Two-dimensional porous stratum modeling method based on discrete element method and related device

Abstract

This invention provides a two-dimensional porous formation modeling method and related equipment based on the discrete element method (DEM), comprising: acquiring pore characteristic data of the target formation's pore structure; constructing a porous DEM numerical model based on the pore characteristic data; determining the mechanical property parameters of the target formation; and performing parameter calibration processing on the porous DEM numerical model based on the mechanical property parameters to obtain the target porous DEM numerical model. By constraining the construction of the porous DEM model with pore characteristic data and calibrating it in conjunction with mechanical property parameters, the obtained model exhibits consistency in both the statistical characteristics of the pore structure and the macroscopic mechanical response, reducing the workload of manual trial and error and parameter tuning during the modeling process, and improving the repeatability of model construction and the credibility of numerical simulation.

Description

Technical Field

[0001] This invention relates to the field of intelligent simulation modeling, and in particular to a two-dimensional porous stratum modeling method, apparatus, electronic device and storage medium based on the discrete element method. Background Technology

[0002] The pore structure of porous strata has a significant impact on their bearing capacity, deformation and failure, stability, and related processes such as seepage. The discrete element method (DEM) can characterize the discontinuous features and failure evolution of materials at the particle scale, and is therefore widely used in geotechnical engineering, underground engineering, and related numerical simulation analyses to study the mechanical behavior of strata. To conduct subsequent mechanical analysis and engineering assessment, it is usually necessary to construct a two-dimensional discrete element numerical model that reflects the pore structure characteristics of the target strata.

[0003] The Discrete Element Method (DEM), as a numerical analysis method for discontinuous media, is widely used in geotechnical engineering, deep-sea resource extraction, and geological hazard prediction. In these engineering practices, to balance computational efficiency and simulation scale, two-dimensional (2D) numerical models are often prioritized for mechanism studies and scheme demonstration. However, the distribution characteristics of the microscopic pore structure of formation materials (such as porosity, flattening morphology, and spatial bedding orientation) on a two-dimensional cross-section directly determine the macroscopic mechanical strength and failure mode of the rock. Therefore, constructing a model that can realistically reflect the complex pore characteristics of natural rocks is a prerequisite for high-precision simulation.

[0004] In existing two-dimensional discrete element method (DEM) porous modeling, one approach often uses equivalent porosity or simplified geometric rules to characterize pore structure. However, the pore morphology, scale, and orientation are difficult to match with real strata, resulting in insufficient pore structure representation capability of the model. Another approach, while attempting pore reconstruction based on images / statistical information, is prone to significant deviations between porosity and target porosity during model construction, requiring repeated manual adjustments, leading to poor model construction efficiency and repeatability. Furthermore, there is a mapping relationship between the mesoscopic parameters and macroscopic mechanical properties of porous DEM models. In existing technologies, parameter calibration often relies on experience and trial-and-error iterations, easily resulting in problems such as "pore structure meets requirements but mechanical response does not match" or "mechanical response matches but pore structure deviates," thus affecting the reliability of numerical simulation results.

[0005] Therefore, existing pore modeling methods suffer from problems such as difficulty in matching pore structure characteristics with real strata, difficulty in achieving stable porosity convergence and reliance on repeated manual parameter adjustments, and difficulty in balancing pore structure constraints with macroscopic mechanical parameter calibration, resulting in insufficient model credibility. Summary of the Invention

[0006] This invention provides a two-dimensional porous formation modeling method based on the discrete element method to solve the problems of existing pore modeling methods, such as difficulty in matching pore structure features with real formations, difficulty in achieving stable porosity convergence and reliance on repeated manual parameter adjustments, and difficulty in balancing pore structure constraints with macroscopic mechanical parameter calibration, resulting in insufficient model credibility.

[0007] In a first aspect, embodiments of the present invention provide a two-dimensional porous formation modeling method based on the discrete element method, the method comprising the following steps:

[0008] Acquire pore characteristic data of the target formation's pore structure;

[0009] Based on the pore feature data, a multi-pore discrete element numerical model is constructed;

[0010] Determine the mechanical property parameters of the target formation;

[0011] Based on the mechanical property parameters, the porous discrete element numerical model is calibrated to obtain the target porous discrete element numerical model.

[0012] Optionally, acquiring the pore characteristic data of the target formation's pore structure includes:

[0013] Obtain microscopic structural images of the rocks corresponding to the target strata;

[0014] The microstructure image is subjected to quantification of rock sample structure, and the corresponding processing results are statistically analyzed to obtain pore feature data of the target stratum pore structure. The pore feature data includes target porosity Pt, reference semi-major axis r of the pore, shape factor λ, and angular orientation θ.

[0015] Optionally, constructing a multi-pore discrete element numerical model based on the pore feature data includes:

[0016] Within the boundaries of a pre-defined two-dimensional geometric model, multiple matrix particles are generated, and an initial set of discrete element particles is formed based on these multiple matrix particles.

[0017] Based on the pore feature data, a set of pore seed points is generated within the boundary of the preset two-dimensional geometric model, and the initial value of the number of seed points is determined.

[0018] Configure geometric attributes for the pore seed points in the pore seed point set. The geometric attributes include position coordinates (xS, yS), random semi-major axis R1, angular orientation θ1, and shape factor λ1.

[0019] The initial discrete element particle set is subjected to pore range determination processing to obtain multiple target matrix particles, and the current pore structure is determined based on the multiple target matrix particles;

[0020] Based on the current pore structure, the porosity deviation is determined, and the pore formation process is converged based on the porosity deviation. When the current porosity meets the preset convergence condition, the multi-pore discrete element numerical model is output.

[0021] Optionally, the step of generating a set of pore seed points within the boundary of the preset two-dimensional geometric model based on the pore feature data, and determining an initial value for the number of seed points, includes:

[0022] Determine the boundary parameters of the preset two-dimensional geometric model boundary;

[0023] Based on the boundary parameters and pore feature data, determine the initial value of the number of pore seed points;

[0024] Based on the initial value, a corresponding number of pore seed points are generated within the boundary of the preset two-dimensional geometric model to obtain a seed point set.

[0025] Optionally, the step of performing pore range determination processing on the initial discrete element particle set to obtain multiple target matrix particles, and determining the current pore structure based on the multiple target matrix particles, includes:

[0026] Determine the relative positional relationship of each matrix particle with respect to the pore seed point in the initial discrete element particle set;

[0027] Based on the relative positional relationship, coordinate transformation is performed on each matrix particle to obtain the transformed coordinates of each matrix particle in the local coordinate system of the pore seed point;

[0028] The pore range is determined based on the transformed coordinates and the geometric properties of the pore seed points, and multiple target matrix particles are identified in each matrix particle.

[0029] The current pore structure is determined based on the plurality of target matrix particles.

[0030] Optionally, the step of determining the porosity deviation based on the current pore structure and performing convergence control on the pore formation process based on the porosity deviation, and outputting the multi-pore discrete element numerical model when the current porosity meets the preset convergence condition, includes:

[0031] Based on the current pore structure, determine the current porosity;

[0032] The porosity deviation is determined by comparing the current porosity with the target porosity.

[0033] Determine whether the porosity deviation meets the preset convergence condition;

[0034] If the porosity deviation meets the preset convergence condition, the current pore structure is determined as the pore structure of the multi-pore discrete element numerical model, and the multi-pore discrete element numerical model is output.

[0035] If the porosity deviation does not meet the preset convergence condition, the porosity generation control parameters are updated, and the current porosity is adjusted based on the updated porosity generation control parameters until the porosity deviation meets the preset convergence condition.

[0036] Optionally, the step of performing parameter calibration on the porous discrete element numerical model based on the mechanical property parameters to obtain the target porous discrete element numerical model includes:

[0037] Based on the porous discrete element numerical model, the simulated working condition data corresponding to the mechanical property parameters are determined, and the simulation is performed according to the simulated working condition data to obtain the simulation response results.

[0038] Based on the simulated response results, the calibration deviation data is obtained by comparing them with the mechanical property parameters.

[0039] When the calibration deviation data does not meet the preset consistency condition, the microscopic parameters of the porous discrete element numerical model are adjusted, and the judgment of the calibration deviation data and the preset consistency condition is repeated after adjustment until the calibration deviation data meets the preset consistency condition. Then, the calibrated porous discrete element numerical model is output as the target porous discrete element numerical model.

[0040] Secondly, embodiments of the present invention also provide a two-dimensional porous formation modeling device based on the discrete element method, the two-dimensional porous formation modeling device based on the discrete element method comprising:

[0041] The first acquisition module is used to acquire pore feature data of the pore structure of the target formation.

[0042] The first construction module is used to construct a multi-pore discrete element numerical model based on the pore feature data.

[0043] The first determining module is used to determine the mechanical property parameters of the target formation;

[0044] The first processing module is used to perform parameter calibration processing on the porous discrete element numerical model based on the mechanical property parameters to obtain the target porous discrete element numerical model.

[0045] Thirdly, embodiments of the present invention provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the two-dimensional porous stratum modeling method based on the discrete element method provided in the embodiments of the present invention.

[0046] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the two-dimensional porous stratum modeling method based on the discrete element method provided in the embodiments of the present invention.

[0047] In this embodiment of the invention, pore characteristic data of the target formation's pore structure are acquired; a multi-pore discrete element numerical model is constructed based on the pore characteristic data; mechanical property parameters of the target formation are determined; and parameter calibration processing is performed on the multi-pore discrete element numerical model based on the mechanical property parameters to obtain the target multi-pore discrete element numerical model. By constraining the construction of the multi-pore discrete element model with pore characteristic data and calibrating it in conjunction with mechanical property parameters, the obtained model exhibits consistency in both pore structure statistical characteristics and macroscopic mechanical response, reducing the workload of manual trial and error and parameter tuning during the modeling process, and improving the repeatability of model construction and the credibility of numerical simulation. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of a two-dimensional porous formation modeling method based on the discrete element method provided in an embodiment of the present invention;

[0050] Figure 2 This is a schematic diagram of a two-dimensional porous formation modeling device based on the discrete element method provided in an embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] like Figure 1 As shown, Figure 1This is a flowchart of a two-dimensional porous formation modeling method based on the discrete element method provided in an embodiment of the present invention. The two-dimensional porous formation modeling method based on the discrete element method includes the following steps:

[0054] 101. Obtain pore characteristic data of the target formation pore structure.

[0055] In this embodiment of the invention, the above-mentioned two-dimensional porous formation modeling method based on the discrete element method can be applied to the above-mentioned two-dimensional porous formation modeling platform based on the discrete element method. The above-mentioned two-dimensional porous formation modeling platform based on the discrete element method has functions such as pore modeling data processing, pore modeling data transmission and reception, and pore modeling data memory storage, and can be constructed based on a server or server cluster. The server or server cluster can be an electronic device with pore modeling data processing capabilities.

[0056] The aforementioned pore structure of the target strata refers to the spatial distribution of pores within a two-dimensional cross-section of a rock sample from the target strata. It includes at least the number, size, shape stretching, and orientation characteristics of the pores, used to describe the shape, orientation, and proportion of the pores in the strata. Specifically, the pore structure of the target strata can be observed from microscopic images of the rock sample, such as thin section micrographs, scanned images, or two-dimensional projection images of CT slices.

[0057] The aforementioned pore characteristic data can be a set of data after quantifying the pore structure of the target formation. It can be used to constrain the pore ratio, i.e. the target porosity Pt, and can further constrain the pore geometry, such as the reference semi-major axis r, the shape factor λ, and the pore orientation, such as the angular orientation θ.

[0058] In one possible embodiment, the above-mentioned two-dimensional porous strata modeling platform based on the discrete element method can select several thin section microscopic images of rock samples at the same stratum depth and perform unified calibration on the images (pixel size conversion, contrast normalization, etc.).

[0059] Next, pore regions are extracted from the microstructure image: for example, by separating pores from the matrix through threshold segmentation or region growing methods, and then the pore regions are labeled with connected components to obtain the geometric contour of each pore.

[0060] Furthermore, the aforementioned two-dimensional porous formation modeling platform based on the discrete element method fits an equivalent ellipse to each pore to obtain the semi-major axis, semi-minor axis, and orientation angle of the pore, and statistically obtains pore characteristic data for all pores: for example, the target porosity Pt = pore pixel area / total pixel area; the reference semi-major axis r can be taken as the median or mean of the semi-major axis; the shape factor λ can be taken as the representative value of the ratio of the semi-major axis to the semi-minor axis; the orientation angle θ can be taken as the peak value of the main direction or stored according to the interval distribution.

[0061] 102. Based on pore feature data, construct a multi-pore discrete element numerical model.

[0062] In this embodiment of the invention, the above-mentioned two-dimensional porous stratum modeling platform based on the discrete element method can generate a set of discrete element particles within a preset two-dimensional geometric model boundary and form a porous structure therein, thereby obtaining discrete element model data that can be used for numerical simulation.

[0063] It is understandable that the above construction process can transform pore feature data into rules for generating and filtering discrete element particle sets, such as seed point arrangement, pore range judgment and particle processing, and make the pore structure reach the expected state through the convergence control of porosity deviation, thereby obtaining a multi-porous discrete element numerical model.

[0064] The aforementioned porous discrete element numerical model can refer to a set of numerical model data that uses discrete element particles as basic units and includes porous regions within a two-dimensional model area. It includes at least the position, size, contact relationship of the particles and a description of the porous regions (or the pores formed by the absence of particles), and can be used for stress calculation and failure evolution simulation in a discrete element solver.

[0065] It is understood that the porous discrete element numerical model in this embodiment not only requires the porosity to be close to Pt, but also requires the pore size, shape and orientation to be consistent with or approximately consistent with the pore characteristic data.

[0066] In one possible embodiment, the above-mentioned two-dimensional porous stratum modeling platform based on the discrete element method first sets a preset two-dimensional geometric model boundary, for example, taking a two-dimensional region of 100 mm × 100 mm as the model boundary, and generating multiple matrix particles within the boundary to form an initial discrete element particle set, for example, using random or regular filling methods within a given particle size range to achieve a dense state within the region.

[0067] A set of pore seed points is generated within the boundary of a pre-defined two-dimensional geometric model. For example, the initial number of seed points N0 is determined based on the model region scale and the target porosity Pt, and then N0 seed point locations are generated uniformly or randomly within the region. Then, the aforementioned two-dimensional multi-porosity formation modeling platform based on the discrete element method configures the geometric attributes of the pore seed points, such as assigning each pore seed point a location coordinate (xS, yS), a random semi-major axis R (which can take values ​​around r within a certain fluctuation range), a shape factor λ, and an angle orientation θ.

[0068] The initial discrete element particle set is subjected to pore range determination processing: the relative positional relationship between each matrix particle and the pore seed point is determined, coordinate transformation is performed to obtain the transformed coordinates of the matrix particle in the local coordinate system of the pore seed point, and the pore range is determined in combination with the geometric properties of the pore seed point. Multiple target matrix particles are identified in each matrix particle, and the current pore structure is determined based on the target matrix particles.

[0069] The current porosity Pc is calculated based on the current pore structure and compared with the target porosity Pt to obtain the porosity deviation. The pore generation process is then converged based on the porosity deviation: when the porosity deviation does not meet the preset convergence condition, the pore generation control parameters are updated. Generally, the updated parameters can be obtained by updating the generation strategy of the number of pore seed points or the geometric properties of the seed points, and the pore structure is regenerated until the current porosity meets the preset convergence condition, at which point a multi-pore discrete element numerical model is output.

[0070] Through the above methods and steps, pore seed points are generated and pore structures are formed under the constraints of pore feature data. At the same time, the convergence control of porosity deviation is introduced, which can reduce the situation of "repeated manual trial and error due to porosity deviation from the target", improve the efficiency and repeatability of model construction, and make the output multi-pore discrete element numerical model more stably close to the target formation in terms of pore structure statistical characteristics.

[0071] 103. Determine the mechanical properties of the target formation.

[0072] In this embodiment of the invention, the above-mentioned two-dimensional porous stratum modeling platform based on the discrete element method obtains target parameters or judgment results for subsequent steps, such as determining mechanical property parameters, through data acquisition, data processing, or calculation rules.

[0073] The aforementioned mechanical property parameters can refer to a set of parameters representing the macroscopic mechanical behavior of the target formation, including but not limited to uniaxial compressive strength, elastic modulus, Poisson's ratio, tensile strength, internal friction angle, and cohesion, which are used as target values ​​for the discrete element model calibration. In this embodiment, the mechanical property parameters can be obtained through experimental testing (e.g., uniaxial compression tests on rock samples) or provided by existing engineering survey reports / experimental databases; the aforementioned two-dimensional porous formation modeling platform based on the discrete element method uses these parameters as a "calibration benchmark," ensuring that the simulated macroscopic response of the aforementioned porous discrete element numerical model is consistent with the actual formation.

[0074] In one possible embodiment, the mechanical property parameters of the target stratum can be determined by experimental data acquisition through the above-mentioned two-dimensional porous stratum modeling platform based on the discrete element method: for example, a uniaxial compression test is performed on samples of the same target stratum to obtain the peak stress as the uniaxial compressive strength, the slope of the linear segment of the stress-strain curve is obtained as the elastic modulus, and Poisson's ratio can be obtained based on the ratio of transverse strain to longitudinal strain; or the tensile strength is obtained by performing a Brazilian splitting test on the rock sample.

[0075] Understandably, if an engineering database or historical experimental report exists, the aforementioned two-dimensional porous stratum modeling platform based on the discrete element method can also directly read the target mechanical property parameters of the corresponding stratum as calibration targets.

[0076] 104. Based on the mechanical property parameters, the discrete element numerical model of the porous structure is calibrated to obtain the target porous discrete element numerical model.

[0077] In this embodiment of the invention, the above-mentioned two-dimensional porous formation modeling platform based on the discrete element method can take mechanical property parameters as the target, and adjust the discrete element micro parameters (such as contact stiffness, bond strength, friction coefficient, etc.) iteratively, and repeatedly execute numerical simulations to reduce the deviation between the simulation response and the target value, thereby achieving the purpose of parameter calibration of the porous discrete element numerical model.

[0078] Specifically, after obtaining the porous discrete element numerical model, the aforementioned two-dimensional porous stratum modeling platform based on the discrete element method determines the simulation working condition data corresponding to the mechanical property parameters based on the model. For example, it sets the boundary constraints and loading methods, loading rates and contact solution parameters consistent with the uniaxial compression test, and performs discrete element numerical simulation to obtain the simulation response results, such as peak stress, stress-strain curves, failure modes, etc.

[0079] The simulation response results were compared with the mechanical property parameters to obtain calibration deviation data.

[0080] When the calibration deviation data does not meet the preset consistency conditions, the aforementioned two-dimensional porous formation modeling platform based on the discrete element method adjusts the micro-parameters of the porous discrete element numerical model. For example, it adjusts the contact stiffness parameter to correct the elastic modulus, and adjusts the bond strength or friction parameter to correct the peak strength and failure mode. After adjustment, it repeats the simulation and comparison until the calibration deviation data meets the preset consistency conditions. Then, it outputs the calibrated porous discrete element numerical model as the target porous discrete element numerical model.

[0081] The aforementioned target porous discrete element numerical model can refer to the final model constructed after pore structure constraints and parameter calibration, which simultaneously satisfies: the statistical characteristics of the pore structure reach the preset consistency, such as porosity convergence and morphological orientation conformity, and the macroscopic mechanical response meets the preset consistency, such as strength / modulus deviation within the threshold.

[0082] Through the above methods and steps, the final target porous discrete element numerical model can simultaneously satisfy the consistency between pore structure constraints and macroscopic mechanical response, reduce the problems of "structural similarity but mechanical inconsistency" or "mechanical similarity but structural deviation", and improve the robustness of the model in failure mechanism analysis, parameter sensitivity research and engineering prediction.

[0083] In this embodiment of the invention, pore characteristic data of the target formation's pore structure are acquired; a multi-pore discrete element numerical model is constructed based on the pore characteristic data; mechanical property parameters of the target formation are determined; and parameter calibration processing is performed on the multi-pore discrete element numerical model based on the mechanical property parameters to obtain the target multi-pore discrete element numerical model. By constraining the construction of the multi-pore discrete element model with pore characteristic data and calibrating it in conjunction with mechanical property parameters, the obtained model exhibits consistency in both the statistical characteristics of the pore structure and the macroscopic mechanical response, reducing the workload of manual trial and error and parameter tuning during the modeling process, and improving the repeatability of model construction and the credibility of numerical simulation.

[0084] Optionally, the step of obtaining pore feature data of the target stratum pore structure may also include obtaining a microstructure image of the rock inside the corresponding rock of the target stratum; performing quantification of rock sample structure processing on the microstructure image; and statistically analyzing the corresponding processing results to obtain pore feature data of the target stratum pore structure.

[0085] In this embodiment of the invention, the aforementioned microstructure image may be two-dimensional image data from thin section microscopy imaging (such as polarized light microscopy), scanning electron microscopy imaging, two-dimensional cross-sectional images of industrial CT slices or their two-dimensional projection images, etc., used to reflect the morphology and distribution of pores and matrix inside the target stratum rock at the microscale.

[0086] In one possible embodiment, the aforementioned two-dimensional porous strata modeling platform based on the discrete element method performs a set of image processing and measurement calculation operations on the microstructure image to transform the "pore / matrix morphology in the image" into calculable structural parameters. This can be achieved through methods including but not limited to image preprocessing (denoising, enhancement, calibration), pore region segmentation (distinguishing pores from matrix), connected component labeling (identifying individual pore objects), geometric measurement (pore area, perimeter, equivalent ellipse parameters, etc.), and statistical summarization (mean / median / distribution), thereby completing the process of quantifying rock sample structure processing.

[0087] The above processing results can refer to the intermediate and final results output by the quantitative rock sample structure processing, including but not limited to the segmentation map of the pore region (e.g., binary map or label map), the identification information of the pore objects (the number and connected domain of each pore), the geometric measurement results of the pore objects (e.g., area, equivalent half axis, orientation angle, etc.), and the statistical summary table (e.g., porosity, representative value of half axis, representative value of shape factor, etc.).

[0088] The aforementioned pore characteristic data may include, but is not limited to, target porosity Pt, the reference semi-major axis r of the pores, shape factor λ, and angular orientation θ. Specifically, the target porosity (Pt) can be the percentage of pores in a typical rock sample, serving as the convergence target value for subsequent models; the reference semi-major axis (r) can be the average or median of the semi-major axes of all extracted independent pores, fitted as an elliptical geometry, serving as a benchmark measure of pore size; the distribution form can be the probability statistics of pore size data, fitting its distribution law, and determining the mathematical distribution model it conforms to (such as exponential distribution, log-normal distribution, or Weibull distribution); the shape factor (λ) can be the ratio of the major axis to the minor axis of the statistical pores (λ = major axis / minor axis, and λ≥1), used to characterize the flatness of the pores; the angular orientation (θ) can be the diagonal distribution of the major axis of the statistical pores relative to the reference coordinate axis. If the angular distribution is approximately uniform, it is determined to be a random distribution; if it is concentrated in a specific interval, it is determined to have a dominant directionality (i.e., bedding characteristics).

[0089] Optionally, the step of constructing a multi-porous discrete element numerical model based on pore feature data further includes: generating multiple matrix particles within the boundary of a preset two-dimensional geometric model, and forming an initial discrete element particle set based on the multiple matrix particles; generating a set of pore seed points within the boundary of the preset two-dimensional geometric model based on pore feature data, and determining an initial value for the number of seed points; configuring geometric attributes for the pore seed points in the pore seed point set; performing pore range determination processing on the initial discrete element particle set to obtain multiple target matrix particles, and determining the current pore structure based on the multiple target matrix particles; determining the porosity deviation based on the current pore structure, and performing convergence control on the pore generation process based on the porosity deviation; and outputting a multi-porous discrete element numerical model when the current porosity meets a preset convergence condition.

[0090] In this embodiment of the invention, the above-mentioned geometric attributes include position coordinates (xS, yS), random semi-major axis R1, angular orientation θ1, and shape factor λ1. It should be noted that the pore feature data is used to describe the statistical characteristics of the pore structure of the target stratum; the geometric attributes of the pore seed point are used to describe the specific parameters of a single pore instance. In this embodiment, the random semi-major axis R1, angular orientation θ1, and shape factor λ1 are generated or sampled from the scale and distribution rules represented by the above-mentioned pore feature data.

[0091] More specifically, the coordinates (xS, yS) mentioned above can be randomly generated position coordinates within the model area, used to determine the center position of the pores;

[0092] The aforementioned random semi-major axis R1 can be a random variable (following a statistical distribution with the reference semi-major axis r as a characteristic parameter) generated based on the pore distribution form determined in step 101. This parameter determines the size of the pore semi-major axis generated by a single seed point, thereby reproducing the pore size non-uniformity of the real rock sample in the model;

[0093] The aforementioned angle θ1 can be consistent with the pore angle in step 101. If step 101 determines that it is random, it is randomly generated within the range of (0~180°); if step 101 determines that it has a main directionality, it is generated within a specific angle range to simulate the actual direction of the pore.

[0094] The shape factor λ1 mentioned above can be consistent with the pore shape factor in step 101. When λ1=1, the generated pores are circular; when λ1>1, the generated pores are elliptical.

[0095] The aforementioned matrix particles refer to the discrete element basic units generated within the boundaries of a preset two-dimensional geometric model by the aforementioned two-dimensional porous formation modeling platform based on the discrete element method. These units possess attributes such as location, equivalent particle size (or radius), and density, and form contact and force transmission networks during the discrete element solution process. They are used to characterize the granular discrete body of the rock matrix in the formation. They can be simply understood as the skeleton of the non-porous portion.

[0096] The aforementioned initial discrete element particle set refers to the set of data composed of multiple matrix particles before the aforementioned two-dimensional porous formation modeling platform based on the discrete element method generates the pore structure. Simply put, this set can first constitute a controllable and repeatable initial state of matrix particles, and then pore generation processing is performed on it based on the pore data.

[0097] The aforementioned set of pore seed points can refer to the set of multiple pore center points (or pore generation reference points) generated within the preset two-dimensional geometric model boundary by the two-dimensional multi-porosity stratum modeling platform based on the discrete element method. It can be understood that each pore seed point corresponds to a pore instance to be generated, which is used to drive the subsequent pore range determination process.

[0098] The initial value of the number of seed points mentioned above can refer to the number of pore seed points N0 used by the two-dimensional porous stratum modeling platform based on the discrete element method in the first pore generation iteration. Generally, it can be estimated by combining the model area scale parameters and pore characteristic data.

[0099] In one possible embodiment, the aforementioned two-dimensional porous formation modeling platform based on the discrete element method can instantiate the geometric parameters of each pore seed point, such as pore center coordinates (xS, yS), pore scale parameters (e.g., semi-major axis R), shape factor λ, and angular orientation θ, thereby achieving the purpose of transforming the "statistical characteristics / representative values / distribution rules" reflected in the pore feature data into "specific values" for each pore instance. These specific values ​​can be used as calculation data for subsequent range determination.

[0100] In another possible embodiment, the aforementioned two-dimensional porous formation modeling platform based on the discrete element method can, for matrix particles in the initial discrete element particle set, determine whether each matrix particle falls within a preset pore range of a certain pore by combining the geometric properties of the pore seed points, thereby filtering out target matrix particles. Specifically, this can be achieved by determining the relative position of the particles and the pore seed points, performing coordinate transformation to obtain local coordinates, performing range judgment based on geometric properties, and outputting the judgment result.

[0101] The aforementioned target matrix particles can refer to a subset of matrix particles selected from the initial discrete element particle set after pore range determination processing. These particles are used to subsequently determine the current pore structure and serve as the matrix framework for the multi-pore discrete element numerical model.

[0102] The aforementioned current pore structure can refer to the spatial distribution of pores obtained by the two-dimensional multi-porosity formation modeling platform based on the discrete element method after a certain pore generation iteration. It is used to characterize the position, scale, shape and orientation of the pore region in the model at this time. It can be determined by the spatial distribution of the target matrix particles and the pore range determination results, and serves as the basic input for porosity calculation and deviation assessment.

[0103] The aforementioned porosity deviation can refer to the deviation obtained by the two-dimensional multi-porosity formation modeling platform based on the discrete element method comparing the current porosity Pc with the target porosity Pt. It is used to measure the difference between the current pore structure and the target pore structure in terms of pore ratio, and can be used as a driving force for convergence control to adjust the pore generation process toward the target porosity.

[0104] In another possible embodiment, the aforementioned two-dimensional porous formation modeling platform based on the discrete element method can iteratively adjust the pore generation process according to the porosity deviation, so that the porosity deviation gradually decreases and eventually meets the preset convergence condition. For example, when the deviation is too large, the pore generation control parameters (such as the number of pore seed points, the seed point geometric attribute generation strategy, etc.) are adjusted and pore generation is re-executed, thereby approximating the target porosity within a finite number of iterations.

[0105] The aforementioned preset convergence conditions may refer to the judgment conditions used by the two-dimensional porous formation modeling platform based on the discrete element method to determine whether the porosity generation process has reached the expected conditions, including but not limited to the porosity deviation being less than the threshold ε, or the number of iterations reaching the upper limit, or the porosity deviation changing less than the threshold in several consecutive iterations.

[0106] Optionally, the step of generating a set of pore seed points within the boundary of a preset two-dimensional geometric model based on pore feature data and determining an initial value for the number of seed points further includes determining boundary parameters of the preset two-dimensional geometric model boundary; determining an initial value for the number of pore seed points based on the boundary parameters and pore feature data; and generating a corresponding number of pore seed points within the boundary of the preset two-dimensional geometric model according to the initial value to obtain a set of seed points.

[0107] In this embodiment of the invention, the boundary parameters mentioned above can refer to a set of parameters describing the boundary of a preset two-dimensional geometric model, including at least the geometric scale parameters and coordinate constraint parameters of the model region. For example, the boundary parameters may include, but are not limited to, the length L of the model region. x Width L y The area of ​​the model region A=L x ×L y Boundary location range [x min ,x max ]、[y min ,y max And boundary types (such as rigid boundaries / periodic boundaries, etc.).

[0108] The initial value mentioned above can refer to the initial value N0 of the number of pore seed points, used as the input for the number of pore instances in the first pore generation iteration. Specifically, the initial value can be calculated using the following formula:

[0109]

[0110] Among them, A r The area is the geometric model area.

[0111] For example, the geometric area A of the model r =200mm×400mm=80000mm2, target porosity P t =20%, reference semi-major axis r=5, shape factor λ=2.2. Substitute the above parameters into the above formula N to calculate:

[0112]

[0113] Therefore, the initial number of virtual seed points N is set to 448.

[0114] Optionally, the steps of determining the pore range of the initial discrete element particle set to obtain multiple target matrix particles, and determining the current pore structure based on the multiple target matrix particles, further include determining the relative positional relationship of each matrix particle relative to the pore seed point in the initial discrete element particle set; performing coordinate transformation on each matrix particle based on the relative positional relationship to obtain the transformed coordinates of each matrix particle in the local coordinate system of the pore seed point; determining the pore range based on the transformed coordinates and the geometric properties of the pore seed point to identify multiple target matrix particles in each matrix particle; and determining the current pore structure based on the multiple target matrix particles.

[0115] In this embodiment of the invention, the aforementioned relative positional relationship may refer to a set of information on the spatial relative relationship between a matrix particle and a pore seed point, such as the displacement vector, displacement direction quadrant, and displacement length between the center of the matrix particle and the center of the pore seed point.

[0116] The aforementioned coordinate transformation process can refer to the process by which the two-dimensional porous stratum modeling platform based on the discrete element method transforms the global coordinates of matrix particles into the local coordinate system corresponding to the pore seed point. This includes translation transformation with the pore seed point as the origin, and rotation alignment of the coordinate axes according to the angular orientation of the pore seed point (even if the pore has an orientation, the judgment is made in the coordinate system of its "own orientation").

[0117] The aforementioned transformed coordinates refer to the coordinate values ​​of the matrix particles in the local coordinate system of the pore seed point after the coordinate transformation process is completed. This reflects the relative position of the matrix particles with respect to the center of the pore instance and after the orientation of the pore instance is aligned. It can be used as input for pore range determination, enabling the aforementioned two-dimensional multi-porosity formation modeling platform based on the discrete element method to determine whether the matrix particles are within the preset pore range of the pore instance.

[0118] The aforementioned pore range determination refers to the process by which the two-dimensional multi-porosity formation modeling platform based on the discrete element method determines whether matrix particles fall within the pore range based on transformed coordinates and the geometric properties of pore seed points. The specific determination algorithm can be explained as follows:

[0119] First, calculate the relative distances dx and dy between the matrix particle i (xi, yi) and the seed point S (xS, yS):

[0120]

[0121]

[0122] Transform the relative coordinates to the local coordinate system (dx', dy') of the seed point using a rotation matrix:

[0123]

[0124] The elliptic geometry criterion is applied for screening. If the following inequality is satisfied, the particle is determined to be located inside the pore and is removed:

[0125]

[0126] Optionally, the steps of determining the porosity deviation based on the current pore structure, and performing convergence control on the pore generation process based on the porosity deviation, and outputting a multi-pore discrete element numerical model when the current porosity meets the preset convergence condition, further include: determining the current porosity based on the current pore structure; comparing the current porosity with the target porosity to determine the porosity deviation; determining whether the porosity deviation meets the preset convergence condition; if the porosity deviation meets the preset convergence condition, determining the current pore structure as the pore structure of the multi-pore discrete element numerical model, and outputting the multi-pore discrete element numerical model; if the porosity deviation does not meet the preset convergence condition, updating the pore generation control parameters, and adjusting the current porosity based on the updated pore generation control parameters until the porosity deviation meets the preset convergence condition.

[0127] In this embodiment of the invention, the aforementioned current porosity can refer to the porosity ratio calculated by the two-dimensional multi-porosity formation modeling platform based on the discrete element method after a certain porosity generation iteration, based on the current pore structure. This ratio represents the proportion of the pore region within the preset two-dimensional geometric model boundary in the current model. Specifically, it can be calculated using the following formula:

[0128] The current porosity (Pc) is calculated by adding the area of ​​the geometric model (Ar = 200 mm × 400 mm) to the area of ​​the remaining particles:

[0129]

[0130] Where n is the total number of remaining particles, and Ai is the area of ​​the i-th particle.

[0131] The aforementioned target porosity can refer to the target formation porosity ratio parameter obtained from the pore feature data by the aforementioned two-dimensional multi-porosity formation modeling platform based on the discrete element method. It is used as an alignment target for the pore generation process and can be obtained by quantifying the rock sample structure from the microstructure image. That is, it is the proportion of the pore area to the total area of ​​the image. The aforementioned two-dimensional multi-porosity formation modeling platform based on the discrete element method can use it as a benchmark value for pore generation and convergence control.

[0132] The aforementioned porosity deviation refers to the deviation obtained by comparing the current porosity with the target porosity using the two-dimensional multi-porosity formation modeling platform based on the discrete element method. It is used to characterize the difference between the current and target pore structures in terms of pore proportion. Generally, it can be calculated directly using the absolute value of the difference between the current and target porosity.

[0133] The aforementioned preset convergence conditions can refer to the set of conditions used by the two-dimensional porous formation modeling platform based on the discrete element method to determine whether the porosity generation process has reached an acceptable target, including but not limited to porosity deviation being less than or equal to a preset threshold, maximum iteration count constraint, or stability conditions where the deviation change is less than a threshold after several consecutive iterations.

[0134] Specifically, convergence can be determined through the following steps:

[0135] Set the allowable error threshold ε = 3% between the current porosity (Pc) and the target porosity of 20%.

[0136]

[0137] If the condition is met (i.e., the calculation deviation is less than a set threshold of 3%), then convergence is determined, the file data of the porous discrete element numerical model is output, and the process proceeds to the next step. If the condition is not met, the number of seed points N for the next round is dynamically adjusted according to the direction of the deviation.

[0138] Specifically, when the current porosity is insufficient, i.e., P c If the percentage is less than 20%, the number of seed points needs to be increased to prevent oscillations. A damping constraint factor F = 1.3 is introduced. Simultaneously, to prevent the number of seed points from ceasing to increase due to rounding in the later stages of iteration, an adjustment constant ΔN = 5 is introduced. The calculation formula is as follows:

[0139]

[0140] Where, N n Set the new seed points.

[0141] When the current porosity is excessive, i.e., P c If the percentage is greater than 20%, the number of seed points needs to be reduced proportionally.

[0142]

[0143] That is, N n The above-mentioned pore generation control parameters are used to guide the porosity toward the target porosity when the porosity deviation does not meet the convergence condition.

[0144] Optionally, the step of calibrating the porous discrete element numerical model based on mechanical property parameters to obtain the target porous discrete element numerical model further includes determining the simulation working condition data corresponding to the mechanical property parameters based on the porous discrete element numerical model, performing simulation based on the simulation working condition data to obtain the simulation response result; comparing the simulation response result with the mechanical property parameters to obtain calibration deviation data; when the calibration deviation data does not meet the preset consistency condition, adjusting the micro-parameters of the porous discrete element numerical model, and repeatedly performing the judgment of the calibration deviation data and the preset consistency condition after adjustment until the calibration deviation data meets the preset consistency condition, and outputting the calibrated porous discrete element numerical model as the target porous discrete element numerical model.

[0145] In this embodiment of the invention, the simulated working condition data mentioned above may refer to a set of simulation input data determined by the two-dimensional porous stratum modeling platform based on the discrete element method in order to reproduce the experimental / engineering loading process corresponding to the mechanical property parameters on the porous discrete element numerical model. This includes, but is not limited to, boundary constraint methods, loading methods and loading process parameters, such as loading direction, loading rate, displacement / force control mode, initial stress state, boundary fixing conditions and solution step size or iteration control parameters.

[0146] The aforementioned simulation response results may refer to the output results obtained by the two-dimensional porous formation modeling platform based on the discrete element method after performing simulation on the porous discrete element numerical model based on the simulated working condition data. These include, but are not limited to, macroscopic response quantities that can be compared with mechanical property parameters, such as peak strength, stress-strain curve characteristics (elastic segment slope, post-peak softening trend), failure mode or fracture evolution characteristics, etc. They may also include intermediate results used for interpretation, such as energy evolution and contact force chain distribution.

[0147] The aforementioned calibration deviation data can refer to the deviation quantity or set of deviations obtained by comparing the simulated response results with mechanical property parameters using the aforementioned two-dimensional porous formation modeling platform based on the discrete element method. This includes, but is not limited to, strength deviation, modulus deviation, Poisson's ratio deviation, and curve shape deviation, which are used to quantify the difference between the current model and the target formation in terms of mechanical response. Generally, it can be expressed in the form of an interpolation or a relative error.

[0148] The aforementioned pre-set consistency conditions can refer to the set of judgment conditions used by the two-dimensional porous stratum modeling platform based on the discrete element method to determine whether the calibration has reached the target. For example, the calibration deviation data is less than or equal to a preset threshold. It can also be conditions such as multiple indicators simultaneously meeting the threshold, curve shape similarity meeting the threshold, or the number of iterations reaching the upper limit.

[0149] The aforementioned micro-parameters can refer to the set of parameters used at the discrete element level by the two-dimensional porous strata modeling platform based on the discrete element method to describe the micro-interactions such as contact / bonding / friction between particles and between particles and boundaries. These parameters include contact stiffness parameters, damping parameters, friction coefficient, bond strength parameters (normal / tangential bond strength), fracture threshold, etc. Adjusting the micro-parameters can be used to change the simulated elastic modulus, strength level, and failure mode, thereby enabling the simulated response to gradually approach the mechanical property parameters.

[0150] In this embodiment, a parallel bond model (PBM) can be used to define the contact behavior between discrete element particles. Based on this, a rock mechanics experiment consistent with the aforementioned mechanical property parameters is constructed in the numerical model. The failure process and macroscopic stress-strain response of the model are monitored. By repeatedly adjusting the microscopic parameters between particles (such as stiffness ratio, bond strength, friction coefficient, etc.), until the numerical simulation results match the macroscopic mechanical properties of the rock obtained from the aforementioned mechanical property parameters, the final microscopic parameters are determined, and the construction of the target porous discrete element numerical model is completed.

[0151] like Figure 2 As shown, this embodiment of the invention also provides a two-dimensional porous formation modeling device 200 based on the discrete element method, which includes:

[0152] The first acquisition module 201 is used to acquire pore feature data of the pore structure of the target stratum.

[0153] The first construction module 202 is used to construct a multi-pore discrete element numerical model based on the pore feature data;

[0154] The first determining module 203 is used to determine the mechanical property parameters of the target formation;

[0155] The first processing module 204 is used to perform parameter calibration processing on the porous discrete element numerical model based on the mechanical property parameters to obtain the target porous discrete element numerical model.

[0156] Optionally, the first acquisition module 201 mentioned above includes:

[0157] The first acquisition submodule is used to acquire images of the microstructure inside the rock corresponding to the target stratum;

[0158] The second acquisition submodule is used to quantify the rock sample structure of the microstructure image and statistically analyze the corresponding processing results to obtain pore feature data of the target stratum pore structure. The pore feature data includes the target porosity Pt, the reference semi-major axis r of the pore, the shape factor λ, and the angle orientation θ.

[0159] Optionally, the first building module 202 mentioned above includes:

[0160] The first construction submodule is used to generate multiple matrix particles within the boundary of a preset two-dimensional geometric model, and to form an initial discrete element particle set based on the multiple matrix particles.

[0161] The second construction submodule is used to generate a set of pore seed points within the boundary of the preset two-dimensional geometric model based on the pore feature data, and to determine the initial value of the number of seed points.

[0162] The third construction submodule is used to configure geometric attributes for the pore seed points in the pore seed point set. The geometric attributes include position coordinates (xS, yS), random semi-major axis R1, angular orientation θ1, and shape factor λ1.

[0163] The fourth construction submodule is used to perform pore range determination processing on the initial discrete element particle set to obtain multiple target matrix particles, and to determine the current pore structure based on the multiple target matrix particles;

[0164] The fifth construction submodule is used to determine the porosity deviation based on the current pore structure, and to perform convergence control on the pore generation process based on the porosity deviation. When the current porosity meets the preset convergence condition, the multi-pore discrete element numerical model is output.

[0165] Optionally, the second construction submodule mentioned above includes:

[0166] The first construction unit is used to determine the boundary parameters of the preset two-dimensional geometric model boundary;

[0167] The second construction unit is used to determine the initial value of the number of pore seed points based on the boundary parameters and pore feature data.

[0168] The third construction unit is used to generate a corresponding number of pore seed points within the boundary of the preset two-dimensional geometric model according to the initial value, so as to obtain a seed point set.

[0169] Optionally, the fourth construction submodule mentioned above includes:

[0170] The fourth construction unit is used to determine the relative positional relationship of each matrix particle with respect to the pore seed point in the initial discrete element particle set;

[0171] The fifth construction unit is used to perform coordinate transformation processing on each matrix particle based on the relative positional relationship to obtain the transformed coordinates of each matrix particle in the local coordinate system of the pore seed point;

[0172] The sixth construction unit is used to determine the pore range based on the transformed coordinates and the geometric properties of the pore seed points, and to identify multiple target matrix particles in each matrix particle.

[0173] The seventh construction unit is used to determine the current pore structure based on the plurality of target matrix particles.

[0174] Optionally, the fifth construction submodule mentioned above includes:

[0175] The eighth construction unit is used to determine the current porosity based on the current pore structure;

[0176] The ninth construction unit is used to compare the current porosity with the target porosity to determine the porosity deviation;

[0177] The tenth construction unit is used to determine whether the porosity deviation meets the preset convergence condition;

[0178] The eleventh construction unit is used to determine the current pore structure as the pore structure of the multi-pore discrete element numerical model when the porosity deviation meets the preset convergence condition, and output the multi-pore discrete element numerical model.

[0179] The twelfth construction unit is used to update the pore generation control parameters if the porosity deviation does not meet the preset convergence condition, and adjust the current porosity based on the updated pore generation control parameters until the porosity deviation meets the preset convergence condition.

[0180] Optionally, the first processing module 204 mentioned above includes:

[0181] The first processing submodule is used to determine the simulated working condition data corresponding to the mechanical property parameters based on the porous discrete element numerical model, and to perform simulation based on the simulated working condition data to obtain the simulation response results.

[0182] The second processing submodule is used to compare the simulation response results with the mechanical property parameters to obtain calibration deviation data.

[0183] The third processing submodule is used to adjust the microscopic parameters of the porous discrete element numerical model when the calibration deviation data does not meet the preset consistency conditions, and to repeatedly perform the judgment of the calibration deviation data and the preset consistency conditions after adjustment until the calibration deviation data meets the preset consistency conditions, and then output the calibrated porous discrete element numerical model as the target porous discrete element numerical model.

[0184] like Figure 3As shown, this embodiment of the invention also provides an electronic device 300, including a processor, which can execute any of the above-mentioned two-dimensional porous strata modeling methods based on the discrete element method.

[0185] Specifically, it includes a processor 301 and a memory 302, as well as a computer program stored in the memory 302 and capable of running on the processor 301, which executes a two-dimensional porous formation modeling method based on the discrete element method, wherein:

[0186] The processor 301 executes the calculator program for a two-dimensional porous formation modeling method based on the discrete element method, stored in the runtime memory 302, and performs the following steps:

[0187] Acquire pore characteristic data of the target formation's pore structure;

[0188] Based on the pore feature data, a multi-pore discrete element numerical model is constructed;

[0189] Determine the mechanical property parameters of the target formation;

[0190] Based on the mechanical property parameters, the porous discrete element numerical model is calibrated to obtain the target porous discrete element numerical model.

[0191] Optionally, the processor 301 performs the process of acquiring pore feature data of the target formation pore structure, including:

[0192] Obtain microscopic structural images of the rocks corresponding to the target strata;

[0193] The microstructure image is subjected to quantification of rock sample structure, and the corresponding processing results are statistically analyzed to obtain pore feature data of the target stratum pore structure. The pore feature data includes target porosity Pt, reference semi-major axis r of the pore, shape factor λ, and angular orientation θ.

[0194] Optionally, the processor 301 executes the construction of a multi-pore discrete element numerical model based on the pore feature data, including:

[0195] Within the boundaries of a pre-defined two-dimensional geometric model, multiple matrix particles are generated, and an initial set of discrete element particles is formed based on these multiple matrix particles.

[0196] Based on the pore feature data, a set of pore seed points is generated within the boundary of the preset two-dimensional geometric model, and the initial value of the number of seed points is determined.

[0197] Configure geometric attributes for the pore seed points in the pore seed point set. The geometric attributes include position coordinates (xS, yS), random semi-major axis R1, angular orientation θ1, and shape factor λ1.

[0198] The initial discrete element particle set is subjected to pore range determination processing to obtain multiple target matrix particles, and the current pore structure is determined based on the multiple target matrix particles;

[0199] Based on the current pore structure, the porosity deviation is determined, and the pore formation process is converged based on the porosity deviation. When the current porosity meets the preset convergence condition, the multi-pore discrete element numerical model is output.

[0200] Optionally, the processor 301 executes the process of generating a set of pore seed points within the boundary of the preset two-dimensional geometric model based on the pore feature data, and determines an initial value for the number of seed points, including:

[0201] Determine the boundary parameters of the preset two-dimensional geometric model boundary;

[0202] Based on the boundary parameters and pore feature data, determine the initial value of the number of pore seed points;

[0203] Based on the initial value, a corresponding number of pore seed points are generated within the boundary of the preset two-dimensional geometric model to obtain a seed point set.

[0204] Optionally, the processor 301 performs the pore range determination process on the initial discrete element particle set to obtain multiple target matrix particles, and determines the current pore structure based on the multiple target matrix particles, including:

[0205] Determine the relative positional relationship of each matrix particle with respect to the pore seed point in the initial discrete element particle set;

[0206] Based on the relative positional relationship, coordinate transformation is performed on each matrix particle to obtain the transformed coordinates of each matrix particle in the local coordinate system of the pore seed point;

[0207] The pore range is determined based on the transformed coordinates and the geometric properties of the pore seed points, and multiple target matrix particles are identified in each matrix particle.

[0208] The current pore structure is determined based on the plurality of target matrix particles.

[0209] Optionally, the processor 301 executes the step of determining the porosity deviation based on the current pore structure and performing convergence control on the pore formation process based on the porosity deviation. When the current porosity meets the preset convergence condition, the processor outputs the multi-pore discrete element numerical model, including:

[0210] Based on the current pore structure, determine the current porosity;

[0211] The porosity deviation is determined by comparing the current porosity with the target porosity.

[0212] Determine whether the porosity deviation meets the preset convergence condition;

[0213] If the porosity deviation meets the preset convergence condition, the current pore structure is determined as the pore structure of the multi-pore discrete element numerical model, and the multi-pore discrete element numerical model is output.

[0214] If the porosity deviation does not meet the preset convergence condition, the porosity generation control parameters are updated, and the current porosity is adjusted based on the updated porosity generation control parameters until the porosity deviation meets the preset convergence condition.

[0215] Optionally, the processor 301 performs parameter calibration processing on the porous discrete element numerical model based on the mechanical property parameters to obtain the target porous discrete element numerical model, including:

[0216] Based on the porous discrete element numerical model, the simulated working condition data corresponding to the mechanical property parameters are determined, and the simulation is performed according to the simulated working condition data to obtain the simulation response results.

[0217] Based on the simulated response results, the calibration deviation data is obtained by comparing them with the mechanical property parameters.

[0218] When the calibration deviation data does not meet the preset consistency condition, the microscopic parameters of the porous discrete element numerical model are adjusted, and the judgment of the calibration deviation data and the preset consistency condition is repeated after adjustment until the calibration deviation data meets the preset consistency condition. Then, the calibrated porous discrete element numerical model is output as the target porous discrete element numerical model.

[0219] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the two-dimensional porous stratum modeling method based on the discrete element method provided in this invention, or the application-side two-dimensional porous stratum modeling method based on the discrete element method, and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0220] Those skilled in the art will understand that implementing all or part of the processes in the above embodiments can be accomplished by a computer program instructing related hardware, and can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0221] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for modeling a two-dimensional porous formation based on the discrete element method, characterized in that, include: Acquire pore characteristic data of the target formation's pore structure; Within the boundaries of a pre-defined two-dimensional geometric model, multiple matrix particles are generated, and an initial set of discrete element particles is formed based on these multiple matrix particles. Based on the pore feature data, a set of pore seed points is generated within the boundary of the preset two-dimensional geometric model, and the initial value of the number of seed points is determined. Configure geometric attributes for the pore seed points in the pore seed point set. The geometric attributes include position coordinates (xS, yS), random semi-major axis R1, angular orientation θ1, and shape factor λ1. The initial discrete element particle set is subjected to pore range determination processing to obtain multiple target matrix particles, and the current pore structure is determined based on the multiple target matrix particles; Based on the current pore structure, the porosity deviation is determined, and the pore formation process is converged based on the porosity deviation. When the current porosity meets the preset convergence condition, a multi-pore discrete element numerical model is output. Determine the mechanical property parameters of the target formation; Based on the mechanical property parameters, the porous discrete element numerical model is calibrated to obtain the target porous discrete element numerical model.

2. The two-dimensional porous formation modeling method based on the discrete element method as described in claim 1, characterized in that, The acquisition of pore feature data of the target formation pore structure includes: Obtain microscopic structural images of the rocks corresponding to the target strata; The microstructure image is subjected to quantification of rock sample structure, and the corresponding processing results are statistically analyzed to obtain pore feature data of the target stratum pore structure. The pore feature data includes the target porosity Pt, the reference semi-major axis r of the pore, the shape factor λ, and the angular orientation θ.

3. The two-dimensional porous formation modeling method based on the discrete element method as described in claim 1, characterized in that, The step of generating a set of pore seed points within the boundary of the preset two-dimensional geometric model based on the pore feature data, and determining an initial value for the number of seed points, includes: Determine the boundary parameters of the preset two-dimensional geometric model boundary; Based on the boundary parameters and pore feature data, determine the initial value of the number of pore seed points; Based on the initial value, a corresponding number of pore seed points are generated within the boundary of the preset two-dimensional geometric model to obtain a seed point set.

4. The two-dimensional porous formation modeling method based on the discrete element method as described in claim 1, characterized in that, The step of performing pore range determination processing on the initial discrete element particle set to obtain multiple target matrix particles, and determining the current pore structure based on the multiple target matrix particles, includes: Determine the relative positional relationship of each matrix particle with respect to the pore seed point in the initial discrete element particle set; Based on the relative positional relationship, coordinate transformation is performed on each matrix particle to obtain the transformed coordinates of each matrix particle in the local coordinate system of the pore seed point; The pore range is determined based on the transformed coordinates and the geometric properties of the pore seed points, and multiple target matrix particles are identified in each matrix particle. The current pore structure is determined based on the plurality of target matrix particles.

5. The two-dimensional porous formation modeling method based on the discrete element method as described in claim 1, characterized in that, The process involves determining the porosity deviation based on the current pore structure, and performing convergence control on the pore formation process based on the porosity deviation. When the current porosity meets the preset convergence condition, the multi-pore discrete element numerical model is output, including: Based on the current pore structure, determine the current porosity; The porosity deviation is determined by comparing the current porosity with the target porosity. Determine whether the porosity deviation meets the preset convergence condition; If the porosity deviation meets the preset convergence condition, the current pore structure is determined as the pore structure of the multi-pore discrete element numerical model, and the multi-pore discrete element numerical model is output. If the porosity deviation does not meet the preset convergence condition, the porosity generation control parameters are updated, and the current porosity is adjusted based on the updated porosity generation control parameters until the porosity deviation meets the preset convergence condition.

6. The two-dimensional porous formation modeling method based on the discrete element method as described in claim 1, characterized in that, The step of calibrating the porous discrete element numerical model based on the mechanical property parameters to obtain the target porous discrete element numerical model includes: Based on the porous discrete element numerical model, the simulated working condition data corresponding to the mechanical property parameters are determined, and the simulation is performed according to the simulated working condition data to obtain the simulation response results. Based on the simulated response results, the calibration deviation data is obtained by comparing them with the mechanical property parameters. When the calibration deviation data does not meet the preset consistency condition, the microscopic parameters of the porous discrete element numerical model are adjusted, and the judgment of the calibration deviation data and the preset consistency condition is repeated after adjustment until the calibration deviation data meets the preset consistency condition. Then, the calibrated porous discrete element numerical model is output as the target porous discrete element numerical model.

7. The two-dimensional porous formation modeling device based on the discrete element method as described in claim 1, characterized in that, include: The first acquisition module is used to acquire pore feature data of the pore structure of the target formation. The first construction module is used to construct a multi-pore discrete element numerical model based on the pore feature data. The first determining module is used to determine the mechanical property parameters of the target formation; The first processing module is used to perform parameter calibration processing on the porous discrete element numerical model based on the mechanical property parameters to obtain the target porous discrete element numerical model.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the two-dimensional porous formation modeling method based on the discrete element method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps in the two-dimensional porous formation modeling method based on the discrete element method as described in any one of claims 1 to 6.

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