A method and system for constructing a digital core mechanical parameter field

Abstract

The application relates to the technical field of digital core modeling, and discloses a digital core mechanical parameter field construction method and system. The method comprises the following steps: multi-scale image acquisition; multi-scale image space registration; mineral particle identification and numbering; target indentation test point or scratch test path arrangement; micro-nano mechanical test and parameter acquisition; test point coordinate conversion and parameter mapping; mineral particle mechanical parameter statistics and heterogeneity coefficient determination; mineral particle internal mechanical parameter heterogeneity field construction; and digital core mineral mechanical parameter field model construction. The application can solve the problems in traditional digital core modeling, such as that a single kind of mineral is usually endowed with a single homogeneous mechanical parameter, the correspondence between test points and mineral particles is unclear, and micro-test parameters are difficult to accurately map to a digital core model, and the authenticity and fine degree of digital core mechanical parameter assignment are improved.

Description

Technical Field

[0001] This invention relates to the field of digital core modeling technology, specifically to a method and system for constructing a digital core mechanical parameter field. Background Technology

[0002] Digital core technology is a fundamental method in rock mechanics, reservoir evaluation, and numerical simulation of underground engineering. It acquires microstructural information about the interior of rocks, including mineral composition, porosity, fractures, bedding, and mineral interfaces, through multi-scale characterization techniques such as CT, SEM, EDS, QEMSCAN, FIB-SEM, and optical microscopy. This information is then used to construct digital core models for mechanical, seepage, or multi-field coupled simulations. Compared to traditional homogeneous rock models, digital core models can more realistically represent the heterogeneous structure within rocks, which is of great significance for rock strength prediction, crack propagation analysis, reservoir stimulation evaluation, rockburst tendency analysis, and engineering-scale parameter inversion.

[0003] In existing digital core mechanics modeling processes, rocks are typically classified into different components such as quartz, feldspar, carbonate minerals, clay minerals, organic matter, and pores and fractures based on mineral identification results. A uniform elastic modulus, Poisson's ratio, strength parameter, or fracture parameter is assigned to the same mineral phase. While this method facilitates modeling and calculation, it neglects the dispersion of mechanical parameters within the same mineral due to crystal defects, grain size, mineral boundaries, microfracture development, pore proximity effects, and diagenetic differences. In real rocks, even within the same mineral, the mechanical properties can vary significantly between different mineral grains and within the same mineral grain. Using a single homogeneous parameter for assignment can easily reduce the digital core model's ability to represent real micromechanical behavior, thus affecting the reliability of predictions regarding rock fracture, crack propagation, and macroscopic strength.

[0004] Micro- and nano-mechanical testing methods, such as nanoindentation, microindentation, and scratch testing, can obtain the elastic modulus, hardness, strength parameters, fracture parameters, and damage parameters of local areas of mineral particles or mineral interfaces, providing direct testing basis for assigning mechanical parameters to digital cores. However, existing test results are often used in the form of individual test points or statistical averages, and the spatial correspondence between test points and specific mineral particles, mineral interfaces, and adjacent areas of pores and fractures is not clear enough, making it difficult to achieve accurate mapping of test parameters to digital core structure models. At the same time, differences in resolution, field of view, coordinate reference, and imaging scale exist between multi-scale images, making it difficult to directly correspond QEMSCAN, SEM, EDS, and CT images with indentation test points.

[0005] Currently, while some digital core modeling methods consider mineral composition, pore and fracture structure, or mineral interface distribution, and some methods introduce rock parameter heterogeneity through random fields or Weibull distributions, most still lack a process for constructing a mineral grain-scale parameter field based on real mineral grain identification, test point coordinate registration, and targeted indentation test results. In other words, existing technologies cannot simultaneously solve problems such as "accurate mineral grain identification," "correspondence between indentation test points and mineral grains," "spatial mapping of mineral mechanical parameters," and "construction of heterogeneous fields within single mineral grains." Summary of the Invention

[0006] To address the aforementioned shortcomings in existing technologies, this invention provides a method and system for constructing a digital core mechanical parameter field. This addresses the problems encountered in current digital core mechanical modeling processes, such as assigning a single homogeneous mechanical parameter to the same mineral, unclear spatial correspondence between micro / nano mechanical test points and specific mineral particles, difficulty in accurately mapping test parameters to the digital core model, and the challenge in characterizing the heterogeneity of mechanical parameters within a single mineral particle. This invention achieves refined assignment of digital core mechanical parameters through multi-scale image spatial registration, mineral particle numbering, targeted indentation test point arrangement, test parameter coordinate mapping, and the construction of a heterogeneous field for mechanical parameters within a single mineral particle. This enhances the ability of digital core models to characterize the true microstructure and mechanical heterogeneity of rocks.

[0007] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: In a first aspect, the present invention proposes a method for constructing a digital core mechanical parameter field, comprising the following steps: Acquire multi-scale image information of rock samples; the multi-scale image information characterizes mineral composition, mineral spatial distribution, porosity, fractures, and mineral interfaces; Spatial registration is performed on the multi-scale image information to establish a unified coordinate system for rock sample images; Based on the registered multi-scale image information, mineral phase identification is performed, and boundary extraction and particle numbering are performed on different mineral particles to obtain a spatial distribution model of mineral particles. Based on the spatial distribution model of the mineral particles, target test points or scratch test paths are determined, and a correspondence is established between the target test points or scratch test paths and specific mineral particles, mineral interfaces, pore adjacent areas, or fracture adjacent areas. Obtain the mineral mechanical parameters and test coordinates of each test point obtained by conducting micro-nano mechanical tests according to the target test points or scratch test paths. The test coordinates are transformed into the coordinate system of the rock sample image. The mineral mechanical parameters are classified and mapped according to the mineral grain number to which the test point belongs, and the correspondence between the mineral grain number, the test point coordinates and the mineral mechanical parameters is established. Based on the correspondence between mineral particle number, test point coordinates and mineral mechanical parameters, a heterogeneous field of internal mechanical parameters of a single mineral particle is constructed. The heterogeneous field of mechanical parameters is then coupled with the spatial distribution of minerals, pores, fractures and mineral interfaces to generate a digital core mineral mechanical parameter field model.

[0008] Furthermore, spatial registration is performed on the multi-scale image information to establish a unified coordinate system for the rock sample image, including: The boundaries of mineral particles, pores, fractures, and mineral interfaces in images at different scales are identified, and structural feature points are extracted. Feature description information is established for the structural feature points and feature point matching is performed. Feature points with incorrect matching are eliminated and stable matching feature point pairs are retained. Calculate the coordinate transformation parameters based on the stable matching feature point pairs, and transform the coordinates of the image to be registered and the test point to the reference image coordinate system; The coordinate transformation parameters are iteratively corrected based on boundary or contour deviations until the registration error meets the preset requirements, thereby establishing the unified rock sample image coordinate system.

[0009] Furthermore, mineral phase identification is performed based on the registered multi-scale image information, and boundary extraction and particle numbering are performed on different mineral particles to obtain a spatial distribution model of mineral particles, including: Mineral phase identification is performed based on at least one of the following in the registered multi-scale image information: grayscale, color, elemental composition, mineral phase diagram, and texture features. Connected regions of the same type of mineral are divided into independent mineral particles. Boundary extraction is performed on each independent mineral particle, and a unique particle number is assigned. Record the particle attribute information corresponding to each particle number. The particle attribute information includes at least one of the following: area, perimeter, equivalent diameter, shape factor, major and minor axis directions, adjacent mineral type, and interface length. The spatial distribution model of the mineral particles is generated based on the particle number and the particle attribute information.

[0010] Further, determining the target test point or scratch test path based on the aforementioned mineral particle spatial distribution model includes: The number and location of indentation test points inside the particles are determined based on the differences in the area of ​​the mineral particles, and the locations cover the central and edge areas of the particles; Test points or scratch test paths are arranged in the areas near mineral interfaces, pores, cracks, and contact areas of different minerals. Establish a correspondence between mineral particle numbers and target test points or scratch test paths.

[0011] Further, the test coordinates are transformed into the coordinate system of the rock sample image, and the mineral mechanical parameters are classified and mapped according to the mineral grain number to which the test point belongs, establishing a correspondence between the mineral grain number, the test point coordinates, and the mineral mechanical parameters, including: The test coordinates of each test point are transformed into the coordinate system of the rock sample image to obtain the registered test point coordinates; Determine the mineral particle region and corresponding particle number to which the registered test point coordinates belong; Mineral mechanical parameters are classified and stored according to their relationship. Test point parameters belonging to the same mineral grain region are stored under the corresponding grain number. Test point parameters located in the mineral interface adjacent area are stored under the corresponding mineral interface category. Test point parameters located in the pore adjacent area or fracture adjacent area are classified and stored according to their distance from the pore or fracture. Based on the classification and storage results, establish the correspondence between mineral particle numbers, test point coordinates, local structural environment, and mineral mechanical parameters.

[0012] Furthermore, after establishing the correspondence between mineral grain numbers, test point coordinates, and mineral mechanical parameters, the process also includes: For mineral particles lacking targeted indentation test points, parameter estimation or parameter field assignment is performed on the mineral particles lacking test points based on the mechanical parameters of mineral particles that have been tested under the same mineral type, similar particle size, similar mineral adjacency relationship, or similar interface environment.

[0013] Furthermore, based on the correspondence between mineral grain number, test point coordinates, and mineral mechanical parameters, a heterogeneous field of internal mechanical parameters for a single mineral grain is constructed, including: The mean, standard deviation, and coefficient of variation of the same type of mineral are calculated based on the mechanical parameters of multiple test points within the same mineral particle. The Weibull shape parameters of the mineral grains can be determined by using the discrete coefficients. Using the average value as the average level and the Weibull shape parameter as the distribution shape, the mechanical parameter values ​​of each computational unit inside the mineral particle are generated, forming a heterogeneous field of mechanical parameters inside the particle.

[0014] Furthermore, the heterogeneous field of the mechanical parameters is coupled with the spatial distribution of minerals, porosity, fractures, and mineral interfaces to generate a digital core mineral mechanical parameter field model, including: Porous and fractured regions can be assigned void, weakening, or contact properties. Weakening mechanical parameters or contact properties are assigned to the mineral interface region, and the contact properties include at least one of interface strength, interface stiffness, interface damage parameters and interface friction parameters. The heterogeneous field of internal mechanical parameters of the particles is coupled with the void properties, weakened mechanical parameters or contact properties according to the spatial position relationship to generate the digital core mineral mechanical parameter field model; wherein, the digital core mineral mechanical parameter field model includes mineral phase spatial distribution information, mineral particle number information, pore and fracture spatial distribution information, mineral interface information and mineral particle mechanical parameter spatial distribution information.

[0015] Furthermore, after generating the digital core mineral mechanical parameter field model, the following is also included: The digital core mineral mechanical parameter field model is transformed into at least one of the following: finite element model, discrete element model, finite difference model, or coupled model. During the transformation process, the grid is divided according to the mineral grain boundaries, pore and fracture boundaries, and mineral interface positions. Each grid cell is then matched with the mineral type, grain number, and mechanical parameters of its location to form a digital core calculation model for rock mechanics simulation. And / or, extend the two-dimensional parametric field model into a three-dimensional parametric field model by multi-slice stacking or three-dimensional reconstruction.

[0016] Secondly, this invention proposes a digital core mechanical parameter field construction system, which applies the aforementioned digital core mechanical parameter field construction method, including: The image acquisition module is used to acquire multi-scale image information of rock samples; the multi-scale image information characterizes mineral composition, mineral spatial distribution, porosity, fractures and mineral interfaces; The spatial registration module is used to perform spatial registration on the multi-scale image information and establish a unified coordinate system for rock sample images. The particle identification and numbering module is used to identify mineral phases based on the registered multi-scale image information, extract the boundaries of different mineral particles and number the particles to obtain a spatial distribution model of mineral particles. The targeted testing module is used to determine the targeted test points or scratch test paths according to the spatial distribution model of the mineral particles, and to establish a correspondence between the targeted test points or scratch test paths and specific mineral particles, mineral interfaces, pore adjacent areas or fracture adjacent areas. The test parameter acquisition module is used to acquire the mineral mechanical parameters and test coordinates of each test point obtained by conducting micro-nano mechanical tests according to the target test point or scratch test path. The parameter mapping module is used to convert the test coordinates to the rock sample image coordinate system, classify and map the mineral mechanical parameters according to the mineral grain number to which the test point belongs, and establish the correspondence between the mineral grain number, the test point coordinates and the mineral mechanical parameters. The parameter field model generation module is used to construct a heterogeneous field of internal mechanical parameters of a single mineral particle based on the correspondence between mineral particle number, test point coordinates and mineral mechanical parameters, and couple the heterogeneous field of mechanical parameters with mineral spatial distribution, pores, fractures and mineral interfaces to generate a digital core mineral mechanical parameter field model.

[0017] The present invention has the following beneficial effects: (1) This invention establishes a unified coordinate system for rock sample images through multi-scale image registration, so that mineral particles, pores, cracks and mineral interfaces in QEMSCAN, SEM, EDS, CT, FIB-SEM or optical microscopy images can correspond to each other in spatial position, solving the problem of inconsistent coordinate references and difficulty in fusion of structural information between images of different scales.

[0018] (2) By extracting mineral particle boundaries and numbering particles, this invention establishes a correspondence between the targeted indentation test points or scratch test paths and specific mineral particles, mineral interfaces, pore adjacent areas and crack adjacent areas, thus solving the problem of unclear spatial correspondence between traditional micro-nano mechanical test results and specific mineral particles.

[0019] (3) The present invention maps the mineral mechanical parameters obtained by targeted indentation test or scratch test to the digital core structure model according to the test point coordinates, which can realize the accurate transfer of test parameters from point data to digital core parameter field, and improve the authenticity and refinement of digital core mechanical parameter assignment.

[0020] (4) The present invention constructs a non-homogeneous field of mechanical parameters inside a single mineral particle, which breaks through the limitation of the traditional digital core modeling that usually assigns a single homogeneous parameter to the same mineral, and can more realistically characterize the discreteness of mechanical parameters between different particles of the same mineral and within the same particle.

[0021] (5) The digital core mineral mechanical parameter field model generated by the present invention contains both mineral structure information and spatial distribution information of mechanical parameters, which can provide a more reliable digital core basic model for rock micromechanical simulation, crack propagation analysis, strength prediction, rockburst tendency analysis and engineering scale mechanical parameter inversion. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of a digital core mechanical parameter field construction method according to the present invention; Figure 2 This is a schematic diagram illustrating the mapping between mineral particle numbers and targeted indentation parameters in this invention; Figure 3 This is a schematic diagram illustrating the construction of a heterogeneous field (Weibull distribution) of internal mechanical parameters of a single mineral particle in this invention. Detailed Implementation

[0023] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0024] This invention provides a method and system for constructing a digital core mechanical parameter field, primarily addressing the problems of overly homogeneous mineral mechanical parameter assignment, unclear correspondence between indentation test points and mineral particles, difficulty in characterizing the heterogeneity of internal mechanical parameters within mineral particles, and difficulty in accurately mapping test parameters to the digital core model during digital core mechanical modeling. This method achieves refined assignment of micro- and nano-scale test parameters to the digital core computational model through multi-scale image registration, mineral particle identification and numbering, targeted indentation testing, test point coordinate mapping, heterogeneity coefficient determination, and mechanical parameter field construction.

[0025] like Figure 1 As shown, the present invention provides a method for constructing a digital core mechanical parameter field, comprising the following steps S1 to S7: S1. Obtain multi-scale image information of rock samples; the multi-scale image information characterizes mineral composition, mineral spatial distribution, porosity, fractures, and mineral interfaces; In an optional embodiment of the present invention, step S1 acquires multi-scale image information of the rock sample. The multi-scale image information includes one or more of QEMSCAN images, SEM images, EDS elemental distribution images, CT images, FIB-SEM images, or optical microscopic images, which are used to characterize the microstructure information of the rock sample, such as mineral composition, mineral spatial distribution, pores, fractures, bedding, and mineral interfaces.

[0026] This embodiment begins with rock sample preparation. Representative rock samples are selected, cut, ground, and polished to create a smooth test surface suitable for image acquisition and micro / nano mechanical testing. To facilitate subsequent image registration, artificial markers can be placed on the sample surface or edges, or natural feature points can be used, such as sample boundaries, pores, fissures, mineral grain intersections, or mineral interface intersections.

[0027] Then, multi-scale image acquisition was performed. QEMSCAN, SEM, EDS, CT, FIB-SEM, or optical microscopy images were acquired from the rock samples to obtain multi-scale structural information such as mineral composition, spatial distribution of minerals, porosity, fractures, bedding, and mineral interfaces. Specifically, QEMSCAN images were used to identify mineral types and spatial distribution; SEM images were used to identify microstructure, pores, fractures, and mineral interfaces; EDS elemental distribution images were used to assist in mineral identification; and CT or FIB-SEM images were used to obtain three-dimensional structural information of the rock's interior.

[0028] S2. Spatial registration is performed on the multi-scale image information to establish a unified coordinate system for rock sample images; In an optional embodiment of the present invention, step S2 performs image preprocessing and spatial registration on the multi-scale image information. The image preprocessing includes one or more of image denoising, grayscale correction, brightness correction, boundary enhancement, and scale calibration. The spatial registration includes one or more of coordinate unification, feature point recognition, image rotation correction, translation correction, scaling correction, feature point matching, mineral grain contour matching, and pore and fracture feature matching, thereby establishing a unified rock sample image coordinate system.

[0029] Step S2 involves spatial registration of the multi-scale image information to establish a unified coordinate system for the rock sample image, including: The boundaries of mineral particles, pores, fractures, and mineral interfaces in images at different scales are identified, and structural feature points are extracted. Feature description information is established for the structural feature points and feature point matching is performed. Feature points with incorrect matching are eliminated and stable matching feature point pairs are retained. Calculate the coordinate transformation parameters based on the stable matching feature point pairs, and transform the coordinates of the image to be registered and the test point to the reference image coordinate system; The coordinate transformation parameters are iteratively corrected based on boundary or contour deviations until the registration error meets the preset requirements, thereby establishing the unified rock sample image coordinate system.

[0030] This embodiment first performs denoising, grayscale correction, brightness correction, contrast enhancement, scale calibration, and boundary enhancement processing on the acquired multi-scale images. Then, it identifies mineral grain boundaries, pore boundaries, fracture boundaries, and mineral interfaces in images at different scales, extracting feature points suitable for image registration. These feature points include one or more of the following: artificially marked points, sample boundary corners, mineral grain boundary intersections, pore boundary points, fracture endpoints, fracture intersections, mineral interface intersections, and local feature points with significant differences in grayscale, color, or elemental composition.

[0031] In one specific implementation, feature point extraction can be performed as follows: First, QEMSCAN images, SEM images, EDS elemental images, or optical microscopic images are subjected to grayscale conversion, filtering and denoising, and boundary enhancement processing. Then, threshold segmentation, edge detection, mineral phase segmentation, or contour tracing methods are used to extract the boundaries of mineral particles, pores, fractures, and mineral interfaces. Candidate feature points are further determined based on the endpoints, intersections, turning points, corners, curvature abrupt change points, or intersections of different mineral phases along the boundary lines. For fracture structures, fracture endpoints, bifurcation points, and intersections can be extracted as feature points through fracture skeletonization processing. For pore structures, corners, centroids, or locations with significant changes in boundary curvature on the pore boundaries can be extracted as feature points. For mineral particles, particle boundary intersections, particle centroids, particle boundary corners, or multi-mineral contact points can be extracted as feature points.

[0032] In one optional implementation, feature description information is established for each extracted feature point. This feature description information includes one or more of the following: feature point coordinates, neighborhood grayscale values, neighborhood texture features, mineral type, adjacent mineral types, boundary direction, local curvature, and distance to pores or fractures. Subsequently, feature points in the image to be registered are matched with those in the reference image based on this feature description information. Feature point matching can be achieved using one or more of the following methods: nearest neighbor matching, distance-constrained matching, angle-constrained matching, mineral type consistency matching, boundary contour similarity matching, or random sampling consistency matching. Feature points with incorrect matching can be discarded based on matching distance, direction deviation, local structural similarity, or registration residuals, retaining stable matching feature point pairs.

[0033] In one optional implementation, a QEMSCAN mineral phase image is used as a reference image, and the SEM image, EDS element image, or indentation test area image is transformed into the coordinate system of the reference image. Coordinate transformation can be achieved using rigid transformation, affine transformation, or local nonlinear registration methods. Specifically, based on the matching feature point pairs selected from the image to be registered and the reference image, the rotation, translation, scaling, or affine transformation parameters of the image are calculated, and the coordinates of the points in the image to be registered are transformed into the coordinate system of the reference image. The coordinate transformation relationship can be expressed as: x' = a11x + a12y + tx; y' = a21x + a22y + ty; In the formula, x and y are the coordinates of the points in the image to be registered; x' and y' are the coordinates of the points after transformation to the reference image coordinate system; a11, a12, a21, and a22 are rotation, scaling, or shearing transformation parameters; and tx and ty are translation parameters. After registration, the boundary positions, morphological features, mineral types, and test points of the same mineral grain in images at different scales can correspond to each other.

[0034] In a further embodiment, to improve registration accuracy, a secondary registration correction can be performed after the initial feature point matching is completed, based on mineral grain boundaries, pore and fracture boundaries, or sample contours. Specifically, the average distance, maximum distance, or overlap area between corresponding boundaries or contours in the image to be registered and the reference image are compared. The registration parameters are iteratively corrected based on the comparison results until the registration error meets preset requirements. The registration error can be determined by the average distance between matched feature points or the average deviation between boundary contours.

[0035] S3. Based on the registered multi-scale image information, mineral phase identification is performed, and boundary extraction and particle numbering are performed on different mineral particles to obtain a spatial distribution model of mineral particles. In an optional embodiment of the present invention, step S3 identifies the mineral phases in the rock sample based on the registered multi-scale image, and performs boundary extraction, particle segmentation, and particle numbering on different mineral particles to obtain a spatial distribution model of mineral particles. The spatial distribution model of mineral particles includes one or more of the following information: mineral type, mineral particle number, particle boundary, particle area, particle morphology, particle adjacency relationship, and mineral interface location.

[0036] Step S3 involves mineral phase identification based on the registered multi-scale image information, boundary extraction and particle numbering for different mineral particles, resulting in a spatial distribution model of mineral particles, including: Mineral phase identification is performed based on at least one of the following in the registered multi-scale image information: grayscale, color, elemental composition, mineral phase diagram, and texture features. Connected regions of the same type of mineral are divided into independent mineral particles. Boundary extraction is performed on each independent mineral particle, and a unique particle number is assigned. Record the particle attribute information corresponding to each particle number. The particle attribute information includes at least one of the following: area, perimeter, equivalent diameter, shape factor, major and minor axis directions, adjacent mineral type, and interface length. The spatial distribution model of the mineral particles is generated based on the particle number and the particle attribute information.

[0037] This embodiment identifies mineral phases in rock samples based on registered multi-scale images and performs boundary extraction, particle segmentation, and particle numbering for different mineral grains. Mineral phase identification can be achieved using one or more methods selected from direct identification using mineral phase diagrams, elemental composition discrimination, grayscale threshold segmentation, color feature classification, texture feature recognition, or machine learning classification.

[0038] In one specific implementation, when using QEMSCAN mineral phase images, the mineral type can be directly determined based on different colors or mineral phase labels in the QEMSCAN image, and the spatial distribution areas of different mineral phases can be extracted. When using EDS elemental distribution images, different mineral types can be identified based on the relative content and spatial distribution characteristics of elements such as Si, Al, Ca, Mg, Fe, K, Na, C, and O. For example, Si-rich areas can be identified as quartz or silica minerals, Ca and Mg-rich areas can be identified as carbonate minerals, Al, Si, and K-rich areas can be identified as clay minerals or feldspar minerals, and C-rich areas can be identified as organic matter or carbonaceous components. When using SEM images or optical microscopic images, different mineral particles can be identified by grayscale differences, particle morphology, boundary features, texture features, and registration results with other images.

[0039] In one optional implementation, mineral phase identification can be performed as follows: First, the mineral type distribution is determined using QEMSCAN images or EDS elemental distribution images; then, the mineral type distribution results are mapped onto SEM images or optical micrographs to correct mineral boundaries, pore boundaries, and fracture boundaries; next, different mineral particles within the same mineral phase are segmented based on mineral type, grayscale range, elemental composition, and spatial connectivity. For similar mineral particles that are in contact with each other or partially connected, particle separation can be performed based on boundary depressions, particle morphology, watershed segmentation, distance transformation, or manual correction methods to obtain independent mineral particle regions.

[0040] In one optional implementation, each identified mineral particle is assigned a unique number. For example, quartz particles are numbered Q1, Q2, and Q3; carbonate mineral particles are numbered C1, C2, and C3; clay mineral regions are numbered Cl1, Cl2, and Cl3; and organic matter regions are numbered O1, O2, and O3. Each mineral particle number corresponds to the mineral type, particle boundary, particle area, equivalent diameter, shape factor, major and minor axis directions, adjacent mineral types, interface length, and spatial relationship with pores and fractures.

[0041] like Figure 2 As shown, based on the mineral grain identification results, different mineral grains in the digital core image can be numbered separately, and a correspondence between the mineral grain number and its spatial location can be established. Each mineral grain number corresponds to an independent mineral grain region, and subsequent targeted indentation points, scratch paths, and mechanical parameters can be assigned and stored based on this number.

[0042] S4. Determine the target test point or scratch test path according to the spatial distribution model of the mineral particles, and establish a correspondence between the target test point or scratch test path and the specific mineral particles, mineral interface, pore adjacent area or crack adjacent area. In an optional embodiment of the present invention, step S4 determines the targeted indentation test points or scratch test paths based on the spatial distribution model of the mineral particles, thereby establishing a correspondence between the targeted indentation test points or scratch test paths and specific mineral particles, mineral interfaces, pore adjacent areas, or fracture adjacent areas. The targeted indentation test points include one or more of the following: test points in the central region of the mineral particles, test points in the edge region of the mineral particles, test points near the mineral interfaces, test points near pores, test points near fractures, and test points in contact areas between different minerals.

[0043] Step S4 determines the target test point or scratch test path based on the spatial distribution model of the mineral particles, including: The number and location of indentation test points inside the particles are determined based on the differences in the area of ​​the mineral particles, and the locations cover the central and edge areas of the particles; Test points or scratch test paths are arranged in the areas near mineral interfaces, pores, cracks, and contact areas of different minerals. Establish a correspondence between mineral particle numbers and target test points or scratch test paths.

[0044] This embodiment determines targeted nanoindentation, microindentation, or nanoscratch test sites based on the aforementioned mineral particle spatial distribution model. Test sites can be arranged in the central region of mineral particles, the edge region of mineral particles, the region near mineral interfaces, the region near pores, the region near fractures, and the contact area between different minerals. In this way, each indentation test site or scratch test path can correspond to a specific mineral particle, mineral interface, or region near pores or fractures.

[0045] In one optional embodiment, for larger mineral particles, multiple indentation test points are arranged at the particle center, particle edge, and near the mineral interface; for smaller mineral particles, one or more indentation test points are arranged inside the particle; for the mineral interface region, test points or scratch paths are arranged along both sides of the interface or along the interface direction to obtain parameters of the internal mineral particles and parameters of the adjacent area of ​​the mineral interface. Figure 2 As shown, for mineral particles numbered 1, 2, 3, 4, and 5, indentation points P1,1, P1,2, ..., P1,n, P2,1, P2,2, ..., P2,n can be arranged respectively, thereby establishing the correspondence between the mineral particle number and the target indentation point.

[0046] S5. Obtain the mineral mechanical parameters and test coordinates of each test point obtained by conducting micro-nano mechanical tests according to the target test points or scratch test paths. In an optional embodiment of the present invention, step S5 involves performing nanoindentation, microindentation, or scratch testing on the targeted indentation test point or scratch test path to obtain the mineral mechanical parameters at the corresponding location. The mineral mechanical parameters include one or more of the following: elastic modulus, hardness, indentation modulus, fracture toughness, tensile strength, compressive strength, cohesion, internal friction angle, interfacial strength, damage parameters, and fracture parameters.

[0047] In this embodiment, nanoindentation, microindentation, or nanoscratch tests are performed at the targeted indentation test points or scratch test paths to obtain one or more of the following parameters at the corresponding locations: elastic modulus, hardness, indentation modulus, equivalent fracture toughness, interfacial strength, damage parameters, or fracture parameters. For indentation test results, the elastic modulus and hardness can be determined based on the load-displacement curve, and the equivalent fracture toughness can be determined by combining the indentation crack morphology. For scratch test results, local strength parameters, equivalent scratch hardness, equivalent interfacial fracture parameters, or apparent friction coefficient can be determined based on the scratch force, normal load, tangential force, scratch depth, and scratch path.

[0048] In one specific implementation, for the j-th indentation point on the i-th mineral particle, the maximum load Pmax,i,j, the maximum indentation depth hmax,i,j, and the unloading contact stiffness Si,j are determined based on the load-displacement curve obtained from the nanoindentation test. The unloading contact stiffness Si,j can be determined by the slope of the initial segment of the unloading curve, and its expression is: Si,j = dP / dh; In the formula, P is the indentation load; h is the indentation depth; and Si,j is the unloading contact stiffness of the j-th indentation point on the i-th mineral particle.

[0049] According to the Oliver-Pharr method, the contact depth hc,i,j of the j-th indentation point on the i-th mineral particle can be calculated from the maximum indentation depth hmax,i,j, the maximum load Pmax,i,j, and the unloading contact stiffness Si,j. Its expression is: hc,i,j = hmax,i,j - ε Pmax,i,j / Si,j; In the formula, hc,i,j is the contact depth; ε is a constant related to the shape of the indenter. For Berkovich indenters or conical indenters, ε can be taken as 0.72 to 0.75.

[0050] The contact area Ac,i,j between the indenter and the measured mineral is calculated based on the contact depth hc,i,j, and its expression is as follows: Ac,i,j = f(hc,i,j); In the formula, Ac,i,j is the contact area of ​​the j-th indentation point on the i-th mineral particle; f(hc,i,j) is the indenter area function.

[0051] When using an ideal Berkovich indenter, the contact area can be expressed as: Ac,i,j = 24.5 hc,i,j 2 ; Taking into account the blunting of the indenter tip or the area function calibration, the contact area can also be expressed as: Ac,i,j = C0hc,i,j 2 + C1hc,i,j + C2hc,i,j1 / 2 + C3hc,i,j1 / 4 + ...; In the formula, C0, C1, C2, and C3 are calibration parameters for the pressure head area function.

[0052] The hardness Hi,j of the j-th indentation point on the i-th mineral grain is calculated based on the maximum load Pmax,i,j and the contact area Ac,i,j, and its expression is as follows: Hi,j = Pmax,i,j / Ac,i,j; In the formula, Hi,j represents the hardness of the j-th indentation point on the i-th mineral grain.

[0053] The reduced elastic modulus Er,i,j of the j-th indentation point on the i-th mineral particle is calculated based on the unloading contact stiffness Si,j and the contact area Ac,i,j. Its expression is as follows: ; In the formula, Er,i,j is the reduced elastic modulus; β is the correction coefficient related to the shape of the indenter; Si,j is the unloading contact stiffness; and Ac,i,j is the contact area.

[0054] Based on the reduced elastic modulus Er,i,j, the elastic modulus Ei,j of the tested mineral is further calculated, and its expression is: 1 / Er,i,j = (1 - νi,j 2 ) / Ei,j + (1 - νt 2 ) / Et; Therefore, we can conclude that: Ei,j = (1 - νi,j 2 ) / [1 / Er,i,j - (1 - νt 2 ) / Et]; In the formula, Ei,j is the elastic modulus of the j-th indentation point on the i-th mineral particle; νi,j is the Poisson's ratio of the tested mineral; Et is the elastic modulus of the indenter; and νt is the Poisson's ratio of the indenter.

[0055] In one alternative embodiment, when an identifiable radial crack forms at the corner of the indentation or around the indentation after the indentation test, the equivalent fracture toughness KIC,i,j at the j-th indentation point on the i-th mineral grain can be calculated based on the indentation crack length, and its expression is: KIC,i,j = α (Ei,j / Hi,j)1 / 2 Pmax,i,j / ci,j3 / 2; In the formula, KIC,i,j represents the equivalent fracture toughness of the j-th indentation point on the i-th mineral grain; α is an empirical coefficient related to the indenter shape and crack morphology; Ei,j is the elastic modulus; Hi,j is the hardness; Pmax,i,j is the maximum indentation load; and ci,j is the distance from the center of the indentation to the tip of the radial crack. When no stable and identifiable crack has formed around the indentation, the equivalent fracture toughness of that indentation point can be omitted from the calculation or discarded as invalid fracture toughness data.

[0056] For nano-scratch testing, the normal load N, tangential force Ft, scratch depth hs, and scratch width bs during the scratching process can be recorded. The equivalent scratch hardness, interfacial strength, or apparent coefficient of friction can be calculated based on the scratch contact area or cross-sectional area. The apparent coefficient of friction μ can be expressed as: μ = Ft / N; In the formula, μ is the apparent friction coefficient; Ft is the scratch tangential force; and N is the scratch normal load. For scratch paths that cross mineral interfaces, the equivalent interface strength parameters or interface damage parameters of the adjacent region of the mineral interface can be determined based on the abrupt changes in scratch force, scratch depth, or material spalling near the interface.

[0057] Through the above calculations, the elastic modulus Ei,j, hardness Hi,j, reduced elastic modulus Er,i,j, equivalent fracture toughness KIC,i,j, or scratch interface parameters of the j-th indentation point on the i-th mineral particle can be obtained. Furthermore, for multiple test points on the same mineral particle, the average elastic modulus, average hardness, average equivalent fracture toughness, as well as the coefficients of heterogeneity for elastic modulus, hardness, and equivalent fracture toughness can be statistically obtained. For example... Figure 2 As shown, by establishing a correspondence table of "mineral particle number - indentation point - mechanical parameter - heterogeneity coefficient", the precise correlation between the targeted indentation test parameters and the mineral particle number can be achieved.

[0058] S6. Transform the test coordinates to the coordinate system of the rock sample image, classify and map the mineral mechanical parameters according to the mineral particle number to which the test point belongs, and establish the correspondence between the mineral particle number, the test point coordinates and the mineral mechanical parameters. In an optional embodiment of the present invention, step S6 transforms the coordinates of the targeted indentation test point or scratch test path to a unified rock sample image coordinate system, and classifies and stores the mineral mechanical parameters according to the mineral particle number to which the test point belongs, the distance between the test point and the mineral interface, and the distance between the test point and the pores or cracks, and establishes the correspondence between the mineral particle number, the test point coordinates and the mineral mechanical parameters.

[0059] Step S6 transforms the test coordinates into the rock sample image coordinate system, and classifies and maps the mineral mechanical parameters according to the mineral grain number to which the test point belongs, establishing the correspondence between the mineral grain number, the test point coordinates, and the mineral mechanical parameters, including: The test coordinates of each test point are transformed into the coordinate system of the rock sample image to obtain the registered test point coordinates; Determine the mineral particle region and corresponding particle number to which the registered test point coordinates belong; Mineral mechanical parameters are classified and stored according to their relationship. Test point parameters belonging to the same mineral grain region are stored under the corresponding grain number. Test point parameters located in the mineral interface adjacent area are stored under the corresponding mineral interface category. Test point parameters located in the pore adjacent area or fracture adjacent area are classified and stored according to their distance from the pore or fracture. Based on the classification and storage results, establish the correspondence between mineral particle numbers, test point coordinates, local structural environment, and mineral mechanical parameters.

[0060] This embodiment transforms the coordinates of indentation test points or scratch test paths into a unified rock sample image coordinate system, and determines the mineral grain number, mineral type, distance to the mineral interface, distance to pores, and distance to fractures based on the test point coordinates. Subsequently, the mineral mechanical parameters obtained at the corresponding locations are mapped to the registered digital core structure model, establishing a correspondence between "mineral grain number - test point coordinates - local structural environment - mineral mechanical parameters".

[0061] Specifically, if the coordinates of a test point are located within the region Ri of the i-th mineral grain, then the elastic modulus, hardness, or equivalent fracture parameter corresponding to that test point is assigned to the mechanical parameter database of the i-th mineral grain; if the test point is located in the region adjacent to the interface of two mineral grains, then it is assigned to the parameter database of the corresponding mineral interface adjacent region; if the test point is close to a pore or fracture, then its distance from the pore or fracture is recorded simultaneously for subsequent parameter field correction. The assignment relationship can be expressed as follows: Pi,j ∈ Ri; In the formula, Pi,j represents the j-th test point on the i-th mineral particle; Ri represents the image area occupied by the i-th mineral particle. This method enables the correspondence between test points and mineral particle numbers, mineral types, local structural environments, and mechanical parameters.

[0062] In an optional embodiment of the present invention, after establishing the correspondence between mineral particle numbers, test point coordinates, and mineral mechanical parameters in step S6, the method further includes: For mineral particles lacking targeted indentation test points, parameter estimation or parameter field assignment is performed on the mineral particles lacking test points based on the mechanical parameters of mineral particles that have been tested under the same mineral type, similar particle size, similar mineral adjacency relationship, or similar interface environment.

[0063] S7. Based on the correspondence between mineral particle number, test point coordinates and mineral mechanical parameters, construct a heterogeneous field of internal mechanical parameters of a single mineral particle, and couple the heterogeneous field of mechanical parameters with mineral spatial distribution, pores, fractures and mineral interfaces to generate a digital core mineral mechanical parameter field model.

[0064] In an optional embodiment of the present invention, step S7 constructs a heterogeneous field of internal mechanical parameters of a single mineral particle based on the aforementioned correspondence. This heterogeneous field of internal mechanical parameters of a single mineral particle can be constructed based on one or more of the following: mechanical parameters from multiple test points within the same mineral particle; statistical results of mechanical parameters from multiple mineral particles of the same mineral type; Weibull distribution; random field method; or spatial interpolation method.

[0065] Step S7 constructs a heterogeneous field of internal mechanical parameters for a single mineral particle based on the correspondence between mineral particle number, test point coordinates, and mineral mechanical parameters, including: The mean, standard deviation, and coefficient of variation of the same type of mineral are calculated based on the mechanical parameters of multiple test points within the same mineral particle. The Weibull shape parameters of the mineral grains can be determined by using the discrete coefficients. Using the average value as the average level and the Weibull shape parameter as the distribution shape, the mechanical parameter values ​​of each computational unit inside the mineral particle are generated, forming a heterogeneous field of mechanical parameters inside the particle.

[0066] This embodiment calculates the average value, standard deviation, and coefficient of variation of the mechanical parameters inside the same mineral particle based on the mechanical parameters obtained from multiple test points within the particle, and determines the heterogeneity coefficient or Weibull shape parameter m of the mineral particle accordingly. For the i-th mineral particle, if n indentation test points are arranged inside it, n elastic modulus, hardness, or equivalent fracture toughness test values ​​can be obtained respectively, and statistical analysis can be performed on the corresponding mechanical parameters.

[0067] In one optional implementation, for a certain type of mechanical parameter of the i-th mineral particle, its n test values ​​can be denoted as P1, P2, ..., Pn, and the average value Pmean and standard deviation S of the mechanical parameter can be calculated. The calculation relationship can be expressed as follows: Pmean = (P1 + P2 + ... + Pn) / n; ; In the formula, P1, P2, ..., Pn represent the mechanical parameters obtained from different indentation test points inside the same mineral particle; Pmean represents the average value of the corresponding mechanical parameter of the mineral particle; S represents the standard deviation of the mechanical parameter; and n represents the number of indentation test points inside the mineral particle.

[0068] Furthermore, the coefficient of variation of the internal mechanical parameters of the mineral grains can be determined based on the ratio of the standard deviation to the mean. The calculation relationship can be expressed as follows: CV = S / Pmean; In the formula, CV represents the coefficient of variation of the mechanical parameters within the mineral grain. The larger the CV value, the stronger the dispersion of the mechanical parameters within the same mineral grain; the smaller the CV value, the more concentrated the mechanical parameters within the same mineral grain.

[0069] Furthermore, the Weibull shape parameter m corresponding to the mineral grain can be determined based on the dispersion coefficient CV. For mechanical parameters following a Weibull distribution, the relationship between the dispersion coefficient CV and the Weibull shape parameter m can be expressed as: ; In the formula, m represents the Weibull shape parameter; Gamma() represents the Gamma function. Based on the CV value obtained from the test, the Weibull shape parameter m can be obtained by looking up a table, numerical iteration, or minimum error fitting method. The larger the value of m, the more concentrated the distribution of mechanical parameters and the more uniform the mineral particles are; the smaller the value of m, the more dispersed the distribution of mechanical parameters and the stronger the heterogeneity of the mineral particles.

[0070] In one optional implementation, statistical analysis can be performed on the elastic modulus, hardness, and equivalent fracture toughness of the i-th mineral particle to obtain the average elastic modulus Emean,i, the average hardness Hmean,i, and the average equivalent fracture toughness Kmean,i, and the coefficients of heterogeneity for elastic modulus mi,E, hardness mi,H, and equivalent fracture toughness mi,KIC, respectively. For mineral particles with few or no indentation test points, parameters can be supplemented based on the statistical parameters of previously tested mineral particles of the same mineral type, similar particle size, similar mineral adjacency, similar pore and fracture environment, or similar interface environment.

[0071] like Figure 3 As shown, different Weibull shape parameters *m* correspond to different degrees of heterogeneity. After determining the average mechanical parameters and Weibull shape parameter *m* of the mineral particles, the mechanical parameters of different pixels, voxels, or computational units within the mineral particles can be made to conform to a Weibull distribution, thereby constructing a heterogeneous field of mechanical parameters within the mineral particles. The computational relationship can be expressed as: xk = xmean / Gamma(1 + 1 / m) × [-ln(1 - rk)] (1 / m); In the formula, xk represents the mechanical parameter of the k-th pixel, voxel, or computational unit; xmean represents the average mechanical parameter obtained from targeted indentation testing or statistical analysis; Gamma() represents the Gamma function; rk is a random number used to generate the Weibull random variable; and m is the Weibull shape parameter, used to characterize the heterogeneity of the mechanical parameters. The generated xk follows a Weibull distribution with xmean as the average level. The larger the value of m, the more concentrated the mechanical parameters and the higher the material homogeneity; the smaller the value of m, the more dispersed the mechanical parameters and the stronger the material heterogeneity.

[0072] In one optional implementation, different Weibull shape parameters can be set for the elastic modulus, hardness, and equivalent fracture toughness of different mineral particles. For example, for the i-th mineral particle, the elastic modulus Weibull shape parameter mi,E, the hardness Weibull shape parameter mi,H, and the equivalent fracture toughness Weibull shape parameter mi,KIC can be set respectively, thereby constructing an elastic modulus heterogeneous field, a hardness heterogeneous field, and an equivalent fracture toughness heterogeneous field. For the interface region, the interface parameter field can also be set according to the equivalent scratch hardness, equivalent interface fracture parameter, and apparent friction coefficient determined by nano-scratch testing.

[0073] In an optional embodiment of the present invention, step S7 couples the heterogeneous field of the internal mechanical parameters of the single mineral grain with the spatial distribution of minerals, pores, fractures, bedding, and mineral interfaces to generate a digital core mineral mechanical parameter field model. The digital core mineral mechanical parameter field model includes mineral phase spatial distribution information, mineral grain numbering information, pore and fracture spatial distribution information, mineral interface information, and spatial distribution information of mineral grain mechanical parameters.

[0074] Step S7 couples the heterogeneous field of mechanical parameters with the spatial distribution of minerals, porosity, fractures, and mineral interfaces to generate a digital core mineral mechanical parameter field model, including: Porous and fractured regions can be assigned void, weakening, or contact properties. Weakening mechanical parameters or contact properties are assigned to the mineral interface region, and the contact properties include at least one of interface strength, interface stiffness, interface damage parameters and interface friction parameters. The heterogeneous field of internal mechanical parameters of the particles is coupled with the void properties, weakened mechanical parameters or contact properties according to the spatial position relationship to generate the digital core mineral mechanical parameter field model; wherein, the digital core mineral mechanical parameter field model includes mineral phase spatial distribution information, mineral particle number information, pore and fracture spatial distribution information, mineral interface information and mineral particle mechanical parameter spatial distribution information.

[0075] This embodiment couples the heterogeneous field of internal mechanical parameters of the mineral particles with the spatial distribution of minerals, porosity, fractures, bedding, and mineral interfaces to form a digital core mineral mechanical parameter field model. The digital core mineral mechanical parameter field model includes mineral phase spatial distribution information, mineral particle numbering information, pore and fracture spatial distribution information, mineral interface information, and spatial distribution information of mineral particle mechanical parameters.

[0076] In this embodiment, each pixel, voxel, or computational unit in the digital core mineral mechanical parameter field model has a corresponding mineral type, grain number, and mechanical parameters. For pore and fracture regions, void properties, weakening properties, or contact properties can be assigned; for mineral interface regions, interface strength, interface stiffness, interface damage parameters, friction parameters, or weakening parameters can be assigned based on the equivalent scratch hardness, equivalent interface fracture parameters, apparent friction coefficient, or mechanical parameters of the interface adjacent region determined by nano-scratch tests; for the internal regions of mineral grains, corresponding elastic modulus, hardness, strength parameters, or fracture parameters are assigned based on the constructed heterogeneous field of mechanical parameters.

[0077] In an optional embodiment of the present invention, after generating the digital core mineral mechanical parameter field model in step S7, the method further includes: The digital core mineral mechanical parameter field model is transformed into at least one of the following: finite element model, discrete element model, finite difference model, or coupled model. During the transformation process, the grid is divided according to the mineral grain boundaries, pore and fracture boundaries, and mineral interface positions. Each grid cell is then matched with the mineral type, grain number, and mechanical parameters of its location to form a digital core calculation model for rock mechanics simulation. And / or, extend the two-dimensional parametric field model into a three-dimensional parametric field model by multi-slice stacking or three-dimensional reconstruction.

[0078] This embodiment transforms the digital core mineral mechanical parameter field model into a finite element model, a discrete element model, a finite difference model, or a coupled finite element-discrete element model. During the transformation process, a mesh can be generated based on the mineral grain boundaries, pore and fracture boundaries, and mineral interface locations. Each mesh cell is then mapped to its corresponding mineral type, grain number, and mechanical parameters, thereby forming a digital core calculation model that can be used for rock mechanics simulation.

[0079] In one optional implementation, for any grid cell, its mineral grain number, pore / fracture region, or mineral interface region is first determined based on its center point coordinates. Then, based on the determination result, the corresponding mineral grain mechanical parameter field, interface parameter field, or pore / fracture weakening parameter is retrieved and assigned to the grid cell. This method achieves a one-to-one correspondence between the digital core structure model and the mechanical parameter field model.

[0080] Digital core computational models can be used for rock strength prediction, crack propagation simulation, rockburst tendency analysis, energy storage capacity evaluation, and engineering-scale mechanical parameter inversion. Because this model simultaneously considers the actual mineral structure, mineral grain numbering, indentation test parameters, and the heterogeneity of mechanical parameters within individual mineral grains, it can more realistically reflect the micromechanical heterogeneity within rocks compared to traditional digital core models that assign a single homogeneous parameter to the same mineral.

[0081] In another embodiment, the digital core mineral mechanical parameter field model can be constructed as a two-dimensional digital core parameter field model, or as a three-dimensional digital core parameter field model based on CT images, FIB-SEM images, or multi-slice images. When constructing a three-dimensional model, the two-dimensional mineral grain numbers, indentation test parameters, and statistical distribution of mineral mechanical parameters can be mapped to the three-dimensional voxel model, and the parameter field can be expanded according to the three-dimensional mineral spatial distribution, pore and fracture connectivity characteristics, and mineral interface relationships.

[0082] This invention achieves the precise transfer of micro- and nano-scale test parameters to a digital core model through image registration, mineral particle identification and numbering, targeted indentation testing, test point coordinate mapping, heterogeneity coefficient determination, and the construction of a heterogeneous field of internal mechanical parameters of mineral particles. This solves the problems of homogenization of mechanical parameters for the same mineral, unclear correspondence between test points and mineral particles, and difficulty in characterizing the internal heterogeneity of mineral particles in traditional digital core modeling. Using the above methods, a correspondence can be established between mineral particle numbering, indentation test points, mechanical parameters, heterogeneity coefficients, and digital core computational units, further forming a digital core mechanical parameter field model that can be used for numerical simulation.

[0083] This invention also provides a digital core mechanical parameter field construction system, which applies the above-described digital core mechanical parameter field construction method, including: The image acquisition module is used to acquire multi-scale image information of rock samples; the multi-scale image information characterizes mineral composition, mineral spatial distribution, porosity, fractures and mineral interfaces; The spatial registration module is used to perform spatial registration on the multi-scale image information and establish a unified coordinate system for rock sample images. The particle identification and numbering module is used to identify mineral phases based on the registered multi-scale image information, extract the boundaries of different mineral particles and number the particles to obtain a spatial distribution model of mineral particles. The targeted testing module is used to determine the targeted test points or scratch test paths according to the spatial distribution model of the mineral particles, and to establish a correspondence between the targeted test points or scratch test paths and specific mineral particles, mineral interfaces, pore adjacent areas or fracture adjacent areas. The test parameter acquisition module is used to acquire the mineral mechanical parameters and test coordinates of each test point obtained by conducting micro-nano mechanical tests according to the target test point or scratch test path. The parameter mapping module is used to convert the test coordinates to the rock sample image coordinate system, classify and map the mineral mechanical parameters according to the mineral grain number to which the test point belongs, and establish the correspondence between the mineral grain number, the test point coordinates and the mineral mechanical parameters. The parameter field model generation module is used to construct a heterogeneous field of internal mechanical parameters of a single mineral particle based on the correspondence between mineral particle number, test point coordinates and mineral mechanical parameters, and couple the heterogeneous field of mechanical parameters with mineral spatial distribution, pores, fractures and mineral interfaces to generate a digital core mineral mechanical parameter field model.

[0084] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0088] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for constructing a digital core mechanical parameter field, characterized in that, Includes the following steps: Acquire multi-scale image information of rock samples; the multi-scale image information characterizes mineral composition, mineral spatial distribution, porosity, fractures, and mineral interfaces; Spatial registration is performed on the multi-scale image information to establish a unified coordinate system for rock sample images; Based on the registered multi-scale image information, mineral phase identification is performed, and boundary extraction and particle numbering are performed on different mineral particles to obtain a spatial distribution model of mineral particles. Based on the spatial distribution model of the mineral particles, target test points or scratch test paths are determined, and a correspondence is established between the target test points or scratch test paths and specific mineral particles, mineral interfaces, pore adjacent areas, or fracture adjacent areas. Obtain the mineral mechanical parameters and test coordinates of each test point obtained by conducting micro-nano mechanical tests according to the target test points or scratch test paths. The test coordinates are transformed into the coordinate system of the rock sample image. The mineral mechanical parameters are classified and mapped according to the mineral grain number to which the test point belongs, and the correspondence between the mineral grain number, the test point coordinates and the mineral mechanical parameters is established. Based on the correspondence between mineral particle number, test point coordinates and mineral mechanical parameters, a heterogeneous field of internal mechanical parameters of a single mineral particle is constructed. The heterogeneous field of mechanical parameters is then coupled with the spatial distribution of minerals, pores, fractures and mineral interfaces to generate a digital core mineral mechanical parameter field model.

2. The method for constructing a digital core mechanical parameter field according to claim 1, characterized in that, Spatial registration of the multi-scale image information to establish a unified coordinate system for rock sample images includes: The boundaries of mineral particles, pores, fractures, and mineral interfaces in images at different scales are identified, and structural feature points are extracted. Feature description information is established for the structural feature points and feature point matching is performed. Feature points with incorrect matching are eliminated and stable matching feature point pairs are retained. Calculate the coordinate transformation parameters based on the stable matching feature point pairs, and transform the coordinates of the image to be registered and the test point to the reference image coordinate system; The coordinate transformation parameters are iteratively corrected based on boundary or contour deviations until the registration error meets the preset requirements, thereby establishing the unified rock sample image coordinate system.

3. The method for constructing a digital core mechanical parameter field according to claim 1, characterized in that, Based on the registered multi-scale image information, mineral phase identification is performed, and boundary extraction and particle numbering are carried out for different mineral particles to obtain a spatial distribution model of mineral particles, including: Mineral phase identification is performed based on at least one of the following in the registered multi-scale image information: grayscale, color, elemental composition, mineral phase diagram, and texture features. Connected regions of the same type of mineral are divided into independent mineral particles. Boundary extraction is performed on each independent mineral particle, and a unique particle number is assigned. Record the particle attribute information corresponding to each particle number. The particle attribute information includes at least one of the following: area, perimeter, equivalent diameter, shape factor, major and minor axis directions, adjacent mineral type, and interface length. The spatial distribution model of the mineral particles is generated based on the particle number and the particle attribute information.

4. The method for constructing a digital core mechanical parameter field according to claim 1, characterized in that, Determining the target test point or scratch test path based on the aforementioned mineral particle spatial distribution model includes: The number and location of indentation test points inside the particles are determined based on the differences in the area of ​​the mineral particles, and the locations cover the central and edge areas of the particles; Test points or scratch test paths are arranged in the areas near mineral interfaces, pores, cracks, and contact areas of different minerals. Establish a correspondence between mineral particle numbers and target test points or scratch test paths.

5. The method for constructing a digital core mechanical parameter field according to claim 1, characterized in that, The test coordinates are transformed into the coordinate system of the rock sample image. The mineral mechanical parameters are then categorized and mapped according to the mineral grain number to which the test point belongs, establishing a correspondence between the mineral grain number, test point coordinates, and mineral mechanical parameters. This includes: The test coordinates of each test point are transformed into the coordinate system of the rock sample image to obtain the registered test point coordinates; Determine the mineral particle region and corresponding particle number to which the registered test point coordinates belong; Mineral mechanical parameters are classified and stored according to their relationship. Test point parameters belonging to the same mineral grain region are stored under the corresponding grain number. Test point parameters located in the mineral interface adjacent area are stored under the corresponding mineral interface category. Test point parameters located in the pore adjacent area or fracture adjacent area are classified and stored according to their distance from the pore or fracture. Based on the classification and storage results, establish the correspondence between mineral particle numbers, test point coordinates, local structural environment, and mineral mechanical parameters.

6. The method for constructing a digital core mechanical parameter field according to claim 1, characterized in that, After establishing the correspondence between mineral grain numbers, test point coordinates, and mineral mechanical parameters, the following is also included: For mineral particles lacking targeted indentation test points, parameter estimation or parameter field assignment is performed on the mineral particles lacking test points based on the mechanical parameters of mineral particles that have been tested under the same mineral type, similar particle size, similar mineral adjacency relationship, or similar interface environment.

7. The method for constructing a digital core mechanical parameter field according to claim 1, characterized in that, Based on the correspondence between mineral grain number, test point coordinates, and mineral mechanical parameters, a heterogeneous field of internal mechanical parameters of a single mineral grain is constructed, including: The mean, standard deviation, and coefficient of variation of the same type of mineral are calculated based on the mechanical parameters of multiple test points within the same mineral particle. The Weibull shape parameters of the mineral grains can be determined by using the discrete coefficients. Using the average value as the average level and the Weibull shape parameter as the distribution shape, the mechanical parameter values ​​of each computational unit inside the mineral particle are generated, forming a heterogeneous field of mechanical parameters inside the particle.

8. The method for constructing a digital core mechanical parameter field according to claim 1, characterized in that, The heterogeneous field of the aforementioned mechanical parameters is coupled with the spatial distribution of minerals, porosity, fractures, and mineral interfaces to generate a digital core mineral mechanical parameter field model, including: Porous and fractured regions can be assigned void, weakening, or contact properties. Weakening mechanical parameters or contact properties are assigned to the mineral interface region, and the contact properties include at least one of interface strength, interface stiffness, interface damage parameters and interface friction parameters. The heterogeneous field of internal mechanical parameters of the particles is coupled with the void properties, weakened mechanical parameters or contact properties according to the spatial position relationship to generate the digital core mineral mechanical parameter field model; wherein, the digital core mineral mechanical parameter field model includes mineral phase spatial distribution information, mineral particle number information, pore and fracture spatial distribution information, mineral interface information and mineral particle mechanical parameter spatial distribution information.

9. The method for constructing a digital core mechanical parameter field according to claim 1, characterized in that, After generating the digital core mineral mechanical parameter field model, the following is also included: The digital core mineral mechanical parameter field model is transformed into at least one of the following: finite element model, discrete element model, finite difference model, or coupled model. During the transformation process, the grid is divided according to the mineral grain boundaries, pore and fracture boundaries, and mineral interface positions. Each grid cell is then matched with the mineral type, grain number, and mechanical parameters of its location to form a digital core calculation model for rock mechanics simulation. And / or, extend the two-dimensional parametric field model into a three-dimensional parametric field model by multi-slice stacking or three-dimensional reconstruction.

10. A digital core mechanical parameter field construction system, employing the digital core mechanical parameter field construction method as described in any one of claims 1 to 9, characterized in that, include: The image acquisition module is used to acquire multi-scale image information of rock samples; The multi-scale image information characterizes mineral composition, mineral spatial distribution, porosity, fractures, and mineral interfaces; The spatial registration module is used to perform spatial registration on the multi-scale image information and establish a unified coordinate system for rock sample images. The particle identification and numbering module is used to identify mineral phases based on the registered multi-scale image information, extract the boundaries of different mineral particles and number the particles to obtain a spatial distribution model of mineral particles. The targeted testing module is used to determine the targeted test points or scratch test paths according to the spatial distribution model of the mineral particles, and to establish a correspondence between the targeted test points or scratch test paths and specific mineral particles, mineral interfaces, pore adjacent areas or fracture adjacent areas. The test parameter acquisition module is used to acquire the mineral mechanical parameters and test coordinates of each test point obtained by conducting micro-nano mechanical tests according to the target test point or scratch test path. The parameter mapping module is used to convert the test coordinates to the rock sample image coordinate system, classify and map the mineral mechanical parameters according to the mineral grain number to which the test point belongs, and establish the correspondence between the mineral grain number, the test point coordinates and the mineral mechanical parameters. The parameter field model generation module is used to construct a heterogeneous field of internal mechanical parameters of a single mineral particle based on the correspondence between mineral particle number, test point coordinates and mineral mechanical parameters, and couple the heterogeneous field of mechanical parameters with mineral spatial distribution, pores, fractures and mineral interfaces to generate a digital core mineral mechanical parameter field model.

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