Coal mine area gas control and safety guarantee method and system under complex geological conditions

Abstract

The invention relates to the technical field of data processing, and discloses a coal mine area gas control and safety guarantee method and system under complex geological conditions. The method comprises the steps that geophysical prospecting, drilling and logging exploration are conducted on a coal mine area, and geological parameters are obtained; performing statistical analysis on the data, and constructing a three-dimensional gas model; identifying a high-gas area based on the model, and arranging a directional drilling network; differential extraction and anti-reflection measures are implemented according to the drilling network; fusing extraction and sensor data, and predicting a gas change trend by using a neural network; and performing intelligent diagnosis based on the early warning matrix to form a disposal scheme. Under the complex geological condition, the gas distribution rule is accurately mastered, scientific arrangement of directional drilling and optimization design of differential extraction parameters are achieved, meanwhile, a gas early warning decision support system based on artificial intelligence is established, the accuracy of gas treatment and the timeliness of early warning are improved, and therefore safe production of a coal mine is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for gas control and safety assurance in coal mine areas under complex geological conditions. Background Art

[0002] In the process of coal mining, gas is one of the main safety hazards, especially in complex geological conditions, such as fault development, complex geological structure, high gas area and other environments, the gas control problem is particularly prominent. Traditional gas control methods mainly include ventilation dilution method, gas extraction method and regional pre-extraction method. The ventilation dilution method reduces the gas concentration by increasing the ventilation volume, but the effect is limited in high-gas mines; the gas extraction method extracts the gas from the coal seam by drilling holes, which can effectively reduce the gas content, but the traditional drilling arrangement often lacks scientific basis; the regional pre-extraction method extracts the coal seam gas in advance before mining, which can reduce the gas outflow during the mining process, but the implementation cycle is long and the efficiency is not high. At the same time, the existing gas monitoring technology mainly relies on fixed measuring points for sampling and detection, and the monitoring data is relatively scattered, making it difficult to form an overall grasp of the gas distribution and migration rules.

[0003] However, the existing gas control methods have many shortcomings. First, under complex geological conditions, especially in special geological structural areas such as faults and folds, the gas distribution is highly non-uniform, and the traditional uniform arrangement of extraction drilling is inefficient; second, the measurement of gas parameters is inaccurate, and it is difficult to accurately grasp the gas occurrence law, resulting in the lack of pertinence in the design of extraction plans; third, the existing gas monitoring system generally has lags, and it is impossible to effectively predict the trend of gas concentration changes, and it is difficult to achieve early warning; fourth, there is a lack of differentiated design of gas extraction parameters under different geological conditions, and a single parameter is difficult to adapt to the complex and changeable geological environment; fifth, in terms of emergency disposal, there is a lack of differentiated disposal processes based on risk levels, and the emergency response efficiency is low. These problems seriously restrict the safe and efficient mining of coal mines under complex geological conditions. Summary of the invention

[0004] The present application provides a method and system for gas control and safety assurance in coal mine areas under complex geological conditions. It is used to accurately grasp the gas distribution law under complex geological conditions, realize the scientific arrangement of directional drilling and the optimal design of differentiated extraction parameters, and at the same time establish a gas early warning decision support system based on artificial intelligence to improve the accuracy of gas control and the timeliness of early warning, thereby ensuring safe production in coal mines.

[0005] In the first aspect, the present application provides a method for gas control and safety assurance in coal mine areas under complex geological conditions, and the method for gas control and safety assurance in coal mine areas under complex geological conditions includes: conducting geophysical exploration, drilling, and well logging exploration in the coal mine area to obtain multidimensional geological parameter data; conducting geostatistical analysis on the multidimensional geological parameter data to construct a three-dimensional gas distribution model; identifying high-gas areas based on the three-dimensional gas distribution model, designing and implementing a directional drilling network layout plan, and generating a borehole space network; implementing differentiated extraction parameters and permeability enhancement measures for different pressure zones according to the borehole space network and gas pressure distribution, and establishing an efficient gas extraction system; performing outlier detection and data fusion processing on the real-time data of the efficient gas extraction system and the sensor network data, calculating the gas concentration change trend through a neural network algorithm, and generating a multi-level early warning judgment matrix; based on the multi-level early warning judgment matrix, performing intelligent diagnosis and analysis on the causes of gas anomalies, and generating risk level assessment results and a differentiated disposal process database.

[0006] In the second aspect, the present application provides a gas control and safety assurance system for coal mine areas under complex geological conditions, and the gas control and safety assurance system for coal mine areas under complex geological conditions includes:

[0007] The exploration module is used to conduct geophysical exploration, drilling and logging exploration in the coal mining area to obtain multi-dimensional geological parameter data;

[0008] An analysis module, used for performing geostatistical analysis on the multi-dimensional geological parameter data to construct a three-dimensional gas distribution model;

[0009] An identification module, used to identify high-gas areas based on the three-dimensional gas distribution model, design and implement a directional drilling network layout plan, and generate a drilling space network;

[0010] A drainage module, for implementing differentiated drainage parameters and permeability enhancement measures for different pressure zones according to the borehole space network and gas pressure distribution, and establishing an efficient gas drainage system;

[0011] A fusion module is used to perform abnormal value detection and data fusion processing on the real-time data of the efficient gas extraction system and the sensor network data, calculate the gas concentration change trend through a neural network algorithm, and generate a multi-level early warning judgment matrix;

[0012] The diagnosis module is used to perform intelligent diagnosis and analysis on the causes of gas anomalies based on the multi-level early warning judgment matrix, and generate risk level assessment results and a differentiated disposal process database.

[0013] In a third aspect, a gas control and safety assurance device for coal mine areas under complex geological conditions is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the gas control and safety assurance device for coal mine areas under complex geological conditions executes the above-mentioned gas control and safety assurance method for coal mine areas under complex geological conditions.

[0014] In a fourth aspect, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium, and when the computer-readable storage medium is run on a computer, the computer is enabled to execute the above-mentioned method for gas control and safety assurance in coal mine areas under complex geological conditions.

[0015] In the technical solution provided by the present application, the multi-dimensional geological parameter data obtained by geophysical prospecting, drilling and logging exploration of the coal mine area is combined with the three-dimensional gas distribution model constructed by geostatistical analysis, which not only realizes the accurate grasp of the gas distribution law under complex geological conditions and improves the accuracy of gas distribution prediction, but also provides a scientific basis for the subsequent directional drilling arrangement; based on the three-dimensional gas distribution model, the high-gas area is identified and the directional drilling network is designed and implemented. Through the fan-shaped layout method and the three-layer layout principle, the intersection probability of the borehole and the gas-enriched zone is significantly improved, and the coverage rate of the borehole network reaches more than 85%, creating basic conditions for efficient extraction; according to the differentiated extraction parameters and permeability enhancement measures implemented according to the borehole space network and gas pressure distribution, different negative pressure values ​​and extraction time strategies are adopted for different pressure areas, and hydraulic fracturing, CO are implemented according to the difference in permeability coefficient. 2 Targeted permeability enhancement technologies such as phase change blasting and negative pressure pulse have greatly improved the efficiency of gas extraction and reduced the gas content in the working face to a safe allowable range. The real-time data of the efficient gas extraction system and the sensor network data are subjected to outlier detection and data fusion processing. By applying a hybrid prediction model constructed by convolutional neural networks and long short-term memory networks, an accurate prediction of the trend of gas concentration changes in the next 4 hours is achieved, which significantly enhances the timeliness and accuracy of the early warning. The application of neural network algorithms enables the system to automatically identify the nonlinear patterns and time series characteristics of gas concentration changes, taking into account the complex interactions between multiple influencing factors, and achieving more accurate concentration predictions through deep learning of historical data. Intelligent diagnosis and analysis of the causes of gas anomalies are performed based on a multi-level early warning judgment matrix. The possible causes of gas anomalies are identified through decision trees and rule inference algorithms, and risk level assessment results and a differentiated disposal process database are generated, realizing the transition from passive response to active prevention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 This is a schematic diagram of an embodiment of a method for gas control and safety assurance in a coal mine area under complex geological conditions in an embodiment of the present application;

[0018] Figure 2 This is a schematic diagram of an embodiment of a coal mine area gas control and safety assurance system under complex geological conditions in the embodiment of the present application;

[0019] Figure 3 It is a schematic block diagram of the structure of coal mine area gas control and safety assurance equipment under complex geological conditions in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The embodiment of the present application provides a method and system for gas control and safety assurance in coal mine areas under complex geological conditions. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of the method for gas control and safety assurance in coal mine areas under complex geological conditions includes:

[0022] Step S101, conducting geophysical exploration, drilling and logging exploration in the coal mine area to obtain multi-dimensional geological parameter data;

[0023] Step S102, performing geostatistical analysis on the multi-dimensional geological parameter data to construct a three-dimensional gas distribution model;

[0024] Step S103, identifying high-gas areas based on the three-dimensional gas distribution model, designing and implementing a directional drilling network layout plan, and generating a drilling space network;

[0025] Step S104: according to the borehole space network and gas pressure distribution, differentiated extraction parameters and permeability enhancement measures are implemented for different pressure zones to establish an efficient gas extraction system;

[0026] Step S105: perform outlier detection and data fusion processing on the real-time data of the efficient gas extraction system and the sensor network data, calculate the gas concentration change trend through the neural network algorithm, and generate a multi-level early warning judgment matrix;

[0027] Step S106: Based on the multi-level early warning judgment matrix, intelligent diagnosis and analysis are performed on the causes of gas anomalies to generate risk level assessment results and a differentiated disposal process database.

[0028] It is understandable that the execution subject of this application can be a coal mine area gas control and safety assurance system under complex geological conditions, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking a server as the execution subject as an example.

[0029] Specifically, multi-dimensional geological parameter data are collected through geophysical exploration, drilling, logging and other means. Specifically, geophysical exploration uses high-precision seismic exploration technology to obtain underground geological structure data in the coal mine area by setting a survey line spacing of no more than 50 meters and a detection point spacing of no more than 10 meters; the drilling layout adopts a grid layout principle of 30 meters × 30 meters (near the fault) and 50 meters × 50 meters (other areas) according to the distribution of faults in the coal mine area, and collects data such as the thickness, inclination, and gas pressure of the coal seam through drilling; well logging provides more detailed physical parameter data of the underground rock formation through acoustic wave, density, natural potential and resistivity logging. After these data are processed by professional software, outliers and interference signals are removed to generate standardized geological parameter data sets, further laying the foundation for subsequent analysis. Geostatistical analysis is performed on the collected multi-dimensional geological parameter data to construct a three-dimensional gas distribution model. Data processing methods include using variogram analysis and Kriging interpolation algorithms, which can model the spatial variability of coal mine areas and the correlation between different parameters. By calculating the degree of variation of geological parameters in space, the variogram analysis helps determine how various geological parameters change in different locations, revealing the spatial distribution patterns of parameters such as gas content, gas pressure and coal seam permeability in the coal mine area. Next, the Kriging interpolation method will interpolate and estimate areas lacking data based on the calculation results of the variogram to fill in the data gaps in space, thereby ensuring that the generated three-dimensional gas distribution model has sufficient accuracy. Based on the three-dimensional gas distribution model, high-gas areas are identified and a directional drilling network layout plan is designed. This process mainly relies on identifying different gas area grades through gas content data: special grade (gas content>15m 3 / t), Level 1 (gas content 10-15m 3 / t), Level II (gas content 5-10m 3 / t) and level 3 (gas content <5m 3 / t). According to these regional divisions, the drilling density is designed. The drilling spacing in the special area is not more than 15 meters, the first-level area is not more than 25 meters, the second-level area is not more than 35 meters, and the third-level area is not more than 50 meters. The rationality and accuracy of the drilling layout are crucial to the effect of subsequent extraction work. Based on different gas pressure zones, they are divided into high-pressure zones, medium-pressure zones and low-pressure zones. Each zone corresponds to different extraction parameters: the high-pressure zone adopts a low-negative pressure long-term extraction strategy, the medium-pressure zone adopts a medium-negative pressure extraction strategy, and the low-pressure zone uses a high-negative pressure short-term extraction strategy. At the same time, for areas with poor coal seam permeability, permeability enhancement measures such as hydraulic fracturing, CO 2 Phase change blasting technology and negative pressure pulse technology, etc. These technical means can significantly improve the gas extraction efficiency by enhancing the permeability of coal seams, ensuring the safety and efficiency of extraction.

[0030] Outlier detection is performed by fusing the real-time data of the efficient gas extraction system with the sensor network data. This process mainly uses neural network algorithms, such as convolutional neural networks (CNN) and long short-term memory networks (LSTM), to predict real-time gas concentration data. The data fusion process combines data from different regions and different sensors to correct potential noise and errors, ensuring the accuracy of the prediction of gas concentration trend changes. The prediction results will be evaluated through a multi-level early warning judgment matrix to identify the risk points of gas anomalies and provide a scientific basis for subsequent safety decisions. Based on the multi-level early warning judgment matrix, intelligent diagnosis and analysis of the causes of gas anomalies are performed. Through decision trees and rule reasoning algorithms, possible causes of gas anomalies are analyzed, such as ventilation system failures, sudden geological changes, or extraction system anomalies. The analysis results will calculate the probability, impact range, and degree of harm of different gas accidents, and formulate differentiated emergency response processes based on risk level classification. Special emergency plans will be launched for particularly serious risks, while corresponding emergency treatment measures will be launched for major and relatively large risks to ensure that the mine can respond quickly and effectively when gas accidents occur.

[0031] In the embodiment of the present application, the multi-dimensional geological parameter data obtained by geophysical prospecting, drilling and logging exploration of the coal mine area is combined with the three-dimensional gas distribution model constructed by geostatistical analysis, which not only realizes the accurate grasp of the gas distribution law under complex geological conditions and improves the accuracy of gas distribution prediction, but also provides a scientific basis for the subsequent directional drilling arrangement; based on the three-dimensional gas distribution model, the high-gas area is identified and the directional drilling network is designed and implemented. Through the fan-shaped layout method and the three-layer layout principle, the intersection probability of the borehole and the gas-enriched zone is significantly improved, and the borehole network coverage rate reaches more than 85%, creating basic conditions for efficient extraction; according to the differentiated extraction parameters and permeability enhancement measures implemented according to the borehole space network and gas pressure distribution, different negative pressure values ​​and extraction time strategies are adopted for different pressure areas, and hydraulic fracturing, CO are implemented according to the difference in permeability coefficient. 2 Targeted permeability enhancement technologies such as phase change blasting and negative pressure pulse have greatly improved the efficiency of gas extraction and reduced the gas content in the working face to a safe allowable range. The real-time data of the efficient gas extraction system and the sensor network data are subjected to outlier detection and data fusion processing. By applying a hybrid prediction model constructed by convolutional neural networks and long short-term memory networks, an accurate prediction of the trend of gas concentration changes in the next 4 hours is achieved, which significantly enhances the timeliness and accuracy of the early warning. The application of neural network algorithms enables the system to automatically identify the nonlinear patterns and time series characteristics of gas concentration changes, taking into account the complex interactions between multiple influencing factors, and achieving more accurate concentration predictions through deep learning of historical data. Intelligent diagnosis and analysis of the causes of gas anomalies are performed based on a multi-level early warning judgment matrix. The possible causes of gas anomalies are identified through decision trees and rule inference algorithms, and risk level assessment results and a differentiated disposal process database are generated, realizing the transition from passive response to active prevention.

[0032] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0033] Obtain underground geological structure data through high-precision seismic exploration with a survey line spacing of no more than 50 meters and a detection point spacing of no more than 10 meters;

[0034] Drill holes were arranged according to the grid principle of 30m x 30m near the main faults and 50m x 50m in other areas to collect data on coal seam thickness, dip and gas pressure;

[0035] Perform acoustic logging, density logging, natural potential logging and resistivity logging on each borehole to generate logging curve data;

[0036] Eliminate outliers from underground geological structure data, coal seam thickness, inclination, gas pressure data, and well logging curve data to generate a standardized geological parameter data set;

[0037] Apply variogram analysis and Kriging interpolation algorithm to the standardized geological parameter data set to calculate the spatial variation pattern and parameter correlation;

[0038] According to the spatial variation law and parameter correlation, the coal mining area is divided into ultra-high complexity area, high complexity area, medium complexity area and low complexity area to generate geological complexity zoning data.

[0039] Specifically, underground geological structure data are obtained through high-precision seismic exploration with a survey line spacing of no more than 50 meters and a detection point spacing of no more than 10 meters. These geological structure data mainly include the depth, inclination, and changes in the strata of the coal seam, which are crucial for the construction of the subsequent gas distribution model. Next, the grid principle of borehole layout is based on the fault distribution in the coal mine area. In order to ensure a full understanding of the thickness, inclination and gas pressure of the coal seam, a dense borehole grid of 30 meters × 30 meters is usually arranged near the main fault, while the borehole spacing in other areas is 50 meters × 50 meters. These boreholes will collect the basic physical parameters of the coal seam, including gas pressure, coal seam thickness, inclination, etc. In addition, the acoustic logging, density logging, natural potential logging and resistivity logging data collected by the boreholes are comprehensively analyzed to form logging curves, which can reflect the physical properties of the coal seam and its permeability, so as to make a preliminary judgment on the storage and release of gas.

[0040] After outliers are removed from geological structure data, coal seam thickness, inclination, gas pressure, and well logging data, these data are integrated into a standardized geological parameter data set. Outlier removal is achieved by identifying and removing data points that do not conform to geological laws or have too large detection errors. The data set after outliers are removed can more realistically reflect the geological characteristics of the coal mining area and ensure the accuracy of subsequent analysis. Once the standardized geological parameter data set is completed, the next step is to apply variogram analysis and kriging interpolation algorithms to these data to calculate spatial variation laws and parameter correlations. Variogram analysis is a statistical method that helps determine whether the gas occurrence in certain areas is highly concentrated or uneven by measuring the degree of spatial variation of geological parameters. For example, coal seams are more likely to be gas-rich in areas close to faults, while they may be gas-poor in certain locations. Through the calculation of variograms, these spatial variation characteristics can be quantified, thereby identifying different gas distribution areas. The kriging interpolation algorithm predicts the gas distribution in the missing data area through the spatial information obtained based on the variogram, forming a complete gas spatial distribution map.

[0041] Through the analysis results of spatial variation and parameter correlation, the coal mining area is divided into different geological complexity zones, including ultra-high complexity zone, high complexity zone, medium complexity zone and low complexity zone. Specifically, the division of these areas is determined by multiple factors such as gas content, coal seam permeability and fault distribution. In high-complexity and ultra-high-complexity areas, due to the high gas content and poor coal seam permeability, special gas control measures are taken. These areas are usually the focus of extraction. In low-complexity areas, because there is less gas or better permeability, control measures can be relatively relaxed.

[0042] The gas distribution model can accurately reflect the gas reserves, distribution and change trends in the coal mining area, and support the formulation of subsequent extraction strategies and safety measures. For example, in high-complexity areas, the gas pressure may be as high as 1.2MPa, and the coal seam permeability is poor. This information can be used to predict the gas accumulation trend of the entire area through the Kriging interpolation method, so as to design a dense drilling network in a targeted manner and adopt hydraulic fracturing and other permeability enhancement measures when necessary to improve extraction efficiency and ensure mine safety. On the contrary, for low-complexity areas, the gas pressure is lower and the permeability is better, so the frequency and intensity of extraction can be relatively reduced.

[0043] The core of data processing is not only to obtain and analyze gas distribution data, but also to closely integrate these data with actual mine extraction operations. Through comparative analysis with real-time sensor monitoring data, extraction parameters can be further optimized to reduce the occurrence of gas accidents. For example, during the extraction process, by real-time monitoring of gas concentration and extraction flow, when it is detected that the gas concentration of a certain borehole is lower than the design standard for a long time, the system will automatically adjust the extraction strategy, increase negative pressure or enable supplementary drilling, thereby ensuring the continuity and safety of the extraction operation.

[0044] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0045] Based on the standardized geological parameter data set and geological complexity zoning data, a 3D skeleton model of regional geological structure with an accuracy of 5m×5m×1m was established;

[0046] The gas content, gas pressure and coal seam permeability data in the multi-dimensional geological parameter data are mapped to the three-dimensional skeleton model through the trilinear interpolation algorithm to generate the initial gas distribution grid;

[0047] Apply the random forest algorithm to the data sparse areas in the initial gas distribution grid to predict parameters and generate gas parameter spatial distribution data;

[0048] Based on the spatial distribution data of gas parameters and the zoning data of geological complexity, the finite element method is used to calculate the stress distribution state in the region and establish a stress-gas coupling analysis model;

[0049] The fault influence coefficient is set for the fault zone in the stress-gas coupling analysis model, and the influence weights of 0.8, 0.6, and 0.4 are assigned according to the size of the fault, and the fault gas enrichment coefficient is calculated;

[0050] The fault gas enrichment coefficient and stress-gas coupling analysis model are input into the multiphase flow numerical simulator to calculate the gas migration law under different mining conditions and generate a three-dimensional gas distribution model.

[0051] Specifically, based on standardized geological parameter data sets and geological complexity zoning data, a three-dimensional skeleton model of regional geological structure with an accuracy of 5m×5m×1m is constructed. The construction of the three-dimensional skeleton model depends on the spatial coordinates and morphological characteristics of geological data. Through high-precision measurement and data acquisition, combined with known geological structure information, this three-dimensional skeleton model can accurately describe the morphology of coal seams, the distribution of faults, and the spatial distribution of other geological features. This model provides a spatial basis for subsequent gas distribution simulation and extraction optimization. Each grid cell in the three-dimensional skeleton model represents a small area of ​​the coal mine area. The accuracy of the model (5m×5m×1m) ensures that the spatial distribution of gas can be accurately depicted between different coal seams and geological areas.

[0052] The important parameters such as gas content, gas pressure and coal seam permeability in the multidimensional geological parameter data are mapped to the three-dimensional skeleton model through the trilinear interpolation algorithm to generate the initial gas distribution grid. The trilinear interpolation algorithm is a method commonly used for three-dimensional spatial data interpolation. It estimates the unknown area based on the surrounding environment of the known data points, thereby generating the distribution of parameters such as gas content and pressure in three-dimensional space. Specifically, the trilinear interpolation algorithm calculates the gas content and gas pressure at each position in the three-dimensional space through the known values ​​around each grid point to form a complete gas distribution grid. After the initial gas distribution grid is generated, the sparse areas of the data need to be supplemented by the random forest algorithm. Random forest is a machine learning algorithm based on decision tree integration, which can predict the gas parameters of areas that are not directly collected by analyzing the existing geological parameter data. The random forest algorithm can effectively fill the data gaps, optimize the spatial prediction of gas distribution, and make the spatial distribution data of gas parameters more accurate. During the specific implementation process, the random forest algorithm will learn the relationship between features from the existing gas pressure, content and permeability data, and infer the gas parameters of the missing area based on the combination of these features.

[0053] Based on the generated spatial distribution data of gas parameters and geological complexity zoning data, the finite element method is used to calculate the stress distribution state in the area. The finite element method (FEM) is a numerical analysis technique that is widely used to calculate the numerical solution of complex physical phenomena. In this process, the coal mine area is divided into a number of finite small areas, each of which is assigned corresponding geological and mechanical properties, and then the stress distribution in the area is calculated by solving the stress-strain equation. Stress distribution is a key factor affecting gas release and migration. Accurate stress calculation can reveal the stress state and possible pressure accumulation areas between different coal seams. For the fault zone area, the fault influence coefficient is set, and different influence weights are assigned according to the size of the fault. For example, the influence coefficient of a large fault is 0.8, that of a medium fault is 0.6, and that of a small fault is 0.4. Fault areas are usually key areas of gas enrichment. Therefore, by calculating the fault influence coefficient, the contribution of these areas to gas enrichment can be clarified, and key guidance can be provided for subsequent extraction work. By adding these fault influence coefficients to the stress-gas coupling analysis model, the distribution and migration trend of gas in the fault zone area can be calculated more accurately. The fault gas enrichment coefficient and the stress-gas coupling analysis model are input into the multiphase flow numerical simulator to simulate the gas migration law. Multiphase flow numerical simulation technology can simulate the dynamic migration process of gas in coal seams under different mining conditions. By considering the impact of stress redistribution on gas release, the simulator can predict the change law of gas in coal seams under different mining stages and gas extraction methods. By comparing with actual coal mine extraction data, the parameters of the model can be continuously optimized to ensure that it accurately reflects the dynamic changes of gas and generate a complete three-dimensional gas distribution model.

[0054] For example, in a certain mining area, preliminary exploration data showed that the gas pressure in the area varied greatly at different depths, especially near the fault zone of the coal seam, where gas enrichment was more significant. On this basis, the stress distribution results calculated by the three-dimensional gas distribution model and the finite element method showed that the stress state of the coal seam was highly correlated with the gas-enriched area. The gas pressure data of some unexplored areas were predicted by the random forest algorithm, and combined with the influence coefficient of the fault, a complete gas distribution map was finally generated. The map clearly shows which areas are more difficult to extract gas and which areas have higher gas pressure, optimizes the gas extraction plan, and predicts the dynamic distribution of gas at different mining stages through the multiphase flow simulator.

[0055] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0056] According to the gas content data in the three-dimensional gas distribution model, the coal mine area is divided into special-grade area, first-grade area, second-grade area and third-grade area, and the gas area classification data is generated;

[0057] Based on the gas area classification data, the drilling density standard is formulated, with the spacing of special-grade areas not exceeding 15 meters, first-grade areas not exceeding 25 meters, second-grade areas not exceeding 35 meters, and third-grade areas not exceeding 50 meters;

[0058] The drilling density standard and the three-dimensional gas distribution model are input into the computer-aided design system, and a three-layer layout plan is designed for areas with coal seam thickness greater than 3 meters, and a drilling plan perpendicular to the coal seam trend is designed for areas with inclination angles greater than 15°, to generate the initial drilling trajectory design;

[0059] The fan-shaped layout method is applied to the area near the fault in the initial drilling trajectory design. The holes are arranged at 15° angles around the fault to generate a drilling layout design diagram.

[0060] Drilling construction is carried out according to the drilling layout design drawing. The magnetic direction finding system and gyroscope direction finding system are used to monitor the drilling trajectory in real time. When the deviation from the designed trajectory exceeds 0.5 meters, the trajectory is corrected to complete the drilling construction;

[0061] Perform borehole imaging detection and pressure testing on completed boreholes, calculate the borehole network coverage, add additional holes when the coverage is lower than 85%, and generate a borehole space network database containing the borehole space coordinates, length, direction and diameter.

[0062] Specifically, the gas area classification of coal mine areas is carried out based on the gas content data in the three-dimensional gas distribution model. This process first analyzes the gas content data in different areas of the coal mine and divides the entire area into special-grade areas, primary areas, secondary areas, and tertiary areas. Special-grade areas usually refer to areas with extremely high gas content, and their gas content usually exceeds the set warning standard; primary areas refer to areas with high gas content; secondary areas are areas with moderate gas content; and tertiary areas are areas with low gas content. Through this division, the gas risks of different areas can be clearly identified.

[0063] After completing the gas area classification, the drilling density standard is designed according to the risk level of each area. For the special-grade area with extremely high gas content, the spacing between boreholes is set to no more than 15 meters to ensure that intensive extraction can effectively reduce the gas content in the area; for the first-grade area with high gas content, the drilling spacing is set to no more than 25 meters; for the second-grade area with moderate gas content, the drilling spacing is no more than 35 meters; for the third-grade area with low gas content, the drilling spacing is 50 meters. The setting of the drilling density standard ensures that the extraction work in each area can be differentiated according to the actual gas content, thereby improving the extraction efficiency and reducing the waste of resources during the extraction process.

[0064] After the drilling density standard is set, these data will be input into the computer-aided design system. The system will further optimize the drilling layout based on the thickness, inclination and gas distribution characteristics of the coal seam. For areas where the coal seam thickness exceeds 3 meters, a three-layer layout is designed, that is, drilling holes are arranged in the upper, middle and lower layers respectively to ensure that the gas in each coal seam is effectively extracted; for areas where the coal seam inclination is greater than 15°, the drilling holes will be arranged in a direction perpendicular to the coal seam strike to ensure that the extraction drilling holes can intersect with the gas-rich zone to the maximum extent and improve the extraction efficiency. Based on these requirements, a preliminary drilling trajectory design diagram is generated to ensure the rationality and accuracy of the design.

[0065] For special areas in the drilling trajectory design, such as areas near fault zones, the fan-shaped layout method is used for drilling arrangement. Near the fault, the drill holes are arranged at 15° intervals around the fault. This ensures that the gas-rich area around the fault zone can be fully covered, thereby reducing the risk of missed mining and improving the effect of gas extraction. Finally, a drilling layout design drawing is generated based on this plan, and drilling construction is carried out according to the drawing.

[0066] During the drilling process, the magnetic direction finding system and the gyroscope direction finding system are used to monitor the trajectory of the borehole in real time. When the deviation between the actual trajectory of the borehole and the designed trajectory exceeds 0.5 meters, the system will automatically sound an alarm and require trajectory correction to ensure that the borehole can be accurately positioned according to the design requirements. In this way, the reduction in extraction efficiency or safety hazards caused by the deviation of the borehole trajectory can be avoided, thereby ensuring the efficiency and safety of the gas control process. After the drilling construction is completed, borehole imaging detection and pressure testing are carried out. Through imaging detection, the actual shape of the borehole can be clearly understood to ensure that there are no problems such as borehole blockage; pressure testing can verify the sealing of the borehole and the distribution of gas pressure. Through these tests, it can be ensured that the actual effect of each borehole meets the design standards. During the test, the coverage rate of the borehole network is calculated. If the coverage rate of a certain area is less than 85%, additional boreholes need to be added and the borehole layout needs to be adjusted until a reasonable coverage rate standard is reached. The design of the supplementary boreholes should be adjusted according to factors such as coal seam permeability and gas pressure to ensure that the gas control effect of each area meets the expected standards.

[0067] After drilling construction and acceptance inspection, a detailed borehole spatial network database is formed. The database contains the spatial coordinates, length, direction, diameter and other information of each borehole, providing important data support for subsequent gas extraction work. The database can not only provide accurate technical support for subsequent extraction work, but also dynamically evaluate the extraction effect based on real-time monitoring data, and provide a basis for subsequent improvement and optimization.

[0068] For example, in a certain area, after three-dimensional gas distribution model analysis, it was found that the gas content in this area was high and located in the first-level area. According to the drilling density standard, the drilling spacing was set to no more than 25 meters, and a three-layer layout was adopted. After the drilling construction, through drilling imaging detection and pressure testing, it was found that the drilling in some areas deviated from the design trajectory by 0.6 meters, resulting in the gas extraction efficiency in this area not meeting expectations. Based on this situation, the drilling trajectory was adjusted and additional drilling was added to ensure that the coverage rate of the area reached more than 85%, thereby improving the efficiency of gas extraction and ensuring that the gas content in the area can be effectively controlled within a safe range.

[0069] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0070] Based on the gas pressure data in the borehole spatial network database and the three-dimensional gas distribution model, the coal mine area is divided into high-pressure area, medium-pressure area and low-pressure area, and a gas pressure zoning map is generated;

[0071] According to the gas pressure zoning map, a low-negative-pressure long-term extraction strategy with a negative pressure value of no more than 10 kPa and a duration of no less than 30 days is implemented in the high-pressure area, a medium-negative-pressure extraction strategy with a negative pressure value of 15-25 kPa and a duration of 15-25 days is implemented in the medium-pressure area, and a high-negative-pressure extraction strategy with a negative pressure value of 25-35 kPa and a duration of 7-15 days is implemented in the low-pressure area, generating differentiated extraction parameter plans;

[0072] According to the coal seam permeability data in the three-dimensional gas distribution model, the permeability coefficient is less than 0.1m 2 / (MPa 2 ·d) Hydraulic fracturing technology is implemented in the area with a permeability coefficient of 0.1-0.5m 2 / (MPa 2 ·d) Implement CO in the area between 2 Phase change blasting technology, air permeability coefficient greater than 0.5m 2 / (MPa 2 ·D) Implement negative pressure pulse technology to generate anti-reflection technology solutions;

[0073] Carry out permeability enhancement operation on the borehole according to the permeability enhancement technical plan, verify the permeability enhancement effect through pressure monitoring and flow monitoring, and improve the permeability of the coal seam;

[0074] Design the main and branch pipe network structure based on the borehole spatial network database. The main pipe diameter is not less than 219 mm, the branch pipe diameter is not less than 159 mm, and the single borehole extraction pipe diameter is not less than 89 mm. Generate the extraction pipeline system design plan;

[0075] According to the differentiated extraction parameter plan and the extraction pipeline system design plan, the extraction pump station equipment is configured, and gas extraction operations are carried out. Monitoring points are set up every 50 meters to monitor the extraction effect in real time. When the extraction concentration and extraction volume do not meet the standards, the extraction parameters are automatically adjusted to establish an efficient gas extraction system that includes data collection, parameter adjustment and gas utilization classification.

[0076] Specifically, the gas pressure zones in coal mine areas are divided into high-pressure zones, medium-pressure zones, and low-pressure zones. The core of this process is to use the gas pressure data in the three-dimensional gas distribution model, combined with the geological information in the borehole space network database, to classify the gas pressure in different areas in detail. The distribution of gas pressure is a key factor in determining the gas extraction strategy, so it is necessary to accurately divide the mining area according to different levels of gas pressure. Specifically, the gas pressure in the high-pressure zone is usually more than 1.0MPa, the medium-pressure zone is between 0.25MPa and 0.74MPa, and the low-pressure zone is less than 0.25MPa. By distinguishing the gas pressure in different areas, a detailed basis can be provided for the formulation of subsequent extraction strategies.

[0077] According to the gas pressure zoning map, differentiated extraction strategies are then formulated. High-pressure areas are usually key areas of gas enrichment. Therefore, for these areas, a low-negative-pressure, long-term extraction strategy is adopted, with a negative pressure value not exceeding 10 kPa and an extraction duration of not less than 30 days. This strategy helps to avoid damage to the coal seam structure caused by excessive extraction and maintain the stability of the coal seam. For medium-pressure areas, the extraction negative pressure value is set between 15-25 kPa, and the extraction duration is set to 15 to 25 days. Appropriately increasing the negative pressure can effectively improve the gas release efficiency and avoid the impact of excessive extraction on the coal seam. The extraction strategy for low-pressure areas uses a higher negative pressure value, set at 25-35 kPa, and the extraction duration is shorter, between 7 and 15 days. The purpose of this strategy is to extract gas quickly and efficiently to ensure that the gas concentration meets safety standards. Through these differentiated extraction strategies, the extraction efficiency of gas can be maximized, while avoiding coal seam damage or gas waste caused by excessive extraction. When implementing the extraction strategy, it is also necessary to select the permeability enhancement technology based on the permeability data of the coal seam. The permeability coefficient is an important parameter that affects the gas extraction effect. Through the analysis of permeability data, the permeability enhancement requirements of different coal seams can be determined. 2 / (MPa 2 ·d) In the area, the coal seam has poor permeability and gas is difficult to release, so hydraulic fracturing technology is needed. Hydraulic fracturing technology injects high-pressure liquid into the coal seam to crack the coal seam and increase its permeability, thereby improving the gas desorption rate and extraction efficiency. 2 / (MPa 2 d) to 0.5m2 / (MPa 2 ·d) In the area between 2 Phase change blasting technology. This technology injects CO into the coal seam. 2 gas, and utilize CO 2 The phase change characteristics (change from gas to liquid) produce expansion force, thereby generating micro cracks, enhancing the permeability of the coal seam, and are suitable for areas with medium permeability. 2 / (MPa 2 · In the area of ​​d), the coal seam has good permeability and does not require too many permeability enhancement measures, but negative pressure pulse technology can be used to further stimulate micro-cracks in the coal seam and enhance the gas release efficiency. The selection and implementation of each permeability enhancement technology needs to be verified through pressure monitoring and flow monitoring to ensure the effectiveness and safety of the permeability enhancement operation.

[0078] In terms of the design of the gas extraction pipeline, a main-branch network structure was designed based on the borehole space network database to ensure the smooth flow and stability of the pipeline during gas extraction. The diameter of the main pipeline is set to no less than 219mm, the diameter of the branch pipeline is no less than 159mm, and the diameter of the extraction pipe of a single borehole is no less than 89mm. These designs ensure the flow demand and pressure resistance of the pipeline during the extraction process. During the extraction operation, a monitoring point is set every 50 meters to monitor the extraction effect in real time. When the gas concentration and extraction volume at the monitoring point do not meet the predetermined standards, the extraction parameters are automatically adjusted to ensure the smooth completion of the extraction task. This automatic adjustment mechanism helps to respond to emergencies that may occur during the extraction process in a timely manner, such as abnormal fluctuations in gas concentration or insufficient extraction volume.

[0079] Through these differentiated extraction strategies and permeability enhancement technology solutions, the efficiency and safety of gas extraction can be effectively improved, ensuring safe production in coal mines. At the same time, the design of the extraction pipeline system and the real-time monitoring mechanism ensure the efficient implementation of gas extraction operations and minimize the occurrence of gas accidents. In the specific implementation process, with the continuous updating of extraction data, the system can dynamically adjust the extraction plan and permeability enhancement technology to provide continuous and stable technical support for gas control in coal mine areas.

[0080] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0081] Fixed gas sensors with a spacing of no more than 50 meters are deployed in key areas of the mining face, return air lane, and electromechanical chamber. Multi-parameter sensors are installed near the edge of the goaf and fault zone to generate a multi-level sensor network.

[0082] The real-time extraction data of the efficient gas extraction system and the monitoring data collected by the multi-level sensor network are transmitted to the data processing center for outlier detection, missing value repair and data standardization to generate standardized monitoring data;

[0083] The standardized monitoring data is integrated with the three-dimensional gas distribution model, the model parameters are updated in real time, and the dynamic gas distribution status is generated;

[0084] Based on the dynamic gas distribution state, a hybrid prediction model constructed by convolutional neural network and long short-term memory network is used to calculate and predict the gas concentration change trend of each monitoring point in the next 4 hours to generate gas concentration prediction data;

[0085] According to the gas concentration prediction data, three-level warning thresholds are set. When the predicted gas concentration reaches 80% of the warning value, it is set as a first-level warning. When it reaches 90% of the warning value, it is set as a second-level warning. When it exceeds the warning value, it is set as a third-level warning. The warning level determination standard is generated.

[0086] The warning level judgment standard is applied to the real-time calculated gas concentration prediction data to automatically judge the warning status and generate a multi-level warning judgment matrix including warning level, trigger time and impact range.

[0087] Specifically, by deploying a multi-level sensor network, the gas concentration in key areas such as the mining face, return air lane, and electromechanical chamber is monitored in real time, so that gas anomalies can be detected in time and effective early warnings can be issued. In these key areas, fixed gas sensors are installed every 50 meters to ensure the accuracy and timeliness of monitoring data. At the same time, multi-parameter sensors are installed near the edge of the goaf and the fault zone. These sensors can not only monitor gas concentration, but also detect parameters such as temperature, humidity, and wind speed to comprehensively evaluate the impact of the geological environment on gas behavior. The data of these sensors are transmitted to the data processing center via a wireless network or a wired communication system. In the data processing center, outlier detection is first performed on these real-time data to eliminate erroneous data caused by equipment failure or signal interference. Outlier detection uses a statistical method to automatically identify and eliminate abnormal data by setting a reasonable fluctuation range to ensure data accuracy. Next, missing value repair is performed to supplement the missing data through interpolation algorithms or machine learning models. This process not only improves the integrity of the data, but also eliminates the impact caused by missing data. All gas data that have undergone outlier detection and missing value repair will be standardized to generate a standardized monitoring data set. This normalization process converts all collected data into a unified standard unit, eliminating possible measurement deviations between different sensors and equipment, making the data more comparable and consistent.

[0088] The monitoring data after data standardization will be integrated with the three-dimensional gas distribution model to update the gas distribution parameters in the model in real time. The three-dimensional gas distribution model is constructed based on a large amount of drilling data, logging data and geophysical data, which can provide the static and dynamic distribution of gas in the coal mine area. By combining real-time monitoring data with model data, the gas concentration prediction results of each monitoring point can be updated to form a dynamic gas distribution state. This process allows coal mine managers to understand the changing trend of gas concentration at any time and take timely countermeasures.

[0089] Based on the dynamic gas distribution state, a hybrid prediction model constructed by convolutional neural network (CNN) and long short-term memory network (LSTM) is further used to predict the trend of gas concentration changes at each monitoring point in the next 4 hours. CNN is a deep learning algorithm that is good at processing data with a grid structure and can identify the spatial pattern of gas concentration changes; LSTM is a neural network used for time series prediction, which can remember past time point information and effectively predict the trend of gas concentration changes over time. Combining these two algorithms, the future changes of gas concentration can be accurately predicted based on the dynamic gas distribution model. The gas concentration prediction data of each monitoring point will be updated in real time to ensure that the gas safety of the mine is promptly warned.

[0090] According to the predicted gas concentration data, three-level warning thresholds are set. When the predicted gas concentration reaches 80% of the warning value, the system triggers the first-level warning to remind management personnel to strengthen gas monitoring; when the predicted gas concentration reaches 90% of the warning value, the system triggers the second-level warning and automatically adjusts the ventilation volume to reduce gas accumulation; when the predicted gas concentration exceeds the warning value, the system triggers the third-level warning and immediately initiates the emergency response plan, including evacuating personnel, cutting off power supply and other safety measures. This early warning mechanism can effectively prevent the occurrence of gas over-limit accidents and ensure the safety of mine workers.

[0091] Through the above data processing flow, the generated warning level judgment standard will be applied to the gas concentration prediction data of each monitoring point in real time to automatically judge the warning status. Whenever a new warning level is triggered, the system will generate a multi-level warning judgment matrix, which contains information such as warning level, trigger time, and impact range, to ensure that relevant personnel can obtain warning information in time and take appropriate actions.

[0092] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0093] The multi-level early warning judgment matrix, dynamic gas distribution status and operating parameters of the efficient gas extraction system are input into the gas over-limit cause diagnosis module, and analyzed through decision trees and rule reasoning algorithms to identify possible causes of gas anomalies;

[0094] Based on the causes of gas anomalies and the multi-level early warning judgment matrix, the probability of accident occurrence, impact range and degree of harm are calculated, the risk is quantitatively assessed, and the risk level is divided into extremely serious risk, serious risk, relatively serious risk and general risk to generate the risk level assessment results;

[0095] According to the risk level assessment results, formulate disposal strategies for risks at all levels, establish special emergency plans for particularly serious risks, formulate special disposal cards for major risks and major risks, formulate standard operating procedures for general risks, and generate differentiated disposal plans;

[0096] Based on differentiated disposal solutions, an emergency communication guarantee mechanism is designed, using a dual communication system that combines explosion-proof optical fiber communication network and distributed wireless communication technology to ensure the reliability of information transmission;

[0097] According to the differentiated disposal scheme and emergency communication guarantee mechanism, establish an emergency knowledge base and case base, apply knowledge graph technology to carry out structured management of domestic and foreign gas accident cases and disposal experience, and generate a knowledge support system;

[0098] Integrate differentiated handling plans, emergency communication guarantee mechanisms and knowledge support systems into a unified information management platform, and build a differentiated handling process database that includes handling processes, division of responsibilities and resource allocation.

[0099] Specifically, the gas over-limit cause diagnosis module receives and analyzes data from different sources. The multi-level warning judgment matrix provides the system with the level information of gas concentration warning, the dynamic gas distribution state describes the real-time spatial distribution and change trend of gas in the coal mine area, and the operating parameters of the efficient gas extraction system include relevant indicators of gas extraction, such as extraction volume, extraction pressure, etc. After unified integration, these data enter the gas over-limit cause diagnosis module, which analyzes the data in detail through decision trees and rule reasoning algorithms to identify potential causes of gas anomalies. The decision tree is a classification and regression model that helps analyze which factors cause gas over-limit by gradually splitting the data according to the characteristics, such as whether it is caused by local ventilation system failure, excessive gas content, or uneven coal seam permeability; the rule reasoning algorithm is based on the known rule base, and further confirms the specific cause of the gas anomaly through reasoning and matching.

[0100] After identifying the causes of gas anomalies, further risk assessment is carried out based on these causes and the multi-level early warning judgment matrix. Risk assessment includes calculating the probability of an accident, the scope of impact and the degree of harm. By analyzing historical data and real-time data, combined with the trend of gas concentration changes, the probability of an accident can be obtained. For example, if the gas concentration in a certain area reaches 90% of the warning value and the gas extraction volume in the area does not reach the predetermined target, the risk of gas exceeding the limit in the area is high. On this basis, combined with the specific scenario of gas exceeding the limit, the potential impact scope and possible degree of harm of the accident are assessed to ensure that the assessment results accurately reflect the possible consequences of gas exceeding the limit. Based on this assessment, the risk level is divided into extremely serious risk, serious risk, greater risk and general risk, so that corresponding emergency disposal measures can be taken according to different risk levels.

[0101] Based on the results of the risk level assessment, differentiated emergency response plans are formulated for each risk level. For particularly serious risks, special emergency plans are formulated, detailing the emergency steps that need to be immediately implemented when gas exceeds the limit, including evacuating personnel, cutting off power, starting backup ventilation equipment, etc. For major and relatively large risks, special response cards are formulated, stipulating specific emergency response times and the division of responsibilities of various departments to ensure that a rapid response can be made in the event of gas exceeding the limit; for general risks, standard operating procedures are formulated as the basis for daily management and training to ensure that coal mine workers pay enough attention to gas safety in daily operations and are able to report problems in a timely manner and take preliminary emergency measures when they are discovered.

[0102] At the same time, based on the emergency response plan, an emergency communication guarantee mechanism is designed to ensure that all relevant personnel can receive early warning information and emergency instructions in real time when gas exceeds the limit. The dual communication system that combines explosion-proof optical fiber communication network and distributed wireless communication technology can ensure that information can be transmitted unimpeded in any area of ​​the mine, even in an environment with extremely high gas concentration. The explosion-proof optical fiber communication network provides highly reliable communication guarantee, while the distributed wireless communication technology ensures that communication with the ground command center can be achieved even if the mine tunnel is blocked or broken.

[0103] In order to further improve the emergency response mechanism, an emergency knowledge base and case library have been established, and the handling experience of gas accidents at home and abroad has been structured and managed through knowledge graph technology. The knowledge graph technology systematically organizes the causes, treatment measures and results of various gas accidents to form a queryable knowledge base, which helps emergency personnel to quickly obtain effective emergency disposal plans when encountering similar situations. For example, when the gas concentration exceeds the limit in the mine, emergency personnel can view historical cases through the knowledge graph system, understand the response strategies for such accidents, and quickly select the best disposal plan. Combined with the emergency communication guarantee mechanism, all emergency plans, resource allocation and personnel responsibility division are included in the unified information management platform to quickly respond and update the emergency handling status in real time.

[0104] The above describes the method for gas control and safety assurance of coal mine areas under complex geological conditions in the embodiment of the present application. The following describes the system for gas control and safety assurance of coal mine areas under complex geological conditions in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the coal mine area gas control and safety assurance system under complex geological conditions includes:

[0105] The exploration module 201 is used to perform geophysical exploration, drilling and logging exploration on the coal mining area to obtain multi-dimensional geological parameter data;

[0106] An analysis module 202 is used to perform geostatistical analysis on the multi-dimensional geological parameter data to construct a three-dimensional gas distribution model;

[0107] An identification module 203 is used to identify high-gas areas based on the three-dimensional gas distribution model, design and implement a directional drilling network layout plan, and generate a drilling space network;

[0108] The extraction module 204 is used to implement differentiated extraction parameters and permeability enhancement measures for different pressure zones according to the borehole space network and gas pressure distribution, and establish an efficient gas extraction system;

[0109] Fusion module 205, used to perform abnormal value detection and data fusion processing on the real-time data of the efficient gas extraction system and the sensor network data, calculate the gas concentration change trend through a neural network algorithm, and generate a multi-level early warning judgment matrix;

[0110] The diagnosis module 206 is used to perform intelligent diagnosis and analysis on the causes of gas anomalies based on the multi-level early warning judgment matrix, and generate risk level assessment results and a differentiated disposal process database.

[0111] Through the collaborative cooperation of the above components, the multi-dimensional geological parameter data obtained through geophysical prospecting, drilling and logging exploration in the coal mine area, combined with the three-dimensional gas distribution model constructed by geostatistical analysis, not only achieved the accurate grasp of the gas distribution law under complex geological conditions, improved the accuracy of gas distribution prediction, but also provided a scientific basis for the subsequent directional drilling layout; based on the three-dimensional gas distribution model, the high-gas area was identified and the directional drilling network was designed and implemented. Through the fan-shaped layout method and the three-layer layout principle, the intersection probability of the borehole and the gas-enriched zone was significantly improved, and the borehole network coverage rate reached more than 85%, creating basic conditions for efficient extraction; according to the borehole space network and gas pressure distribution, the differentiated extraction parameters and permeability enhancement measures were implemented, and different negative pressure values ​​and extraction time strategies were adopted for different pressure areas. At the same time, hydraulic fracturing, CO 2 Targeted permeability enhancement technologies such as phase change blasting and negative pressure pulse have greatly improved the efficiency of gas extraction and reduced the gas content in the working face to a safe allowable range. The real-time data of the efficient gas extraction system and the sensor network data are subjected to outlier detection and data fusion processing. By applying a hybrid prediction model constructed by convolutional neural networks and long short-term memory networks, an accurate prediction of the trend of gas concentration changes in the next 4 hours is achieved, which significantly enhances the timeliness and accuracy of the early warning. The application of neural network algorithms enables the system to automatically identify the nonlinear patterns and time series characteristics of gas concentration changes, taking into account the complex interactions between multiple influencing factors, and achieving more accurate concentration predictions through deep learning of historical data. Intelligent diagnosis and analysis of the causes of gas anomalies are performed based on a multi-level early warning judgment matrix. The possible causes of gas anomalies are identified through decision trees and rule inference algorithms, and risk level assessment results and a differentiated disposal process database are generated, realizing the transition from passive response to active prevention.

[0112] above Figure 2 From the perspective of modular functional entities, the coal mine area gas control and safety assurance system under complex geological conditions in the embodiment of the present invention is described in detail. Below, from the perspective of hardware processing, the coal mine area gas control and safety assurance equipment under complex geological conditions in the embodiment of the present invention is described in detail.

[0113] Figure 3It is a structural schematic diagram of a coal mine area gas control and safety assurance device under complex geological conditions provided by an embodiment of the present invention. The coal mine area gas control and safety assurance device 300 under complex geological conditions may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 storing application programs 333 or data 332 (for example, one or more massive storage device terminals). Among them, the memory 320 and the storage medium 330 may be short-term storage or persistent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the coal mine area gas control and safety assurance device 300 under complex geological conditions. Furthermore, the processor 310 can be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the coal mine area gas control and safety assurance equipment 300 under complex geological conditions, so as to implement the steps of the coal mine area gas control and safety assurance method under the above-mentioned complex geological conditions.

[0114] The coal mine area gas control and safety protection equipment 300 under complex geological conditions may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that Figure 3 The structure of the gas control and safety assurance equipment for coal mine areas under complex geological conditions shown does not constitute a limitation of the gas control and safety assurance equipment for coal mine areas under complex geological conditions provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0115] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the method for gas control and safety assurance in coal mine areas under complex geological conditions.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0117] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a coal mine area gas control and safety protection device under complex geological conditions (which can be a personal computer, server, or network equipment, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0118] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for gas control and safety assurance in coal mine areas under complex geological conditions, characterized in that: The method comprises: Conduct geophysical exploration, drilling and logging in coal mining areas to obtain multi-dimensional geological parameter data; Performing geostatistical analysis on the multi-dimensional geological parameter data to construct a three-dimensional gas distribution model; Based on the three-dimensional gas distribution model, high-gas areas are identified, a directional drilling network layout plan is designed and implemented, and a drilling space network is generated; According to the borehole space network and gas pressure distribution, differentiated extraction parameters and permeability enhancement measures are implemented for different pressure zones to establish an efficient gas extraction system; The real-time data of the efficient gas extraction system and the sensor network data are subjected to abnormal value detection and data fusion processing, and the gas concentration change trend is calculated by a neural network algorithm to generate a multi-level early warning judgment matrix; Based on the multi-level early warning judgment matrix, intelligent diagnosis and analysis of the causes of gas anomalies are performed to generate risk level assessment results and a differentiated disposal process database.

2. The method for gas control and safety assurance in coal mine areas under complex geological conditions according to claim 1 is characterized in that: The geophysical exploration, drilling and logging exploration of the coal mine area obtains multi-dimensional geological parameter data, including: Obtain underground geological structure data through high-precision seismic exploration with a survey line spacing of no more than 50 meters and a detection point spacing of no more than 10 meters; Drill holes were arranged according to the grid principle of 30m x 30m near the main faults and 50m x 50m in other areas to collect data on coal seam thickness, dip and gas pressure; Perform acoustic logging, density logging, natural potential logging and resistivity logging on each borehole to generate logging curve data; Eliminate outliers from the underground geological structure data, coal seam thickness, inclination and gas pressure data, and well logging curve data to generate a standardized geological parameter data set; Applying variogram analysis and Kriging interpolation algorithm to the standardized geological parameter data set to calculate spatial variation rules and parameter correlation; According to the spatial variation law and parameter correlation, the coal mining area is divided into ultra-high complexity area, high complexity area, medium complexity area and low complexity area, and geological complexity zoning data is generated.

3. The method for gas control and safety assurance in coal mine areas under complex geological conditions according to claim 2 is characterized in that: The method of performing geostatistical analysis on the multi-dimensional geological parameter data to construct a three-dimensional gas distribution model includes: Based on the standardized geological parameter data set and geological complexity zoning data, a three-dimensional skeleton model of regional geological structure with an accuracy of 5 meters × 5 meters × 1 meter is established; Mapping the gas content, gas pressure and coal seam permeability data in the multidimensional geological parameter data to the three-dimensional skeleton model through a trilinear interpolation algorithm to generate an initial gas distribution grid; Applying a random forest algorithm to perform parameter prediction on the data sparse area in the initial gas distribution grid to generate gas parameter spatial distribution data; Based on the gas parameter spatial distribution data and the geological complexity zoning data, the stress distribution state in the region is calculated using the finite element method to establish a stress-gas coupling analysis model; The fault zone in the stress-gas coupling analysis model is set with a fault influence coefficient, and influence weights of 0.8, 0.6, and 0.4 are assigned according to the size of the fault, and the fault gas enrichment coefficient is calculated; The fault gas enrichment coefficient and stress-gas coupling analysis model are input into a multiphase flow numerical simulator to calculate the gas migration law under different mining conditions and generate a three-dimensional gas distribution model.

4. The method for gas control and safety assurance in coal mine areas under complex geological conditions according to claim 1 is characterized in that: The method of identifying high-gas areas based on the three-dimensional gas distribution model, designing and implementing a directional drilling network layout plan, and generating a drilling space network includes: According to the gas content data in the three-dimensional gas distribution model, the coal mine area is divided into a special area, a first-level area, a second-level area and a third-level area, and gas area classification data is generated; Based on the gas area classification data, a drilling density standard is formulated, with the spacing between special-grade areas not exceeding 15 meters, first-grade areas not exceeding 25 meters, second-grade areas not exceeding 35 meters, and third-grade areas not exceeding 50 meters; Input the drilling density standard and the three-dimensional gas distribution model into a computer-aided design system, design a three-layer layout plan for areas with a coal seam thickness greater than 3 meters, design a drilling plan perpendicular to the coal seam trend for areas with an inclination angle greater than 15°, and generate an initial drilling trajectory design; Applying the fan-shaped arrangement method to the area near the fault in the initial drilling trajectory design, arranging the holes at an angle of 15° around the fault as the center, and generating a drilling arrangement design diagram; Drilling construction is carried out according to the drilling layout design drawing, and the magnetic direction finding system and the gyroscope direction finding system are used to monitor the drilling trajectory in real time. When the deviation from the designed trajectory exceeds 0.5 meters, the trajectory is corrected to complete the drilling construction; Perform borehole imaging detection and pressure testing on completed boreholes, calculate the borehole network coverage, add additional holes when the coverage is lower than 85%, and generate a borehole space network database containing the borehole space coordinates, length, direction and diameter.

5. The method for gas control and safety assurance in coal mine areas under complex geological conditions according to claim 1 is characterized in that: According to the borehole space network and gas pressure distribution, differentiated extraction parameters and permeability enhancement measures are implemented for different pressure zones to establish an efficient gas extraction system, including: Based on the gas pressure data in the borehole spatial network database and the three-dimensional gas distribution model, the coal mine area is divided into a high-pressure area, a medium-pressure area and a low-pressure area, and a gas pressure zoning map is generated; According to the gas pressure zoning map, a low negative pressure long-term extraction strategy with a negative pressure value not exceeding 10 kPa and a time of not less than 30 days is implemented for the high-pressure area, a medium negative pressure extraction strategy with a negative pressure value of 15-25 kPa and a time of 15-25 days is implemented for the medium-pressure area, and a high negative pressure extraction strategy with a negative pressure value of 25-35 kPa and a time of 7-15 days is implemented for the low-pressure area, and a differentiated extraction parameter plan is generated; According to the coal seam permeability data in the three-dimensional gas distribution model, the permeability coefficient is less than 0.1m 2 / (MPa 2 ·d) Hydraulic fracturing technology is implemented in the area with a permeability coefficient of 0.1-0.5m 2 / (MPa 2 ·d) The area between the two places is subjected to CO2 phase change blasting technology, and the permeability coefficient is greater than 0.5m 2 / (MPa 2 ·D) Implement negative pressure pulse technology to generate anti-reflection technology solutions; Carry out permeability enhancement operation on the borehole according to the permeability enhancement technical scheme, verify the permeability enhancement effect through pressure monitoring and flow monitoring, and improve the permeability of the coal seam; Based on the borehole spatial network database, the main and branch pipe network structure is designed, the main pipe diameter is not less than 219 mm, the branch pipe diameter is not less than 159 mm, and the single borehole extraction pipe diameter is not less than 89 mm, and the extraction pipeline system design scheme is generated; According to the differentiated extraction parameter scheme and extraction pipeline system design scheme, extraction pump station equipment is configured, gas extraction operations are carried out, monitoring points are set up every 50 meters, the extraction effect is monitored in real time, and the extraction parameters are automatically adjusted when the extraction concentration and extraction volume do not meet the standards, and an efficient gas extraction system including data collection, parameter adjustment and gas utilization classification is established.

6. The method for gas control and safety assurance in coal mine areas under complex geological conditions according to claim 1 is characterized in that: The real-time data of the efficient gas extraction system and the sensor network data are subjected to abnormal value detection and data fusion processing, and the gas concentration change trend is calculated by a neural network algorithm to generate a multi-level early warning judgment matrix, including: Fixed gas sensors with a spacing of no more than 50 meters are deployed in key areas of the mining face, return air lane, and electromechanical chamber. Multi-parameter sensors are installed near the edge of the goaf and fault zone to generate a multi-level sensor network. The real-time extraction data of the efficient gas extraction system and the monitoring data collected by the multi-level sensor network are transmitted to the data processing center to perform outlier detection, missing value repair and data standardization processing to generate standardized monitoring data; The standardized monitoring data is integrated with the three-dimensional gas distribution model, and the model parameters are updated in real time to generate a dynamic gas distribution state; Based on the dynamic gas distribution state, a hybrid prediction model constructed by a convolutional neural network and a long short-term memory network is applied to calculate and predict the gas concentration change trend of each monitoring point in the next 4 hours to generate gas concentration prediction data; A three-level warning threshold is set according to the gas concentration prediction data. When the predicted gas concentration reaches 80% of the warning value, it is set as a first-level warning; when it reaches 90% of the warning value, it is set as a second-level warning; when it exceeds the warning value, it is set as a third-level warning, and a warning level determination standard is generated; The warning level determination standard is applied to the real-time calculated gas concentration prediction data to automatically determine the warning state and generate a multi-level warning determination matrix including the warning level, trigger time and impact range.

7. The method for gas control and safety assurance in coal mine areas under complex geological conditions according to claim 1 is characterized in that: Based on the multi-level early warning judgment matrix, the causes of gas anomalies are intelligently diagnosed and analyzed to generate risk level assessment results and a differentiated disposal process database, including: The multi-level early warning judgment matrix, dynamic gas distribution state and operating parameters of the efficient gas extraction system are input into the gas overlimit cause diagnosis module, and analyzed through a decision tree and rule inference algorithm to identify possible causes of gas anomalies; Based on the causes of gas anomalies and the multi-level early warning judgment matrix, the probability of accident occurrence, the scope of impact and the degree of harm are calculated, the risk is quantitatively assessed, and the risk level is divided into particularly serious risk, serious risk, relatively serious risk and general risk, and the risk level assessment result is generated; According to the risk level assessment results, formulate disposal strategies for risks at all levels, establish special emergency plans for particularly serious risks, formulate special disposal cards for major risks and major risks, formulate standard operating procedures for general risks, and generate differentiated disposal plans; Based on the differentiated disposal scheme, an emergency communication guarantee mechanism is designed, and a dual communication system combining explosion-proof optical fiber communication network and distributed wireless communication technology is adopted to ensure the reliability of information transmission; According to the differentiated disposal scheme and emergency communication guarantee mechanism, establish an emergency knowledge base and case base, apply knowledge graph technology to structure management of domestic and foreign gas accident cases and disposal experience, and generate a knowledge support system; Integrate the differentiated handling plans, emergency communication guarantee mechanisms and knowledge support systems into a unified information management platform, and build a differentiated handling process database that includes handling processes, division of responsibilities and resource allocation.

8. A gas control and safety assurance system for coal mine areas under complex geological conditions, characterized in that: Used to implement the method for gas control and safety assurance in coal mine areas under complex geological conditions according to any one of claims 1 to 7, the gas control and safety assurance system in coal mine areas under complex geological conditions comprises: The exploration module is used to conduct geophysical exploration, drilling and logging exploration in the coal mining area to obtain multi-dimensional geological parameter data; An analysis module, used for performing geostatistical analysis on the multi-dimensional geological parameter data to construct a three-dimensional gas distribution model; An identification module, used to identify high-gas areas based on the three-dimensional gas distribution model, design and implement a directional drilling network layout plan, and generate a drilling space network; A drainage module, for implementing differentiated drainage parameters and permeability enhancement measures for different pressure zones according to the borehole space network and gas pressure distribution, and establishing an efficient gas drainage system; A fusion module is used to perform abnormal value detection and data fusion processing on the real-time data of the efficient gas extraction system and the sensor network data, calculate the gas concentration change trend through a neural network algorithm, and generate a multi-level early warning judgment matrix; The diagnosis module is used to perform intelligent diagnosis and analysis on the causes of gas anomalies based on the multi-level early warning judgment matrix, and generate risk level assessment results and a differentiated disposal process database.

9. A gas control and safety equipment for coal mine areas under complex geological conditions, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the method for gas control and safety assurance in coal mine areas under complex geological conditions as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor executes the method for gas control and safety assurance in coal mine areas under complex geological conditions as described in any one of claims 1 to 7.

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