A tomography-based artificial reconstruction of coal body fracture network inversion system and method

Abstract

This invention pertains to the field of coal mine mining and modification technology, specifically relating to a tomographic imaging-based inversion system and method for retrieving fracture networks in artificially modified coal seams. This invention involves deploying a resistivity detection electrode array in the artificially modified coal seam region, using resistivity tomography to collect real-time data on the coal seam resistivity distribution, and then combining this data with artificial modification process parameters to construct a correlation model between the fracture network and resistivity. An inversion algorithm is then used to reconstruct the spatial distribution, development range, and connectivity characteristics of the coal seam fractures. Furthermore, this invention is relatively easy to operate, has high inversion accuracy, and can track the dynamic evolution of coal seam fractures in real time during the artificial modification process, providing crucial data support for optimizing coal mine gas extraction efficiency and assessing coal seam stability.

Description

Technical Field

[0001] This invention belongs to the field of coal mining and modification technology, specifically relating to a system and method for inverting the fracture network of artificially modified coal bodies based on tomographic imaging. Background Technology

[0002] In the process of coal mining, high-gas and low-permeability coal seams suffer from low gas extraction efficiency and insufficient coal strength. These problems have always been key obstacles to safe production. To address these issues, the industry has widely adopted a series of hydraulic measures, such as hydraulic cavity creation, hydraulic fracturing, and hydraulic slotting, to artificially modify the coal body. By constructing a well-developed fracture network, the permeability of the coal body is improved, the gas extraction effect is enhanced, and the stress distribution of the coal body is optimized, thereby improving the safety of mining.

[0003] However, the development of the fracture network during artificial coal seam modification, such as its spatial distribution, aperture, and connectivity, directly determines the effectiveness of the modification. If the evolution of fractures cannot be accurately grasped, it can easily lead to unreasonable matching of modification parameters. For example, excessively high fracturing pump pressure can cause excessive coal seam fragmentation, or insufficient cavity creation range can limit the effect of improving permeability, affecting gas extraction efficiency and even triggering the risk of coal seam instability.

[0004] Currently, traditional methods for detecting coal seam fractures mainly include borehole inspection and acoustic testing. Borehole inspection can only obtain images of fractures at localized locations within the borehole, failing to achieve large-scale three-dimensional detection. Acoustic testing is significantly affected by the heterogeneity of the coal seam, making its accuracy susceptible to interference, and it is difficult to track the dynamic changes of fractures during the modification process in real time. Furthermore, existing methods mostly rely on empirical judgment to establish the correlation between fractures and detection signals, lacking quantitative model support, resulting in large errors in the inversion results and failing to meet the requirements of precision mining. A technical solution is needed that can detect artificially modified coal seam fracture networks in real time, accurately, and over a large area to address these problems. Summary of the Invention

[0005] The purpose of this invention is to provide a system and method for inverting the fracture network of artificially modified coal bodies based on tomographic imaging. This system can capture the dynamic evolution of coal body fractures in real time during the artificial modification process and provide data support for safe coal mine production by quantitatively inverting fracture parameters.

[0006] The specific technical solution adopted by this invention is as follows:

[0007] A method for inverting the fracture network of artificially modified coal seams based on tomographic imaging includes:

[0008] The detection range of the artificially modified coal body is obtained, and a movable electrode array is deployed on the borehole wall and around the roadway within the area of ​​the artificially modified coal body according to the spatial size of the detection range.

[0009] A probe current is applied to the coal body using a movable electrode array, and the resistivity response data of the coal body is collected. The resistivity response data of the coal body is then preprocessed.

[0010] The parameters of hydraulic measures for artificially modified coal bodies are obtained, and the matching basic model is retrieved from the mapping relationship database of fracture network and resistivity correlation model. The basic model is then calibrated on-site in conjunction with the initial physical parameters of the target coal body to obtain a personalized correlation model suitable for the modification conditions.

[0011] Substitute the preprocessed coal resistivity response data into the personalized correlation model to obtain the fracture network parameters of the coal body during the artificial modification process.

[0012] The fracture network parameters obtained by inversion are verified, and after successful verification, the fracture network parameters are output in the form of three-dimensional images and reports.

[0013] In a preferred embodiment, when deploying the movable electrode array, the deployment spacing of the movable electrodes is determined by a formula for calculating the electrode spacing. The formula for calculating the electrode spacing is as follows:

[0014] ;

[0015] In the formula, Indicates the spacing between movable electrodes. Indicates the accuracy of target crack location. This represents the resolution coefficient.

[0016] In a preferred embodiment, the sampling interval of the coal resistivity response data is determined according to an adaptive calculation formula for the data acquisition frequency, which is:

[0017] ;

[0018] In the formula, Indicates the actual data collection frequency. Indicates the basic sampling frequency. Indicates the pressure change sensitivity coefficient. This indicates the rate of change of pressure in hydraulic measures. This indicates the maximum design pressure of the hydraulic measures.

[0019] In a preferred embodiment, after the acquisition frequency of the coal resistivity response data is output, the resistivity tomography host is started, a detection current of a preset frequency is applied to the movable electrode, and the electrode is controlled to switch between power supply mode and measurement mode in sequence to acquire the coal resistivity response data of the coal body at each time point during the process of artificially modifying the coal body.

[0020] The real-time acquired coal resistivity response data is subjected to power frequency filtering and signal amplification to remove noise interference and improve the signal-to-noise ratio.

[0021] Temperature interference correction is applied to the real-time acquired coal resistivity response data based on the resistivity-temperature correction formula, thus completing the preprocessing of the coal resistivity response data. The resistivity-temperature correction formula is as follows:

[0022] ;

[0023] In the formula: This represents the resistivity of the coal body standardized to 25°C. Indicates actual temperature The measured resistivity of the coal body, This represents the temperature coefficient of coal resistivity. This indicates the actual measured temperature downhole.

[0024] In a preferred embodiment, the core mapping relationships of the basic model include:

[0025] The formula for the quantitative mapping relationship between coal resistivity and fracture density:

[0026] ;

[0027] In the formula, This represents the actual resistivity of the coal body. Indicates the resistivity of the coal matrix. Indicates the porosity of the coal body. Indicates the cementation index, Indicates the fracture density. Indicates the influence index of fractures;

[0028] And, the formula relating fracture aperture to preresistivity:

[0029] ;

[0030] In the formula, Indicates the crack aperture. Represents the resistivity of the fluid within the fracture. Indicates the average length of the crack. This represents the resistivity of pure coal without cracks.

[0031] In a preferred embodiment, the step of substituting the preprocessed coal resistivity response data into a personalized correlation model to invert and obtain the fracture network parameters of the coal body during the artificial modification process includes:

[0032] A regularization error correction formula is introduced to reduce the inversion error caused by coal body heterogeneity. The regularization error correction formula is as follows:

[0033] ;

[0034] In the formula, This indicates the error in the corrected fracture parameter inversion. Indicates the initial inversion error. This represents the correction factor for heterogeneity. This represents the standard deviation of coal resistivity. This represents the average resistivity of the coal body.

[0035] A three-dimensional image of the resistivity distribution of the coal body was obtained by iterative calculation and inversion.

[0036] Based on the mapping relationship in the personalized association model, the fracture parameters corresponding to the resistivity anomaly region are extracted, including fracture density, fracture aperture, fracture orientation and connectivity.

[0037] Among them, fracture connectivity is quantitatively evaluated using the fracture connectivity rate calculation formula, which is:

[0038] ;

[0039] In the formula, Indicates the fracture connectivity. This indicates the number of interconnected fracture pairs. The total number of fracture pairs within the detection area is represented by the following formula:

[0040] ;

[0041] In the formula, The total number of fractures within the detection area is calculated from the fracture density and the detection volume using the following formula:

[0042] ;

[0043] In the formula, Indicates the volume of the probe.

[0044] In a preferred embodiment, the iterative calculation is performed 20-50 times, and the iteration is terminated directly when the sum of the squares of the resistivity differences between two adjacent iterations is less than the iteration convergence threshold.

[0045] In a preferred embodiment, the development verification of the inverted fracture network parameters includes the following steps:

[0046] Multiple verification boreholes were randomly selected within the detection area, and coal seam fracture development parameters were observed based on the verification boreholes.

[0047] The parameters of coal body fracture development are compared with the parameters of fracture network obtained by inversion, and the inversion error rate is output.

[0048] When the inversion error rate exceeds the allowable error threshold, the personalized association model should be recalibrated.

[0049] If the inversion error rate does not exceed the allowable error threshold, the inverted fracture network parameters are deemed to have passed the development verification.

[0050] This invention also provides a tomographic imaging-based inversion system for fracture networks in artificially modified coal seams, using the aforementioned tomographic imaging-based inversion method for fracture networks in artificially modified coal seams, comprising:

[0051] The electrode deployment module is used to obtain the detection range of the artificially modified coal body, and to deploy a movable electrode array on the borehole inner wall and around the roadway within the artificially modified coal body area according to the spatial size of the detection range.

[0052] The data acquisition module is used to apply a probe current to the coal body through a movable electrode array and acquire the coal body resistivity response data, and to preprocess the coal body resistivity response data.

[0053] The model calibration module is used to obtain the hydraulic measures parameters of artificial coal body modification, retrieve the matching basic model from the mapping relationship database of fracture network and resistivity correlation model, and perform on-site calibration of the basic model in combination with the initial physical parameters of the target coal body to obtain a personalized correlation model suitable for the modification conditions.

[0054] The inversion module is used to substitute the pre-processed coal resistivity response data into the personalized correlation model to invert the fracture network parameters of the coal body during the artificial modification process.

[0055] The verification output module is used to verify the development of the fracture network parameters obtained by inversion, and outputs the fracture network parameters in the form of three-dimensional images and reports after the verification is passed.

[0056] And, an electronic device, the electronic device comprising:

[0057] At least one processor;

[0058] and a memory communicatively connected to the at least one processor;

[0059] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the above-described method for inverting artificially modified coal fracture networks based on tomography.

[0060] The technical effects achieved by this invention are as follows:

[0061] This invention upgrades traditional qualitative detection to quantitative inversion by constructing a fracture network and resistivity correlation model based on indoor experiments and combining it with a regularized constraint inversion algorithm. The fracture density inversion error is much higher than that of borehole inspection and acoustic testing methods, which can accurately characterize the details of the fracture network. It also supports continuous acquisition of resistivity data throughout the entire manual modification process, with flexible time intervals, and can capture the dynamic changes of fractures in real time during the modification process, providing dynamic data support for analyzing the fracture evolution mechanism. It solves the defect of traditional static detection methods that cannot reflect the process. The movable electrode supports two modes: borehole deployment and roadway surface deployment, adapting to different mining scenarios. It is also compatible with various hydraulic modification measures such as hydraulic cavity creation, fracturing, and slotting, making it widely applicable. Furthermore, the combination of wireless transmission technology avoids the cumbersome and safety hazards of underground wiring. The inversion process is highly automated, requiring no manual intervention. It can be operated by staff in the ground or underground control room, reducing the intensity and safety risks of underground operations. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0063] Figure 2 This is a schematic diagram of the movable electrode array layout of the present invention;

[0064] Figure 3 This is a schematic diagram of the system modules of the present invention;

[0065] Figure 4 This is a schematic diagram of the electronic device structure of the present invention. Detailed Implementation

[0066] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0067] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0068] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0069] Please see Figure 1 As shown, this invention provides a system and method for inverting the fracture network of artificially modified coal seams based on tomographic imaging, comprising:

[0070] S1. Obtain the detection range of the artificially modified coal body, and according to the spatial dimensions of the detection range, deploy a movable electrode array on the borehole inner wall and around the roadway within the artificially modified coal body area.

[0071] In step S1, after the target coal body area is determined, its spatial range is defined as the detection range. A movable electrode array is then deployed according to its spatial dimensions to ensure that the detection area covers the modified area and the surrounding area with an influence radius of 1-2 times. The movable electrodes are made of highly conductive copper alloy with a polytetrafluoroethylene (PTFE) anti-corrosion coating to ensure stable operation in humid, high-gas underground environments. The ends are rounded to avoid damaging the coal body. Other highly conductive materials can also be used, depending on actual needs. The electrode fixing device uses a retractable spring clamp structure to adapt to different borehole diameters. A rubber anti-slip pad is provided on the inner side of the clamp to ensure close contact between the electrode and the coal body wall, reducing contact resistance interference. When deploying the movable electrode array, the spacing of the movable electrodes is determined using the electrode spacing calculation formula:

[0072] ;

[0073] In the formula, Indicates the spacing between movable electrodes. Indicates the accuracy of target crack location. Indicates the resolution coefficient;

[0074] In specific deployment, movable electrodes are installed using either the drilling method or the roadway surface deployment method. If drilling is used, several parallel detection boreholes are constructed in the detection area, and the movable electrodes are arrayed along the borehole depth direction using an electrode fixing device. The electrode spacing is determined according to the detection accuracy requirements.

[0075] If the electrodes are installed on the surface of the roadway, they are attached to the coal surface on both sides and the roof of the roadway using a fixing device to form a planar electrode array.

[0076] After the electrodes are installed, check the contact resistance between the electrodes and the coal body. If the contact resistance is too high, optimize the contact state by applying conductive paste or adjusting the tightness of the clamps.

[0077] S2. Apply a probe current to the coal body through a movable electrode array and collect the coal body resistivity response data, and preprocess the coal body resistivity response data;

[0078] In step S2, after the movable electrode array is deployed, the corresponding data acquisition task can be executed, so that each electrode acquires the coal resistivity response data in the detection area according to the corresponding data acquisition frequency. The sampling interval of the coal resistivity response data is determined according to the adaptive calculation formula of the data acquisition frequency. The adaptive calculation formula of the data acquisition frequency is:

[0079] ;

[0080] In the formula, Indicates the actual data collection frequency. Indicates the basic sampling frequency. Indicates the pressure change sensitivity coefficient. The pressure change rate, representing the hydraulic measures, is collected in real time by on-site pressure sensors. Indicates the maximum design pressure of the hydraulic measures;

[0081] Secondly, after the coal resistivity response data acquisition frequency is output, the resistivity tomography host is started, a detection current of a preset frequency is applied to the movable electrode, and the electrode is controlled to switch between power supply mode and measurement mode in sequence to collect coal resistivity response data of the coal body at each time point during the process of artificially modifying the coal body.

[0082] The real-time acquired coal resistivity response data is subjected to power frequency filtering and signal amplification to remove noise interference and improve the signal-to-noise ratio.

[0083] Temperature interference correction is applied to the real-time acquired coal resistivity response data based on the resistivity-temperature correction formula, thus completing the preprocessing of the coal resistivity response data. The resistivity-temperature correction formula is as follows:

[0084] ;

[0085] In the formula, This represents the resistivity of the coal body standardized to 25°C. Indicates actual temperature The measured resistivity of the coal body, This indicates the temperature coefficient of coal resistivity (the specific value needs to be determined according to the type of coal). This indicates the actual measured temperature downhole;

[0086] Specifically, the resistivity tomography host is first started, detection parameters are set, and data acquisition modes are set to continuous acquisition and triggered acquisition. Continuous acquisition is used for routine monitoring, while triggered acquisition is used for enhanced monitoring during critical stages of the modification process, such as the peak time of fracturing and pressure increase. Data acquisition is initiated according to the progress of the manual modification process. During this process, the raw coal resistivity response data is preprocessed. First, the signal conditioning unit filters out power frequency interference and high-frequency noise, amplifies the signal to the industrial standard analog signal acquisition range, and converts it into a digital signal after A / D conversion. At the same time, temperature interference correction is performed on the acquired resistivity data based on the resistivity temperature correction formula to eliminate well... The influence of ambient temperature fluctuations on resistivity measurement results was investigated. The pre-processed coal resistivity response data was temporarily stored in a data storage unit using a dual-storage design (SD card + solid-state drive). The SD card temporarily stores the raw data, while the solid-state drive stores the processed data, supporting automatic data backup and fault recovery. In case of storage device failure, the system automatically switches to the backup storage path to prevent data loss. The data is then transmitted wirelessly (supporting real-time uploading and resume capability, meeting the transmission requirements of complex underground environments) to the subsequent fracture network inversion module for analysis. At this point, the uploaded digital signal undergoes secondary preprocessing, as detailed below:

[0087] Use the moving average method to remove random errors:

[0088] Set the sliding window size to N data points (N is a positive integer, dynamically adjusted according to the data acquisition frequency; a larger window is used for high-frequency acquisition). Calculate the arithmetic mean of the resistivity data within the window based on the time series, using the following formula:

[0089] ;

[0090] In the formula, For the first in the sliding window The original coal resistivity response data (arranged in time series). Given a sliding window size (with odd values ​​to ensure central symmetry), calculate the range of summations. to The original data index for window coverage (when) When the data is close to the beginning of the data sequence, the portion of the window edge that is insufficient is calculated based on the actual available data. By moving the window point by point and repeating the calculation, a smoothed resistivity data sequence is obtained, eliminating the random fluctuation error of a single acquisition.

[0091] Using outlier detection algorithms (such as 3) (Guideline) Remove outlier data that exceeds a reasonable range:

[0092] Calculate the average value of the preprocessed resistivity dataset and standard deviation , where the average value This is the arithmetic mean of all valid data points after moving average processing (this is a progression from the previous section "Arithmetic Mean in Moving Average Method," where the arithmetic mean was used for single-window data smoothing; here...). The standard deviation is the overall arithmetic mean of the data after full smoothing, reflecting the overall level of the dataset. The formula used to characterize the dispersion of the full dataset is as follows:

[0093] ;

[0094] In the formula, Standard deviation, The total number of data points in the smoothed dataset. For the first in the smoothed dataset Data points, This represents the average value of the smoothed dataset.

[0095] The reasonable range is determined as [ -3 , +3 The determination of this reasonable range is based on the statistical law of normal distribution, which is consistent with the change law of resistivity data in the process of coal body modification (when there are no sudden working conditions, the resistivity data shows a continuous and gradual distribution, and the proportion of extreme outliers is extremely low).

[0096] Data exceeding the above range are identified as outliers and removed. If three or more consecutive data points exceed the range, the intermediate data points are retained and marked as pending verification. Combined with manual verification under on-site conditions, a clean coal resistivity response dataset can be obtained.

[0097] S3. Obtain the hydraulic parameters of the artificial coal body modification, retrieve the matching basic model from the mapping relationship database of fracture network and resistivity correlation model, and perform on-site calibration of the basic model in combination with the initial physical parameters of the target coal body to obtain a personalized correlation model suitable for the modification working condition.

[0098] In step S3, when retrieving the basic model, specifically based on the parameters of the specific hydraulic measures adopted in the artificial modification (such as pump pressure and discharge rate of hydraulic fracturing, cavity diameter of hydraulic cavity creation, etc.), a matching basic model is retrieved from the mapping relationship database of the fracture network-resistivity correlation model. The core mapping relationship of the basic model includes:

[0099] The formula for the quantitative mapping relationship between coal resistivity and fracture density:

[0100] ;

[0101] In the formula, This represents the actual resistivity of the coal body. Indicates the resistivity of the coal matrix. Indicates the porosity of the coal body. Indicates the cementation index, Indicates the fracture density. Indicates the influence index of fractures;

[0102] And, the formula relating fracture aperture to preresistivity:

[0103] ;

[0104] In the formula, Indicates the crack aperture. Represents the resistivity of the fluid within the fracture. Indicates the average length of the crack. Represents the resistivity of pure coal without cracks;

[0105] The initial resistivity data of the coal seam collected before the modification is selected and substituted into the basic model to calculate the corresponding initial fracture parameters. These parameters are then compared with the initial measured fracture parameters obtained by borehole inspection to output the parameter error. The parameter error is calculated as: |initial fracture parameter - initial measured fracture parameter| / measured value × 100%. If the parameter error does not exceed the allowable threshold, the basic model is deemed not to require calibration. Otherwise, on-site calibration of the basic model is required. The specific steps are as follows:

[0106] Clearly define the weighting coefficients and their constraints: The contribution weight of fracture density to coal resistivity. The contribution weight of fracture aperture to coal resistivity is determined by satisfying... Generally speaking, The value is greater than ;

[0107] Initialize weight coefficients: Generally, the default initial value is =0.65、 =0.35 (a general initial value suitable for most coal types and hydraulic transformation conditions).

[0108] Iterative adjustment of weight coefficients: The target is an error ≤ 3% (this is just an example threshold; the specific threshold needs to be set according to actual conditions and requirements), adjusting sequentially according to the preset compensation. (Updated synchronously) =1- After each adjustment, the initial resistivity data is substituted into the corrected personalized correlation model (correction formula: ,in The resistivity contribution value corresponding to the fracture density. (For the resistivity contribution value corresponding to the fracture aperture), recalculate the theoretical fracture parameters and errors;

[0109] Stop Iteration and Model Determination: When the calculation error is ≤3%, stop adjusting the weight coefficients and record the current value. , The numerical values, based on the correlation model with these weighting coefficients, are used as a personalized correlation model adapted to the current modification conditions. This personalized correlation model is obtained by modifying the basic model based on the fracture network and resistivity correlation model. Specifically, it retains the core structure of the basic model, including the quantitative mapping formula between coal resistivity and fracture density, and the correlation formula between fracture aperture and resistivity, by adjusting the weighting coefficients. , The contribution ratio of fracture density and fracture aperture to coal resistivity is optimized to adapt the model to the specific physical characteristics of the coal body (such as initial porosity and coal type) and hydraulic measures parameters of this modification, and finally form a personalized correlation model adapted to the modification conditions.

[0110] S4. Substitute the preprocessed coal resistivity response data into the personalized correlation model to obtain the fracture network parameters of the coal body during the artificial modification process.

[0111] In step S4, during the fracture network inversion analysis, the preprocessed coal resistivity response dataset (containing resistivity distribution data at different time points) is imported, and the inversion parameters are set: the number of iterations for iterative calculation is 20-50, and the iteration is terminated directly when the sum of the squares of the resistivity difference between two adjacent iterations is less than the iteration convergence threshold.

[0112] Method for obtaining the sum of squares of resistivity differences: Determine the calculation object, using all valid grids in the 3D mesh model of the coal body as units, extract the grid resistivity calculation values ​​for the k-th iteration and the (k+1)-th iteration respectively, and calculate the single-grid difference: For each grid, calculate the resistivity difference between the two iterations. ,in For the first The first grid The calculated resistivity value of the next iteration. For the first The first grid The resistivity calculation value is obtained by +1 iteration, and finally the resistivity difference of all grids is squared and summed.

[0113] The regularization factor is adjusted according to the data noise level, taking a larger value when the noise is high;

[0114] The steps for substituting the preprocessed coal resistivity response data into a personalized correlation model to invert and obtain the fracture network parameters of the coal body during artificial modification include:

[0115] A regularization error correction formula is introduced to reduce the inversion error caused by coal body heterogeneity. The regularization error correction formula is as follows:

[0116] ;

[0117] In the formula, This indicates the error in the corrected fracture parameter inversion. Indicates the initial inversion error. This represents the correction factor for heterogeneity. This represents the standard deviation of coal resistivity. This represents the average resistivity of the coal body.

[0118] A three-dimensional image of the resistivity distribution of the coal body was obtained by iterative calculation and inversion.

[0119] Based on the mapping relationship in the personalized association model, the fracture parameters corresponding to the resistivity anomaly region are extracted, including fracture density, fracture aperture, fracture orientation and connectivity.

[0120] Specifically, the first step is to initialize a three-dimensional mesh model of the coal body. This involves dividing the area into several small meshes based on the spatial range of the detection zone, using a structured meshing method and ensuring the mesh size matches the electrode spacing (e.g., a mesh size of 0.5m × 0.5m × 0.5m if the electrode spacing is 0.5m). After meshing, each mesh is assigned initial physical parameters (initial density, initial porosity, initial resistivity), which are the initial physical parameters of the target coal body. This achieves a match between the mesh model and the physical properties of the actual coal body. This model serves as the spatial carrier for inversion calculations, used to calculate the mapping relationship between fracture parameters and resistivity, requiring no additional training. Then, based on the personalized association model and the initial physical parameters of the mesh, the theoretical resistivity value of each mesh is calculated. ,Will Measured resistivity values ​​of the corresponding grid after preprocessing Compare and calculate the residuals. The residual is defined as (The residual is the absolute deviation between the theoretical and measured resistivity values, used for inversion and iterative optimization.)

[0121] Then, the fracture parameters are adjusted using the least squares method: with the goal of minimizing the sum of the squares of the residuals of all grids, the fracture parameters (fracture density) of each grid are iteratively adjusted using the least squares method. Opening degree , towards The specific steps are as follows:

[0122] Establish the objective function: minS = Σr² = Σ( - ) 2,in, The formula is obtained from the quantitative mapping formula between coal resistivity and fracture density, and the correlation formula between fracture aperture and resistivity. =f( , , , ,φ);

[0123] Take partial derivatives of the objective function with respect to the fracture parameters: , , Taking the partial derivatives and setting them to zero, we obtain the system of equations: , , ,in The partial differential symbol is used to represent the rate of change of a function in a multivariable function when a single variable changes, where S is the objective function (the sum of squares of all grid residuals), i.e., Σ( )²;

[0124] Solving the system of equations: Solve the above linear equations using matrix operations to obtain the correction amount for the fracture parameters. , , ;

[0125] Update crack parameters: Press , , Update the mesh fracture parameters and recalculate the theoretical resistivity value and residuals;

[0126] Convergence criterion: Repeat the least squares method to adjust the fracture parameters until the sum of the squares of the resistivity differences between two adjacent iterations is less than or equal to the iteration convergence threshold, then stop the iteration to complete the inversion;

[0127] The fracture parameters of all grids were extracted and integrated to obtain the three-dimensional distribution data of the coal body fracture network, including fracture density, aperture, orientation, and connectivity at different locations (connectivity was determined by the consistency of fracture orientation between adjacent grids; consistency ≥ 80% was considered connectivity). Fracture connectivity was quantitatively evaluated using a fracture connectivity rate calculation formula:

[0128] ;

[0129] In the formula, Indicates the fracture connectivity. This indicates the number of interconnected fracture pairs. The total number of fracture pairs within the detection area is represented by the following formula:

[0130] ;

[0131] In the formula, The total number of fractures within the detection area is calculated from the fracture density and the detection volume using the following formula:

[0132] ;

[0133] In the formula, Indicates the volume of the probe.

[0134] S5. Verify the development of the fracture network parameters obtained by inversion, and output the fracture network parameters in the form of three-dimensional images and reports after the verification is passed.

[0135] In step S5, after the inversion is completed, to ensure the reliability of the inversion results, a corresponding development verification will be performed on the inversion results. The development verification of the fracture network parameters obtained by the inversion includes the following steps:

[0136] Multiple verification boreholes were randomly selected within the detection area, and coal seam fracture development parameters were observed based on the verification boreholes.

[0137] The parameters of coal body fracture development are compared with the parameters of fracture network obtained by inversion, and the inversion error rate is output.

[0138] When the inversion error rate exceeds the allowable error threshold, the personalized association model should be recalibrated.

[0139] If the inversion error rate does not exceed the allowable error threshold, the inverted fracture network parameters are deemed to have passed the development verification.

[0140] Specifically, when verifying the inversion results, firstly, multiple verification boreholes are randomly selected within the detection area (generally 3-5 verification boreholes, evenly distributed in high-fracture, medium-fracture, and low-fracture zones). A borehole inspection instrument is used to observe the fracture development on the borehole inner wall, recording coal fracture development parameters (density, aperture, and orientation). These parameters are then compared with the inverted fracture network parameters, and the average error is calculated. The average error is the mean deviation between the fracture network parameters and the measured parameters. If the average error exceeds the allowable error threshold (generally 5%), the weight coefficients of the personalized correlation model are readjusted, and the inversion is performed again. Otherwise, the inversion result is valid. The spatial distribution of the inverted fracture network is then displayed as a three-dimensional image (showing the coal resistivity distribution in the form of a color cloud map, with different colors representing different resistivity values; for example, red represents a low resistivity region, corresponding to...). The system displays high-fracture development zones (blue represents high resistivity areas, corresponding to low-fracture development zones; users can drag and drop to rotate, zoom, and section, allowing for intuitive observation of fracture spatial distribution), contour maps (outputting fracture density contour lines and fracture aperture contour lines at different depths, marking the coordinates of the maximum fracture development area; contour data is calculated based on the quantitative mapping formula between coal resistivity and fracture density, and the correlation formula between fracture aperture and resistivity), and generates data analysis reports containing key fracture parameters (automatically calculating key parameters of the fracture network, including average fracture density, maximum fracture aperture, fracture connectivity, and effective modification volume, generating Excel-formatted reports for subsequent analysis). Users can evaluate the effectiveness of manual modification based on the output results, such as determining whether fracture development meets design requirements; if not, adjusting modification parameters (e.g., increasing fracturing pump pressure, increasing the number of cavity creations) to optimize subsequent modification plans.

[0141] Please see Figure 2 A tomographic imaging-based inversion system for artificially modified coal seam fracture networks, using the aforementioned tomographic imaging-based inversion method for artificially modified coal seam fracture networks, includes:

[0142] The electrode deployment module is used to obtain the detection range of the artificially modified coal body, and to deploy a movable electrode array on the borehole inner wall and around the roadway within the artificially modified coal body area according to the spatial size of the detection range.

[0143] The data acquisition module is used to apply a probe current to the coal body through a movable electrode array and acquire the coal body resistivity response data, and to preprocess the coal body resistivity response data.

[0144] The model calibration module is used to obtain the hydraulic measures parameters of artificial coal body modification, retrieve the matching basic model from the mapping relationship database of fracture network and resistivity correlation model, and perform on-site calibration of the basic model in combination with the initial physical parameters of the target coal body to obtain a personalized correlation model suitable for the modification conditions.

[0145] The inversion module is used to substitute the pre-processed coal resistivity response data into the personalized correlation model to invert the fracture network parameters of the coal body during the artificial modification process.

[0146] The verification output module is used to verify the development of the fracture network parameters obtained by inversion, and outputs the fracture network parameters in the form of three-dimensional images and reports after the verification is passed.

[0147] The execution process of the above inversion system corresponds exactly to the process of the aforementioned method, so it will not be repeated here.

[0148] Please see Figure 3 An electronic device, comprising:

[0149] At least one processor;

[0150] and memory that is communicatively connected to at least one processor;

[0151] The memory stores a computer program that can be executed by at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the above-described method for inverting artificially modified coal fracture networks based on tomography.

[0152] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A method for inverting the fracture network of artificially modified coal seams based on tomographic imaging, characterized in that: include: The detection range of the artificially modified coal body is obtained, and a movable electrode array is deployed on the borehole wall and around the roadway within the area of ​​the artificially modified coal body according to the spatial size of the detection range. A probe current is applied to the coal body using a movable electrode array, and the resistivity response data of the coal body is collected. The resistivity response data of the coal body is then preprocessed. The parameters of hydraulic measures for artificially modified coal bodies are obtained, and the matching basic model is retrieved from the mapping relationship database of fracture network and resistivity correlation model. The basic model is then calibrated on-site in conjunction with the initial physical parameters of the target coal body to obtain a personalized correlation model suitable for the modification conditions. Substitute the preprocessed coal resistivity response data into the personalized correlation model to obtain the fracture network parameters of the coal body during the artificial modification process. The fracture network parameters obtained by inversion are verified, and after successful verification, the fracture network parameters are output in the form of three-dimensional images and reports.

2. The method for inverting the fracture network of artificially modified coal seams based on tomographic imaging according to claim 1, characterized in that: When deploying the movable electrode array, the deployment spacing of the movable electrodes is determined by the electrode spacing calculation formula, which is: ； In the formula, Indicates the spacing between movable electrodes. Indicates the accuracy of target crack location. This represents the resolution coefficient.

3. The method for inverting the fracture network of artificially modified coal seams based on tomographic imaging according to claim 1, characterized in that: The sampling interval for the coal resistivity response data is determined according to an adaptive calculation formula for the data acquisition frequency. The adaptive calculation formula for the data acquisition frequency is: ； In the formula, Indicates the actual data collection frequency. Indicates the basic sampling frequency. Indicates the pressure change sensitivity coefficient. This indicates the rate of change of pressure in hydraulic measures. This indicates the maximum design pressure of the hydraulic measures.

4. The method for inverting the fracture network of artificially modified coal seams based on tomographic imaging according to claim 1, characterized in that: After the acquisition frequency of the coal resistivity response data is output, the resistivity tomography host is started, a detection current of a preset frequency is applied to the movable electrode, and the electrode is controlled to switch between power supply mode and measurement mode in sequence to acquire the coal resistivity response data of the coal body at each time point during the process of artificially modifying the coal body. The real-time acquired coal resistivity response data is subjected to power frequency filtering and signal amplification to remove noise interference and improve the signal-to-noise ratio; Temperature interference correction is applied to the real-time acquired coal resistivity response data based on the resistivity-temperature correction formula, thus completing the preprocessing of the coal resistivity response data. The resistivity-temperature correction formula is as follows: ； In the formula, This represents the resistivity of the coal body standardized to 25°C. Indicates actual temperature The measured resistivity of the coal body, This represents the temperature coefficient of coal resistivity. This indicates the actual measured temperature downhole.

5. The method for inverting the fracture network of artificially modified coal seams based on tomographic imaging according to claim 1, characterized in that: The core mapping relationships of the basic model include: The formula for the quantitative mapping relationship between coal resistivity and fracture density: ； In the formula, This represents the actual resistivity of the coal body. Indicates the resistivity of the coal matrix. Indicates the porosity of the coal body. Indicates the cementation index, Indicates the fracture density. Indicates the influence index of fractures; And, the formula relating fracture aperture to preresistivity: ； In the formula, Indicates the crack aperture. Represents the resistivity of the fluid within the fracture. Indicates the average length of the crack. This represents the resistivity of pure coal without cracks.

6. The method for inverting the fracture network of artificially modified coal seams based on tomographic imaging according to claim 1, characterized in that: The step of substituting the preprocessed coal resistivity response data into a personalized correlation model to invert and obtain the fracture network parameters of the coal body during the artificial modification process includes: A regularization error correction formula is introduced to reduce the inversion error caused by coal body heterogeneity. The regularization error correction formula is as follows: ； In the formula, This indicates the error in the corrected fracture parameter inversion. Indicates the initial inversion error. This represents the correction factor for heterogeneity. This represents the standard deviation of coal resistivity. This represents the average resistivity of the coal body. A three-dimensional image of the resistivity distribution of the coal body was obtained by iterative calculation and inversion. Based on the mapping relationship in the personalized association model, the fracture parameters corresponding to the resistivity anomaly region are extracted, including fracture density, fracture aperture, fracture orientation and connectivity. Among them, fracture connectivity is quantitatively evaluated through the fracture connectivity rate calculation formula, which is: ； In the formula, Indicates the fracture connectivity. This indicates the number of interconnected fracture pairs. The total number of fracture pairs within the detection area is represented by the following formula: ； In the formula, The total number of fractures within the detection area is calculated from the fracture density and the detection volume using the following formula: ； In the formula, Indicates the volume of the probe.

7. The method for inverting the fracture network of artificially modified coal seams based on tomographic imaging according to claim 6, characterized in that: The iterative calculation is performed 20-50 times, and the iteration is terminated directly when the sum of the squares of the resistivity difference between two adjacent iterations is less than the iteration convergence threshold.

8. The method for inverting the fracture network of artificially modified coal seams based on tomographic imaging according to claim 1, characterized in that: The process of verifying the development of the fracture network parameters obtained by inversion includes the following steps: Multiple verification boreholes were randomly selected within the detection area, and coal seam fracture development parameters were observed based on the verification boreholes. The parameters for coal fracture development are compared with the parameters for the fracture network obtained by inversion, and the inversion error rate is output. When the inversion error rate exceeds the allowable error threshold, the personalized association model should be recalibrated. If the inversion error rate does not exceed the allowable error threshold, the inverted fracture network parameters are deemed to have passed the development verification.

9. A system for inverting fracture networks in artificially modified coal seams based on tomographic imaging, characterized in that: The method for inverting artificially modified coal fracture networks based on tomographic imaging as described in any one of claims 1 to 8 includes: The electrode deployment module is used to obtain the detection range of the artificially modified coal body, and to deploy a movable electrode array on the borehole inner wall and around the roadway within the artificially modified coal body area according to the spatial size of the detection range. The data acquisition module is used to apply a probe current to the coal body through a movable electrode array and acquire the coal body resistivity response data, and to preprocess the coal body resistivity response data. The model calibration module is used to obtain the hydraulic measures parameters of artificial coal body modification, retrieve the matching basic model from the mapping relationship database of fracture network and resistivity correlation model, and perform on-site calibration of the basic model in combination with the initial physical parameters of the target coal body to obtain a personalized correlation model suitable for the modification working condition. The inversion module is used to substitute the pre-processed coal resistivity response data into the personalized correlation model to invert the fracture network parameters of the coal body during the artificial modification process. The verification output module is used to verify the development of the fracture network parameters obtained by inversion, and outputs the fracture network parameters in the form of three-dimensional images and reports after the verification is passed.

10. An electronic device, characterized in that: The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the inversion method for artificially modified coal fracture networks based on tomographic imaging as described in any one of claims 1 to 8.

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