Method and device for modeling microstructure of polycrystalline rocks

Abstract

The embodiments of this specification provide a method and device for modeling the mesostructure of polycrystalline rocks, wherein the method for modeling the mesostructure of polycrystalline rocks includes: determining a model region, establishing boundary walls and initial particles based on the model region and attribute parameters; converting initial particles into block units, and classifying the block units to determine target block units; performing particle filling based on the target block units to determine initial filling particles, wherein the particle filling includes filling based on multiple particles of different sizes; determining contact model parameters, and determining a rock model based on the contact model parameters and the initial filling particles. By generating different rigid blocks to replace multiple mineral particles, then deleting the rigid blocks and memorizing the regional positions, different-sized particles with parameter attributes corresponding to each mineral component are filled in different regions, thereby more accurately simulating the heterogeneous structure of the rock.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of rock mechanics technology, and in particular to a method for modeling the mesostructure of polycrystalline rocks. Background Art

[0002] The development and spatial utilization of deep underground resources are closely related to complex underground engineering problems. In order to accurately assess the stability, bearing capacity and engineering risks of rocks, it is necessary to have a deep understanding of the internal structure and mechanical properties of rocks. With the development of computer technology and numerical simulation methods, researchers have begun to explore various rock micro-modeling methods. Numerical models are constantly being improved to reproduce the internal micro-structure of rock materials in a more realistic way, providing a scientific basis for rock engineering design, geological disaster prediction and resource exploration and mining.

[0003] In previous studies, rock mesoscopic modeling is generally based on granular flow codes, which characterize the heterogeneous structure of rocks by grouping particles, grouping all basic elements to represent different types of minerals, and then calibrating them using corresponding microscopic parameters; or using the Voronoi method to simulate the numerical model of rock polycrystalline division. Constructing a numerical model that can clearly reproduce the real microstructure of polycrystalline rocks has become the main step in the numerical study of granular rocks. However, the above methods still have limitations in simulating the real three-dimensional geometry and mechanical properties of polycrystalline rocks: the previous DEM-based 3DGBM can only complete the division of mineral particles relative to geometric size, and the mineral particles are not further divided to represent different mineral components, such as feldspar, quartz and biotite, which leads to the inability to accurately reproduce the internal structure of crystalline rocks; at the same time, studies have shown that the polycrystalline numerical model developed by the Voronoi method currently used has defects, such as the lack of ability to accurately characterize various types of mineral grains in rocks in three-dimensional space and solve intercrystalline microcracks. Therefore, a better solution is urgently needed. Summary of the invention

[0004] In view of this, the embodiments of this specification provide a method for modeling the mesostructure of polycrystalline rocks. One or more embodiments of this specification also relate to a device for modeling the mesostructure of polycrystalline rocks, a computing device, a computer-readable storage medium, and a computer program to solve the technical defects existing in the prior art.

[0005] According to a first aspect of an embodiment of this specification, a method for modeling a polycrystalline rock mesostructure is provided, comprising:

[0006] Determine the model area, and establish boundary walls and initial particles based on the model area and attribute parameters;

[0007] Convert initial particles into block units, classify the block units, and determine target block units;

[0008] Performing particle filling based on the target block unit to determine initial filling particles; wherein the particle filling includes filling based on multiple particles of different sizes;

[0009] The contact model parameters are determined, and the rock model is determined based on the contact model parameters and the initial filling particles.

[0010] In a possible implementation, determining a model region, and establishing a boundary wall and initial particles based on the model region and attribute parameters include:

[0011] Create a model area based on the set area, and establish a boundary wall based on the model area;

[0012] Conduct mineral composition analysis on rock samples and determine the analysis results;

[0013] Generate initial particles within the boundary wall based on analysis results and property parameters.

[0014] In a possible implementation, initial particles are generated within the boundary wall based on analysis results and property parameters, including:

[0015] Determine quantity parameters, size parameters and distribution parameters based on the analysis results;

[0016] determining boundary wall parameters and contact parameters based on the attribute parameters;

[0017] Initial particles are generated within the boundary wall based on the number parameter, the size parameter, the distribution parameter, the boundary wall parameter, and the contact parameter.

[0018] In a possible implementation, converting initial particles into block units, classifying the block units, and determining target block units includes:

[0019] Based on the initial particles, block units are obtained by mosaic technology;

[0020] A category identifier is determined, the block units are grouped based on the category identifier, and a target block unit is determined.

[0021] In a possible implementation, performing particle filling based on a target block unit and determining initial filling particles includes:

[0022] Delete the target block unit and determine the filling space;

[0023] Filling a plurality of particles in the filling space; wherein the plurality of particles have different sizes;

[0024] A filling parameter is determined, and initial filling particles are determined based on the filling parameter and the plurality of particles.

[0025] In a possible implementation, determining a rock model based on contact model parameters and initial filling particles includes:

[0026] Setting parameters of initial filling particles based on contact model parameters to determine target filling particles;

[0027] generating an initial rock model based on target filling particles;

[0028] The suspended particles in the initial rock model are identified and deleted to obtain the rock model.

[0029] In one possible implementation, the contact model parameters include stiffness, strength, friction angle, and friction coefficient.

[0030] According to a second aspect of an embodiment of this specification, a device for modeling a polycrystalline rock microstructure is provided, comprising:

[0031] A particle establishment module is configured to determine a model region, and to establish boundary walls and initial particles based on the model region and attribute parameters;

[0032] A unit classification module is configured to convert initial particles into block units, classify the block units, and determine target block units;

[0033] A particle filling module is configured to perform particle filling based on a target block unit and determine initial filling particles; wherein the particle filling includes filling based on a plurality of particles of different sizes;

[0034] The model building module is configured to determine contact model parameters, and determine a rock model based on the contact model parameters and initial filling particles.

[0035] According to a third aspect of an embodiment of this specification, a computing device is provided, including:

[0036] Memory and processor;

[0037] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the above-mentioned polycrystalline rock mesostructure modeling method are implemented.

[0038] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the above-mentioned polycrystalline rock mesostructure modeling method are implemented.

[0039] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned method for modeling the mesostructure of polycrystalline rocks.

[0040] The embodiments of this specification provide a method and device for modeling the mesostructure of polycrystalline rocks, wherein the method for modeling the mesostructure of polycrystalline rocks includes: determining a model region, establishing boundary walls and initial particles based on the model region and attribute parameters; converting initial particles into block units, and classifying the block units to determine target block units; performing particle filling based on the target block units to determine initial filling particles, wherein the particle filling includes filling based on multiple particles of different sizes; determining contact model parameters, and determining a rock model based on the contact model parameters and the initial filling particles. By generating different rigid blocks to replace multiple mineral particles, then deleting the rigid blocks and memorizing the regional positions, different-sized particles with parameter attributes corresponding to each mineral component are filled in different regions, thereby more accurately simulating the heterogeneous structure of the rock. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flow chart of a method for modeling a polycrystalline rock mesostructure provided by an embodiment of this specification;

[0042] Figure 2 It is a schematic diagram of particle generation of a polycrystalline rock mesostructure modeling method provided by an embodiment of this specification;

[0043] Figure 3 It is a block unit schematic diagram of a polycrystalline rock mesostructure modeling method provided by an embodiment of this specification;

[0044] Figure 4 It is a grouping schematic diagram of a method for modeling a polycrystalline rock mesostructure provided by an embodiment of this specification;

[0045] Figure 5 It is a schematic diagram of a rock model of a method for modeling a polycrystalline rock mesostructure provided by an embodiment of this specification;

[0046] Figure 6 It is a structural schematic diagram of a polycrystalline rock mesostructure modeling device provided by an embodiment of this specification;

[0047] Figure 7 It is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0048] Many specific details are described in the following description to facilitate a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of this specification, so this specification is not limited to the specific implementation disclosed below.

[0049] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0050] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0051] In this specification, a method for modeling the mesostructure of polycrystalline rocks is provided. This specification also relates to an apparatus for modeling the mesostructure of polycrystalline rocks, a computing device, and a computer-readable storage medium, which are described in detail one by one in the following embodiments.

[0052] See also Figure 1 , Figure 1 A flow chart of a method for modeling the mesostructure of polycrystalline rocks provided according to an embodiment of the present specification is shown, which specifically includes the following steps.

[0053] Step 101: Determine the model region, and establish boundary walls and initial particles based on the model region and attribute parameters.

[0054] In practical applications, PFC software is used to specify the model area, establish the boundary wall of the rock sample, generate initial grains with larger diameters based on the mineral composition analysis, and assign attribute parameters to the wall and particles.

[0055] Specifically, the mineral composition of the rock sample under study is analyzed to determine the main mineral types and their relative contents that may exist in the rock sample; a model region is created, and a boundary wall is added to the model to define the boundary of the rock sample, ensuring that the geometry of the boundary wall matches the geometry of the model region and maintains a certain distance from the boundary of the rock sample to avoid excessive impact of the particles on the boundary wall; initial particles with larger diameters are generated in the model region, which represent the main mineral particles in the rock sample. According to the information given in the mineral composition analysis, the number, size and distribution of the generated particles are adjusted to simulate the actual rock sample as accurately as possible; appropriate attribute parameters are assigned to the boundary wall and the initial particles to define their physical properties and mechanical behaviors. For the boundary wall, parameters such as the size, direction, and angle of the sample are set; for the contact model, appropriate parameters such as the elastic modulus, stiffness ratio and friction coefficient are set; for the initial particles, according to the mineral type and particle characteristics they represent, parameters such as the minimum particle size, maximum particle size, volume fraction, density and damping coefficient are set so that they behave similarly to actual rocks in the simulation.

[0056] In one possible implementation, a model area is determined, and boundary walls and initial particles are established based on the model area and attribute parameters, including: creating a model area based on a set area, and establishing a boundary wall based on the model area; performing a mineral component analysis on a rock sample and determining the analysis results; and generating initial particles within the boundary wall based on the analysis results and attribute parameters.

[0057] For example, it is necessary to measure the physical and mechanical properties of the rock samples under study, including obtaining parameters such as porosity and elastic modulus. Secondly, the grain sizes of various mineral components and their proportions in the rock samples are measured; combined with the PFC simulation software, the domain size and boundary conditions are defined, and the wall generate command is used to generate six walls, and the ball distribute command is used to place grain seeds of different sizes inside the rock sample model; the porosity, radius range, density, damping coefficient, friction coefficient and other properties are assigned to the particles, and the contact mechanical parameters between particles and between particles and walls, including elastic modulus, stiffness ratio and friction coefficient, are assigned, and the contact force is solved so that each particle reaches the initial equilibrium state.

[0058] Step 102: converting the initial particles into block units, classifying the block units, and determining target block units.

[0059] In practical applications, the Voronoi tessellation technique is used to convert the initial particles into block units. The block units are classified into several different types of units by assigning values ​​to the initial particle parameters and marked with corresponding colors to represent different minerals, such as feldspar, quartz and biotite. Then, the Fish language in the software is used to write functions to group the rigid blocks and memorize the geometric positions.

[0060] Specifically, the Voronoi tessellation technique is used to automatically generate block units corresponding to each mineral particle through the software, and the generated block units are optimized and adjusted to ensure that they match the position and shape of the original particles as much as possible. According to the initial particle parameter information, the generated block units are classified. The block units can be classified into different types such as feldspar, quartz and biotite according to different parameter values; Fish language is written to achieve the required functions: traverse all rigid blocks, group them according to their attribute information, remember the geometric position of each rigid block, and divide the spatial area group.

[0061] In a possible implementation, initial particles are generated in the boundary wall based on the analysis results and the attribute parameters, including: determining quantity parameters, size parameters and distribution parameters based on the analysis results; determining boundary wall parameters and contact parameters based on the attribute parameters; and generating initial particles in the boundary wall based on the quantity parameters, size parameters, distribution parameters, boundary wall parameters and contact parameters.

[0062] For example, the rblock construct from-balls command is used to construct a rigid block based on the sphere. The initial particles created by the ball distribute command in step 101 provide seed positions and weights for the Voronoi structure. The initial particles will not be circulated and will be deleted after running.

[0063] In a possible implementation, initial particles are converted into block units, and the block units are classified to determine target block units, including: obtaining block units based on the initial particles through a mosaic technique; determining a category identifier, grouping the block units based on the category identifier, and determining the target block unit.

[0064] For example, a function is written in Fish language, a loop is used to iterate over each rigid block rb(rblock) in rblock.list, the ID of rblock is stored in idRblock, a command is executed to export the geometric information of rblock to the specified group name, and the function is called; a new Fish function is defined to prepare for filling particles in the simulation. The function is represented as a single value of the geometric count rigid block. Mathematical operations are performed in combination with mineral analysis to determine the groups for the created different rigid block groups to represent the main mineral components in the rock sample, and then different group names are specified to fill their respective particles.

[0065] Step 103: performing particle filling based on the target block unit and determining initial filling particles; wherein the particle filling includes filling based on multiple particles of different sizes.

[0066] In practical applications, the rigid blocks are deleted and filled with particles of different sizes: after all blocks are deleted, particles of different sizes are filled in each acquired spatial region group and their attribute parameters are assigned.

[0067] Specifically, the rigid blocks are deleted to obtain the spatial region groups. For each spatial region group, particles of different size ranges are filled in it. The geometric dimensions of the filled particles are determined by mineral component analysis to ensure that the sizes of particles filled in different spatial region groups are different. For particles of each size, attribute parameters are assigned as needed, and material properties, density, elastic parameters or other related parameters are set. The layout and distribution of the particles are adjusted to ensure that they fill the entire spatial region group and are in harmony with the surrounding environment.

[0068] In a possible implementation, performing particle filling based on a target block unit and determining initial filling particles include: deleting the target block unit and determining a filling space; filling a plurality of particles in the filling space; wherein the sizes of the plurality of particles are different; and determining a filling parameter, and determining the initial filling particle based on the filling parameter and the plurality of particles.

[0069] For example, use the rblock.delete command to delete rigid blocks; fill different sized particles in different groups according to the mineral component analysis, set the properties of each group of particles, including porosity, radius range, density and damping coefficient, use a loop to generate particles in sequence, import geometric information into the wall, set the model state to calm, and delete all particles except the currently generated particles; characterize the distribution of different minerals by calling the ball group color attribute; execute the model solution and save the results, and delete the imported wall. Use the ball distribute command to distribute the spheres within a specific range in the geometric space, set the porosity and radius, and use the ball attribute command to set the density and damping parameters for the spheres.

[0070] Step 104: Determine contact model parameters, and determine a rock model based on the contact model parameters and initial filling particles.

[0071] In practical applications, the corresponding contact model parameters are assigned to complete the establishment of the rock polycrystalline structure model.

[0072] Specifically, according to the simulation needs, appropriate contact model parameters such as stiffness, friction coefficient, etc. are set, and suspended particles in the simulation environment are checked and deleted, which may affect the contact between particles.

[0073] In one possible implementation, a rock model is determined based on contact model parameters and initial filling particles, including: setting parameters of the initial filling particles based on the contact model parameters to determine target filling particles; generating an initial rock model based on the target filling particles; identifying suspended particles in the initial rock model, and deleting the suspended particles to obtain a rock model.

[0074] For example, first define the material contact type and set the simulation parameters. The default contact condition is linear contact, and set the material deformation performance parameters, such as elastic modulus, stiffness ratio, etc.; for the "ball-ball" type of contact, a linear parallel bonding contact model is established, and the contact gap is set to 2e -3 , specify the deformation contact parameters of this type, as well as the properties of the bonding contact method, set the friction coefficient, and apply it to the "ball-ball" and "ball-wall" types of contact respectively; perform mechanical solution on the model and adjust the time step, adjust the ratio of the mechanical time step, generally 1e -4 , and automatically set the mechanical time step, execute the model solution operation to reach the equilibrium state; define the Fish function ball_delete: delete the redundant particles in the simulation: loop through each sphere and store it in a group named 'floaters1'. For each sphere, obtain its position coordinates in three-dimensional space and reallocate it to the 'floaters1' group according to certain conditions, including the situation where the sphere position is outside the specified range; after the loop ends, execute the command to delete all particles belonging to the 'floaters1' group; define the function identify_floaters to identify and delete suspended particles in the simulation: loop through each sphere and store it in a group named 'floaters2'. For each particle, obtain the contact situation between it and other particles and determine whether it is a suspended particle according to certain conditions, which can be that the contact points are less than or equal to 2; after the loop ends, execute the command to delete all spheres belonging to the 'floaters2' group.

[0075] The embodiments of this specification provide a method and device for modeling the mesostructure of polycrystalline rocks, wherein the method for modeling the mesostructure of polycrystalline rocks includes: determining a model region, establishing boundary walls and initial particles based on the model region and attribute parameters; converting initial particles into block units, and classifying the block units to determine target block units; filling particles based on the target block units to determine initial filling particles; determining contact model parameters, and determining a rock model based on the contact model parameters and initial filling particles. By generating different rigid blocks to replace multiple mineral particles, then deleting the rigid blocks and memorizing the regional positions, different-sized particles with parameter attributes corresponding to each mineral component are filled in different regions, thereby more accurately simulating the heterogeneous structure of the rock.

[0076] In one embodiment, a rock sample meeting the following parameter conditions is constructed and a mesostructure model is performed:

[0077] Rock sample size: length 0.05m, width 0.05m, height 0.1m;

[0078] Initial particle parameters: porosity 0.5; radius range 0.004m - 0.012m; density 2500kg / m 3 ;

[0079] Damping coefficient: 0.7; friction coefficient: 0.3

[0080] Wall properties: friction coefficient 0.0;

[0081] Contact model related parameters: elastic modulus 1e 7 Pa; stiffness ratio 1.0; friction coefficient 0.577;

[0082] Name each rigid block: "geo_number";

[0083] Group names of different rigid blocks: Group different rigid blocks and use "groupName_number" to simply define them to represent the main mineral components;

[0084] Filling particle parameters: porosity 0.4; radius ranges are 0.0009m- 0.0012m, 0.0015m-0.0017m, 0.0018m-0.002m, 0.002m-0.0023m; density 2500kg / m 3 ; Damping coefficient 0.7;

[0085] Furthermore, the operation steps of the rock polycrystalline structure modeling based on spatial region division and multi-size particle filling of the present invention are as follows:

[0086] (1) Create boundary walls and generate different mineral grains according to the mineral composition, such as Figure 2 As shown, Figure 2 It refers to the different mineral particles that form polycrystalline rocks based on the analysis of mineral composition;

[0087] (2) Use the rblock construct from-balls command to generate a rigid block, such as Figure 3 As shown, Figure 3 It means that the mineral grains are converted into rigid block units using Voronoi tessellation technique, and the polyhedra fill the entire domain without gaps and without repeated intersecting tessellated polyhedra;

[0088] (3) Write a Fish function to group different block units, such as Figure 4 As shown, Figure 4It represents the process of grouping block units (grouped by ID) and filling corresponding particles of different sizes into geometric units;

[0089] (4) Delete the block and fill the obtained geometric unit with particles, such as Figure 4 As shown;

[0090] (5) Assign contact model parameters and delete specific types of particles to complete polycrystalline rock modeling, such as Figure 5 As shown, Figure 5 Represents the final three-dimensional heterogeneous rock model.

[0091] Among them, label 1 is the boundary wall; label 2 is quartz mineral; label 3 is feldspar mineral; label 4 is mica mineral; label 5 is other minerals; label 6 is a rigid block unit.

[0092] Compared with the traditional 3D GBM or Voronoi partitioning method for polycrystalline structure, the embodiments of this specification fill the gap that the two cannot accurately simulate the distribution of mineral components inside the rock, and improve the Voronoi partitioning method for polycrystalline structure, grouping block units instead of mineral components, filling geometric units with particles of different sizes, and the modeling process conforms to the physical process of rock crystal structure formation, reproducing the heterogeneous structure of the rock mass in a more accurate way.

[0093] Corresponding to the above method embodiment, this specification also provides a polycrystalline rock microstructure modeling device embodiment, Figure 6 FIG. 1 is a schematic diagram showing a structure of a polycrystalline rock microstructure modeling device provided by an embodiment of the present specification. Figure 6 As shown, the device comprises:

[0094] The particle establishment module 601 is configured to determine a model region, and to establish boundary walls and initial particles based on the model region and attribute parameters;

[0095] The unit classification module 602 is configured to convert the initial particles into block units, classify the block units, and determine the target block units;

[0096] The particle filling module 603 is configured to perform particle filling based on the target block unit and determine initial filling particles; wherein the particle filling includes filling based on multiple particles of different sizes;

[0097] The model building module 604 is configured to determine contact model parameters, and determine a rock model based on the contact model parameters and initial filling particles.

[0098] In a possible implementation, the particle creation module 601 is further configured to:

[0099] Create a model area based on the set area, and establish a boundary wall based on the model area;

[0100] Conduct mineral composition analysis on rock samples and determine the analysis results;

[0101] Generate initial particles within the boundary wall based on analysis results and property parameters.

[0102] In a possible implementation, the particle creation module 601 is further configured to:

[0103] Determine quantity parameters, size parameters and distribution parameters based on the analysis results;

[0104] determining boundary wall parameters and contact parameters based on the attribute parameters;

[0105] Initial particles are generated within the boundary wall based on the number parameter, the size parameter, the distribution parameter, the boundary wall parameter, and the contact parameter.

[0106] In a possible implementation, the unit classification module 602 is further configured to:

[0107] Based on the initial particles, block units are obtained by mosaic technology;

[0108] A category identifier is determined, the block units are grouped based on the category identifier, and a target block unit is determined.

[0109] In a possible implementation, the particle filling module 603 is further configured to:

[0110] Delete the target block unit and determine the filling space;

[0111] Filling a plurality of particles in the filling space;

[0112] A filling parameter is determined, and initial filling particles are determined based on the filling parameter and the plurality of particles.

[0113] In a possible implementation, the model building module 604 is further configured to:

[0114] Setting parameters of initial filling particles based on contact model parameters to determine target filling particles;

[0115] generating an initial rock model based on target filling particles;

[0116] The suspended particles in the initial rock model are identified and deleted to obtain the rock model.

[0117] In a possible implementation, the model building module 604 is further configured to:

[0118] Contact model parameters include stiffness, strength, friction angle, and friction coefficient.

[0119] The embodiments of this specification provide a method and device for modeling the mesostructure of polycrystalline rocks, wherein the device for modeling the mesostructure of polycrystalline rocks includes: determining a model region, and establishing boundary walls and initial particles based on the model region and attribute parameters; converting the initial particles into block units, and classifying the block units to determine target block units; performing particle filling based on the target block units to determine initial filling particles, wherein the particle filling includes filling based on multiple particles of different sizes; determining contact model parameters, and determining a rock model based on the contact model parameters and the initial filling particles. By generating different rigid blocks to replace multiple mineral particles, then deleting the rigid blocks and memorizing the regional positions, different-sized particles with parameter attributes corresponding to each mineral component are filled in different regions, thereby more accurately simulating the heterogeneous structure of the rock.

[0120] The above is a schematic scheme of a polycrystalline rock mesostructure modeling device of this embodiment. It should be noted that the technical scheme of the polycrystalline rock mesostructure modeling device and the technical scheme of the polycrystalline rock mesostructure modeling method described above belong to the same concept, and the details not described in detail in the technical scheme of the polycrystalline rock mesostructure modeling device can be found in the description of the technical scheme of the polycrystalline rock mesostructure modeling method described above.

[0121] Figure 7 The block diagram of a computing device 700 according to an embodiment of the present specification is shown. The components of the computing device 700 include but are not limited to a memory 710 and a processor 720. The processor 720 is connected to the memory 710 via a bus 730, and the database 750 is used to store data.

[0122] The computing device 700 also includes an access device 740 that enables the computing device 700 to communicate via one or more networks 760. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 740 may include one or more of any type of network interface (e.g., a network interface card (NIC)) of wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0123] In one embodiment of the present specification, the above components of the computing device 700 and Figure 7 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 7 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0124] The computing device 700 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 700 may also be a mobile or stationary server.

[0125] The processor 720 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned polycrystalline rock mesostructure modeling method. The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the above-mentioned polycrystalline rock mesostructure modeling method belong to the same concept. For details not described in detail in the technical scheme of the computing device, please refer to the description of the technical scheme of the above-mentioned polycrystalline rock mesostructure modeling method.

[0126] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned polycrystalline rock mesostructure modeling method.

[0127] The above is a schematic scheme of a computer-readable storage medium of this embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the polycrystalline rock mesostructure modeling method described above belong to the same concept, and the details not described in detail in the technical scheme of the storage medium can be referred to the description of the technical scheme of the polycrystalline rock mesostructure modeling method described above.

[0128] An embodiment of the present specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned polycrystalline rock mesostructure modeling method.

[0129] The above is a schematic scheme of a computer program of this embodiment. It should be noted that the technical scheme of the computer program and the technical scheme of the polycrystalline rock mesostructure modeling method described above are of the same concept, and the details not described in detail in the technical scheme of the computer program can be found in the description of the technical scheme of the polycrystalline rock mesostructure modeling method described above.

[0130] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0131] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0132] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0133] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0134] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A method for modeling the mesostructure of polycrystalline rocks, characterized in that: include: Determine a model region, and establish boundary walls and initial particles based on the model region and attribute parameters; Converting the initial particles into block units, classifying the block units, and determining target block units; Performing particle filling based on the target block unit to determine initial filling particles; wherein the particle filling includes filling based on a plurality of particles of different sizes; determining contact model parameters, and determining a rock model based on the contact model parameters and the initial filling particles; The converting the initial particles into block units, classifying the block units, and determining target block units includes: Based on the initial particles, a block unit is obtained by a mosaic technique; Determine a category identifier, group the block units based on the category identifier, and determine a target block unit; Based on the initial particles, a block unit is obtained by a mosaic technique; Determine a category identifier, group the block units based on the category identifier, and determine a target block unit; The converting the initial particles into block units, classifying the block units, and determining target block units also includes: Loop through each rigid block in the rigid block list, store the ID of the rigid block, save the geometric information of the rigid block, determine the rigid block grouping, perform mathematical operations in combination with mineral analysis to determine the main mineral components of the rigid block grouping, and assign different group names to the rigid block grouping; The performing particle filling based on the target block unit and determining initial filling particles includes: Deleting the target block unit to determine the filling space; Filling a plurality of particles in the filling space; wherein the plurality of particles have different sizes; determining a filling parameter, and determining initial filling particles based on the filling parameter and the plurality of particles; The performing particle filling based on the target block unit and determining initial filling particles further includes: Deleting the rigid block to obtain a space region group, for each of the space region groups, filling particles of different size ranges therein, determining the geometric size of the particles by mineral component analysis, and ensuring that the sizes of particles filled in different space region groups are different; For the particles of each size, attribute parameters are assigned to the particles; wherein the attribute parameters include material properties, density and elasticity parameters.

2. The method according to claim 1, characterized in that The step of determining a model region and establishing a boundary wall and initial particles based on the model region and attribute parameters includes: creating a model area based on a set area, and establishing a boundary wall based on the model area; Conduct mineral composition analysis on rock samples and determine the analysis results; Initial particles are generated within the boundary wall based on the analysis results and property parameters.

3. The method according to claim 2, characterized in that The generating initial particles in the boundary wall based on the analysis results and the property parameters comprises: determining a quantity parameter, a size parameter, and a distribution parameter based on the analysis results; determining boundary wall parameters and contact parameters based on the property parameters; Initial particles are generated within the boundary wall based on the number parameter, the size parameter, the distribution parameter, the boundary wall parameter, and the contact parameter.

4. The method according to claim 1, characterized in that: The step of determining a rock model based on the contact model parameters and the initial filling particles comprises: Setting parameters of the initial filling particles based on the contact model parameters to determine target filling particles; generating an initial rock model based on the target filling particles; The suspended particles in the initial rock model are identified and deleted to obtain a rock model.

5. The method according to claim 1, characterized in that The contact model parameters include stiffness, strength, friction angle and friction coefficient.

6. A device for modeling the microstructure of polycrystalline rocks, characterized in that: include: A particle establishment module is configured to determine a model area, and to establish boundary walls and initial particles based on the model area and attribute parameters; A unit classification module is configured to convert the initial particles into block units, classify the block units, and determine target block units; A particle filling module is configured to perform particle filling based on the target block unit and determine initial filling particles; wherein the particle filling includes filling based on a plurality of particles of different sizes; A model building module is configured to determine contact model parameters, and determine a rock model based on the contact model parameters and the initial filling particles; The converting the initial particles into block units, classifying the block units, and determining target block units includes: Based on the initial particles, a block unit is obtained by a mosaic technique; Determine a category identifier, group the block units based on the category identifier, and determine a target block unit; Based on the initial particles, a block unit is obtained by a mosaic technique; Determine a category identifier, group the block units based on the category identifier, and determine a target block unit; The converting the initial particles into block units, classifying the block units, and determining target block units also includes: Loop through each rigid block in the rigid block list, store the ID of the rigid block, save the geometric information of the rigid block, determine the rigid block grouping, perform mathematical operations in combination with mineral analysis to determine the main mineral components of the rigid block grouping, and assign different group names to the rigid block grouping; The performing particle filling based on the target block unit and determining initial filling particles includes: Deleting the target block unit to determine the filling space; Filling a plurality of particles in the filling space; wherein the plurality of particles have different sizes; determining a filling parameter, and determining initial filling particles based on the filling parameter and the plurality of particles; The performing particle filling based on the target block unit and determining initial filling particles further includes: Deleting the rigid block to obtain a space region group, for each of the space region groups, filling particles of different size ranges therein, determining the geometric size of the particles by mineral component analysis, and ensuring that the sizes of particles filled in different space region groups are different; For the particles of each size, attribute parameters are assigned to the particles; wherein the attribute parameters include material properties, density and elasticity parameters.

7. A computing device, characterized in that include: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the polycrystalline rock mesostructure modeling method described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for modeling the mesostructure of polycrystalline rocks as described in any one of claims 1 to 5.

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