Deep resource fluidized mining rock mass fracture mechanism analysis method

Abstract

The invention discloses a deep resource fluidized mining rock mass fracture mechanism analysis method, and belongs to the technical field of deep resource mining. According to the method, deep resource fluidized mining basic parameters are obtained and preprocessed, and a heterogeneous mechanical parameter field and a discrete fracture network model are constructed; based on the acquired geological environment data and the engineering disturbance parameter set, establishing a three-dimensional geological model, coupling a seepage field and a stress field, and dynamically iterating to obtain a time sequence stress field; and introducing correlation coefficients and correlation factors to construct a criterion extension criterion set, importing the heterogeneous mechanical parameter field, the discrete fracture network model, the time sequence stress field and the criterion extension criterion set into a coupling numerical model, and simulating a rock mass fracture process to obtain a rock mass fracture extension path. According to the method, dynamic accurate prediction of rock mass fracture is achieved, support is provided for deep resource fluidization mining parameter optimization and safety boundary delimitation, efficient development of mineral resources is guaranteed, and engineering risks are reduced.

Description

Technical Field

[0001] This invention relates to the field of deep resource extraction technology, specifically to a method for analyzing the rock mass fracture mechanism in fluidized bed mining of deep resources. Background Technology

[0002] With the continued growth of global energy demand, the development and utilization of deep resources has become a key direction for ensuring energy security. Fluidized bed mining technology is a future-oriented exploratory technology. Its core is to convert solid mineral resources into gaseous or liquid states before extraction. Identifying the rock mass fracturing process during this process is crucial for ensuring the safe and efficient extraction of resources. However, deep rock masses exist in complex geological environments characterized by high temperature, high pressure, and high stress. Furthermore, the rock masses themselves exhibit strong heterogeneity and fracture development, further exacerbating the complexity of the rock mass fracturing mechanism. If the evolution of fracturing cannot be accurately analyzed, it can easily lead to formation instability, low resource recovery rates, and engineering safety accidents, thus hindering the large-scale application of fluidized bed mining technology for deep resources.

[0003] Currently, significant progress has been made in the analysis of rock fracture mechanisms. For example, patent application publication number CN118818602A discloses a rock fracture mechanism analysis method based on the dual dominant frequency characteristics of microseismic signals. This method extracts the high and low dominant frequency characteristics of microseismic signals through fast Fourier transform, and combines parametric analysis and polar mean method to establish the correlation between the dual dominant frequency characteristics and tensile failure and shear failure, thus realizing the identification of rock fracture mechanisms at different scales. This method has the characteristics of high accuracy and good universality in fracture type discrimination, providing an important monitoring and analysis approach for the study of rock fracture mechanisms. However, existing technologies still have significant shortcomings in the context of fluidized bed mining of deep resources: First, existing analysis methods mostly rely on microseismic signal monitoring results for post-hoc identification, lacking the ability to dynamically simulate the fracture process. This makes it difficult to combine deep resource fluidized bed mining parameters with key factors such as rock mass heterogeneity and geological structure to achieve forward-looking prediction of fracture initiation and propagation paths. Second, in deep fluidized bed mining, the dynamic coupling of seepage field and stress field is the core factor driving rock mass fracture. However, existing methods do not fully consider the temporal evolution characteristics of this coupling effect, nor do they optimize fracture criteria based on the hierarchical characteristics of dominant and secondary fractures in the rock mass, leading to deviations between analysis results and actual engineering scenarios. Third, patent CN118818602A focuses on the type identification of fracture mechanisms but does not involve the three-dimensional spatial characterization and temporal evolution law quantification of fracture propagation paths, failing to provide direct technical support for optimizing deep resource fluidized bed mining parameters and delineating safety boundaries in fluidized bed mining.

[0004] Therefore, in response to the special needs of fluidized bed mining of deep resources, there is an urgent need for an analytical method that can couple the dynamic evolution of rock mass heterogeneity, engineering disturbance and seepage stress, and integrate fracture mechanism identification and propagation path simulation, so as to solve the problems of insufficient dynamic prediction, poor adaptability and weak engineering guidance in existing technologies, and provide technical support for the safe and efficient implementation of fluidized bed mining of deep resources. Summary of the Invention

[0005] The purpose of this invention is to provide a method for analyzing the rock mass fracture mechanism in the fluidized mining of deep resources, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for analyzing the fracturing mechanism of rock mass in deep resource fluidized mining includes the following steps:

[0008] S1. Obtain basic parameters and geological environment data for fluidized bed mining of deep resources, preprocess the basic parameters for fluidized bed mining of deep resources, and obtain the rock mass parameter distribution field and engineering disturbance parameter set;

[0009] S2. Based on the rock mass parameter distribution field, a heterogeneous mechanical parameter field is constructed using stochastic medium theory and fractal geometry, and a discrete fracture network model is generated by combining Monte Carlo simulation.

[0010] S3. Based on geological environment data and engineering disturbance parameter set, construct a three-dimensional geological model, couple the effects of seepage field and stress field, dynamically iterate according to mining sequence, and output the time-series stress field;

[0011] S4. Based on the heterogeneous mechanical parameter field, time-series stress field and engineering disturbance parameter set, the heterogeneity correction coefficient and engineering disturbance coefficient are introduced to improve the fracture initiation criterion, and the heterogeneous guidance factor and disturbance stress direction factor are introduced to improve the fracture propagation criterion, forming a criterion extension criterion set;

[0012] S5. Import the heterogeneous mechanical parameter field, discrete fracture network model, time-series stress field, and criterion extension set into the coupled numerical model and simulate the rock mass fracture process to obtain the rock mass fracture propagation path.

[0013] Preferably, the basic parameters for deep resource fluidized mining include rock mass physical and mechanical parameters and engineering disturbance parameters. The engineering disturbance parameters include the vibration frequency of the mining equipment, the vibration amplitude of the mining equipment, the resource extraction rate, the spacing of the support structures, the support strength, and the perforation density preset in the mining process design, as well as the well location and well depth determined through well location planning. The wellbore parameters include the perforation density, well location, and well depth. The support data includes the spacing of the support structures and the support strength.

[0014] The physical and mechanical parameters of the rock mass include the elastic modulus, compressive strength, and tensile strength measured by core laboratory tests, as well as fracture observation data obtained through geological exploration, borehole imaging, and field observation. The fracture observation data includes at least the fracture linear density characterizing the degree of fracture development, the fracture orientation and dip angle reflecting the spatial orientation of the fracture, and the fracture length and width describing the geometric dimensions of the fracture.

[0015] Preferably, the data preprocessing process is as follows:

[0016] Outliers were removed from the rock mass physical and mechanical parameters using the 3σ criterion, the Z-score method was used for data standardization, and Kriging interpolation was used for spatial continuum reconstruction to generate the rock mass parameter distribution field.

[0017] The engineering disturbance parameters are classified and organized into a structured set of engineering disturbance parameters according to process parameters and layout parameters. The layout parameters refer to well location, well depth and perforation density, while the process parameters refer to the vibration frequency of the mining equipment, the vibration amplitude of the mining equipment, the resource extraction rate, the spacing of the support structure and the support strength.

[0018] Preferably, the process of constructing the heterogeneous mechanical parameter field is as follows:

[0019] Physical property parameters were extracted from the rock mass parameter distribution field. These physical property parameters included elastic modulus E and compressive strength. and tensile strength Considering that the physical properties of deep rock masses are influenced by factors such as geological evolution and fracture development, their spatial distribution usually exhibits a positively skewed characteristic, and their values ​​are concentrated in a specific interval and decrease towards both ends. Therefore, it is assumed that each physical property parameter X follows either a log-normal distribution or a Weibull distribution, with the probability density functions for both distribution types as follows:

[0020] Log-normal distribution: If the physical property parameter X follows a log-normal distribution, then its probability density function is: ;

[0021] in, The mean of the physical property parameter X after taking the natural logarithm. Let X be the standard deviation after taking the natural logarithm of the physical property parameter. Let X be the probability density function of the physical property parameter X;

[0022] Weibull distribution: If the physical property parameter X follows a Weibull distribution, then its probability density function is: ;

[0023] Where k is the shape parameter. For scale parameters;

[0024] The Kolmogorov-Smirnov test (KS test) was used to select the distribution type with the best fit for the physical property parameters as the spatial distribution model for the physical property parameters. Based on the spatial distribution model, the likelihood function of the physical property parameters was constructed using the maximum likelihood estimation method, and the distribution parameters were obtained by solving the likelihood function. Simultaneously, the box-counting method was used to calculate the fractal dimension of the physical property parameters. The fractal dimension is used to quantitatively characterize the degree of heterogeneity of the physical property parameters. Specifically, the three-dimensional space corresponding to the rock mass parameter distribution field is divided into sections with side lengths of... The box, the side length of the box A series of values ​​can be selected. ( (The integer is non-negative and is set according to the mining accuracy requirements). The three-dimensional space corresponding to the rock mass parameter distribution field is consistent with the preset three-dimensional geological boundary for deep resource mining. Based on this, the length of each side is calculated. Below, the number of boxes containing valid data on physical property parameters. ,right and Taking the logarithm of each data point yields a series of data points. ( (where m is the number of possible side length values) Then, the least squares method is used to perform linear fitting on a series of data points, and the absolute value of the slope of the fitted line is the fractal dimension D;

[0025] Based on the theory of random media, the distribution parameters and fractal dimension are used as inputs, and combined with the preset three-dimensional geological boundary for deep resource mining, fractal interpolation and random simulation are coupled through programming tools to obtain a heterogeneous mechanical parameter field. Each spatial unit in the heterogeneous mechanical parameter field is assigned a corresponding physical property parameter.

[0026] Based on the theory of random media, the distribution parameters Using the fractal dimension D as input, combined with the preset depth resource three-dimensional mining geological boundary (defined as...) Boundary coordinates are The coupling of fractal interpolation and stochastic simulation is achieved through the programming interfaces of programming tools such as Matlab or COMSOL Multiphysics, specifically as follows:

[0027] The pre-defined geological boundary for three-dimensional mining of deep resources. The data is discretized into uniform grid cells, the size of which is set according to the accuracy requirements of the numerical simulation of the mining project, preferably... The coordinates of each grid cell are as follows: ( , , (J, K, L represent the number of grid cells in the x, y, and z directions, respectively). Based on the fractal dimension D, a continuous background field with fractal characteristics is generated using fractal Brownian motion interpolation. Simultaneously, based on a defined spatial distribution model and distribution parameters... A sequential Gaussian simulation method was used for stochastic simulation, with the continuous background field as the trend term of the stochastic simulation. Combined with the spatial correlation of physical property parameters, parameter values ​​were assigned to each grid cell. After assigning parameters to all grid cells, the three-dimensional heterogeneous mechanical parameter field was obtained. In this three-dimensional heterogeneous mechanical parameter field, each grid cell uniquely corresponds to a set of physical property parameters, including elastic modulus E and compressive strength. and tensile strength .

[0028] Preferably, the generation process of the discrete fracture network model is as follows:

[0029] Extract fracture observation data from the rock mass parameter distribution field. The fracture observation data includes fracture linear density. , direction of cracks Crack inclination angle The probability distribution type and distribution parameters of each parameter in the fracture observation data, including fracture length L, fracture width w, and fracture wall roughness coefficient JRC, were determined through statistical analysis. Each parameter refers to the fracture linear density. , direction of cracks Crack inclination angle The crack length L and crack width w are specifically defined as follows: based on the geological origin of deep rock mass fractures and the patterns of engineering measurement data, a targeted probability distribution type hypothesis is proposed for each parameter, including the fracture orientation. Generally, it follows a uniform distribution, with fracture length L and fracture linear density. The fracture width w mostly follows a negative exponential or power-law distribution, while the fracture dip angle mostly follows a log-normal distribution. They usually follow a normal distribution or a bimodal distribution. The KS test is used to verify the fit of the hypothesis that there are multiple possible probability distribution types. The probability distribution type of each parameter is determined, and the maximum likelihood estimation method is used to solve the distribution parameter characteristics of each parameter.

[0030] Based on the parameter distribution characteristics of each parameter, several fracture basic elements are generated by random sampling through Monte Carlo simulation, specifically:

[0031] (1) Sampling constraint setting: All generated fracture base elements are located within the preset depth resource three-dimensional mining geological boundary. Internally, and simultaneously based on the crack linear density The target sample size N is determined by the following formula: In the formula, The total volume of the pre-defined geological boundary for three-dimensional mining of deep resources. For unit volume statistics, Usually taken That is, each cubic meter of rock mass should contain (cracks);

[0032] (2) Multi-parameter joint random sampling: randomly select the coordinates of the fracture center point Coordinates of the fracture center point It conforms to the uniform distribution of the pre-defined geological boundary for three-dimensional mining of deep resources. Simultaneously, it sequentially extracts parameters according to their distribution characteristics and probability distribution type. , L and w form the parameter set for a single fracture. ;

[0033] Crack center point coordinates The sampling formula is:

[0034] ;

[0035] Where (U(0,1)) is a uniformly distributed random number on the interval [0,1];

[0036] (3) Geometric representation of fracture basic unit: Each parameter set is transformed into a geometric entity in the three-dimensional geological boundary of the deep resource mining, namely fracture basic unit, which is represented by a line segment model or a polygonal surface model. The line segment model is suitable for penetrating fractures, and the polygonal surface model is suitable for non-penetrating fractures. Based on this, several fracture basic units carrying complete parameter information and whose spatial positions meet the constraints are generated.

[0037] All fracture base elements are imported into a three-dimensional spatial coordinate system. This system has the center point of the mining area as the origin, the x-axis along the due north direction, the y-axis along the due east direction, and the z-axis along the vertical downward direction. The spatial orientation of each fracture base element is determined based on the fracture strike and dip angle, specifically including:

[0038] Based on the fracture orientation Determine the horizontal projection direction: fracture orientation Corresponding to the positive x-axis direction, Corresponding to the positive y-axis direction;

[0039] Based on fracture dip angle Determine the vertical inclination angle: The corresponding horizontal crack is parallel to the xOy plane. The corresponding vertical crack is parallel to the xOz or yOz plane;

[0040] For cracks in a polygonal surface model, the crack surface normal vector is used... To further accurately characterize the spatial attitude, the normal vector of the fracture surface... With the direction of the fracture fracture dip angle The relationship is: ;

[0041] Simultaneously, the overlap between each fracture base element is calculated. For line segment model fractures, the length overlap ratio is used. For characterization, the area overlap ratio is used for cracks in polygonal surface models. Characterization, if or Then, retain the fracture basic unit with parameters closer to the statistical mean of the sample, and the redundant fracture basic unit, and finally obtain the initial three-dimensional fracture set without excessive overlap.

[0042] The length overlap ratio The calculation method is as follows: Let the two crack segments be... , First, the length of the intersection segment between two crack segments is calculated using a spatial line segment intersection detection algorithm. Then follow the formula Calculate the overlap, where, Two fracture segments respectively and Length;

[0043] The area overlap ratio The calculation method is as follows: Let the two fracture surfaces be... , The intersection area of ​​the two is calculated using a spatial polygon intersection algorithm. Then follow the formula Calculate the overlap, where, These are the areas of the two fracture surfaces, respectively;

[0044] The basic fracture elements in the initial three-dimensional fracture set are classified into categories. Fracture elements with a fracture length ≥ 5m and a fracture width ≥ 2mm are classified as dominant fractures, while fracture elements with a fracture length < 5m or a fracture width < 2mm are classified as minor fractures. Corresponding fracture tags are added. Based on this, all fracture elements with fracture tags are embedded into their corresponding spatial positions in the heterogeneous mechanical parameter field through a grid index mapping algorithm, and associated with the physical property parameters of the grid elements at that spatial position to form a complete discrete fracture network model.

[0045] Preferably, the construction process of the three-dimensional geological model is as follows:

[0046] Using a pre-defined geological boundary for deep resource mining in three dimensions as the spatial range, and with geological environment data as input through three-dimensional geological modeling software, the geological environment data includes geostress data, stratum lithology distribution data, fault occurrence data, aquifer thickness, and pore water pressure data within the pre-defined geological boundary for deep resource mining in three dimensions. The three-dimensional geological modeling software includes GOCAD, Surfer, or COMSOL Multiphysics to accurately construct a three-dimensional geological model that includes the three-dimensional distribution of rock strata, fault spatial interfaces, and the entire aquifer range, providing a realistic geometric and geological constraint basis for subsequent coupling of seepage field and stress field effects.

[0047] Preferably, the method for time-series dynamic iterative calculation is as follows:

[0048] S31. Based on the requirements of the deep resource mining cycle and combined with the characteristics of the fluid injection stage, the deep resource mining time is divided into several time interval calculation steps.

[0049] S32. Based on the pore medium distribution and pore water pressure in the geological environment data from the three-dimensional geological model, the seepage control equation is established using Darcy's law. The core expression of Darcy's law is: ,in The seepage velocity vector, For rock mass permeability tensor, Pore ​​water pressure, The density of pore water, It is the acceleration due to gravity. The elevation is used as the basis for deriving the seepage control equation by combining it with the continuity equation. In the formula The specific water storage coefficient of the rock mass. For time, the finite element method is used to solve the seepage control equation, and finally the global pore water pressure distribution, i.e. the global seepage field distribution, is obtained for this calculation step.

[0050] S33. Using the geometric boundaries of the three-dimensional geological model as constraints, superimpose the pore water pressure of the global seepage field distribution. The stress field in the calculation step is solved using the elasticity equilibrium equation, along with the multiple stress influencing factors corresponding to the engineering disturbance parameter set. These multiple stress influencing factors include in-situ stress, wellbore stress release, perforation stress concentration, vibration-induced additional stress, production rate stress release, and support constraint stress. The in-situ stress is taken from the horizontal in-situ stress data in the geological environment data. , and vertical ground stress The wellbore stress release is calculated using the elastic mechanics formula for circular wellbore stress, constrained by the well location and depth due to the concentration of engineering disturbance parameters, including radial stress around the wellbore. and circumferential stress In the formula For circumferential angles, stress concentration occurs in the perforation. In the formula For the perforation aperture, The perforation spacing is derived from the perforation density in the engineering disturbance parameter set, and the additional vibration stress is derived from the vibration frequency of the mining equipment in the engineering disturbance parameter set. Vibration amplitude of mining equipment Derivation of additional stress from vibration In the formula The vibration stress calibration coefficient is the resource extraction rate determined by the mining rate stress release from the engineering disturbance parameter set. Regulation and stress release of mining rate In the formula For reference mining rate, the support constraint stress is determined by the spacing of the support structures within the engineering disturbance parameter set. and support strength Determine the support constraint stress. In the formula Using the reference support spacing, after superimposing multiple stress influencing factors, the elastic mechanical equilibrium equation is applied. Solve for the equation, where This represents the total stress tensor resulting from the superposition of multiple stress influencing factors. For the density of the rock mass, Given gravitational acceleration, the stress field of the calculation step is obtained;

[0051] S34. Effective stress of the stress field based on the calculation step. ( Calculate the crack opening degree (for the effective stress coefficient). In the formula The initial crack opening degree, The current effective minimum principal stress, The initial effective minimum principal stress, Given the fracture normal stiffness, the rock mass permeability is corrected using the cubic law: ( (Initial permeability), and the corrected rock mass permeability Feedback is fed back to the seepage control equation, and S32-S33 are repeated until the maximum rate of change of the stress field is reached between two adjacent iterations. ( The stress values ​​of the stress field elements in the k-th and (k-1)-th iterations are given respectively, and the stress data for this calculation step is output.

[0052] S35. Complete all calculation steps in S32-S34 sequentially according to the mining time sequence, and perform time-dimension interpolation smoothing on the stress data of each calculation step to form a time-series stress field that covers the deep resource mining cycle and corresponds one-to-one with the spatial coordinates of the three-dimensional geological model, which is used to provide dynamic stress input for subsequent fracture criterion verification.

[0053] Preferably, the criterion extension set includes a fracture initiation criterion and a fracture propagation criterion. The fracture initiation criterion is based on the Mohr-Coulomb criterion, incorporating a non-homogeneity correction coefficient. and engineering disturbance coefficient Construct, where the heterogeneity correction coefficient is... , Let fractal dimension be the nonhomogeneous mechanical parameter field, and engineering perturbation coefficient be the coefficient of variation. In the formula The average compressive strength of the rock mass. For perforation density, This is the calibration coefficient for the mining rate disturbance. For the support constraint calibration coefficient, Given the perforation density, the mathematical expression for the fracture initiation criterion is finally obtained as follows: , These are the maximum and minimum principal stresses of the time-series stress field. This represents the compressive strength of the corresponding grid element in a heterogeneous mechanical field. The internal friction angle of the rock mass is used to determine the rock mass fracturing when the fracturing initiation criterion is met. The initiation threshold for dominant fractures is lowered by 10%, while the threshold for secondary fractures remains unchanged.

[0054] The fracture propagation criteria consist of fracture propagation direction rules and fracture propagation length rules, specifically: introducing a heterogeneous guidance factor. and disturbance stress direction factor Construction, in which heterogeneous guiding factors E is the elastic modulus. The average elastic modulus, The standard deviation of the elastic modulus, The parameter gradient direction is a unit vector, and the perturbation stress direction factor is... , The angle between the direction of the maximum principal stress and the fracture orientation. The angle between the direction of the support structure and the direction of the crack is defined by the regularity of the crack propagation direction. It is confirmed that, among them, The shear stress direction vector. The fracture propagation direction angle is defined by the fracture propagation length rule. , For tensile strength, The fracture propagation length is defined as follows: the fracture propagation length of the dominant fracture increases by 20%, while the fracture propagation length of the secondary fracture remains unchanged.

[0055] Preferably, the coupled numerical model is a coupled FEM and DEM model;

[0056] The steps of the simulated rock mass fracturing process are as follows:

[0057] S51. Import the heterogeneous mechanical parameter field, discrete fracture network model, 3D geological model, and extended criterion set into the FEM and DEM coupled model. The FEM and DEM coupled model includes an FEM module and a DEM module. Initialize the FEM and DEM modules by assigning geometric boundary constraints of the 3D geological model and the elastic modulus, compressive strength, and tensile strength of each grid element in the heterogeneous mechanical parameter field to the FEM module. Assign fracture labels to each fracture base element in the discrete fracture network model and a pre-set initial permeability to the DEM module, including the initial permeability of the dominant fractures. Initial permeability of secondary fractures Meanwhile, the time-series stress field is set as the initial stress input condition for the coupled simulation to ensure that the initial state of the simulation is consistent with the results of the previous coupled seepage field and stress field.

[0058] S52. Each calculation step is subdivided into 5-10 simulation time steps according to the mining sequence. The FEM module is based on the temporal stress field of the current simulation time step, combined with the rock mass permeability corrected in S34. The macroscopic stress distribution data is obtained by solving the elasticity equilibrium equation, and the macroscopic stress distribution data is transmitted to the DEM module in real time according to the spatial coordinate mapping relationship as the stress load for microcrack evolution simulation.

[0059] After receiving macroscopic stress distribution data, the S53.DEM module calls the physical property parameters of the corresponding grid elements in the heterogeneous mechanical parameter field, and verifies the fracture state of dominant and secondary fractures by combining the criterion expansion set. For dominant fractures, it first judges whether the initiation condition is met by the fracture initiation criterion. If it is met, it further calculates the propagation direction by the fracture propagation criterion. For secondary fractures, it only performs the fracture initiation criterion verification. If the verification is passed, it only triggers the fracture initiation and does not actively initiate the fracture propagation criterion process.

[0060] S54. If the criterion verification satisfies the fracture initiation condition, the DEM module generates new fracture elements, and the length of the new fracture elements is determined according to the formula. The calculation is based on the cube law combined with the crack opening degree. Calculated as Simultaneously, corresponding simulation time steps are generated for the new fracture elements; if the dominant fracture satisfies the fracture propagation criterion, the fracture length is extended along the calculated fracture propagation direction angle, the spatial attitude is corrected by updating the coordinates of the two ends of the fracture, and the physical property parameters of the corresponding mesh element are reduced synchronously.

[0061] The S55.DEM module feeds back the updated fracture spatial distribution, new fracture elements, and corrected physical property parameters to the FEM module through the inter-module data interface. Based on this, the FEM module readjusts its calculation model, solves for the macroscopic stress distribution data of the next simulation time step, and repeats steps S52-S54 until no new fractures are generated for three consecutive simulation time steps, or the preset total mining time is reached. At this point, the simulation is considered to have converged and the iteration is stopped.

[0062] S56. Integrate the fracture evolution data from all simulation time steps and extract the fracture spatial coordinates of each fracture element. The system simulates time steps to construct a three-dimensional visualization dataset of rock mass fracture propagation paths, and finally outputs rock mass fracture propagation paths that include fracture topological connectivity, spatial propagation trajectories, and temporal evolution patterns.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] 1. This invention breaks through the limitations of existing technologies that rely on post-event identification of microseismic signals. By coupling heterogeneous mechanical parameter fields with engineering disturbance parameters, it realizes dynamic simulation of the entire process of rock mass fracture from initiation to propagation, and achieves forward-looking and accurate prediction of fracture path.

[0065] 2. This invention fully considers the dynamic coupling and temporal evolution characteristics of the seepage field and stress field in deep fluidized mining, optimizes the fracture criterion for the hierarchical characteristics of dominant and secondary fractures, and greatly improves the adaptability of the analysis results to actual engineering scenarios.

[0066] 3. This invention realizes the three-dimensional spatial topological characterization and temporal evolution law quantification of rock mass fracture propagation path, providing direct and accurate technical support for the optimization of parameters for fluidized mining of deep resources and the delineation of mining safety boundaries. Attached Figure Description

[0067] To more clearly illustrate the technical solutions and advantages 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.

[0068] Figure 1 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0070] Examples, such as Figure 1 As shown, the method for analyzing the rock mass fracture mechanism in deep resource fluidized mining includes the following steps:

[0071] S1. Obtain basic parameters and geological environment data for fluidized bed mining of deep resources, preprocess the basic parameters for fluidized bed mining of deep resources, and obtain the rock mass parameter distribution field and engineering disturbance parameter set;

[0072] S2. Based on the rock mass parameter distribution field, a heterogeneous mechanical parameter field is constructed using stochastic medium theory and fractal geometry, and a discrete fracture network model is generated by combining Monte Carlo simulation.

[0073] S3. Based on geological environment data and engineering disturbance parameter set, construct a three-dimensional geological model, couple the effects of seepage field and stress field, dynamically iterate according to mining sequence, and output the time-series stress field;

[0074] S4. Based on the heterogeneous mechanical parameter field, time-series stress field and engineering disturbance parameter set, the heterogeneity correction coefficient and engineering disturbance coefficient are introduced to improve the fracture initiation criterion, and the heterogeneous guidance factor and disturbance stress direction factor are introduced to improve the fracture propagation criterion, forming a criterion extension criterion set;

[0075] S5. Import the heterogeneous mechanical parameter field, discrete fracture network model, time-series stress field, and criterion extension set into the coupled numerical model and simulate the rock mass fracture process to obtain the rock mass fracture propagation path.

[0076] Furthermore, the working principle of the present invention will be illustrated below using the example of deep coal seam gasification mining:

[0077] The application scenario of this embodiment is a deep coalbed methane extraction block with an extraction depth of 800-1000m, and a preset three-dimensional geological boundary for deep resource extraction. The coordinate range is × × The mining cycle is 180 days. The method of this invention is needed to analyze the rock mass fracture propagation path and provide a basis for optimizing fluid injection parameters and defining safety boundaries.

[0078] S1. Obtain basic parameters and geological environment data for fluidized bed mining of deep resources and perform preprocessing. Among them, the physical and mechanical parameters of the rock mass are measured by core laboratory tests. The average elastic modulus E is 8 GPa, and the compressive strength is... The average value is 25 MPa, tensile strength The mean value is 2.2 MPa. Fracture observation data were obtained through borehole imaging and field observation. Fracture linear density The cracks are distributed in m³, with crack orientation α mainly concentrated between 0° and 90°, crack dip angle β mostly between 30° and 60°, crack length L ranging from 1 to 12 m, and crack width w ranging from 0.5 to 5 mm. Among the engineering disturbance parameters, the pre-set vibration frequency of the mining equipment in the mining process design is also included. Vibration amplitude Resource extraction rate Spacing of support structures Support strength Well location planning determined the well location coordinates (250m, 250m, 900m), well depth 900m, and perforation density. Hole / m; Geological environment data were obtained through geological exploration, and the geostress data are horizontal geostress. , Vertical stress The lithology is mainly siltstone and coal seams. The fault strikes at 30° and dips at 45°. The aquifer thickness is 15m, and the pore water pressure is [not specified]. The three elastic modulus outliers were removed sequentially using the 3σ criterion for the physical and mechanical parameters of the rock mass. After standardization using the Z-score method, spatial continuity reconstruction was performed using Kriging interpolation to generate a rock mass parameter distribution field with a grid size of 1m×1m×1m. The engineering disturbance parameters were classified and organized according to process parameters and layout parameters to form a structured engineering disturbance parameter set.

[0079] S2. Construct a heterogeneous mechanical parameter field based on the rock mass parameter distribution field and generate a discrete fracture network model using Monte Carlo simulation. Extract the elastic modulus E and compressive strength from the rock mass parameter distribution field. ,tensile strength Assuming it follows a log-normal or Weibull distribution, the KS test at a significance level of 0.05 confirms that all three follow a log-normal distribution. The logarithmic mean of the elastic modulus E is then obtained using the maximum likelihood estimation method. Logarithmic standard deviation Using the box dimension method, the side lengths of the boxes were selected as δ = 1m, 2m, 4m, ..., 32m. Statistically, the absolute value of the slope of the fitted line between lnδ and lnN(δ) was found to be 2.6 (i.e., fractal dimension). ), with distribution parameters and fractal dimension Using fractal Brownian motion interpolation and sequential Gaussian simulation as input, a Matlab programming interface is used to couple these two methods. This assigns values ​​to 10,000 grid cells within a pre-defined geological boundary for 3D resource extraction, resulting in each grid cell corresponding to a unique E. , The non-homogeneous mechanical parameter field of the rock mass is analyzed; simultaneously, fracture observation data are extracted from the rock mass parameter distribution field, and the probability distribution types of fracture linear density, fracture orientation, etc., are determined through statistical analysis and KS test. Based on these parameter probability distribution types, sampling constraints are set as follows: Within, according to the target sampling quantity Monte Carlo simulation sampling was performed to randomly select the coordinates, orientation, dip angle, length, and width of the fracture center point, generating fracture base elements with complete parameters. These fracture base elements were represented using a polygonal surface model. All fracture base elements were imported into a three-dimensional spatial coordinate system with the center point of the mining area as the origin, x-axis due north, y-axis due east, and z-axis vertically downwards. The area overlap ratio was then calculated. And remove ≥80% of the redundant fracture basic elements are classified as dominant fractures (approximately 30%) based on fracture length ≥5m and fracture width ≥2mm, while the remainder are classified as secondary fractures (approximately 70%). Fracture labels are added, and a non-homogeneous mechanical parameter field is embedded through a grid index mapping algorithm to form a discrete fracture network model.

[0080] S3. Based on geological environment data and engineering disturbance parameter sets, a three-dimensional geological model is constructed and coupled with seepage field and stress field to generate a time-series stress field. To define the spatial scope, geological environmental data was input into GOCAD software to construct a three-dimensional geological model encompassing the three-dimensional distribution of siltstone and coal seams, fault spatial interfaces, and the entire aquifer area. The 180-day mining cycle was divided into 36 equally spaced calculation steps, each lasting 5 days, based on the pore medium distribution and pore water pressure determined by the three-dimensional geological model. Establish the seepage control equation using Darcy's law. The finite element method was used to obtain the global seepage field distribution for each calculation step. With the geometric boundaries of the three-dimensional geological model as constraints, pore water pressure, wellbore stress release, perforation wall stress concentration factor, vibration-induced stress, production rate stress release, and support constraint stress were superimposed, and the results were obtained through elastic mechanical equilibrium equations. ( =2600 The stress field for each calculation step is obtained by solving the problem, and the effective stress is then used to determine the stress field. ( Calculate the crack opening degree (Initial opening degree) =0.8mm, initial effective minimum principal stress =20MPa, fracture normal stiffness =5GPa / m), according to the cubic law (Initial penetration rate) =10−13 The rock mass permeability is corrected and fed back to the seepage control equation for iteration until the maximum rate of change of the stress field is reached. The stress data for this calculation step is then output. After completing 36 iteration calculations, the stress data for each step are interpolated and smoothed in the time dimension to form a time-series stress field that corresponds one-to-one with the spatial coordinates of the three-dimensional geological model.

[0081] S4. An extended criterion set is constructed based on the heterogeneous mechanical parameter field, time-series stress field, and engineering disturbance parameter set. The fracture initiation criterion is based on the Mohr-Coulomb criterion, incorporating a heterogeneity correction coefficient. and engineering disturbance coefficient The final criterion formula is: (Angle of friction within the rock mass) , The threshold for initiating dominant fractures is lowered by 10%, while the threshold for secondary fractures remains unchanged.

[0082] The fracture propagation criterion introduces heterogeneity guidance. ( Standard deviation of elastic modulus , (where the parameter gradient direction is a unit vector) and the perturbation stress direction factor The expansion direction rule is ( (where shear stress direction vector is the vector), the extension length rule is as follows: The coefficient of the propagation length of the dominant fracture is increased by 20%, while the coefficient of the secondary fracture remains unchanged.

[0083] S5. Import the heterogeneous mechanical parameter field, discrete fracture network model, three-dimensional geological model, time-series stress field, and criterion extension set into the FEM and DEM coupled model and simulate the rock mass fracture process. Assign geometric boundary constraints (fixed displacement boundary, stress free boundary) to the three-dimensional geological model and E, stress, and stress of each grid element in the heterogeneous mechanical parameter field to the FEM module. , Assign fracture basic unit labels and initial permeability (dominant fractures) to the DEM module. Secondary fractures The time-series stress field is set as the initial stress input condition; each calculation step is subdivided into 8 simulation time steps according to the mining sequence, with each step lasting 15 hours. The FEM module solves for the macroscopic stress distribution data through the elasticity equilibrium equation based on the time-series stress field of the current simulation time step and the corrected rock permeability. , , The data is then transmitted to the DEM module in real time. The DEM module calls the physical property parameters of the corresponding mesh element, first verifying the dominant fractures against the fracture initiation criterion; if the criterion is met, it further calculates the propagation direction. For minor fractures, only the fracture initiation criterion is checked; if it is met, initiation is triggered. When the initiation condition is met, the DEM module generates new fracture elements, with the length determined by... Calculate the width according to Calculate and label the generation time step. When the dominant fracture satisfies the propagation criterion, along... The simulation process involves extending the length, updating the spatial orientation, and synchronously reducing the physical property parameters of the corresponding mesh elements, such as reducing compressive strength by 40% and elastic modulus by 30%. The DEM module feeds back the updated crack distribution, new crack elements, and corrected physical property parameters to the FEM module. The FEM module then recalculates the macroscopic stress distribution for the next time step and iterates until day 140. If no new cracks are generated in three consecutive simulation time steps, the simulation is considered converged and iteration stops. Finally, the crack evolution data from all simulation time steps are integrated, and the three-dimensional spatial coordinates of each crack element are extracted. The corresponding simulation time steps are used to construct a three-dimensional visualization dataset of rock mass fracture propagation path, and output a complete rock mass fracture propagation path that includes fracture topological connectivity, spatial propagation trajectory, and temporal evolution law.

[0084] It should be noted that the parameters in this embodiment can be adjusted according to different mining scenarios, but the acquisition method, processing flow and model construction logic must strictly follow the provisions of the claims; the grid size and simulation time step can be adjusted according to the mining accuracy requirements during the simulation, while the core coupling logic remains unchanged; the coefficients in the criterion extension set can be calibrated through multiple sets of experiments.

[0085] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for analyzing the fracture mechanism of rock mass in deep resource fluidized mining, characterized in that, Includes the following steps: S1. Obtain basic parameters and geological environment data for fluidized bed mining of deep resources, preprocess the basic parameters for fluidized bed mining of deep resources, and obtain the rock mass parameter distribution field and engineering disturbance parameter set; S2. Based on the rock mass parameter distribution field, a heterogeneous mechanical parameter field is constructed using stochastic medium theory and fractal geometry, and a discrete fracture network model is generated by combining Monte Carlo simulation. S3. Based on geological environment data and engineering disturbance parameter set, construct a three-dimensional geological model, couple the effects of seepage field and stress field, dynamically iterate according to mining sequence, and output the time-series stress field; S4. Based on the heterogeneous mechanical parameter field, time-series stress field and engineering disturbance parameter set, a fracture initiation criterion is constructed by introducing a heterogeneity correction coefficient and an engineering disturbance coefficient, and a fracture propagation criterion is constructed by introducing a heterogeneous guidance factor and a disturbance stress direction factor, thus forming a criterion extension criterion set; S5. Import the heterogeneous mechanical parameter field, discrete fracture network model, time-series stress field, and criterion extension set into the coupled numerical model and simulate the rock mass fracture process to obtain the rock mass fracture propagation path.

2. The method for analyzing the rock mass fracturing mechanism in deep resource fluidized mining according to claim 1, characterized in that, The basic parameters for fluidized deep resource mining include rock mass physical and mechanical parameters and engineering disturbance parameters. The engineering disturbance parameters include the vibration frequency of the mining equipment, the vibration amplitude of the mining equipment, the resource extraction rate, support data, and wellbore parameters. The rock mass physical and mechanical parameters include elastic modulus, compressive strength, tensile strength, and fracture observation data. The fracture observation data includes at least fracture linear density, fracture orientation, fracture dip angle, fracture length, and fracture width.

3. The method for analyzing the rock mass fracturing mechanism in deep resource fluidized mining according to claim 2, characterized in that, The data preprocessing process is as follows: Outliers were removed from the rock mass physical and mechanical parameters using the 3σ criterion, the Z-score method was used for data standardization, and Kriging interpolation was used for spatial continuum reconstruction to generate the rock mass parameter distribution field. The engineering disturbance parameters are classified and organized into a structured set of engineering disturbance parameters according to process parameters and layout parameters.

4. The method for analyzing the rock mass fracturing mechanism in deep resource fluidized mining according to claim 3, characterized in that, The process of constructing the heterogeneous mechanical parameter field is as follows: Physical property parameters are extracted from the rock mass parameter distribution field. Assuming that the physical property parameters all follow either the log-normal distribution or the Weibull distribution, the distribution type with the best fit of the physical property parameters is selected as its spatial distribution model through the KS test. The fractal dimension of physical property parameters is calculated using the box dimension method. Meanwhile, based on the spatial distribution model, the likelihood function of the physical property parameters is constructed using the maximum likelihood estimation method, and the distribution parameters are obtained by solving the likelihood function. Based on the theory of random media, the distribution parameters and fractal dimension are used as inputs, and combined with the preset three-dimensional geological boundary for deep resource mining, fractal interpolation and random simulation are coupled through programming tools to obtain a heterogeneous mechanical parameter field. Each grid cell in the heterogeneous mechanical parameter field is assigned a corresponding physical property parameter.

5. The method for analyzing the rock mass fracturing mechanism in deep resource fluidized mining according to claim 4, characterized in that, The generation process of the discrete fracture network model is as follows: Extract fracture observation data from the rock mass parameter distribution field, determine the probability distribution type of each parameter in the fracture observation data through statistical analysis, and then use the maximum likelihood estimation method to solve the parameter distribution characteristics of each parameter. Based on the parameter distribution characteristics of each parameter, several fracture basic units are generated by random sampling through Monte Carlo simulation. Each fracture basic unit carries fracture observation data and is located within the preset three-dimensional geological boundary for deep resource mining. Import all fracture base elements into a three-dimensional spatial coordinate system, and determine the spatial orientation of each fracture base element according to the fracture orientation and fracture dip angle. At the same time, calculate the overlap between each fracture base element, remove redundant fracture base elements with an overlap of more than 80%, and construct an initial three-dimensional fracture set. The basic fracture elements in the initial three-dimensional fracture set are classified into categories. Fracture elements with a fracture length ≥ 5m and a fracture width ≥ 2mm are classified as dominant fractures, and fracture elements with a fracture length < 5m or a fracture width < 2mm are classified as minor fractures. Corresponding fracture labels are added. All fracture basic units with added fracture labels are embedded into their corresponding spatial positions in the heterogeneous mechanical parameter field to form a discrete fracture network model.

6. The method for analyzing the rock mass fracturing mechanism in deep resource fluidized mining according to claim 5, characterized in that, The process of constructing the three-dimensional geological model is as follows: Using the preset geological boundary of deep resource 3D mining as the spatial range, and with geological environment data as input, a 3D geological model containing rock strata, faults and aquifers is constructed through 3D geological modeling software.

7. The method for analyzing the rock mass fracturing mechanism in deep resource fluidized mining according to claim 6, characterized in that, The method for time-series dynamic iterative calculation is as follows: S31. Based on the requirements of the deep resource mining cycle, the deep resource mining time is divided into several equal-time interval calculation steps; S32. Based on the three-dimensional geological model, the seepage control equation is established through Darcy's law, and the global seepage field distribution of the calculation step is obtained by solving the equation using the finite element method. S33. Using the geometric boundaries of the three-dimensional geological model as constraints, superimpose the global seepage field distribution and engineering disturbance parameter set, and solve the stress field of the calculation step through the elastic mechanical equilibrium equation; S34. Based on the change in the opening and closing degree of the fracture under the stress field in the calculation step, the rock permeability is corrected by the cubic law. The corrected rock permeability is fed back to the seepage control equation. S32-S3 are repeated until the maximum change rate of the stress field in the calculation step is ≤5%. The stress data of the calculation step is then output. S35. Perform S32-S34 on all calculation steps to obtain stress data for each calculation step, and form a time-series stress field accordingly.

8. The method for analyzing the rock mass fracturing mechanism in deep resource fluidized mining according to claim 7, characterized in that, The coupled numerical model is a coupled model of FEM and DEM; The steps of the simulated rock mass fracturing process are as follows: The heterogeneous mechanical parameter field, discrete fracture network model, three-dimensional geological model and criterion extension set are imported into the coupled numerical model and initialized. Among them, FEM calculates macroscopic stress data and DEM simulates fracture evolution. Synchronously iterating according to the mining sequence, the FEM transmits macroscopic stress data to the DEM. The DEM verifies the fracture state of dominant and secondary fractures in the simulated fracture evolution based on the criterion extension set, generates new fractures or updates fracture morphology and corrects physical property parameters, and then feeds them back to the FEM until no new fractures are generated or the preset mining time is reached. Finally, it outputs the rock mass fracture propagation path containing fracture spatial coordinates and temporal evolution information.

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