform cyclic loading indentation experiments at the same micro-area location, and the corresponding micro-deformation mechanical parameters are collected simultaneously or sequentially.

[0031] Step S108: Based on the pore structure characterization parameters, intrinsic elastic modulus and micro-deformation mechanical parameters corresponding to the micro-region, predict the micro-dynamic stress deformation behavior of the rock sample and obtain the behavior prediction results.

[0032] Microscopic dynamic stress-deformation behavior refers to the dynamic evolution characteristics of rock samples under stress, including stress transmission, local yielding, plastic flow, and residual deformation, exhibited by the coordinated response of internal pores and mineral fabric. Behavioral prediction results include one or more of the following: elastic modulus prediction, effective hardness prediction, and stress-strain relationship curves.

[0033] In one implementation, the above three types of parameters are used as input variables and substituted into a pre-built physical constraint prediction model to output behavioral prediction results such as stress-strain relationship curves covering the elastic and plastic stages, elastic modulus prediction results, and effective hardness prediction results.

[0034] The method for predicting the microscopic dynamic stress-deformation behavior of rocks provided in this invention guides the delineation of micro-regions using backscattered electron imaging, and sequentially conducts intrinsic elastic modulus measurements and cyclic loading indentation experiments on the same micro-region. For the first time, it achieves spatial co-location and data coupling of pore structure characterization parameters, fabric-level intrinsic elastic modulus, and microscopic deformation mechanical parameters at the same physical location. This establishes a direct mapping relationship between pore structure and intrinsic mechanical response, avoiding structural errors caused by sample separation, positional offset, or scale mismatch in traditional methods. The prediction model constructed based on this spatial correspondence significantly improves the accuracy and reliability of predicting the microscopic dynamic stress-deformation behavior of rocks.

[0035] For ease of understanding, this invention provides a specific implementation of a method for predicting the microscopic dynamic stress-deformation behavior of rocks. See also... Figure 2The flowchart of another method for predicting the microscopic dynamic stress-deformation behavior of rocks is shown below: The first step is mineral composition analysis: X-ray diffraction tests are used to obtain the types and relative contents of each mineral phase in the rock sample.

[0036] Typical well cores from the study area were selected, and systematic sampling was conducted on three types of samples: mudstone, limestone, and mudstone-limestone transitional samples. A unified sample numbering system was established, with the format "well number-depth-lithology code-serial number" to ensure traceability. Mineral composition (XRD), total organic carbon (TOC) content, and organic matter type were tested on the samples to clarify their lithofacies type and basic geological characteristics. Based on the test results, a sample database was established, and the samples were classified into lithofacies types according to the following criteria: Mudstone facies: clay mineral content >50%, carbonate mineral content <25%; Calcite facies: carbonate mineral content >75%, clay mineral content <15%; Mixed rock facies: clay mineral content 25%-50%, carbonate mineral content 25%-75%.

[0037] Establish a sample information summary table to record key parameters such as depth, lithology, mineral composition, TOC content, and organic matter type for each sample, providing a basis for subsequent work.

[0038] The second step is to obtain pore structure parameters: using a gas adsorption and mercury porosimetry method, parameters such as pore size distribution, specific surface area and pore volume of rock samples are determined.

[0039] Samples of different lithofacies types were sliced ​​and argon-ion polished. High-resolution field emission scanning electron microscopy and a micro-area mineral composition quantitative system were used to observe and identify the microstructure of various samples, including calcite, dolomite, quartz, pyrite and kerogen, as well as pore types, including organic pores, organic clay complexes, inorganic mineral-related pores and microcracks.

[0040] The third step is to acquire images of the pore structure and physically label the micro-areas: backscattered electron images are obtained using a field emission scanning electron microscope to identify the distribution of minerals and the spatial location of pores, and based on the backscattered electron images, multiple micro-area markers with spatial coordinates are formed on the surface of the rock sample.

[0041] A high-resolution field emission scanning electron microscope (FESEM) was used to perform a full-frame scan of the sample, obtaining a high-magnification two-dimensional large-field-of-view backscattered electron image. Multiple 100μm×100μm micro-regions were marked on the sample surface using photolithography or laser marking as test areas, facilitating subsequent measurements by atomic force microscopy and nanoindentation at the same locations. Geometric parameters of different pore types in the images, including area, radius, and roundness, as well as the relative proportions of pore volume and specific surface area, were extracted using image processing software. The differences in pore type development among samples of different lithofacies types were analyzed.

[0042] The fourth step is to conduct joint characterization of the pore structure across all scales: low-temperature CO2 gas adsorption, N2 gas adsorption, and high-pressure mercury intrusion experiments are carried out to obtain pore structure parameters within different characterization ranges.

[0043] Low-temperature CO2 gas adsorption (for pore sizes less than 2 nm), N2 gas adsorption (for pore sizes between 2 and 50 nm), and high-pressure mercury intrusion porosimetry (for pore sizes greater than 50 nm) experiments were conducted to obtain pore structure parameters within different characterization ranges (characterizing pore sizes from a few nanometers to hundreds of micrometers). By combining multiple testing methods, parameters such as pore volume, specific surface area, pore size distribution, and porosity of samples from different lithofacies types at all scales can be obtained, thus clarifying the differences in the development of pore structure of shale at all scales among samples from different lithofacies types.

[0044] The fifth step is to measure the intrinsic elastic modulus: In each micro-region, in-situ force curve tests are performed on the internal structure of different rocks using an atomic force microscope to obtain their intrinsic elastic modulus.

[0045] Based on the micro-regions marked in the third step, in-situ atomic force microscopy (AFM) measurements were conducted on samples of different lithofacies types. The optical microscope equipped on the AFM instrument was used to selectively scan the regions to obtain surface image information. The interaction forces between the needle tip and the sample surface of different internal rock structures such as organic matter, carbonate minerals, and organic matter-clay complexes in each lithofacies type were measured sequentially. No less than 20 measurement points were set up on each structure type, with a spacing of >5 μm between measurement points to avoid mutual interference. Finally, force-displacement curves were obtained, and the intrinsic elastic modulus of each point was obtained.

[0046] Step 6, Cyclic Load Indentation Experiment: At the same micro-area location, the entire process of loading, holding, and unloading is applied using a nanoindenter to obtain the micro-deformation mechanical parameters.

[0047] Nanoindentation experiments were conducted on samples of different rock facies types using a nanoindenter equipped with in-situ imaging. Within the micro-area scanned by AFM, points were set up for densely pore areas, mineral phase interfaces, and organic matter enrichment areas. The continuous load and displacement applied by the diamond indenter to different internal structures such as organic matter, carbonate minerals, and organic matter-clay complexes in each type of sample were measured sequentially. Load-displacement curves (Ph curves) were plotted, and mechanical parameters such as hardness, elastic modulus, and fracture toughness of mineral particles were obtained.

[0048] First, there is the loading stage of the nanoindentation experiment. As the load on the indenter increases, the indentation depth increases rapidly until it reaches its peak. Then, there is the load holding stage, where the rate of increase in indentation depth slows down and the sample undergoes creep deformation. Finally, there is the unloading stage, where the sample undergoes local plastic deformation and some residual deformation remains on the surface.

[0049] Then, by using the in-situ module of AFM and nanoindentation instrument, the pore deformation (such as crack propagation and particle displacement) during the indentation process is recorded in real time, including pre-indentation imaging (high-resolution AFM scanning of the target area to record the surface morphology and pore distribution before indentation) and post-indentation imaging (AFM scanning at the same location to compare the morphological changes before and after indentation); the loading-unloading is repeated more than 3 times (to analyze the cumulative deformation); at the same time, combined with in-situ scanning electron microscopy (SEM), the sample is transferred to the scanning electron microscope to perform high-magnification imaging of the indentation area, observe the indentation morphology and surrounding pore changes, and thus analyze the microscopic deformation characteristics of different pores.

[0050] Step 7, Microscopic Deformation Mechanism Analysis: Combining mineral composition, pore structure, and deformation data, we analyze the sensitivity and dominant mechanisms of different pore types to mechanical responses, and screen key parameters involved in subsequent behavior prediction.

[0051] First, the peak indentation depth (i.e., the indentation depth corresponding to the maximum load), residual depth (i.e., the residual depth after complete unloading), and creep depth (i.e., the depth increment during the load-holding stage) of the relevant pores in different rock internal structures such as organic matter, inorganic minerals, and organic matter-clay complexes were measured under the same peak load.

[0052] Based on this, the intrinsic elastic modulus and micro-deformation mechanical parameters of different microstructures were obtained in steps five and six. Combined with the micro-deformation characteristics of different pores collected during the nanoindentation experiment and the indentation peak depth, residual depth and creep depth of the aforementioned related pores under the same peak load, the micro-pore mechanical properties and deformation differences of samples of different rock facies types were analyzed and compared.

[0053] First, we analyzed and compared the differences in fabric-level micromechanical parameters and pore characteristics of samples from different lithofacies types. This included three lithofacies types: argillaceous, calcareous, and mixed-matrix; six fabric types: organic matter, calcite, dolomite, quartz, clay minerals, and organic-clay complexes; four mechanical parameters: elastic modulus, hardness, fracture toughness, and creep rate; and five pore characteristics: pore type, pore volume, specific surface area, pore size, and the contact relationship between pores and minerals.

[0054] Second, conduct comparisons of microscopic deformation characteristics, including elastic recovery rate (i.e., comparing the elastic recovery ability of different compositions), creep characteristics (i.e., analyzing the viscoelastic behavior of organic matter and clay minerals), damage mode (i.e., distinguishing between brittle fracture of carbonate minerals and plastic deformation of organic matter), and porosity sensitivity (i.e., analyzing the degree of influence of the presence of pores on mechanical response).

[0055] Third, establish the correlation between pore characteristics and mechanical parameters, analyze the influence of pore structure (that is, analyze the correlation between pore volume and specific surface area and mechanical parameters such as elastic modulus and hardness) and pore size effect (that is, analyze the correlation between pore size and mechanical parameters, and study the influence of different pore size ranges on mechanical parameters).

[0056] Step 8, prediction of dynamic stress-deformation behavior: Based on the aforementioned parameters, a physical constraint model is constructed to predict the effective elastic modulus, effective hardness, and complete stress-strain relationship curve of the micro-region. In this embodiment of the invention, pore structure parameters are matched with mechanical data (i.e., intrinsic elastic modulus and microscopic deformation mechanical parameters) to quantify the contribution of pore heterogeneity to the mechanical response of samples of different rock facies types. This reveals the coupling characteristics between rock micropore structure and mechanical deformation, establishes a coupling prediction model between rock micropore structure and mechanical deformation, and predicts dynamic deformation behavior. The core idea is to establish a mathematical relationship between pore structure parameters and mechanical test data to achieve the goal of predicting mechanical response from structural parameters. The specific process is as follows: (1) Data integration and summarization: The parameters required for the prediction model are divided into three categories: mineral composition, pore structure, and mechanical parameters. The mineral composition parameters are the volume fractions of each mineral, obtained through the aforementioned X-ray diffraction experiments; Pore ​​structure parameters include pore volume, specific surface area, pore size distribution, and pore morphology parameters. The pore volume, specific surface area, and pore size distribution were obtained through the aforementioned gas adsorption pressurized mercury experiment, while the pore morphology parameters were obtained through the aforementioned field emission scanning electron microscopy image analysis. The mechanical parameters include intrinsic elastic modulus and intrinsic hardness, wherein the intrinsic elastic modulus is obtained by the aforementioned atomic force microscopy experiment and the intrinsic hardness is obtained by the aforementioned nanoindentation experiment.

[0057] (2) Data matching: Spatial matching of data obtained from different testing methods: Sample-level matching: Using the sample number as the keyword, link the X-ray diffraction experimental data, gas adsorption pressurized mercury experimental data, field emission scanning electron microscope image data, atomic force microscope experimental data, and nanoindentation experimental data of the same sample. Micro-area level matching: Using the micro-area number as the keyword, it associates field emission scanning electron microscope images, atomic force microscope test points, and nanoindentation test points within the same 100-micrometer by 100-micrometer area; Data format is standardized: all parameters are converted to standard units, with length in micrometers or nanometers, mechanical parameters in gigapascals, and content in percentages or decimals.

[0058] Table 1. Example of parameter data matching

[0059] (3) Based on the pore structure characterization parameters, intrinsic elastic modulus, and micro-deformation mechanical parameters corresponding to the micro-regions, the micro-dynamic stress-deformation behavior of the rock samples is predicted, and the behavior prediction results are obtained, including: (3.1) Convert the mineral content corresponding to the micro-region into the volume fractions of the various minerals contained in the micro-region. In one example, the volume fraction Vi of the i-th mineral is obtained by converting the mineral content obtained through the aforementioned X-ray diffraction experiment.

[0060] (3.2) Based on the volume fraction of various minerals, the pore structure characterization parameters of the micro-region, and the intrinsic elastic modulus, the elastic modulus prediction results of the micro-region during the micro-deformation process are predicted.

[0061] In this embodiment of the invention, the elastic modulus of rock is mainly determined by its mineral composition and pore structure. This model employs a combination of matrix modulus and pore reduction: first, the matrix elastic modulus is calculated using the mineral composition obtained from the aforementioned X-ray diffraction experiment; then, reduction is performed using the pore characteristics obtained from the aforementioned gas adsorption pressurized mercury experiment and field emission scanning electron microscopy image analysis. The specific implementation process is as follows: (A) The matrix elastic modulus of the micro-region is obtained by weighted summing the products of the volume fractions of various minerals and the intrinsic elastic moduli of the various minerals contained in the micro-region. Specifically, the weighted average method is used, and the formula is that the matrix elastic modulus is equal to the sum of the products of the volume fractions of each mineral phase and their corresponding intrinsic elastic moduli. The expression is as follows: ; where the volume fraction of each mineral is... The intrinsic elastic modulus of each mineral was obtained by converting the mineral content obtained from the aforementioned X-ray diffraction experiment. Obtained through the aforementioned atomic force microscopy experiment.

[0062] Table 2 Reference values ​​of intrinsic elastic modulus of major mineral phases

[0063] Calculation example: The XRD test results of a sample are: quartz 30%, calcite 25%, clay minerals 35%, organic matter 10%. Therefore, Em = 0.30 × 95 + 0.25 × 75 + 0.35 × 20 + 0.10 × 5 = 54.75 GPa.

[0064] (B) Based on the pore structure characterization parameters, a comprehensive pore heterogeneity index corresponding to the micro-region is determined, and a modulus correction coefficient is determined in conjunction with the corresponding micro-porosity. The pore structure characterization parameters include: porosity data under multiple viewpoints, pore volume data for multiple pore size intervals, specific surface area data for multiple pore size intervals, and pore volume ratio for multiple pore size intervals. Pore heterogeneity is a key intermediate parameter for predicting mechanical response, reflecting the degree of non-uniformity in pore distribution. Specifically, the process for determining the comprehensive pore heterogeneity index is as follows: (B1) The ratio of the standard deviation of porosity data across multiple view planes to the mean of porosity data across multiple view planes is used as the porosity heterogeneity index. Specifically, the calculation formula is: .in, The standard deviation of the face rate Both are average face proportions, calculated statistically from face proportion data obtained from multiple fields of view using the aforementioned field emission scanning electron microscopy image analysis. Calculation example: If the face proportion data for a sample obtained from the aforementioned field emission scanning electron microscopy image analysis for 10 fields of view are 5%, 8%, 6%, 12%, 7%, 9%, 4%, 11%, 6%, and 8%, then... =7.6%, =2.5%, PHI=2.5 / 7.6=0.33.

[0065] (B2) The ratio of the standard deviation of pore volume data across multiple pore size ranges to the mean of pore volume data across multiple pore size ranges is used as the pore volume heterogeneity index. Specifically, the calculation formula is: .in, The standard deviation of pore volume. Both are average pore volumes, calculated statistically from pore volume data across different pore size ranges obtained in the aforementioned gas adsorption pressurized mercury experiment. Calculation example: If a sample yields 10 sets of pore volume data (cm³ / g) through the aforementioned gas adsorption pressurized mercury experiment, with values ​​of 0.015, 0.022, 0.018, 0.025, 0.020, 0.016, 0.024, 0.019, 0.021, and 0.017, then... =0.0197cm³ / g, =0.0033cm³ / g, PVHI=0.0033 / 0.0197=0.17.

[0066] (B3) The ratio of the standard deviation of the specific surface area data across multiple pore size ranges to the mean of the specific surface area data across multiple pore size ranges is used as the heterogeneity index of the specific surface area data. Specifically, the calculation formula is as follows: .in, The standard deviation of the specific surface area. Both are average specific surface areas, calculated statistically from specific surface area data for different pore size ranges obtained in the aforementioned gas adsorption experiments. Calculation example: If a sample yields 10 sets of specific surface area data (unit: square meters per gram) through the aforementioned gas adsorption experiments, with values ​​of 12.5, 15.8, 14.2, 18.6, 13.9, 16.4, 14.8, 17.2, 15.1, and 13.6, then... =15.21m² / g =1.78m² / g, SSAHI=1.78 / 15.21=0.12.

[0067] (B4) Determine the pore size distribution heterogeneity index based on the pore volume ratio across multiple pore size ranges. Specifically, the calculation formula is as follows: .in, The pore volume ratio of the i-th pore size range is obtained from the pore size distribution curves obtained by the aforementioned carbon dioxide adsorption, nitrogen adsorption, and mercury intrusion porosimetry experiments. The number of aperture intervals is determined by the resolution of the test data, and is usually between 10 and 20 intervals.

[0068] (B5) The porosity heterogeneity index, pore volume heterogeneity index, specific surface area heterogeneity index, and pore size distribution heterogeneity index are weighted and fused to obtain the comprehensive porosity heterogeneity index corresponding to the micro-region. Specifically, the calculation formula is as follows: .in, The face rate heterogeneity index It is the index of pore volume heterogeneity. It is the index of surface area heterogeneity. The four indices represent the heterogeneity of pore size distribution. They were all obtained through the aforementioned field emission scanning electron microscopy image analysis, gas adsorption pressurized mercury experiment, and pore size distribution curve analysis. It comprehensively reflects the heterogeneity of pores in two dimensions: spatial distribution and pore size distribution. The value ranges from 0 to 1, with a larger value indicating stronger heterogeneity.

[0069] Based on the aforementioned pore heterogeneity index, and combined with the corresponding microscopic porosity, the modulus correction coefficient is determined. For example, the expression for the modulus correction coefficient is: .

[0070] (C) The product of the matrix elastic modulus and the modulus correction factor is used as the predicted elastic modulus of the micro-region during the micro-deformation process. Specifically, the calculation formula is as follows: .in, The effective elastic modulus is expressed in gigapascals (GPa). The matrix elastic modulus is calculated using the aforementioned matrix elastic modulus. Porosity, expressed as a decimal, is obtained from the total porosity data acquired through the aforementioned gas adsorption pressurized mercury experiment. The comprehensive porosity heterogeneity index is calculated using the aforementioned comprehensive porosity heterogeneity index. Calculation example: Given... =54.75GPa, =0.08, =0.33, then =54.75×(1 1.5×0.08+0.5×0.33)=54.5GPa.

[0071] Furthermore, the model predictions were compared with the measured effective elastic modulus values ​​obtained from the aforementioned nanoindentation experiments. Comparison: The formula for calculating the relative error is: The pass / fail standard is a relative error of less than 10%. If the relative error exceeds 10%, the accuracy of the mineral content data obtained by the aforementioned X-ray diffraction experiment and the porosity data obtained by the aforementioned gas adsorption pressurized mercury experiment needs to be verified.

[0072] (3.3) Based on the volume fractions of various minerals, the pore structure characterization parameters of micro-regions, and the micro-deformation mechanical parameters, the effective hardness prediction results of micro-regions during micro-deformation are predicted. Hardness reflects the ability of rocks to resist local plastic deformation and is closely related to mineral hardness and pore structure. This model adopts a combination of mineral hardness weighted averaging and pore weakening. The specific implementation process is as follows: (A) The matrix hardness of the micro-region during micro-deformation is obtained by weighted summing the products of the volume fractions of various minerals and the intrinsic hardness of the various minerals contained in the micro-region. Specifically, the calculation formula is as follows: .in, The matrix hardness is expressed in gigapascals (GPa). The volume fraction of the i-th mineral is obtained through the aforementioned X-ray diffraction experiment; The intrinsic hardness of the i-th mineral, expressed in gigapascals, is obtained through the aforementioned nanoindentation experiment.

[0073] Table 3. Reference values ​​of intrinsic hardness for major mineral phases

[0074] (B) Determine the hardness correction factor based on the microscopic porosity. For example, the expression for the hardness correction factor is: .

[0075] (C) The product of matrix hardness and hardness correction factor is used as the predicted effective hardness of the micro-region during micro-deformation. Specifically, the calculation formula is as follows: .in, Effective hardness, measured in gigapascals (GPa). The matrix hardness is calculated using the aforementioned matrix hardness method. Porosity, expressed as a decimal, is obtained from the total porosity data obtained through the aforementioned gas adsorption pressurized mercury experiment. Calculation example: A sample, as determined by X-ray diffraction, contains 30% quartz, 25% calcite, 35% clay minerals, and 10% organic matter, with a porosity of 0.08. Therefore, Hm = 0.30 × 12 + 0.25 × 3.5 + 0.35 × 1.0 + 0.10 × 0.5 = 4.875 GPa, Heff = 4.875 × (1 (2.0 × 0.08) = 4.1 GPa.

[0076] (3.4) Based on the predicted elastic modulus and effective hardness, a stress-strain relationship curve for the micro-region during micro-deformation is constructed. In this embodiment of the invention, based on the predicted effective elastic modulus and effective hardness, and combined with the calculated comprehensive porosity heterogeneity index, a stress-strain relationship prediction model is established; the stress-strain curve is divided into elastic and plastic stages and modeled separately. The specific process is as follows: (A) In the elastic stage, the predicted strain is determined based on the predicted elastic modulus and the applied stress. Specifically, the calculation formula is as follows: .in, Strain is expressed as a percentage; The stress is measured in megapascals (MPa). The effective elastic modulus, in gigapascals (GPa), is obtained through the aforementioned prediction of the effective elastic modulus. Calculation example: Given Eeff = 54.5 GPa and applied stress σ = 50 MPa, then ε = 50 / 54500 = 0.000917 = 0.092%.

[0077] (B) During the plastic stage, the yield strength corresponding to the micro-region is determined based on the comprehensive porosity heterogeneity index and effective hardness prediction results. This yield strength is then used to determine the strain prediction results for different strain levels. Specifically, the calculation formula is as follows: .in, Yield strength, in megapascals (MPa); The effective hardness, measured in gigapascals, is obtained through the aforementioned effective hardness prediction. The comprehensive porosity heterogeneity index is calculated using the aforementioned comprehensive porosity heterogeneity index. Calculation example: Given Heff = 4.1 GPa and CPHI = 0.33, then σy = 3 × 4.1 × (1 0.33)=12.3×0.67=8.24GPa=8240MPa.

[0078] (C) Based on the strain prediction results of the elastic stage and the plastic stage, the stress-strain relationship curve of the micro-region during the micro-deformation process is constructed. Specifically, based on the aforementioned model, the complete stress-strain curve can be predicted: Elastic Stage Plastic stage (σ≥σy): ;in, , This is the hardening index, with a value ranging from 0.1 to 0.3, adjusted according to the rock type.

[0079] Furthermore, this embodiment of the invention proposes to verify the above-mentioned behavioral prediction results, namely: using the measured values ​​of elastic modulus and effective hardness, the validity of the predicted elastic modulus and effective hardness results are verified respectively; if the validity verification is passed, the predicted elastic modulus, predicted effective hardness, and stress-strain relationship curves are saved. Specifically, the predicted effective elastic modulus is compared with the measured elastic modulus value obtained from the aforementioned nanoindentation experiment; the predicted effective hardness is compared with the measured hardness value obtained from the aforementioned nanoindentation experiment; the relative error should be less than 15%; if the relative error exceeds 15%, the accuracy of the mineral composition data obtained from the aforementioned X-ray diffraction experiment, the porosity data obtained from the aforementioned gas adsorption pressurized mercury experiment, and the calculation of the aforementioned comprehensive porosity heterogeneity index needs to be verified.

[0080] Table 4 Validation Criteria

[0081] After the prediction is complete, the following will be output: Input Parameter Summary: List all input parameters and their sources, including mineral composition obtained through the aforementioned X-ray diffraction experiment, pore structure parameters obtained through the aforementioned gas adsorption pressurized mercury experiment and field emission scanning electron microscopy image analysis, hardness data obtained through the aforementioned nanoindentation experiment, etc. Intermediate calculation results: combining porosity heterogeneity index, matrix elastic modulus, and matrix hardness; Final prediction results: effective elastic modulus, effective hardness, yield strength; Stress-strain curve data: strain values ​​corresponding to different stress levels; Verification and comparison results: Comparison of predicted values ​​with the measured values ​​of the aforementioned nanoindentation experiments and analysis of relative errors.

[0082] The aforementioned predictive model allows for the quantitative prediction of rock elastic modulus, hardness, and stress-strain relationships based on its mineral composition and pore structure parameters. All parameters in the model have clearly defined sources and acquisition pathways: mineral composition was obtained through the aforementioned X-ray diffraction experiments; pore structure parameters were obtained through the aforementioned gas adsorption pressurized mercury experiments and field emission scanning electron microscopy image analysis; and fundamental mechanical data were obtained through the aforementioned nanoindentation experiments and atomic force microscopy experiments. The prediction results can be directly used for oil and gas reservoir evaluation and drilling fracturing design.

[0083] In summary, the embodiments of the present invention can achieve the following technical effects: establishing a spatial correspondence between pore structure and mechanical properties; realizing in-situ monitoring of pore mechanical deformation; and establishing a mechanical property prediction model based on mineral composition and pore structure.

[0084] Based on the foregoing embodiments, this invention provides a system for predicting the microscopic dynamic stress-deformation behavior of rocks. (See also...) Figure 3 The diagram shows a structural schematic of a rock micro-dynamic stress-deformation behavior prediction system. The device mainly includes the following parts: Image acquisition module 302 is used to acquire backscattered electron images of rock samples within the study area; The micro-region delineation module 304 is used to delineate multiple micro-regions on the surface of a rock sample based on backscattered electron images and obtain the corresponding pore structure characterization parameters of the micro-regions. Experimental module 306 is used to control the in-situ measurement experimental equipment to measure the intrinsic elastic modulus of different rock internal structures in the same defined micro-region, and to control the nanoindenter to perform cyclic loading indentation experiments on the micro-region to obtain the micro-deformation mechanical parameters of the micro-region. The prediction module 308 is used to predict the microscopic dynamic stress-deformation behavior of rock samples based on the pore structure characterization parameters, intrinsic elastic modulus and microscopic deformation mechanical parameters corresponding to the micro-region, and obtain the behavior prediction results.

[0085] The rock micro-dynamic stress-deformation behavior prediction system provided in this invention guides micro-region delineation using backscattered electron imaging and sequentially conducts intrinsic elastic modulus measurement and cyclic load indentation experiments on the same micro-region. For the first time, it achieves spatial co-location and data coupling of pore structure characterization parameters, fabric-level intrinsic elastic modulus, and micro-deformation mechanical parameters at the same physical location. This establishes a direct mapping relationship between pore structure and intrinsic mechanical response, avoiding structural errors caused by sample separation, positional offset, or scale mismatch in traditional methods. The prediction model constructed based on this spatial correspondence significantly improves the accuracy and reliability of predicting rock micro-dynamic stress-deformation behavior.

[0086] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0087] This invention provides an electronic device, specifically, the electronic device includes a processor and a memory; the memory stores a computer program, which, when run by the processor, executes the method described in any of the above embodiments.

[0088] Figure 4 The present invention provides a schematic diagram of the structure of an electronic device 100, which includes a processor 40, a memory 41, a bus 42 and a communication interface 43. The processor 40, the communication interface 43 and the memory 41 are connected through the bus 42. The processor 40 is used to execute executable modules, such as computer programs, stored in the memory 41.

[0089] The memory 41 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 43 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0090] Bus 42 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0091] The memory 41 is used to store programs. After receiving an execution instruction, the processor 40 executes the program. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0092] Processor 40 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 40 or by instructions in software form. Processor 40 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 41. The processor 40 reads the information in memory 41 and, in conjunction with its hardware, completes the steps of the above method.

[0093] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0094] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting the microscopic dynamic stress-deformation behavior of rocks, characterized in that, include: Backscattered electron images of rock samples within the study area were collected; Based on the backscattered electron image, multiple micro-regions are delineated on the surface of the rock sample, and the pore structure characterization parameters corresponding to the micro-regions are obtained. The micro-regions serve as a unified positioning reference. For the same defined micro-region, in-situ measurement experimental equipment is controlled to measure the intrinsic elastic modulus of different rock internal structures within the micro-region, and a nanoindenter is controlled to perform cyclic loading indentation experiments on the micro-region to obtain the corresponding microscopic deformation mechanical parameters of the micro-region. This includes: based on the identification of the micro-region, the actual rock sample is first placed under an atomic force microscope to locate the position of the rock internal structure and measure its intrinsic elastic modulus; then it is transferred to a nanoindenter, and cyclic loading indentation experiments are performed at the same micro-region position, while the corresponding microscopic deformation mechanical parameters are collected synchronously or sequentially. Based on the pore structure characterization parameters, intrinsic elastic modulus, and microscopic deformation mechanical parameters corresponding to the micro-region, the microscopic dynamic stress deformation behavior of the rock sample is predicted, and the behavior prediction results are obtained.

2. The method for predicting the microscopic dynamic stress-deformation behavior of rocks according to claim 1, characterized in that, Based on the pore structure characterization parameters, intrinsic elastic modulus, and microscopic deformation mechanical parameters corresponding to the microregion, the microscopic dynamic stress-deformation behavior of the rock sample is predicted, and the behavior prediction results are obtained, including: The mineral content corresponding to the micro-region is converted into the volume fraction of the various minerals contained in the micro-region; Based on the volume fraction of various minerals, the pore structure characterization parameters of the micro-region, and the intrinsic elastic modulus, the elastic modulus prediction results of the micro-region during the micro-deformation process are predicted. Based on the volume fractions of various minerals, the pore structure characterization parameters of the micro-region, and the micro-deformation mechanical parameters, the effective hardness prediction results of the micro-region during the micro-deformation process are predicted. Based on the predicted elastic modulus and the predicted effective hardness, the stress-strain relationship curve of the micro-region during the micro-deformation process is constructed. The prediction results include one or more of the following: the elastic modulus prediction results, the effective hardness prediction results, and the stress-strain relationship curve.

3. The method for predicting the microscopic dynamic stress-deformation behavior of rocks according to claim 2, characterized in that, Based on the volume fractions of various minerals, the pore structure characterization parameters of the microregion, and the intrinsic elastic modulus, the elastic modulus prediction results of the microregion during the micro-deformation process are made, including: The matrix elastic modulus of the microregion is obtained by weighted summing the product of the volume fractions corresponding to the various minerals and the intrinsic elastic moduli corresponding to the various minerals contained in the microregion. Based on the pore structure characterization parameters, a comprehensive pore heterogeneity index corresponding to the micro-region is determined, and a modulus correction coefficient is determined in combination with the porosity corresponding to the micro-region. The product of the matrix elastic modulus and the modulus correction coefficient is used as the predicted elastic modulus of the micro-region during the micro-deformation process.

4. The method for predicting the microscopic dynamic stress-deformation behavior of rocks according to claim 3, characterized in that, The pore structure characterization parameters include: porosity data under multiple view planes, pore volume data for multiple pore size intervals, specific surface area data for multiple pore size intervals, and pore volume percentage for multiple pore size intervals; based on the pore structure characterization parameters, a comprehensive pore heterogeneity index corresponding to the micro-region is determined, including: The ratio between the standard deviation of the porosity data under multiple view planes and the mean of the porosity data under multiple view planes is used as the porosity heterogeneity index. The ratio between the standard deviation of pore volume data for multiple pore size ranges and the mean of pore volume data for multiple pore size ranges is used as the pore volume heterogeneity index. The ratio of the standard deviation of the specific surface area data for multiple pore size ranges to the mean of the specific surface area data for multiple pore size ranges is used as the heterogeneity index of the specific surface area data. The pore size distribution heterogeneity index is determined based on the pore volume ratio of multiple pore size ranges. The porosity heterogeneity index, the pore volume heterogeneity index, the specific surface area data heterogeneity index, and the pore size distribution heterogeneity index are weighted and fused to obtain the comprehensive pore heterogeneity index corresponding to the micro-region.

5. The method for predicting the microscopic dynamic stress-deformation behavior of rocks according to claim 2, characterized in that, The micro-deformation mechanical parameters include at least the intrinsic hardness of various minerals; Based on the volume fractions of various minerals, the pore structure characterization parameters of the micro-region, and the microscopic deformation mechanical parameters, the effective hardness prediction results of the micro-region during the microscopic deformation process are predicted, including: The volume fractions corresponding to various minerals are weighted and summed with the products of the intrinsic hardness corresponding to various minerals contained in the micro-region to obtain the matrix hardness of the micro-region during the micro-deformation process. The hardness correction coefficient is determined based on the porosity corresponding to the microstructure. The product of the matrix hardness and the hardness correction coefficient is used as the effective hardness prediction result of the micro-region during the micro-deformation process.

6. The method for predicting the microscopic dynamic stress-deformation behavior of rocks according to claim 2, characterized in that, Based on the predicted elastic modulus and the predicted effective hardness, the stress-strain relationship curve of the microregion during the microdeformation process is constructed, including: In the elastic phase, the strain prediction result is determined based on the elastic modulus prediction result and the applied stress; During the plastic stage, the yield strength corresponding to the micro-region is determined based on the comprehensive porosity heterogeneity index and the effective hardness prediction result, so as to determine the strain prediction result for different strain levels based on the yield strength. Based on the strain prediction results of the elastic stage and the strain prediction results of the plastic stage, the stress-strain relationship curve of the micro-region during the micro-deformation process is constructed.

7. The method for predicting the microscopic dynamic stress-deformation behavior of rocks according to claim 2, characterized in that, The microscopic deformation mechanical parameters also include measured values ​​of elastic modulus and effective hardness; the method further includes: The effectiveness of the predicted elastic modulus and the predicted effective hardness is verified by using the measured values ​​of the elastic modulus and the effective hardness, respectively. If the validity verification is passed, save the elastic modulus prediction result, the effective hardness prediction result, and the stress-strain relationship curve.

8. A system for predicting the microscopic dynamic stress-deformation behavior of rocks, characterized in that, include: The image acquisition module is used to acquire backscattered electron images of rock samples within the study area; The micro-region delineation module is used to delineate multiple micro-regions on the surface of the rock sample based on the backscattered electron image, and to obtain the pore structure characterization parameters corresponding to the micro-regions, wherein the micro-regions serve as a unified positioning reference. The experimental module is used to control in-situ measurement equipment to measure the intrinsic elastic modulus of different rock internal structures within the same defined micro-region, and to control a nanoindenter to perform cyclic loading indentation experiments on the micro-region to obtain the corresponding microscopic deformation mechanical parameters of the micro-region. This includes: based on the micro-region's identification, first placing a real rock sample under an atomic force microscope to locate the position of the rock's internal structure and measuring its intrinsic elastic modulus; then transferring it to the nanoindenter to perform cyclic loading indentation experiments at the same micro-region location, and simultaneously or sequentially acquiring the corresponding microscopic deformation mechanical parameters. The prediction module is used to predict the microscopic dynamic stress-deformation behavior of the rock sample based on the pore structure characterization parameters, intrinsic elastic modulus and microscopic deformation mechanical parameters corresponding to the micro-region, and obtain the behavior prediction result.

9. An electronic device, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.

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