Method for predicting microscopic fractures in granite inner reservoir, electronic device and medium

Abstract

The application relates to the technical field of oil and natural gas, and discloses a method for predicting fine microscopic cracks in a granite inner reservoir, an electronic device and a medium, which comprises the following steps: obtaining initial rock fabric data corresponding to the granite, wherein the initial rock fabric data comprises mineral component information, structure information, tectonic information and mineral particle size information; constructing an initial rock fabric numerical model comprising a plurality of grain clusters and a plurality of sub-grains distributed in the grain clusters based on the initial rock fabric data; correcting the contact mechanical parameters between the grain clusters and the contact mechanical parameters between the sub-grains in the initial rock fabric numerical model based on rock mechanics parameters of the granite, so as to obtain a target rock fabric numerical model; applying axial strain to the target rock fabric numerical model until a preset damage threshold is reached, simulating crack characteristic data of the target rock fabric numerical model, and determining fine microscopic cracks in the granite inner reservoir, so as to realize accurate prediction of the fine microscopic cracks in the granite inner reservoir.

Description

Technical Field

[0001] This application relates to the technical field of oil and gas, specifically to a method, electronic device, and medium for predicting microscopic fractures in granite internal reservoirs. Background Technology

[0002] Microfractures within granite reservoirs refer to natural fracture systems ranging from millimeters to micrometers that develop within the granite body and are controlled by the rock's mineral composition. These microfractures are the main spaces for oil and gas accumulation and seepage within granite reservoirs.

[0003] While existing technologies employ a variety of methods, including field outcrops, core description, well logging identification, seismic identification, and tectonic stress field analysis, to predict micro-fractures in granite-containing reservoirs, the extremely complex distribution of micro-fractures and the unclear development mechanism of these fractures make it difficult to accurately predict them. This severely restricts oil and gas exploration in granite-containing reservoirs. Summary of the Invention

[0004] The purpose of this application is to provide a method, electronic device, and medium for predicting microscopic fractures in granite internal reservoirs, in order to solve the technical defects of existing technologies that make it difficult to accurately predict microscopic fractures in granite internal reservoirs, thereby severely restricting the oil and gas exploration of granite internal reservoirs.

[0005] To achieve the above objectives, the first aspect of this application provides a method for predicting fine-grained fractures in granite internal reservoirs, the method comprising: Obtain the initial rock texture data corresponding to the granite sample, which includes mineral composition information, structural information, texture information and mineral grain size information; Based on the initial rock texture data, an initial rock texture numerical model is constructed, which includes multiple grain clusters and multiple subgrains distributed within the grain clusters. Based on the rock mechanical parameters of granite samples, the contact mechanical parameters between grain clusters and between subgrains in the initial rock fabric numerical model were corrected to obtain the target rock fabric numerical model. Axial strain is applied to the numerical model of the target rock fabric until the axial strain reaches the preset failure threshold, and the crack characteristic data of the numerical model of the target rock fabric are obtained by simulation. Fine-grained fractures in granite internal reservoirs were identified based on fracture characteristic data.

[0006] In this embodiment of the application, an initial rock texture numerical model is constructed based on the initial rock texture data, including: Based on the initial rock texture data, multiple grain seeds are generated to fill the preset space, resulting in a grain seed distribution model. Based on the grain seed distribution model, multiple subgrains are generated to fill the preset space, resulting in the grain seed-subgrain distribution model. Based on the component information, location information and grain size information of the grain seed, the sub-grains associated with each grain seed in the grain seed-subgrain distribution model are determined, and a grain cluster is generated for each grain seed and its associated sub-grains. Based on all the grain clusters, a numerical model of the grain cluster structure is obtained; Based on the numerical model of grain cluster structure, the subgrain pairs in phase contact within each grain cluster and the subgrain pairs in phase contact between each grain cluster are identified. For each subgrain, a parallel bonding model of the subgrain pair is constructed based on the contact mechanical parameters within the subgrain pair to obtain the initial numerical model of the rock fabric.

[0007] In this embodiment, based on the component information, location information, and grain size information of the grain seed, the subgrains associated with each grain seed in the grain seed-subgrain distribution model are determined, and a grain cluster is generated for each grain seed and its associated subgrain; based on all the grain clusters, a numerical model of the grain cluster structure is obtained, including: For each grain seed, based on the location information and grain size information, the spatial envelope range of the grain seed is constructed to obtain the grain spatial domain; Based on the grain spatial domain and the grain seed-subgrain distribution model, the spatial domain to which the center point of the subgrain falls is determined, and the subgrains associated with each grain seed in the grain seed-subgrain distribution model are identified. Based on the composition information of the subgrains and grain seeds associated with each grain seed, the subgrains are assigned corresponding mineral attribute tags to obtain a set of subgrains. Based on the subgrain set, all grain clusters are generated to obtain a numerical model of the grain cluster structure.

[0008] In this embodiment, the parallel bond model includes a tensile strength model and a shear strength model.

[0009] In this embodiment of the application, based on the rock mechanical parameters of the granite sample, the contact mechanical parameters between grain clusters and between subgrains in the initial rock texture numerical model are corrected to obtain the target rock texture numerical model, including: Uniaxial mechanical tests were conducted on granite samples to obtain the rock mechanical parameters of the granite samples; The difference between the rock mechanical parameters and the contact mechanical parameters between the grain clusters is taken as the first mechanical parameter difference. The difference between the rock mechanical parameters and the contact mechanical parameters between subgrains is taken as the second mechanical parameter difference. Based on the difference between the first and second mechanical parameters, the contact mechanical parameters between grain clusters and between subgrains are corrected to obtain the corrected contact mechanical parameters between grain clusters and between subgrains. When the difference between the first mechanical parameter corresponding to the corrected contact mechanical parameters between grain clusters and the difference between the second mechanical parameters corresponding to the corrected contact mechanical parameters between subgrains are both less than the preset difference threshold, a numerical model of the target rock texture is obtained based on the corrected contact mechanical parameters between grain clusters and the corrected contact mechanical parameters between subgrains.

[0010] In the embodiments of this application, the rock mechanical parameters of the granite sample include Young's modulus, Poisson's ratio, uniaxial compressive strength, and internal friction angle.

[0011] In this embodiment of the application, axial strain is applied to the numerical model of the target rock fabric until the axial strain reaches a preset failure threshold, and the crack characteristic data of the numerical model of the target rock fabric are simulated, including: By using mineral composition information, mineral grain size information, and crystal morphology in the target rock fabric numerical model as variables, the target rock fabric numerical model is adjusted to obtain the adjusted target rock fabric numerical model. Axial strain is applied to the adjusted target rock fabric numerical model until the axial strain reaches the preset failure threshold, and multiple sets of crack characteristic data of the adjusted target rock fabric numerical model are obtained through simulation.

[0012] In this embodiment of the application, the prediction method further includes: Based on X-ray diffraction analysis and polarized light microscopy identification, the mineral composition information of the granite sample was obtained; Based on hand specimen identification and polarized light microscopy, structural information, texture information, and mineral grain size information of the granite sample were obtained.

[0013] A second aspect of this application provides an electronic device, comprising: The memory is configured to store instructions; The processor is configured to retrieve instructions from memory and, when executing the instructions, to implement a method for predicting microscopic fractures in granite internal reservoirs as provided in the first aspect.

[0014] A third aspect of this application provides a machine-readable storage medium storing instructions for causing a machine to perform a method for predicting fine-scale fractures in granite internal reservoirs as provided in the first aspect.

[0015] The above technical solution obtains initial rock fabric data by quantitatively characterizing the mineral composition, structure, texture, and mineral grain size of granite samples. Then, it constructs an initial numerical model of the rock fabric including grain clusters and subgrains. Next, based on rock mechanics parameters, it corrects the contact mechanics parameters between grain clusters and subgrains in the model to obtain a target rock fabric numerical model. Finally, it applies axial strain to the target rock fabric numerical model until a preset failure threshold is reached, and fracture characteristic data is extracted. This achieves a complete quantitative prediction process from quantitative analysis of rock fabric to simulation of fracture development characteristics. This application overcomes the technical limitations of relying solely on indirect methods such as field outcrops and well logging, which are insufficient to reveal the development patterns of fractures at the microscale. By accurately reflecting the control effect of different mineral compositions, grain sizes, and crystal forms of granite on fracture development in the numerical model, it provides a quantifiable technical means for predicting the spatial distribution and evaluating the development degree of microscale fractures in granite-internal reservoirs, thereby effectively supporting the oil and gas exploration deployment in granite-internal reservoirs.

[0016] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 The schematic diagram illustrates a flow chart of a method for predicting microscopic fractures in granite internal reservoirs according to an embodiment of this application. Figure 2 The schematic diagram illustrates multiple sets of uniaxial compression test results of granite according to embodiments of this application; Figure 3 This illustration schematically shows a statistical diagram of fine fractures in a granite internal reservoir according to Example 1 of this application; Figure 4 This illustration schematically shows a statistical diagram of fine fractures in a granite internal reservoir according to Example 2 of this application; Figure 5 This illustration schematically shows a statistical diagram of fine fractures in a granite internal reservoir according to Example 3 of this application; Figure 6 The illustration shows a statistical diagram of fine fractures in a granite internal reservoir according to Example 4 of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0020] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0021] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0022] Figure 1 The illustration schematically shows a flowchart of a method for predicting microscopic fractures in granite internal reservoirs according to an embodiment of this application. Figure 1 As shown in the embodiments of this application, a method for predicting fine-grained fractures in granite internal reservoirs is provided. The prediction method may include the following steps: Step S110: Obtain the initial rock texture data corresponding to the granite sample, wherein the initial rock texture data includes mineral composition information, structural information, texture information and mineral grain size information; Step S120: Based on the initial rock texture data, construct an initial rock texture numerical model, wherein the initial rock texture numerical model includes multiple grain clusters and multiple subgrains distributed within the grain clusters; Step S130: Based on the rock mechanical parameters of the granite sample, the contact mechanical parameters between grain clusters and between subgrains in the initial rock fabric numerical model are corrected to obtain the target rock fabric numerical model. Step S140: Apply axial strain to the numerical model of the target rock fabric until the axial strain reaches the preset failure threshold, and simulate the crack characteristic data of the numerical model of the target rock fabric. Step S150: Identify fine-grained fractures in the granite internal reservoir based on fracture characteristic data.

[0023] In step S110, granite is an acidic plutonic intrusive rock, mainly composed of quartz, feldspar, and a small amount of dark minerals. It has a typical granular structure and often occurs in massive or directional formations. It is hard, has high mechanical strength, and is one of the most widely distributed rocks in the Earth's crust. It is also an important research object for fractured internal reservoirs in oil and gas exploration. Mineral composition information refers to the types of various mineral components in the granite sample and their mass or volume percentage. Structural information refers to the size, shape, arrangement, and intergranular bonding of mineral grains in the granite sample. Tectonic information refers to the spatial orientation or distribution characteristics of mineral grains in the granite sample. Mineral grain size information refers to the size distribution range of each mineral grain in the granite sample, including the maximum grain size, minimum grain size, and average grain size. The initial petrographic data of the granite sample are the basic rock characteristic information obtained after quantitative characterization of the mineral composition, structure, texture, and mineral grain size of the granite.

[0024] In step S120, the initial rock texture numerical model is a multi-particle discrete element model established in a computer that reflects the true spatial distribution characteristics of minerals in the granite sample. It includes multiple grain clusters and multiple subgrains distributed within the grain clusters. A grain cluster refers to an aggregate of mineral crystals composed of multiple subgrains, used to simulate individual mineral crystal grains in granite. A subgrain is the basic circular unit that constitutes a grain cluster, used to describe the microstructure within the mineral crystal.

[0025] In step S130, the rock mechanical parameters are macroscopic mechanical property indicators of the granite sample obtained through indoor uniaxial compression tests, characterizing the overall strength and deformation capacity of the granite sample. The contact mechanical parameters between grain clusters are microscopic parameters used to describe the mechanical behavior on the contact surface between adjacent grain clusters in the initial rock fabric numerical model, reflecting the cementation strength at grain boundaries. The contact mechanical parameters between subgrains are microscopic parameters used to describe the mechanical behavior on the contact surface between adjacent subgrains within the same grain cluster in the initial rock fabric numerical model, reflecting the cementation strength within the mineral crystals. During the correction process of the initial rock fabric numerical model, the rock mechanical parameters of the granite sample are used as the target benchmark. The contact mechanical parameters between grain clusters and between subgrains are adjusted to make the overall macroscopic mechanical response of the rock fabric numerical model consistent with the real granite sample, thus obtaining the corrected target rock fabric numerical model.

[0026] In step S140, axial strain refers to the relative displacement generated when compressive deformation is applied to the target rock fabric numerical model along the vertical direction. The preset failure threshold is a pre-set critical value of axial strain. When the deformation of the target rock fabric numerical model reaches the preset failure threshold, the granite sample is considered to have undergone macroscopic failure. The crack characteristic data is a set of information reflecting the internal fracture state of the target rock fabric numerical model obtained through numerical simulation, including the spatial distribution of cracks and the statistical count of various microcracks. During the simulation, a constant relative motion is applied to the upper and lower boundaries of the target rock fabric numerical model, causing the target rock fabric numerical model to gradually generate axial strain. When the axial strain reaches the preset failure threshold, the location and statistical information of all cracks generated due to contact fracture inside the target rock fabric numerical model are extracted. This crack characteristic data is used as a prediction basis to determine the development degree and spatial distribution of microscopic cracks in the granite internal reservoir.

[0027] In step S150, the fracture characteristic data of the target rock fabric numerical model are the microscopic fractures in the granite internal reservoir.

[0028] Steps S110 to S150 of this embodiment obtain initial rock fabric data by quantitatively characterizing the mineral composition, structure, texture, and mineral grain size of granite samples. Then, an initial rock fabric numerical model containing grain clusters and subgrains is constructed. Next, the contact mechanical parameters between grain clusters and subgrains in the model are corrected based on rock mechanics parameters to obtain a target rock fabric numerical model. Finally, axial strain is applied to the target rock fabric numerical model until a preset failure threshold is reached, and fracture characteristic data are extracted. This achieves a full-process quantitative prediction from quantitative rock fabric analysis to fracture development characteristic simulation. This embodiment overcomes the technical limitations of relying solely on indirect methods such as field outcrops and well logging to reveal the development law of fine-scale fractures. By accurately reflecting the control effect of different mineral compositions, mineral grain sizes, and mineral crystal forms of granite on fracture development in the numerical model, it provides a quantifiable technical means for predicting the spatial distribution and evaluating the development degree of fine-scale fractures in granite-internal reservoirs, thereby effectively supporting the oil and gas exploration deployment in granite-internal reservoirs.

[0029] Further, step S120: Based on the initial rock texture data, constructing an initial rock texture numerical model may include the following steps: Step S121: Based on the initial rock texture data, generate multiple grain seeds to fill the preset space to obtain a grain seed distribution model; Step S122: Based on the grain seed distribution model, generate multiple subgrains to fill the preset space, and obtain the grain seed-subgrain distribution model; Step S123: Based on the component information, location information and grain size information of the grain seed, determine the sub-grains associated with each grain seed in the grain seed-sub-grain distribution model, and generate a grain cluster for each grain seed and associated sub-grain. Step S124: Based on all the grain clusters, obtain the numerical model of the grain cluster structure; Step S125: Based on the numerical model of the grain cluster structure, identify the subgrain pairs in contact within each grain cluster and the subgrain pairs in contact between each grain cluster. Step S126: For each subgrain, based on the contact mechanical parameters within the subgrain pair, construct a parallel bonding model of the subgrain pair to obtain an initial numerical model of the rock fabric.

[0030] In step S121, the grain seed is the initial geometric core used to simulate a single mineral crystal grain in the granite sample. In the initial rock fabric numerical model, it is represented by an elliptical region. The spatial location and size of the elliptical region are determined by the mineral content and grain size distribution information in the initial rock fabric data. The preset space refers to the pre-defined rectangular boundary range in the initial rock fabric numerical model, which defines the generation and distribution area of ​​grain seeds and subgrains. During the generation process, based on the volume fraction and grain size distribution characteristics of various minerals in the initial rock fabric data, an appropriate number and size of elliptical grain seeds are randomly placed within the preset space. The ratio of the major and minor axes and the major axis direction angle of each grain seed are specified, ensuring that the distribution density and arrangement of the grain seeds in space reflect the spatial distribution characteristics of the minerals in the real granite sample. The resulting set of grain seeds is the grain seed distribution model.

[0031] In step S122, subgrains are the basic circular units that constitute grain clusters. The diameter of a subgrain is smaller than that of a grain seed, and it is used to describe the microstructural features inside mineral crystals in the initial numerical model of rock fabrication. When generating subgrains, based on the grain seed distribution model, a large number of circular subgrains are randomly generated within the entire rectangular boundary of a preset space. These subgrains conform to a certain grain size distribution range and fill the areas in the preset space that the grain seeds cannot fill as densely as possible, ensuring that the gaps between grain seeds are also occupied by subgrains. After the generation of subgrains is completed, both grain seeds and subgrains are distributed within the preset space. The spatial distribution model that includes both grain seeds and subgrains is called the grain seed-subgrain distribution model.

[0032] In step S123, component information refers to the mineral type identifier corresponding to the seed grain, used to distinguish different types of mineral components in the granite sample. Location information refers to the coordinates of the seed grain's center in a preset space. Grain size information refers to the size of the seed grain along its major and minor axes. When determining the association relationship, the boundary range of each seed grain in the preset space is defined based on its location and grain size information. Subgrains whose center points fall within the boundary range are identified as subgrains associated with the seed grain. A grain cluster refers to a mineral crystal aggregate composed of a seed grain and all associated subgrains. Grain clusters are used to simulate a complete mineral crystal grain in the initial rock fabric numerical model. The aforementioned operation of determining the association relationship is performed on each seed grain, aggregating each seed grain and its associated subgrains into a grain cluster, thereby generating corresponding grain clusters for each seed grain.

[0033] In step S124, the numerical model of the grain cluster structure is a spatial structure model composed of all generated grain clusters arranged according to their positions in a preset space. Each grain cluster in the numerical model retains its component information, spatial location, geometric dimensions, and the set of subgrains it contains during its generation. By summarizing the spatial distribution information of all grain clusters, an overall structural model reflecting the spatial arrangement and compositional characteristics of mineral crystals in the granite sample is formed, serving as the numerical model of the grain cluster structure.

[0034] In step S125, a contacting subgrain pair refers to a combination of two subgrains that satisfy the contact criterion in a preset space. The contact criterion is that the straight-line distance between the center points of the two contacting subgrains is less than or equal to the sum of their radii. During identification, the positional relationships between all subgrains in the numerical model of the grain cluster structure are traversed, and judgments are made from two levels: the first level is to determine whether any two subgrains within the same grain cluster satisfy the contact criterion, and if they do, they are recorded as a contacting subgrain pair within the grain cluster; the second level is to determine whether any two subgrains belonging to different grain clusters satisfy the contact criterion, and if they do, they are recorded as a contacting subgrain pair between grain clusters. Through traversal identification, all subgrain pair combinations that satisfy the contact criterion in the numerical model of the grain cluster structure, as well as the classification of each subgrain pair combination, are obtained.

[0035] In step S126, a subgrain pair refers to a pairing of two subgrains identified as being in contact after identification. A subgrain pair includes two subgrains located within the same grain cluster and two subgrains belonging to different grain clusters. The contact mechanical parameters within a subgrain pair are microscopic mechanical property data used to describe the bond strength of the two subgrains in the pair that are in contact, and are used to quantify the resistance of the subgrain pair under stress. When assigning contact mechanical parameters to a subgrain pair, the subgrain pairs are classified into two categories according to their affiliation: intra-grain cluster contact type and inter-grain cluster contact type. Each subgrain pair belonging to the intra-grain cluster contact type is assigned an intra-grain cluster contact mechanical parameter value, and each subgrain pair belonging to the inter-grain cluster contact type is assigned an inter-grain cluster contact mechanical parameter value. The parallel bond model is a constitutive model of the mechanical relationships between each pair of subgrains in phase contact. It simulates the cement-like bonding behavior between subgrains and can transmit normal and shear forces while withstanding bending moments. During the construction process, the contact mechanical parameters assigned to each subgrain pair are input into their respective parallel bond models, ensuring that the mechanical behavior of each subgrain pair is described by a parallel bond model. Once all contacting subgrain pairs in the preset space have been assigned contact mechanical parameters and their respective parallel bond models established, an initial numerical model of rock texture capable of simulating the stress, deformation, and fracture processes of granite is obtained.

[0036] Steps S121 to S126 of this embodiment transform the mineral composition information, structural information, texture information and mineral grain size information of the granite sample into a grain seed distribution model, a grain seed-subgrain distribution model and a grain cluster structure numerical model, and finally form an initial rock fabric numerical model with mechanical calculation capabilities, thereby realizing a complete mapping from actual rock fabric characteristics to numerical models.

[0037] Further, steps S123-S124: Based on the component information, location information, and grain size information of the grain seed, determine the subgrains associated with each grain seed in the grain seed-subgrain distribution model, and generate a grain cluster for each grain seed and its associated subgrain; based on all the grain clusters, obtain the numerical model of the grain cluster structure, which may include the following steps: Step S123a: For each grain seed, based on the location information and grain size information, construct the spatial envelope range of the grain seed to obtain the grain spatial domain; Step S123b: Based on the grain spatial domain and the grain seed-subgrain distribution model, determine the spatial domain to which the center point of the subgrain falls, and determine the subgrains associated with each grain seed in the grain seed-subgrain distribution model. Step S123c: Based on the composition information of the subgrains and grain seeds associated with each grain seed, assign corresponding mineral attribute tags to the subgrains to obtain a set of subgrains; Step S123d: Based on the subgrain set, generate all grain clusters to obtain a numerical model of the grain cluster structure.

[0038] In step S123a, the position information refers to the coordinates of the center of the grain seed in the preset space. The grain size information refers to the size of the grain seed in the major and minor axis directions. The spatial envelope refers to the elliptical spatial region defined by the center of the grain seed as the geometric center and the major and minor axis dimensions in the grain size information as boundary parameters. The grain space domain refers to the elliptical region occupied by a single grain seed in the preset space, and this region is determined by the spatial envelope. During the construction process, the center coordinates of each grain seed are used as the center of the ellipse, the major and minor axis dimensions of the grain seed are used as the major and minor semi-axes of the ellipse, respectively, and the major axis direction angle of the grain seed is used as the rotation angle of the ellipse in the preset space. The elliptical region corresponding to each grain seed is drawn in the preset space, and this elliptical region is the grain space domain.

[0039] In step S123b, the grain seed-subgrain distribution model is a spatial distribution model in which two types of geometric objects, grain seeds and subgrains, are simultaneously distributed within a preset space. The center point refers to the coordinate position of the center of the subgrain in the preset space. During the determination process, each subgrain in the grain seed-subgrain distribution model is traversed, and the coordinates of the center point of the subgrain are obtained. It is then determined whether the coordinates of the center point of each subgrain are located inside the elliptical boundary of the grain space domain of a certain grain seed. If the center point of a subgrain is located inside the grain space domain of a certain grain seed, then this subgrain is determined to be a subgrain associated with this grain seed. If the center point of a subgrain does not fall into the grain space domain of any grain seed, then this subgrain is determined to be a remaining subgrain. Through the above determination process, all subgrains associated with each grain seed in the grain seed-subgrain distribution model are determined.

[0040] In step S123c, component information refers to the mineral type identifier corresponding to the seed grain, and mineral attribute tags are marking information used to identify the mineral type to which a subgrain belongs. During the assignment process, the mineral type corresponding to each seed grain is determined based on its component information, and the mineral type corresponding to each seed grain is used as a mineral attribute tag and assigned to all subgrains associated with the seed grain. For the remaining subgrains that are not identified as associated with any seed grain, the same mineral attribute tag as the adjacent grain cluster is assigned to the remaining subgrains. After the mineral attribute tag assignment is completed, all subgrains carry mineral attribute tags, and all subgrains carrying mineral attribute tags together constitute a subgrain set.

[0041] In step S123d, during the generation of all grain clusters, based on the mineral attribute tags and spatial affiliation of each sub-grain in the sub-grain set, all sub-grains with the same mineral attribute tags and belonging to the same grain seed are aggregated into a whole, and each aggregate corresponds to a grain cluster. Each grain cluster consists of a grain seed and all sub-grains associated with the grain seed, and carries a corresponding mineral attribute tag. All grain clusters are arranged according to their positions in a preset space, and the spatial distribution information of all grain clusters is summarized to form an overall structural model that can reflect the spatial arrangement and compositional characteristics of mineral crystals in the granite sample, which serves as the numerical model of the grain cluster structure.

[0042] In the embodiments of this application, steps S123a to S123d aggregate discrete sub-grains into grain clusters carrying mineral type information by determining the spatial domain of grain seeds and assigning mineral attribute labels, thereby constructing a numerical model of grain cluster structure that reflects the true mineral composition and spatial distribution characteristics of granite samples.

[0043] In this embodiment, the parallel bond model includes a tensile strength model and a shear strength model.

[0044] The tensile strength model is used to determine whether the bonded element will fracture under normal force on the subgrain pair. The tensile strength model is determined by the normal contact force, bending moment, bond area, and moment of inertia. The normal contact force is the force perpendicular to the contact surface acting on the subgrain pair's contact surface. The bending moment is the rotational moment acting on the bonded element. The bond area is the effective contact area between the bonded elements of the subgrain pair. The moment of inertia is a geometric quantity describing the ability of the bonded element's cross-section to resist bending deformation. The specific expressions for the tensile strength model include:

[0045] In the formula, Indicates the tensile strength of the bonded element. Indicates the normal contact force. Indicates the bonding area. Indicates the torque coefficient. This represents the bending moment of the bonded element. This represents 1 / 2 of the length of the adhesive element. This represents the moment of inertia of the bonded element.

[0046] The shear strength model is used to determine whether the bonded element of a subgrain pair undergoes shear fracture under tangential stress. The shear strength model is determined by the shear contact force and the bond area. The shear contact force is the force acting on the contact surface of the subgrain pair, parallel to the contact surface direction. The specific expression of the shear strength model includes:

[0047] In the formula, Indicates the shear strength of the bonded element. This indicates shear contact force.

[0048] In this embodiment, during the application of axial strain to the numerical model of the target rock fabric, when the actual normal tensile stress between subgrain pairs exceeds the tensile strength limit specified by the tensile strength model, the bonded element undergoes tensile fracture; when the actual shear stress exceeds the shear strength limit specified by the shear strength model, the bonded element undergoes shear fracture. The fracture of the bonded element corresponds to the initiation and propagation of microcracks. The tensile strength model and the shear strength model together constitute a complete determination mechanism for the parallel bonded model to describe the fracture failure of subgrain pairs under stress conditions.

[0049] Further, step S130: Based on the rock mechanical parameters of the granite sample, the contact mechanical parameters between grain clusters and between subgrains in the initial rock fabric numerical model are corrected to obtain the target rock fabric numerical model, which may include the following steps: Step S131: Conduct uniaxial mechanical tests on the granite sample to obtain the rock mechanical parameters of the granite sample; Step S132: The difference between the rock mechanical parameters and the contact mechanical parameters between the grain clusters is taken as the first mechanical parameter difference. Step S133: The difference between the rock mechanical parameters and the contact mechanical parameters between subgrains is taken as the second mechanical parameter difference. Step S134: Based on the difference between the first mechanical parameter and the difference between the second mechanical parameter, correct the contact mechanical parameters between grain clusters and between subgrains to obtain the corrected contact mechanical parameters between grain clusters and between subgrains. Step S135: When the difference between the first mechanical parameter corresponding to the corrected contact mechanical parameters between grain clusters and the difference between the second mechanical parameters corresponding to the corrected contact mechanical parameters between subgrains are both less than the preset difference threshold, a numerical model of the target rock texture is obtained based on the corrected contact mechanical parameters between grain clusters and the corrected contact mechanical parameters between subgrains.

[0050] In step S131, the uniaxial mechanical test of rock is a physical experimental process in which a granite sample is placed in a uniaxial compression testing device, and a monotonically increasing compressive load is applied along the axial direction of the sample until the sample fails. The rock mechanical parameters are macroscopic mechanical property indicators of the granite sample obtained directly through the uniaxial mechanical test of rock. The rock mechanical parameters of the granite sample include Young's modulus, Poisson's ratio, uniaxial compressive strength, and internal friction angle.

[0051] In step S132, the first mechanical parameter difference refers to the numerical difference between the various indices in the rock mechanical parameters and the corresponding items in the contact mechanical parameters between grain clusters.

[0052] In step S133, the second mechanical parameter difference refers to the numerical difference between the various indices in the rock mechanical parameters and the corresponding items in the contact mechanical parameters between subgrains.

[0053] In step S134, based on the sign and magnitude of the difference in the first mechanical parameter, the current value of the contact mechanical parameter between grain clusters is increased or decreased by a certain amount, so that the macroscopic response of the numerical model corresponding to the corrected contact mechanical parameter between grain clusters gradually approaches the rock mechanical parameter. Based on the sign and magnitude of the difference in the second mechanical parameter, the current value of the contact mechanical parameter between subgrains is increased or decreased by a certain amount, so that the macroscopic response of the numerical model corresponding to the corrected contact mechanical parameter between subgrains gradually approaches the rock mechanical parameter.

[0054] In step S135, the preset difference threshold represents the acceptable deviation range between the macroscopic mechanical response of the rock fabric numerical model and the actual rock mechanical parameters. When the difference between the first mechanical parameter corresponding to the corrected contact mechanical parameter between grain clusters is less than the preset difference threshold, it indicates that the correction result of the contact mechanical parameter between grain clusters has reached the accuracy requirement; when the difference between the second mechanical parameter corresponding to the corrected contact mechanical parameter between subgrains is less than the preset difference threshold, it indicates that the correction result of the contact mechanical parameter between subgrains has reached the accuracy requirement, and it is determined that the calibration work of the initial rock fabric numerical model has been completed. The numerical model including the corrected contact mechanical parameter between grain clusters and the corrected contact mechanical parameter between subgrains is taken as the target rock fabric numerical model.

[0055] In the embodiments of this application, steps S131 to S135 involve obtaining the rock mechanical parameters of the granite sample through uniaxial rock mechanical tests as a calibration benchmark. Based on the differences between the rock mechanical parameters and the contact mechanical parameters between grain clusters and between subgrains, the contact mechanical parameters between grain clusters and between subgrains are corrected accordingly until both sets of differences meet the preset difference threshold. This makes the macroscopic mechanical response of the initial rock fabric numerical model approximate the mechanical behavior of the real granite sample, thereby obtaining the corrected target rock fabric numerical model.

[0056] Further, step S140: Apply axial strain to the numerical model of the target rock fabric until the axial strain reaches a preset failure threshold, and simulate the crack characteristic data of the numerical model of the target rock fabric, including: Step S141: Using mineral composition information, mineral grain size information, and crystal morphology in the target rock fabric numerical model as variables, adjust the target rock fabric numerical model to obtain the adjusted target rock fabric numerical model. Step S142: Apply axial strain to the adjusted target rock fabric numerical model until the axial strain reaches the preset failure threshold, and simulate multiple sets of crack characteristic data of the adjusted target rock fabric numerical model.

[0057] In step S141, during the adjustment process of the target rock fabric numerical model, the proportion of various types of grain seeds corresponding to the mineral composition information, the distribution of grain seed size corresponding to the mineral grain size information, and the ratio of major and minor axes and the direction angle of major axis of the grain seeds corresponding to the crystal morphology are changed respectively. Each time, one variable is changed or multiple variables are changed at the same time, so that the target rock fabric numerical model corresponds to granite samples with different fabric characteristics. The target rock fabric numerical model after variable adjustment is used as the adjusted target rock fabric numerical model.

[0058] In step S142, during the simulation of fracture characteristic data, a constant relative motion is applied to the upper and lower boundaries of the adjusted target rock fabric numerical model, causing the adjusted target rock fabric numerical model to gradually generate axial strain. When the axial strain reaches a preset failure threshold, the locations and statistical information of all fractures generated due to contact rupture within the adjusted target rock fabric numerical model are extracted to obtain fracture characteristic data. By performing the aforementioned simulation process on multiple adjusted target rock fabric numerical models obtained in step S141, multiple sets of fracture characteristic data corresponding to different fabric conditions are obtained. Based on multiple sets of fracture characteristic data, the differences in the development degree and spatial distribution pattern of microscopic fractures in granite internal reservoirs with different fabrics can be determined.

[0059] In the embodiments of this application, steps S141 to S142 involve using mineral composition information, mineral grain size information, and crystal morphology as variables to adjust the numerical model of the target rock texture. Axial strain is then applied to multiple sets of numerical models corresponding to different texture characteristics after adjustment until a preset failure threshold is reached, thereby obtaining the fracture characteristic data corresponding to each numerical model. This enables quantitative prediction of the differences in the degree of development and spatial distribution of microscopic fractures in granite internal reservoirs with different textures.

[0060] Furthermore, the prediction method in this application embodiment may also include the following steps: Step S210: Based on X-ray diffraction analysis and polarized light microscopy identification, obtain the mineral composition information of the granite sample; Step S2210: Based on hand specimen identification and polarized light microscopy identification, obtain the structural information, texture information and mineral grain size information of the granite sample.

[0061] In step S210, X-ray diffraction analysis refers to the technique of determining the types and relative contents of various mineral phases in granite samples by utilizing the diffraction phenomenon produced by X-rays in granite sample powder. X-ray diffraction analysis can provide qualitative identification results and quantitative percentage data of various minerals in granite samples. Polarizing microscopy identification refers to the technique of observing granite samples under a polarizing microscope after preparing thin rock sections, and identifying mineral types and estimating mineral contents based on the optical properties of minerals under polarized light. In the process of obtaining mineral composition information of granite samples, qualitative and quantitative results of the main mineral phases in granite samples are obtained through X-ray diffraction analysis, and polarizing microscopy identification is used to verify and supplement the results of X-ray diffraction analysis. By combining the test results of the two methods, the mineral composition information of granite samples is finally determined.

[0062] In step S220, hand specimen identification refers to the technique of directly observing the macroscopic appearance characteristics of a granite sample with the naked eye or a handheld magnifying glass to identify the rock type and make a preliminary judgment on the rock structure and texture. Polarizing microscopy identification refers to the technique of preparing a thin section of the granite sample and observing it under a polarizing microscope, observing the interrelationships, spatial arrangement, and particle size of mineral grains based on the optical properties of minerals under polarized light. Hand specimen identification provides a preliminary judgment on the macroscopic structure and texture of the granite sample, while polarizing microscopy provides precise measurements of the microscopic structure, contact relationships between mineral grains, and grain size distribution. By combining the results of hand specimen identification and polarizing microscopy identification, the structural information, texture information, and mineral grain size information of the granite sample are finally determined.

[0063] To further demonstrate the reliability of the embodiments of this application, multiple sets of granite samples were selected for verification. The multiple sets of granite samples include Sample 1, Sample 2, Sample 3 and Sample 4, and the types of each granite sample are different. Figure 2 The schematic diagram illustrates multiple sets of uniaxial compression test results of granite according to embodiments of this application, such as... Figure 2 As shown, for multiple groups of granite samples, the results of the numerical model simulation of the target rock texture were compared and analyzed with the results of the laboratory test. The actual stress-strain curves of the granite samples were compared with the simulated stress-strain curves. The actual stress-strain curves were all similar to the simulated stress-strain curves. The uniaxial compression results simulated by the numerical model of the target rock texture are reliable and can replace the actual uniaxial compression test. Figure 3 This illustration schematically shows a statistical diagram of fine fractures in a granite internal reservoir according to Example 1 of this application; Figure 4 This illustration schematically shows a statistical diagram of fine fractures in a granite internal reservoir according to Example 2 of this application; Figure 5 This illustration schematically shows a statistical diagram of fine fractures in a granite internal reservoir according to Example 3 of this application; Figure 6 This schematically illustrates a statistical diagram of fine fractures in a granite internal reservoir according to Example 4 of this application, as shown below. Figures 3 to 6 As shown, the development process of micro-scale fractures in granite internal reservoirs of granite samples 1-4 was simulated. The micro-scale fractures in granite internal reservoirs include mineral intercrystalline fractures, quartz crystal fractures, plagioclase crystal fractures, alkali feldspar crystal fractures, and dark mineral crystal fractures. By comparing the degree of fracture development of different types of granite samples under the same axial strain, quantitative prediction of granite fracture development can be achieved.

[0064] This application also provides an electronic device, including: The memory is configured to store instructions; The processor is configured to retrieve instructions from memory and, when executing the instructions, to implement the aforementioned method for predicting microscopic fractures in granite internal reservoirs.

[0065] It is understood that the electronic device provided in this application embodiment can realize each process of the prediction method for fine micro-fractures in granite internal reservoirs in the above embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0066] This application also provides a machine-readable storage medium storing instructions for causing a machine to perform the aforementioned method for predicting microscopic fractures in granite internal reservoirs.

[0067] It is understood that the machine-readable storage medium provided in the embodiments of this application can implement each process of the prediction method for fine micro-fractures in granite internal reservoirs in the above embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0068] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0070] 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.

[0071] 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.

[0072] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0073] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0074] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0075] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0076] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for predicting fine-grained fractures in granite internal reservoirs, characterized in that, The prediction method includes: Obtain initial rock texture data corresponding to the granite sample, wherein the initial rock texture data includes mineral composition information, structural information, texture information and mineral grain size information; Based on the initial rock texture data, an initial rock texture numerical model is constructed, wherein the initial rock texture numerical model includes multiple grain clusters and multiple subgrains distributed within the grain clusters; Based on the rock mechanics parameters of the granite sample, the contact mechanics parameters between grain clusters and between subgrains in the initial rock fabric numerical model are corrected to obtain the target rock fabric numerical model. Axial strain is applied to the numerical model of the target rock fabric until the axial strain reaches a preset failure threshold, thereby simulating the crack characteristic data of the numerical model of the target rock fabric. The fine-grained fractures in the granite internal reservoir were determined based on the fracture characteristic data.

2. The method according to claim 1, characterized in that, The construction of an initial rock texture numerical model based on the initial rock texture data includes: Based on the initial rock texture data, multiple grain seeds are generated to fill a preset space, resulting in a grain seed distribution model. Based on the grain seed distribution model, multiple subgrains are generated to fill the preset space, resulting in a grain seed-subgrain distribution model. Based on the component information, location information and grain size information of the grain seed, the sub-grains associated with each grain seed in the grain seed-subgrain distribution model are determined, and a grain cluster is generated for each grain seed and associated sub-grain. Based on all the aforementioned grain clusters, a numerical model of the grain cluster structure is obtained; Based on the numerical model of the grain cluster structure, the subgrain pairs in contact within each grain cluster and the subgrain pairs in contact between each grain cluster are identified. For each subgrain, a parallel bonding model of the subgrain pair is constructed based on the contact mechanical parameters within the subgrain pair to obtain an initial numerical model of rock texture.

3. The method according to claim 2, characterized in that, Based on the component information, location information, and grain size information of the grain seed, the subgrains associated with each grain seed in the grain seed-subgrain distribution model are determined, and a grain cluster is generated for each grain seed and its associated subgrain; based on all the grain clusters, a numerical model of the grain cluster structure is obtained, including: For each of the aforementioned grain seeds, based on the location information and the grain size information, the spatial envelope range of the grain seed is constructed to obtain the grain spatial domain; Based on the grain spatial domain and the grain seed-subgrain distribution model, determine the spatial domain to which the center point of the subgrain falls, and determine the subgrains associated with each grain seed in the grain seed-subgrain distribution model. Based on the subgrains associated with each of the aforementioned grain seeds and the component information of the grain seeds, the subgrains are assigned corresponding mineral attribute tags to obtain a set of subgrains; Based on the subgrain set, all the grain clusters are generated to obtain a numerical model of the grain cluster structure.

4. The method according to claim 2, characterized in that, The parallel bond model includes a tensile strength model and a shear strength model.

5. The method according to claim 1, characterized in that, The contact mechanical parameters between grain clusters and between subgrains in the initial rock fabric numerical model are corrected based on the rock mechanical parameters of the granite sample to obtain the target rock fabric numerical model, including: The rock mechanical parameters of the granite sample were obtained by conducting uniaxial mechanical tests on the granite sample. The difference between the rock mechanical parameters and the contact mechanical parameters between the grain clusters is taken as the first mechanical parameter difference. The difference between the rock mechanical parameters and the contact mechanical parameters between the subgrains is taken as the second mechanical parameter difference. Based on the first mechanical parameter difference and the second mechanical parameter difference, the contact mechanical parameters between the grain clusters and the contact mechanical parameters between the subgrains are corrected to obtain the corrected contact mechanical parameters between the grain clusters and the corrected contact mechanical parameters between the subgrains. When the difference between the first mechanical parameter corresponding to the corrected contact mechanical parameters between grain clusters and the difference between the second mechanical parameter corresponding to the corrected contact mechanical parameters between subgrains are both less than a preset difference threshold, the numerical model of the target rock texture is obtained based on the corrected contact mechanical parameters between grain clusters and the corrected contact mechanical parameters between subgrains.

6. The method according to claim 1, characterized in that, The rock mechanical parameters of the granite sample include Young's modulus, Poisson's ratio, uniaxial compressive strength, and internal friction angle.

7. The method according to claim 1, characterized in that, The step of applying axial strain to the numerical model of the target rock fabric until the axial strain reaches a preset failure threshold, and simulating the crack characteristic data of the numerical model of the target rock fabric, includes: Using the mineral composition information, the mineral grain size information, and the crystal morphology in the target rock fabric numerical model as variables, the target rock fabric numerical model is adjusted to obtain the adjusted target rock fabric numerical model. The axial strain is applied to the adjusted target rock fabric numerical model until the axial strain reaches the preset failure threshold, and multiple sets of crack characteristic data of the adjusted target rock fabric numerical model are simulated.

8. The method according to claim 1, characterized in that, The prediction method further includes: Based on X-ray diffraction analysis and polarized light microscopy identification, the mineral composition information of the granite sample was obtained; Based on hand specimen identification and polarized light microscopy identification, the structural information, texture information, and mineral grain size information of the granite sample were obtained.

9. An electronic device, characterized in that, include: The memory is configured to store instructions; The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the method for predicting microscopic fractures in granite internal reservoirs according to any one of claims 1 to 8.

10. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform a method for predicting microscopic fractures in granite internal reservoirs according to any one of claims 1 to 8.

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