Method for evaluating grouting transformation effect of complex geological structure mine area

Abstract

The invention provides a complex geological structure mine area grouting transformation effect evaluation method, and relates to the technical field of grouting effect evaluation. The method specifically comprises the following steps that mine geology and hydrogeology characteristics, the space superposition relation and the water yield property of a water-bearing layer and a water-resisting layer, the range of a main coal mining layer, a construction outline map, ground stress analysis, a grouting layer position, grouting hole arrangement and grouting parameters are determined; carrying out fracture effective area analysis, and determining the fracture effective area distribution condition; carrying out fracture effective area water conductivity analysis; carrying out correlation analysis on the effective fracture area and the grouting space; and grouting effect evaluation is carried out, and a grouting effect evaluation model is constructed. According to the technical scheme, the problems that in the prior art, geologic structures and fracture development are not fully considered, the relation between the geologic structures and the fracture development and grouting parameters is not established, and grouting does not have engineering practicability are solved.

Description

Technical Field

[0001] The invention relates to the technical field of grouting effect evaluation, and in particular to a method for evaluating grouting transformation effects in a mine area with complex geological structures. Background Art

[0002] At present, the use of horizontal directional drilling to reinforce the coal seam floor with grouting is the main means of preventing and controlling water hazards in deep coal seam floors. This method greatly increases the length of the effective grouting hole section, increases the area of ​​the fissure zone and water-bearing body exposed by the drilling, reduces the grouting blind area, and improves the efficiency of grouting transformation. However, due to long-term natural geological effects and mining disturbances, the rock mass of the coal seam floor has been deformed and damaged, and the grouting project is hidden. If the combined influence of factors such as natural tectonic effects, rock mass fissure development, and grouting parameters cannot be fully considered, it will be impossible to scientifically evaluate the grouting effect and provide a basis for safe mining of the coal seam floor. There are currently two main evaluation methods. One is to use the grouting final pressure as a reference. This method believes that when the grouting main force is greater than the set grouting final pressure, the grouting is considered to be effective and the effect is good; the second is to use a comprehensive evaluation method, which regards the grouting pressure, grouting rate, core collection rate, etc. as evaluation indicators, and uses a hierarchical analysis method, gray fuzzy and other comprehensive evaluation methods to carry out the evaluation. Both methods do not fully consider the geological structure and fracture development, nor do they establish the relationship between the two and the grouting parameters, making grouting not practical for engineering.

[0003] Therefore, there is a need for a method for evaluating the grouting transformation effect in mine areas with complex geological structures that fully considers the development of geological structures and fractures and has engineering practicality. Summary of the invention

[0004] The main purpose of the present invention is to provide a method for evaluating the grouting transformation effect of a mine area with complex geological structure, so as to solve the problem that the prior art does not fully consider the geological structure and fracture development, and does not establish the relationship between the two and the grouting parameters, making grouting not practical for engineering.

[0005] To achieve the above object, the present invention provides a method for evaluating the effect of grouting transformation in a mine area with complex geological structure, which specifically comprises the following steps:

[0006] S1. Clarify the geological and hydrogeological characteristics of the mine, the spatial superposition relationship and water-richness of the aquifer and aquiclude, the scope of the main mineable coal seams, structural distribution, ground stress distribution, grouting layer, grouting hole layout and grouting parameters.

[0007] S2, conduct fracture effective area analysis to clarify the distribution of fracture effective areas. Fracture effective area analysis includes: constructing a study area model, setting boundary conditions and fracture effective area result analysis. Constructing a study area model includes: constructing a geological model, a mechanical model and a mathematical model.

[0008] S3. Conduct hydraulic conductivity analysis of the effective fracture area, including lateral hydraulic conductivity analysis and vertical hydraulic conductivity analysis.

[0009] S4, conduct spatial correlation analysis between the effective fracture area and grouting, including: constructing a geographically weighted regression model of the effective fracture area and grouting information, analyzing the correlation between the influencing factors at the coordinate positions of each grouting hole and segment and the influence coefficient of the structural fracture, and determining the spatial influence coefficient of the effective fracture area on grouting.

[0010] S5, evaluate the grouting effect, take the grouting effect as the evaluation index, consider the geological conditions, hydrogeological conditions, grouting parameters, and inspection hole verification results, and use the set pair analysis and variable fuzzy set coupling method to construct a grouting effect evaluation model.

[0011] Furthermore, step S2 specifically includes the following steps: S2.1, constructing a geological model, including: determining the size of the study area model according to the boundary of the study area, determining the thickness and stratigraphic distribution of the study area model according to the grouting transformation layer and the drilling column chart, determining the fold and fault structure within the study area according to the structural outline map and three-dimensional seismic detection data, and finally establishing the study area model through CAD and Rhino software, and outputting the ".STL" format file.

[0012] S2.2, constructing a mechanical model, that is, assigning rock mechanical parameters to the strata; obtaining various lithology-related parameters through rock mechanical experiments by field sampling, and calculating the weighted average to determine the required rock mechanical parameters; on the basis of rock mechanical parameters, considering the influence of tectonic action, dividing the area into sedimentary strata, fold belts and fault zones according to the tectonic conditions, considering the fold belts as the hard lithological layers, and the Young's modulus is 150% of the sedimentary strata; the faults are regarded as the weak layers, and the Young's modulus is 50% of the sedimentary strata.

[0013] S2.3, use the equilibrium equation, stress-strain relationship equation and strain-displacement relationship equation to simulate the stress field change and construct a mathematical model; among them, the equilibrium equation represents the conservation of momentum, which is expressed as: (1); In the formula, is the Laplace operator, is the stress tensor, It is the volume force.

[0014] The rock is assumed to be a linear elastic material model and satisfies Hooke's law, so the stress-strain relationship equation is expressed as: (2); In the formula, is the elastic modulus tensor, is the strain tensor.

[0015] Strain-displacement relationship It is expressed as: (3); In the formula, , are the components of the displacement vector, , are spatial coordinates.

[0016] Furthermore, the boundary condition setting in step S2.4 includes ground stress inversion and boundary load setting, and the ground stress inversion includes the following steps: S2.4.1, conduct on-site sampling, and use the samples to prepare standard rock specimens in the horizontal, vertical, 45° and 90° directions to carry out indoor rock acoustic emission experiments.

[0017] S2.4.2, determine the paleo-stress value based on the stress value corresponding to the Kaiser point, and convert the stress value into three-dimensional principal stress. The calculation formula is as follows: (4); (5); (6); (7); In the formula, is the vertical ground stress, unit: MPa; is the stress corresponding to the Kaiser effect of the core in the vertical direction, in MPa; is the effective stress coefficient; is the pore pressure, unit: MPa; is the maximum principal stress in the horizontal direction, in MPa; , , The stresses corresponding to the Kaiser effect of the core at 0°, 45°, and 90° horizontal directions, in MPa; is the horizontal projection angle relative to the 0° direction, unit (°); is the minimum principal stress in the horizontal direction, unit: MPa.

[0018] Furthermore, the boundary load setting in step S2.4 includes the following steps: S2.4.3, based on the measured stress data, the boundary stress condition combinations are designed with equal steps to construct training samples.

[0019] S2.4.4, input the training samples into the neural network, perform iterative updates on the weights and thresholds during the training process of the neural network, and output the boundary stresses in the X, Y, and Z directions of the boundary stress conditions of the neural network model.

[0020] S2.4.5, compare the simulated value of the neural network simulation output with the Kaiser feedback value. When the accuracy rate exceeds 95%, the neural network simulation algorithm is considered to be reliable.

[0021] Furthermore, the crack effective area analysis in step S2 is specifically as follows: Based on the results of the inversion of the ground stress obtained in steps S2.4.3 to S2.4.5, the shear rupture coefficient is introduced using the Mohr-Coulomb strength criterion. To predict cracks; <1, the rock is in a stable state and no fracture occurs; When ≥1, the rock stress state is at or exceeds the Mohr stress circle rupture envelope, and the rock ruptures. The formula is as follows: (8); (9); In the formula, is the maximum horizontal principal stress, MPa; is the minimum horizontal principal stress, MPa; is the cohesion, MPa, is the internal friction angle of rock. The in-situ stress inversion result is used to obtain the distribution of in-situ stress under the influence of tectonic movements in different periods. Substituting it into formula (8) and formula (9), the shear rupture coefficient and effective crack area in different periods are obtained.

[0022] Furthermore, in step S3, the lateral water conduction capacity of the effective fracture area is analyzed by using the lithology juxtaposition diagram method, which specifically includes the following steps: S3.1, based on the borehole columnar data on both sides of the fault or fracture area, fine sandstone, medium sandstone, and coarse sandstone are regarded as aquifers, and mudstone, sandy mudstone, and siltstone are regarded as impermeable layers. The profiles of the aquifers and impermeable layers on both sides of the fracture area are drawn.

[0023] S3.2, according to the spatial position and occurrence of joints and fissures, merge the profiles of aquifers and impermeable layers on both sides of the joints and fissures to calculate the aquifer docking rate , that is, the percentage of the aquifer docking area to the evaluation area, which evaluates the lateral water conductivity of each fracture effective area; the aquifer docking rate The higher the value, the better the water conductivity, which is conducive to slurry injection and diffusion; the aquifer docking rate The lower it is, the worse the water conductivity is, which hinders the injection and diffusion of slurry.

[0024] The vertical water conduction capacity analysis of the effective fracture area in step S3 specifically includes the following steps: S3.3, based on the results of the in-situ stress simulation in the study area, determine the cross-sectional pressure on the effective areas of faults and fissures ,Will It is defined as good water conductivity in the vertical direction, and the water conductivity influence coefficient is set to , It is defined as having better water conductivity in the vertical direction, and the water conductivity influence coefficient is set to , Defined as the vertical water conductivity, the water conductivity coefficient is set to , Defined as the vertical water conductance intersection, the water conductance influence coefficient is set to Defined as the difference in vertical water conductivity, the water conductivity influence coefficient is set to .

[0025] Furthermore, step S3 further includes the following steps: S3.4, conduct hydraulic conductivity analysis on faults and fissures at all locations in the region, compare the lateral hydraulic conductivity with the vertical hydraulic conductivity, take the maximum hydraulic conductivity as the standard, and take the influence coefficient corresponding to the maximum hydraulic conductivity ; The Kriging interpolation method is used to draw the contour map of the water conductivity influence coefficient of the effective area of ​​the fracture in the study area.

[0026] Further, step S4 includes the following steps: S4.1, according to the distribution range of the study area, the geographically weighted regression GWR model was selected, the permeability, grouting volume and grouting final pressure of each grouting section were taken as independent variables, and the water conductivity influence index of the effective fracture area of ​​the study area was taken as the dependent variable. The GWR model was used for regression analysis to quantify the spatial correlation between the independent variables and the dependent variables. The specific formula is as follows: (10); In the formula, represents the effective development area of ​​fractures, is the initial geographically weighted regression coefficient, For the independent variables, including: permeability, grouting volume and final grouting pressure, is the geographically weighted regression coefficient, depending on the grouting section Each network center , is the total number of grouting sections, is the error term.

[0027] S4.2, according to the grouting design plan, determine the center coordinates of each grid in each grouting section , input each grid center coordinate into the GWR model and calculate the geographically weighted regression coefficient , to quantify various grouting parameters, such as permeability X 1i , grouting volume X 2i , grouting final pressure X 3i The spatial correlation between the influence coefficient of water conduction and the water conduction coefficient is calculated as follows: (11); In the formula, It is a distance-based weight matrix that reflects the impact of geographic location on the regression results. is the matrix of independent variables, is the observed dependent variable matrix.

[0028] S4.3, the calculated geographically weighted regression coefficients The Surfer software was imported and combined with the base map of the water conductivity coefficient of the effective area of ​​the fissure in the study area to generate bubble maps for each grouting section. The size of the bubble was calculated based on the OK, the formula is as follows: (12); in, To take the maximum.

[0029] S4.4, through the bubble diagram, intuitively analyze the parameter distribution of each grouting section in space and its correlation with the water conductivity coefficient. At the same time, calculate and analyze Water conductivity coefficient The spatial correlation between : (13); in, for Take the average, for Take the average.

[0030] Further, step S5 includes the following steps: S5.1, based on geological conditions, taking into account the structure and fissures and joints in the study area, the water conductivity coefficient is used as the evaluation index, which is determined specifically according to steps S2~S4; based on hydrogeological conditions, the water pressure of the aquifer, the thickness of the aquiclude and the permeability coefficient are determined as evaluation indicators; based on grouting parameters, the water permeability, the final grouting pressure and the unit grouting volume are determined as evaluation indicators; based on the verification results of the inspection hole, the borehole water inflow, the ratio of the borehole water inflow before and after grouting and the coring rate are determined as evaluation indicators.

[0031] S5.2 Determine the evaluation level standard based on the evaluation index system established in step S5.1, and form a set of evaluation values ​​of each influencing factor index And the evaluation criteria of each indicator Form a set pair ( ),in: (15); (16); In the formula, For the The first evaluation index Rating value; For the The limit value of the evaluation standard corresponding to the evaluation index.

[0032] S5.3, determine the set pair connection degree and relative difference degree, and classify the difference degree in the set pair analysis into excellent and inferior , subdividing the opposition into superior and inferior and inferior , and obtain the multivariate connection degree : (17); In the formula, is the difference coefficient, is the strength coefficient, For the same degree, ; ; ; ; .

[0033] S5.4, establish the relationship between the evaluation value and the evaluation level under the evaluation index , and evaluation level Relative difference between variable fuzzy sets , combined with the evaluation of the grouting effect evaluation model, and the principle of equal division of the difference coefficient and the opposition coefficient in the set pair analysis theory, , ; At the same time, refer to the special coefficient value method and select , , the calculation formula is: (18); (19); (20); (twenty one); (twenty two); In the formula, For the The evaluation value under the evaluation index is The degree of connection of the evaluation level, , , , , , Respectively Each evaluation index corresponds to the limit value of each standard interval.

[0034] S5.5, determine the relative membership, use the relative difference of the variable fuzzy set constructed in step S5.4, and according to the variable fuzzy set evaluation theory, the index evaluation value belongs to the evaluation level The relative membership for: (twenty three).

[0035] Furthermore, step S5 also includes step S5.6: determining the indicator weight based on the entropy weight coefficient method, and determining the objective weight according to the variability of the indicator. The greater the variability, the smaller the entropy value, and the greater the weight. Step S5.6 also includes the following steps: S5.6.1, use the range standardization formula to standardize the data of each evaluation index: (twenty four); In the formula, For the The evaluation index corresponds to Rating value; , Respectively The minimum and maximum values ​​of the evaluation indicators.

[0036] S5.6.2, calculate the information entropy of each indicator. According to the definition of information entropy in information theory, the entropy value of a set of data is: (25); (26); In the formula, For the The entropy value of the evaluation index; For the Under the indicator The proportion of evaluation values; For the Under the evaluation index An evaluation value.

[0037] S5.6.3, determine the weight of each third-level indicator. First, according to the calculation formula of information entropy, calculate the information entropy of each third-level indicator under the four second-level indicators: , , , …, , calculate the weight of each third-level indicator to the second-level indicator through information entropy , , , ..., : (27); is the secondary indicator, Take 1, 2, 3, 4, Indicates the order of the third-level indicators under each second-level indicator, that is, ; ; ; .

[0038] S5.6.4, determine the final weight of each indicator, and calculate the weight of the third-level indicators under each second-level indicator according to the entropy weight coefficient method. Assuming that the influence weights of the four second-level indicators on the evaluation of grouting effect are consistent, they are: The final weight of each index on the grouting effect is for: (28); in, Take 1, 2, 3 and 4, For the The influence weight of the secondary indicators on the evaluation of grouting effect.

[0039] Furthermore, step S5 further includes the following steps: S5.7, Calculate the comprehensive membership : (29); in, is the relative membership, To optimize the criterion parameters, is the least square criterion, is the least squares criterion; is the distance parameter, is the Hamming distance, is the Euclidean distance.

[0040] S5.8, 10 groups of comprehensive membership vectors are calculated, and after normalization of the 10 groups of comprehensive membership vectors, the comprehensive membership vector at any evaluation point is obtained. : (30); in, is the total number of evaluation levels.

[0041] S5.9, using the comprehensive membership vector The cumulative sum of the products of the corresponding evaluation grades is used to obtain the evaluation characteristic values ​​of the grouting effect to be evaluated under 10 model parameters. , analyze the stability of the level eigenvalues ​​and finally determine the level of grouting effect: (31).

[0042] The present invention has the following beneficial effects: The present invention fully considers the geological structure and the development of cracks, establishes the relationship between the two and the grouting parameters, and makes the grouting have engineering practicality considerations. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific implementation of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the specific implementation or the prior art description. Obviously, the drawings described below are some implementations of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 A flow chart of a method for evaluating the effect of grouting transformation in a mine area with complex geological structures according to the present invention is shown. DETAILED DESCRIPTION

[0044] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] like Figure 1 A method for evaluating the effect of grouting transformation in a mine area with complex geological structure is shown, which specifically includes the following steps: S1. Collect and organize the mine production geological report, hydrogeological report, 3D seismic exploration data, borehole columnar and logging data, ground stress measured data, mining engineering plan, grouting design plan, actual grouting data and other information to clarify the mine geological and hydrogeological characteristics, the spatial superposition relationship and water-richness of the aquifer and aquiclude, the scope of the main mineable coal seams, structural distribution, ground stress distribution, grouting layer, grouting hole layout and grouting parameters.

[0046] S2, conduct fracture effective area analysis to clarify the distribution of fracture effective areas. Fracture effective area analysis includes: constructing a study area model, setting boundary conditions and fracture effective area result analysis. Constructing a study area model includes: constructing a geological model, a mechanical model and a mathematical model.

[0047] S3. Conduct hydraulic conductivity analysis of the effective fracture area, including lateral hydraulic conductivity analysis and vertical hydraulic conductivity analysis.

[0048] S4, conduct spatial correlation analysis between the effective fracture area and grouting, including: constructing a geographically weighted regression model of the effective fracture area and grouting information, analyzing the correlation between the influencing factors at the coordinate positions of each grouting hole and segment and the influence coefficient of the structural fracture, and determining the spatial influence coefficient of the effective fracture area on grouting.

[0049] S5, conduct grouting effect evaluation, take grouting effect as the evaluation index, consider geological conditions, hydrogeological conditions, grouting parameters, inspection hole verification results, adopt set pair analysis and variable fuzzy set coupling method, flexibly deal with the uncertainty of the evaluation object, strengthen the systematic and comprehensiveness of the evaluation process, and construct a grouting effect evaluation model.

[0050] Specifically, step S2 includes the following steps:

[0051] S2.1, constructing a geological model, including: determining the size of the geological model according to the boundary of the study area, determining the thickness and stratigraphic distribution of the geological model according to the grouting transformation layer and the drilling column chart, determining the fold and fault structure within the study area according to the structural outline map and three-dimensional seismic detection data, and finally establishing the geological model through CAD and Rhino software, and outputting the ".STL" format file.

[0052] S2.2, constructing a mechanical model means assigning rock mechanical parameters to the stratum; the rock mechanical parameters need to be determined based on the mechanical parameters of the actual stratum, and various lithology-related parameters are obtained through rock mechanics experiments conducted by field sampling, and the required rock mechanical parameters are determined by calculating the weighted average; on the basis of the rock mechanical parameters, the influence of tectonic action is considered, and the area is divided into sedimentary strata, fold belts and fault zones according to the tectonic conditions, and the fold belts are regarded as hard lithological layers with a Young's modulus of 150% of the sedimentary strata; the faults are regarded as weak layers with a Young's modulus of 50% of the sedimentary strata.

[0053] S2.3, mathematical model is the basis for tectonic stress field simulation. Balance equation, stress-strain relationship equation and strain-displacement relationship equation are used to simulate stress field changes and construct mathematical model. Among them, the balance equation represents momentum conservation, which is expressed as: (1); In the formula, is the Laplace operator, is the stress tensor, It is the volume force.

[0054] The rock is assumed to be a linear elastic material model and satisfies Hooke's law, so the stress-strain relationship equation is expressed as: (2); In the formula, is the elastic modulus tensor, is the strain tensor.

[0055] Strain-displacement relationship It is expressed as: (3); In the formula, , are the components of the displacement vector, , are spatial coordinates.

[0056] Specifically, the boundary condition setting in step S2.4 includes ground stress inversion and boundary load setting, and the ground stress inversion includes the following steps: S2.4.1, conduct on-site sampling, and use the samples to prepare standard rock specimens in the horizontal, vertical, 45° and 90° directions to carry out indoor rock acoustic emission experiments.

[0057] S2.4.2, perform uniaxial compression on the rock sample, record the relationship curve between the cumulative number of acoustic emission signals and the external compressive stress, when the acoustic emission signal increases sharply, the Kaiser point where the acoustic emission signal increases sharply reflects the tectonic activities experienced by the rock in history and its corresponding paleo-stress value. The paleo-stress value is determined based on the stress value corresponding to the Kaiser point, and the stress value is converted into three-dimensional principal stress. The calculation formula is as follows: (4); (5); (6); (7); In the formula, is the vertical ground stress, unit: MPa; is the stress corresponding to the Kaiser effect of the core in the vertical direction, in MPa; is the effective stress coefficient; is the pore pressure, unit: MPa; is the maximum principal stress in the horizontal direction, in MPa; , , The stresses corresponding to the Kaiser effect of the core at 0°, 45°, and 90° horizontal directions, in MPa; is the horizontal projection angle relative to the 0° direction, unit (°); is the minimum principal stress in the horizontal direction, unit: MPa.

[0058] Specifically, on the basis of determining the ground stress values ​​at different orientations and different Kaiser points, the ground stress inversion is performed using iterative calculation of boundary stress and neural network algorithm. The boundary load setting in step S2.4 includes the following steps:

[0059] S2.4.3, based on the measured stress data, the boundary stress condition combinations are designed with equal steps to construct training samples.

[0060] S2.4.4, input the training samples into the neural network, perform iterative updates on the weights and thresholds during the training process of the neural network, and output the boundary stresses in the X, Y, and Z directions of the boundary stress conditions of the neural network model.

[0061] S2.4.5, compare the simulated value output by the neural network simulation with the Kaiser feedback value. When the accuracy exceeds 95%, the neural network simulation algorithm is considered reliable. The boundary load boundary mainly adds a rectangular auxiliary border to the geological model of the study area to surround the entire study model, eliminate the influence of boundary conditions, and enhance the convergence of the model.

[0062] Based on the above model settings, the geometric model file format is imported into the COMSOL numerical simulation software for unit mesh division, and the mathematical model and mechanical model are established to start network node calculation and numerical simulation.

[0063] Specifically, the crack effective area analysis in step S2 is as follows: Based on the results of the inversion of the ground stress obtained in steps S2.4.3 to S2.4.5, the shear rupture coefficient is introduced using the Mohr-Coulomb strength criterion. To predict cracks; <1, the rock is in a stable state and no fracture occurs; When ≥1, the rock stress state is at or exceeds the Mohr stress circle rupture envelope, and the rock ruptures. The formula is as follows: (8); (9); In the formula, is the maximum horizontal principal stress, MPa; is the minimum horizontal principal stress, MPa; is the cohesion, MPa, is the internal friction angle of rock. The in-situ stress inversion result is used to obtain the distribution of in-situ stress under the influence of tectonic movements in different periods. Substituting it into formula (8) and formula (9), the shear rupture coefficient and effective crack area in different periods are obtained.

[0064] Specifically, after the effective fracture area is generated, it is necessary to further understand whether the rock layer still has the possibility of conducting water after movement, which mainly includes lateral water conduction capacity analysis and vertical water conduction capacity analysis. In step S3, the lateral water conduction capacity of the effective fracture area is analyzed by using the lithology juxtaposition diagram method, which specifically includes the following steps: S3.1, based on the borehole columnar data on both sides of the fault or fracture area, fine sandstone, medium sandstone, and coarse sandstone are regarded as aquifers, and mudstone, sandy mudstone, and siltstone are regarded as impermeable layers. The profiles of the aquifers and impermeable layers on both sides of the fracture area are drawn.

[0065] S3.2, according to the spatial position and occurrence of joints and fissures, merge the profiles of aquifers and impermeable layers on both sides of the joints and fissures to calculate the aquifer docking rate , that is, the percentage of the aquifer docking area to the evaluation area, which evaluates the lateral water conductivity of each fracture effective area; the aquifer docking rate The higher the value, the better the water conductivity, which is conducive to slurry injection and diffusion; the aquifer docking rate The lower the water conductivity, the worse it is, which hinders the injection and diffusion of slurry. Defined as the lateral water conduction capacity difference, the water conduction influence coefficient is set to ; , defined as poor lateral water conduction capacity, and the water conduction influence coefficient is set to ; D∈[40%,60%), defined as the lateral water conduction capacity, the water conduction influence coefficient is set to , , the lateral water conduction capacity is defined as better, and the water conduction influence coefficient is set to ; , defined as good lateral water conduction capacity, and the water conduction influence coefficient is set to .

[0066] The vertical water conduction capacity analysis of the effective fracture area in step S3 specifically includes the following steps: S3.3, cross-section pressure is an important indicator for judging the vertical closure of joints and fissures. When the cross-section pressure reaches or exceeds 7.5 MPa, the mudstone in the fault plane (zone) undergoes plastic deformation and flows to block the cracks. Therefore, the higher the degree of cross-section closure, the worse the vertical water conductivity and the worse the slurry injectability. Based on the results of the in-situ stress simulation in the study area, the cross-section pressure on the effective area of ​​the fault and the crack is determined. ,Will It is defined as good water conductivity in the vertical direction, and the water conductivity influence coefficient is set to , It is defined as having better water conductivity in the vertical direction, and the water conductivity influence coefficient is set to , Defined as the vertical water conductivity, the water conductivity coefficient is set to , Defined as the vertical water conductance intersection, the water conductance influence coefficient is set to Defined as the difference in vertical water conductivity, the water conductivity influence coefficient is set to .

[0067] Specifically, step S3 also includes the following steps: S3.4, conduct hydraulic conductivity analysis on faults and fissures at all locations in the region, compare the lateral hydraulic conductivity with the vertical hydraulic conductivity, take the maximum hydraulic conductivity as the standard, and take the influence coefficient corresponding to the maximum hydraulic conductivity ; The Kriging interpolation method is used to draw the contour map of the water conductivity influence coefficient of the effective area of ​​the fracture in the study area.

[0068] Specifically, step S4 includes the following steps: S4.1, construct a geographically weighted regression model of fracture effective area and grouting. According to the distribution range of the study area, the geographically weighted regression GWR model is selected, and the permeability, grouting volume and grouting final pressure of each grouting section are used as independent variables, and the water conductivity influence index of the fracture effective area of ​​the study area is used as the dependent variable. The GWR model is used for regression analysis to quantify the spatial correlation between the independent variables and each dependent variable. The specific formula is as follows: (10); In the formula, represents the effective development area of ​​fractures, is the initial geographically weighted regression coefficient, For the independent variables, including: permeability, grouting volume and final grouting pressure, is the geographically weighted regression coefficient, depending on the grouting section The coordinates of each network center , is the total number of grouting sections, is the error term.

[0069] S4.2, Spatial correlation analysis between the coordinate position of each grouting section and the influencing factors. According to the grouting design plan, determine the coordinates of each grid center of each grouting section. , input each grid center coordinate into the GWR model and calculate the geographically weighted regression coefficient , to quantify various grouting parameters, such as permeability , grouting volume , grouting final pressure The spatial correlation between the influence coefficient of water conduction and the water conduction coefficient is calculated as follows: (11); In the formula, It is a distance-based weight matrix that reflects the impact of geographic location on the regression results. is the matrix of independent variables, is the observed dependent variable matrix.

[0070] S4.3, perform visual analysis and display. The calculated geographically weighted regression coefficients The Surfer software was imported and combined with the base map of the water conductivity coefficient of the effective area of ​​the fissure in the study area to generate bubble maps for each grouting section. The size of the bubble was calculated based on the OK, the formula is as follows: (12); in, To obtain the maximum;

[0071] S4.4, Analyze and interpret the correlation results. Through the bubble diagram, intuitively analyze the spatial distribution of parameters of each grouting section and its correlation with the water conductivity coefficient. At the same time, calculate and analyze Water conductivity coefficient The spatial correlation between : (13); in, for Take the average, for Take the average.

[0072] By analyzing the correlation coefficient , identify the grouting parameters that contribute most to the influence index of hydraulic conductivity in the fracture area, and draw the correlation contour maps between permeability, grouting volume, grouting final pressure and hydraulic conductivity. If the overall correlation is good, it means that the effective fracture area matches the grouting engineering information well, and the influence of joints such as structure and fracture on the grouting effect needs to be considered; if the overall correlation is poor, it means that the effective fracture area matches the grouting engineering information poorly.

[0073] Specifically, step S5 includes the following steps: S5.1. Construct an evaluation index system according to the evaluation requirements of grouting effect, including: considering the structure and fissure joints in the study area according to geological conditions, taking the water conductivity coefficient as the evaluation index, which is determined according to steps S2 to S4; determining the water pressure of the aquifer, the thickness of the aquiclude and the permeability coefficient as the evaluation indexes according to the hydrogeological conditions; determining the water permeability, the final grouting pressure and the unit grouting volume as the evaluation indexes according to the grouting parameters; determining the borehole water inflow, the ratio of the borehole water inflow before and after grouting and the coring rate as the evaluation indexes according to the verification results of the inspection hole.

[0074] S5.2 Determine the evaluation level standard based on the evaluation index system established in step S5.1, and form a set of evaluation values ​​of each influencing factor index And the evaluation criteria of each indicator Form a set pair ( ),in: (15); (16); In the formula, For the The first evaluation index Rating value; For the The limit value of the evaluation standard corresponding to the evaluation index.

[0075] S5.3, determine the set pair connection degree and relative difference degree, and classify the difference degree in the set pair analysis into excellent and inferior , subdividing the opposition into superior and inferior and inferior , and obtain the multivariate connection degree : (17); In the formula, is the difference coefficient, is the strength coefficient, For the same degree, ; ; ; ; .

[0076] The grouting effect evaluation model is of the smaller the better type, that is, the smaller the index evaluation value, the better the grouting effect.

[0077] S5.4, establish the relationship between the evaluation value and the evaluation level under the evaluation index , and evaluation level Relative difference between variable fuzzy sets , combined with the evaluation of the grouting effect evaluation model, and the principle of equal division of the difference coefficient and the opposition coefficient in the set pair analysis theory, , ; At the same time, refer to the special coefficient value method and select , , the calculation formula is: (18); (19); (20); (twenty one); (twenty two); In the formula, For the The evaluation value under the evaluation index is The degree of connection of the evaluation level, , , , , , Respectively Each evaluation index corresponds to the limit value of each standard interval; from the calculation, we can know that The closer it is to 1, the more the indicator evaluation value and evaluation level The higher the consistency, the stronger the "sameness" relationship between the two, and vice versa.

[0078] S5.5, determine the relative membership, use the relative difference of the variable fuzzy set constructed in step S5.4, and according to the variable fuzzy set evaluation theory, the index evaluation value belongs to the fuzzy evaluation level The relative membership for: (twenty three).

[0079] Specifically, step S5 also includes step S5.6: determining the indicator weight based on the entropy weight coefficient method, and determining the objective weight according to the variability of the indicator. The greater the variability, the smaller the entropy value, and the greater the weight. Step S5.6 also includes the following steps: S5.6.1, use the range standardization formula to standardize the data of each evaluation index: (twenty four); In the formula, For the The evaluation index corresponds to Rating value; , Respectively The minimum and maximum values ​​of the evaluation indicators.

[0080] S5.6.2, calculate the information entropy of each indicator. According to the definition of information entropy in information theory, the entropy value of a set of data is: (25); (26); In the formula, For the The entropy value of the evaluation index; For the Under the indicator The proportion of evaluation values; For the Under the evaluation index An evaluation value.

[0081] S5.6.3, determine the weight of each third-level indicator. First, according to the calculation formula of information entropy, calculate the information entropy of each third-level indicator under the four second-level indicators: , , , …, , calculate the weight of each third-level indicator to the second-level indicator through information entropy , , , ..., : (27); in, is the secondary indicator, Take 1, 2, 3, 4, Indicates the order of the third-level indicators under each second-level indicator, that is, ; ; ; .

[0082] S5.6.4, determine the final weight of each indicator, and calculate the weight of the third-level indicators under each second-level indicator according to the entropy weight coefficient method. Assuming that the influence weights of the four second-level indicators on the evaluation of grouting effect are consistent, they are: The final weight of each index on the grouting effect is for: (28); in, Take 1, 2, 3 and 4, For the The influence weight of the secondary indicators on the evaluation of grouting effect.

[0083] Specifically, step S5 also includes the following steps: S5.7, Calculate the comprehensive membership : (29); in, is the relative membership, To optimize the criterion parameters, is the least square criterion, is the least squares criterion; is the distance parameter, is the Hamming distance, is the Euclidean distance, and Collectively referred to as variable model parameters; usually there are 4 combinations: ① =1, =1;② =1, =2; ③ =2, =1;④ =2, =2.

[0084] S5.8, determine the level characteristic value and evaluation level. For each level 3 evaluation index, 10 groups of comprehensive membership vectors can be calculated through 10 different combinations of variable model parameters. After normalizing the 10 groups of comprehensive membership vectors, the comprehensive membership vector at any evaluation point can be obtained. : (30); in, is the total number of evaluation levels.

[0085] S5.9, using the comprehensive membership vector The cumulative sum of the products of the corresponding evaluation grades is used to obtain the evaluation characteristic values ​​of the grouting effect to be evaluated under 10 model parameters. , analyze the stability of the level eigenvalues ​​and finally determine the level of grouting effect: (31).

[0086] The present invention further comprises step S6, evaluating the grouting effect classification. For the area where the grouting effect is evaluated as excellent, that is, the grouting meets the design requirements and basically no sudden water gushing will occur; for the area where the grouting effect is evaluated as qualified, that is, the grouting transformation requirements are met, but it should be noted that additional grouting is performed when necessary; for the area where the grouting effect is evaluated as poor, that is, the grouting transformation requirements cannot be met, further grouting plugging treatment is required, and steps S5-S6 are repeated until the grouting transformation requirements are met.

[0087] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for evaluating the effect of grouting transformation in a mine area with complex geological structure, characterized in that: The specific steps include: S1. Clarify the geological and hydrogeological characteristics of the mine, the spatial superposition relationship and water-richness of the aquifer and aquiclude, the scope of the main mineable coal seams, structural distribution, ground stress distribution, grouting layer, grouting hole layout and grouting parameters; S2, conduct fracture effective area analysis to clarify the distribution of fracture effective area. Fracture effective area analysis includes: constructing a study area model, setting boundary conditions and fracture effective area result analysis. Constructing the study area model includes: constructing a geological model, a mechanical model and a mathematical model. S3, conduct hydraulic conductivity analysis of the effective fracture area, including lateral hydraulic conductivity analysis and vertical hydraulic conductivity analysis; S4, conduct spatial correlation analysis between the effective fracture area and grouting, including: constructing a geographically weighted regression model between the effective fracture area and grouting information, analyzing the correlation between the influencing factors at the coordinate positions of each grouting hole and segment and the influence coefficient of the structural fracture, and determining the spatial influence coefficient of the effective fracture area on grouting; S5, evaluate the grouting effect, take the grouting effect as the evaluation index, consider the geological conditions, hydrogeological conditions, grouting parameters, and inspection hole verification results, and use the set pair analysis and variable fuzzy set coupling method to construct a grouting effect evaluation model.

2. The method for evaluating the effect of grouting transformation in a mine area with complex geological structure according to claim 1, characterized in that: Step S2 specifically includes the following steps: S2.1, constructing geological model, including: determining the size of geological model according to the boundary range of the study area, determining the thickness and stratigraphic distribution of geological model according to the grouting transformation layer and the drilling column chart, determining the fold and fault structure within the study area according to the structural outline map and 3D seismic detection data, and finally establishing geological model through CAD and Rhino software, and outputting ".STL" format file; S2.2, construct a mechanical model, that is, assign rock mechanical parameters to the strata; obtain various lithology-related parameters through field sampling and rock mechanical experiments, and calculate the weighted average to determine the required rock mechanical parameters; on the basis of rock mechanical parameters, consider the influence of tectonic action, and divide the area into sedimentary strata, fold belts and fault belts according to the tectonic situation. The fold belts are regarded as hard lithological layers, and the Young's modulus is 150% of the sedimentary strata; the faults are regarded as weak layers, and the Young's modulus is 50% of the sedimentary strata; S2.3, use the equilibrium equation, stress-strain relationship equation and strain-displacement relationship equation to simulate the stress field change and construct a mathematical model; among them, the equilibrium equation represents the conservation of momentum, which is expressed as: （1）； In the formula, is the Laplace operator, is the stress tensor, It is the volume force; The rock is assumed to be a linear elastic material model and satisfies Hooke's law. The stress-strain relationship equation is expressed as: （2）； In the formula, is the elastic modulus tensor, is the strain tensor; Strain-displacement relationship It is expressed as: （3）； In the formula, , are the components of the displacement vector, , are spatial coordinates.

3. The method for evaluating the effect of grouting transformation in a mine area with complex geological structure according to claim 2, characterized in that: The boundary condition setting in step S2.4 includes ground stress inversion and boundary load setting. The ground stress inversion includes the following steps: S2.4.1, conduct on-site sampling, and use the samples to prepare standard rock specimens in the horizontal, vertical, 45° and 90° directions, and carry out indoor rock acoustic emission experiments; S2.4.2, determine the paleo-stress value based on the stress value corresponding to the Kaiser point, and convert the stress value into three-dimensional principal stress. The calculation formula is as follows: （4）； （5）； （6）； （7）； In the formula, is the vertical ground stress, unit: MPa; is the stress corresponding to the Kaiser effect of the core in the vertical direction, in MPa; is the effective stress coefficient; is the pore pressure, unit: MPa; is the maximum principal stress in the horizontal direction, in MPa; , , The stresses corresponding to the Kaiser effect of the core at 0°, 45°, and 90° horizontal directions, in MPa; is the horizontal projection angle relative to the 0° direction, unit (°); is the minimum principal stress in the horizontal direction, unit: MPa.

4. A method for evaluating the effect of grouting transformation in a mine area with complex geological structure according to claim 3, characterized in that: The boundary load setting in step S2.4 includes the following steps: S2.4.3, based on the measured stress data, the boundary stress condition combination is designed with equal step distance to construct the training samples; S2.4.4, input the training samples into the neural network, perform iterative updates on the weights and thresholds during the training process of the neural network, and output the boundary stresses in the X, Y, and Z directions of the boundary stress conditions of the neural network model; S2.4.5, compare the simulated value of the neural network simulation output with the Kaiser feedback value. When the accuracy rate exceeds 95%, the neural network simulation algorithm is considered to be reliable.

5. A method for evaluating the effect of grouting transformation in a mine area with complex geological structure according to claim 4, characterized in that: The specific analysis of the effective crack area in step S2 is as follows: Based on the results of the inversion of the ground stress obtained in steps S2.4.3 to S2.4.5, the shear rupture coefficient is introduced using the Mohr-Coulomb strength criterion. To predict cracks; <1, the rock is in a stable state and no fracture occurs; When ≥1, the rock stress state is at or exceeds the Mohr stress circle rupture envelope, and the rock ruptures. The formula is as follows: （8）； （9）； In the formula, is the maximum horizontal principal stress, MPa; is the minimum horizontal principal stress, MPa; is the cohesion, MPa, is the internal friction angle of rock. The in-situ stress inversion result is used to obtain the distribution of in-situ stress under the influence of tectonic movements in different periods. Substituting it into formula (8) and formula (9), the shear rupture coefficient and effective crack area in different periods are obtained.

6. A method for evaluating the effect of grouting transformation in a mine area with complex geological structure according to claim 4, characterized in that: In step S3, the lateral water conduction capacity of the effective fracture area is analyzed by using the lithology juxtaposition diagram method, which specifically includes the following steps: S3.1, based on the borehole columnar data on both sides of the fault or fracture area, fine sandstone, medium sandstone, and coarse sandstone are regarded as aquifers, and mudstone, sandy mudstone, and siltstone are regarded as aquicludes, and the profiles of aquifers and aquicludes on both sides of the fracture area are drawn; S3.2, according to the spatial position and occurrence of joints and fissures, merge the profiles of aquifers and impermeable layers on both sides of the joints and fissures to calculate the aquifer docking rate , that is, the percentage of the aquifer docking area to the evaluation area, which evaluates the lateral water conductivity of each fracture effective area; the aquifer docking rate The higher the value, the better the water conductivity, which is conducive to slurry injection and diffusion; the aquifer docking rate The lower it is, the worse the water conductivity is, which hinders the injection and diffusion of slurry; The vertical water conduction capacity analysis of the effective fracture area in step S3 specifically includes the following steps: S3.3, based on the results of the in-situ stress simulation in the study area, determine the cross-sectional pressure on the effective areas of faults and fissures ,Will It is defined as good water conductivity in the vertical direction, and the water conductivity influence coefficient is set to , It is defined as having better water conductivity in the vertical direction, and the water conductivity influence coefficient is set to , Defined as the vertical water conductivity, the water conductivity coefficient is set to , Defined as the vertical water conductance intersection, the water conductance influence coefficient is set to Defined as the difference in vertical water conductivity, the water conductivity influence coefficient is set to ; Step S3 also includes the following steps: S3.4, conduct hydraulic conductivity analysis on faults and fissures at all locations in the region, compare the lateral hydraulic conductivity with the vertical hydraulic conductivity, take the maximum hydraulic conductivity as the standard, and take the influence coefficient corresponding to the maximum hydraulic conductivity ; The Kriging interpolation method is used to draw the contour map of the water conductivity influence coefficient of the effective area of ​​the fracture in the study area.

7. The method for evaluating the effect of grouting transformation in a mine area with complex geological structure according to claim 1, characterized in that: Step S4 includes the following steps: S4.1, according to the distribution range of the study area, the geographically weighted regression GWR model was selected, the permeability, grouting volume and grouting final pressure of each grouting section were taken as independent variables, and the water conductivity influence index of the effective fracture area of ​​the study area was taken as the dependent variable. The GWR model was used for regression analysis to quantify the spatial correlation between the independent variables and the dependent variables. The specific formula is as follows: （10）； In the formula, represents the effective development area of ​​fractures, is the initial geographically weighted regression coefficient, For the independent variables, including: permeability, grouting volume and final grouting pressure, is the geographically weighted regression coefficient, depending on the grouting section The coordinates of each network center , is the total number of grouting sections, is the error term; S4.2, according to the grouting design plan, determine the center coordinates of each grid in each grouting section , input each grid center coordinate into the GWR model and calculate the geographically weighted regression coefficient , the specific calculation formula is as follows: （11）； In the formula, It is a distance-based weight matrix that reflects the impact of geographic location on the regression results. is the matrix of independent variables, is the observed dependent variable matrix; S4.3, the calculated geographically weighted regression coefficients The Surfer software was imported and combined with the base map of the water conductivity coefficient of the effective area of ​​the fissure in the study area to generate bubble maps for each grouting section. The size of the bubble was calculated based on the OK, the formula is as follows: （12）； in, To obtain the maximum; S4.4, through the bubble diagram, intuitively analyze the parameter distribution of each grouting section in space and its correlation with the water conductivity coefficient. At the same time, calculate and analyze Water conductivity coefficient The spatial correlation between : （13）； in, for Take the average, for Take the average.

8. The method for evaluating the effect of grouting transformation in a mine area with complex geological structure according to claim 1, characterized in that: Step S5 includes the following steps: S5.1, based on geological conditions, taking into account the structure and fissure joints in the study area, the water conductivity coefficient is used as the evaluation index, which is determined specifically according to steps S2 to S4; based on hydrogeological conditions, the water pressure of the aquifer, the thickness of the aquiclude and the permeability coefficient are determined as the evaluation indexes; based on grouting parameters, the water permeability, the final grouting pressure and the unit grouting volume are determined as the evaluation indexes; based on the verification results of the inspection hole, the borehole water inflow, the ratio of the borehole water inflow before and after grouting and the coring rate are determined as the evaluation indexes; S5.2 Determine the evaluation level standard based on the evaluation index system established in step S5.1, and form a set of evaluation values ​​of each influencing factor index And the evaluation criteria of each indicator Form a set pair ( ),in: （15）； （16）； In the formula, For the The first evaluation index Rating value; For the The limit value of the evaluation standard corresponding to the evaluation index; S5.3, determine the set pair connection degree and relative difference degree, and classify the difference degree in the set pair analysis into excellent and inferior , subdividing the opposition into superior and inferior and inferior , and obtain the multivariate connection degree : （17）； In the formula, is the difference coefficient, is the strength coefficient, For the same degree, ; ; ; ; ; S5.4, establish the relationship between the evaluation value and the evaluation level under the evaluation index , and evaluation level Relative difference between variable fuzzy sets , combined with the evaluation of the grouting effect evaluation model, and the principle of equal division of the difference coefficient and the opposition coefficient in the set pair analysis theory, , ; At the same time, refer to the special coefficient value method and select , , the calculation formula is: （18）； （19）； （20）； （21）； （22）； In the formula, For the The evaluation value under the evaluation index is The degree of connection of the evaluation level, , , , , , Respectively Each evaluation index corresponds to the limit value of each standard interval; S5.5, determine the relative membership, using the relative difference of the variable fuzzy set constructed in step S5.4, according to the variable fuzzy set evaluation theory, the index evaluation value belongs to the evaluation level The relative membership for: （23）。 9. A method for evaluating the effect of grouting transformation in a mine area with complex geological structure according to claim 8, characterized in that: Step S5 also includes step S5.6: determining the indicator weight based on the entropy weight coefficient method, and determining the objective weight according to the variability of the indicator. The greater the variability, the smaller the entropy value, and the greater the weight. Step S5.6 includes the following steps: S5.6.1, use the range standardization formula to standardize the data of each evaluation index: （24）； In the formula, For the The evaluation index corresponds to Rating value; , Respectively The minimum and maximum values ​​under each evaluation index; S5.6.2, calculate the information entropy of each indicator. According to the definition of information entropy in information theory, the entropy value of a set of data is: （25）； （26）； In the formula, For the The entropy value of the evaluation index; For the Under the indicator The proportion of evaluation values; For the Under the evaluation index Rating value; S5.6.3, determine the weight of each third-level indicator. First, according to the calculation formula of information entropy, calculate the information entropy of each third-level indicator under the four second-level indicators: , , , …, , calculate the weight of each third-level indicator to the second-level indicator through information entropy , , , ..., : （27）； is the secondary indicator, Take 1, 2, 3, 4, Indicates the order of the third-level indicators under each second-level indicator, that is, ; ; ; ; S5.6.4, determine the final weight of each indicator, and calculate the weight of the third-level indicators under each second-level indicator according to the entropy weight coefficient method. Assuming that the influence weights of the four second-level indicators on the evaluation of grouting effect are consistent, they are: The final weight of each index on the grouting effect is for: （28）； in, Take 1, 2, 3 and 4, For the The influence weight of the secondary indicators on the evaluation of grouting effect.

10. A method for evaluating the effect of grouting transformation in a mine area with complex geological structure according to claim 9, characterized in that: Step S5 also includes the following steps: S5.7, Calculate the comprehensive membership : （29）； in, is the relative membership, To optimize the criterion parameters, is the least square criterion, is the least squares criterion; is the distance parameter, is the Hamming distance, is the Euclidean distance; S5.8, 10 groups of comprehensive membership vectors are calculated, and after normalization of the 10 groups of comprehensive membership vectors, the comprehensive membership vector at any evaluation point is obtained. : （30）； in, is the total number of evaluation levels; S5.9, using the comprehensive membership vector The cumulative sum of the products of the corresponding evaluation grades is used to obtain the evaluation characteristic values ​​of the grouting effect to be evaluated under 10 model parameters. , analyze the stability of the level eigenvalues ​​and finally determine the level of grouting effect: （31）。

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