Fine investigation method for mining effect of protective layer

Abstract

The invention provides a fine investigation method for the mining effect of a protective layer, and relates to the technical field of geological mining, and the method comprises the following steps: collecting the spatial position relationship, original geological parameters, gas parameters and mechanical parameters of the protective layer and a protected coal seam; constructing a mining prediction model based on the spatial position relation, the original geological parameters, the gas parameters and the mechanical parameters; outputting stress distribution prediction data, gas migration prediction data and gas permeability prediction data through the mining prediction model; dynamically monitoring the mining of the protective layer to obtain real-time stress distribution data, real-time gas migration data and real-time gas permeability data; based on the stress distribution prediction data, the gas migration prediction data, the gas permeability prediction data, the real-time stress distribution data, the real-time gas migration data and the real-time gas permeability data, mining effect analysis is conducted, and a mining effect evaluation result is obtained. According to the method, the comprehensive effect of protective layer mining can be comprehensively and accurately evaluated.

Description

Technical Field

[0001] This invention relates to the field of geological mining technology, and in particular to a method for detailed assessment of the effectiveness of protective layer mining. Background Technology

[0002] In the coal mining sector, gas disasters have always been a key factor seriously threatening mine safety. With the continuous increase in mining depth, the pressure and content of coal seam gas increase significantly, and the frequency and intensity of dynamic disasters such as gas outbursts are also increasing, posing a huge threat to the lives of coal mine workers and mine equipment, and seriously restricting the efficient and safe mining of coal resources.

[0003] Protective seam mining, as a regional gas control technology, plays a crucial role in coal mine safety production. Its basic principle is to mine a protective coal seam that provides pressure relief, causing deformation and fracture development in the protected coal seam. This alters the stress state and permeability of the protected coal seam, promoting gas desorption and migration, reducing gas pressure and content, and ultimately eliminating or mitigating the risk of gas outbursts.

[0004] However, current protective layer mining mainly focuses on examining single indicators such as gas pressure and content in the protected coal seam. This fails to comprehensively and accurately assess the overall effectiveness of protective layer mining, making it difficult to meet the demands of refined management for safe coal mine production. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method for finely examining the mining effect of protective layers, which can solve the technical problem that the prior art cannot comprehensively and accurately evaluate the overall effect of protective layer mining.

[0006] A first aspect of this invention provides a method for detailed evaluation of the effectiveness of protective layer mining, comprising:

[0007] Collect the spatial relationship between the protective layer and the protected coal seam, original geological parameters, gas parameters, and mechanical parameters;

[0008] Based on the spatial location relationship, the original geological parameters, the gas parameters, and the mechanical parameters, a mining prediction model is constructed.

[0009] The mining prediction model outputs stress distribution prediction data, gas migration prediction data, and permeability prediction data.

[0010] Dynamic monitoring is performed on the mining of the protective layer to obtain real-time stress distribution data, real-time gas migration data, and real-time permeability data.

[0011] Based on the stress distribution prediction data, the gas migration prediction data, the permeability prediction data, the real-time stress distribution data, the real-time gas migration data, and the real-time permeability data, the mining effect is analyzed to obtain the mining effect evaluation result.

[0012] Optionally, the original geological parameters include at least the coal seam thickness, burial depth, dip angle, spacing, and lithology of the roof and floor.

[0013] The gas parameters include at least the original gas pressure, the original gas content, and the gas composition;

[0014] The mechanical parameters include at least the elastic modulus, Poisson's ratio, compressive strength, and tensile strength of the coal seam and the roof and floor rocks.

[0015] Optionally, the step of constructing a mining prediction model based on the spatial location relationship, the original geological parameters, the gas parameters, and the mechanical parameters specifically includes:

[0016] The spatial position relationship, the original geological parameters, the gas parameters, and the mechanical parameters are denoised to obtain the target spatial position relationship, the target original geological parameters, the target gas parameters, and the target mechanical parameters.

[0017] A three-dimensional geological model of the protective layer and the protected coal seam is constructed based on the spatial relationship of the target.

[0018] Numerical simulation of the three-dimensional geological model is performed using the original geological parameters of the target, the gas parameters of the target, and the mechanical parameters of the target to obtain a mining prediction model.

[0019] Optionally, the step of outputting stress distribution prediction data, gas migration prediction data, and permeability prediction data through the mining prediction model specifically includes:

[0020] The mining prediction model is used to simulate the mining of the protective layer, and outputs the target pressure relief range, the predicted stress value of each monitoring point, the predicted gas content, the predicted reduction value of gas content, the expected improvement multiple of permeability, and the predicted permeability value.

[0021] The target depressurization range and the predicted stress values ​​at each monitoring point are determined as stress distribution prediction data;

[0022] The predicted gas content and the predicted decrease in gas content are determined as gas migration prediction data;

[0023] The expected improvement factor in breathability and the predicted breathability value are defined as breathability prediction data.

[0024] Optionally, the dynamic monitoring of the protective layer mining to obtain real-time stress distribution data, real-time gas migration data, and real-time permeability data specifically includes:

[0025] During the mining process, real-time stress data, real-time gas content, and real-time air permeability values ​​are collected at each monitoring point.

[0026] After the protective layer is mined out, target stress data, target gas content, and target air permeability values ​​are collected at each monitoring point.

[0027] The real-time stress data and target stress data of each monitoring point are determined as real-time stress distribution data;

[0028] The real-time gas content and target gas content at each monitoring point are defined as real-time gas migration data.

[0029] The real-time air permeability values ​​and target air permeability values ​​at each monitoring point are defined as real-time air permeability data.

[0030] Optionally, the method further includes:

[0031] Risk monitoring is conducted on the mining of the protective layer to obtain real-time surface deformation data and real-time fracture data of the protected coal seam; wherein, the real-time surface deformation data includes at least the cumulative surface rise and fall, cumulative surface displacement, surface rise and fall rate, and surface displacement rate, and the real-time fracture data includes at least the fracture length, fracture width, and fracture depth.

[0032] Risk monitoring is performed on the real-time surface deformation data and the real-time crack data based on safety thresholds, and risk monitoring information is output.

[0033] Optionally, the step of performing risk monitoring on the real-time surface deformation data and the real-time crack data based on a safety threshold, and outputting risk monitoring information, specifically includes:

[0034] If the cumulative surface elevation or subsidence exceeds a preset elevation or subsidence threshold or the surface elevation or subsidence rate exceeds a preset elevation or subsidence rate, then the risk of surface elevation or subsidence will be added to the risk monitoring information.

[0035] If the cumulative surface displacement is greater than a preset cumulative displacement or the surface displacement rate is greater than a preset displacement rate, then the risk of surface displacement is added to the risk monitoring information.

[0036] If the crack length is greater than a preset length, the crack width is greater than a preset width, or the crack depth is greater than a preset depth, then the risk of surface cracks is added to the risk monitoring information.

[0037] Optionally, the step of analyzing the mining effect based on the stress distribution prediction data, the gas migration prediction data, the permeability prediction data, the real-time stress distribution data, the real-time gas migration data, and the real-time permeability data to obtain the mining effect evaluation result specifically includes:

[0038] Based on the real-time stress data and target stress data at each monitoring point, the current pressure relief range is determined;

[0039] Based on the real-time gas content and target gas content at each monitoring point, the current gas content reduction value is determined;

[0040] Based on the real-time air permeability values ​​and target air permeability values ​​at each monitoring point, the expected improvement factor for current air permeability is determined.

[0041] By comparing the target depressurization range and the predicted stress values ​​at each monitoring point with the current depressurization range and the target stress data at each monitoring point, stress effect evaluation information is obtained.

[0042] The predicted gas content and the predicted reduction value of gas content at each monitoring point are compared with the target gas content and the current reduction value of gas content at each monitoring point to obtain gas content evaluation information.

[0043] The expected improvement factor of air permeability at each monitoring point and the predicted air permeability value are compared with the target air permeability value at each monitoring point and the current expected improvement factor of air permeability to obtain air permeability evaluation information.

[0044] The stress effect evaluation information, the gas content evaluation information, and the permeability evaluation information are determined as the mining effect evaluation results.

[0045] A second aspect of this invention provides a system for finely examining the effectiveness of protective layer mining, comprising: a processor and a memory;

[0046] The memory stores programs or instructions that can run on the processor, which, when executed by the processor, implement the steps of the method for detailed investigation of the protective layer mining effect as described in the first aspect.

[0047] A third aspect of the present invention provides a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the method for detailed investigation of the protective layer mining effect as described in the first aspect.

[0048] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0049] In this embodiment of the invention, by collecting various parameters of the protective layer and the protected coal seam, a mining prediction model is constructed. This model can output predicted data on stress distribution, gas migration, and permeability in advance, providing forward-looking guidance for mining planning. Simultaneously, dynamic monitoring of the protective layer mining acquires real-time data, and the predicted data is combined with the real-time data for mining effect analysis. This comprehensive approach considers both pre-mining predictions and actual dynamic changes during the mining process, avoiding the limitations of a single data source or analysis method. It can comprehensively analyze the impact of mining activities on coal seam stress, gas, and permeability from multiple key dimensions, thereby comprehensively and accurately evaluating the overall effect of protective layer mining and providing strong support for ensuring mining safety and improving mining efficiency. Attached Figure Description

[0050] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0051] Figure 1 This is a flowchart illustrating a method for detailed evaluation of the effectiveness of protective layer mining provided in an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of a system for finely examining the mining effect of a protective layer, provided in an embodiment of the present invention. Detailed Implementation

[0053] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0054] The following detailed description, in conjunction with the accompanying drawings, of the method for precise assessment of the protective layer mining effect provided by the embodiments of the present invention through specific examples and application scenarios, will be provided in detail.

[0055] Reference manual attached Figure 1 The diagram shows a flowchart of a method for detailed investigation of the mining effect of protective layers provided by an embodiment of the present invention.

[0056] This invention provides a method for detailed evaluation of the effectiveness of protective layer mining, which may include the following steps:

[0057] S1: Collect the spatial relationship between the protective layer and the protected coal seam, original geological parameters, gas parameters, and mechanical parameters.

[0058] In this embodiment of the application, the original geological parameters include at least the coal seam thickness, burial depth, dip angle, spacing, and lithology of the roof and floor.

[0059] The gas parameters include at least the original gas pressure, the original gas content, and the gas composition;

[0060] The mechanical parameters include at least the elastic modulus, Poisson's ratio, compressive strength, and tensile strength of the coal seam and the roof and floor rocks.

[0061] S2: Based on the spatial location relationship, the original geological parameters, the gas parameters, and the mechanical parameters, construct a mining prediction model.

[0062] As an optional implementation, S2 can construct a mining prediction model based on the spatial location relationship, the original geological parameters, the gas parameters, and the mechanical parameters in the following ways:

[0063] The spatial position relationship, the original geological parameters, the gas parameters, and the mechanical parameters are denoised to obtain the target spatial position relationship, the target original geological parameters, the target gas parameters, and the target mechanical parameters.

[0064] A three-dimensional geological model of the protective layer and the protected coal seam is constructed based on the spatial relationship of the target.

[0065] Numerical simulation of the three-dimensional geological model is performed using the original geological parameters of the target, the gas parameters of the target, and the mechanical parameters of the target to obtain a mining prediction model.

[0066] This implementation method denoises the collected parameters, effectively eliminating interfering information and obtaining accurate target parameters, laying a reliable foundation for subsequent modeling. A three-dimensional geological model is constructed based on the spatial relationship of the target, visually presenting the spatial structure of the protective layer and the protected coal seam. Numerical simulation of the model using the original geological, gas, and mechanical parameters of the target can simulate real mining scenarios. The mining prediction model constructed through this series of operations can more accurately reflect the mining situation, providing a solid basis for subsequent analysis of the protective layer's mining effectiveness and improving the reliability of the assessment.

[0067] In this embodiment of the application, the numerical simulation method can be FLAC3D, UDEC, or PFC.

[0068] S3: Output stress distribution prediction data, gas migration prediction data, and permeability prediction data through the mining prediction model.

[0069] As an optional implementation, S3 may output stress distribution prediction data, gas migration prediction data, and permeability prediction data through the mining prediction model in the following ways:

[0070] The mining prediction model is used to simulate the mining of the protective layer, and outputs the target pressure relief range, the predicted stress value of each monitoring point, the predicted gas content, the predicted reduction value of gas content, the expected improvement multiple of permeability, and the predicted permeability value.

[0071] The target depressurization range and the predicted stress values ​​at each monitoring point are determined as stress distribution prediction data;

[0072] The predicted gas content and the predicted decrease in gas content are determined as gas migration prediction data;

[0073] The expected improvement factor in breathability and the predicted breathability value are defined as breathability prediction data.

[0074] This implementation method, which uses a mining prediction model to simulate protective layer mining, can comprehensively output multiple key data points, including the target pressure relief range and predicted stress values ​​at various monitoring points. Through reasonable classification and integration, the relevant data are identified as stress distribution, gas migration, and permeability prediction data, making the various prediction data more systematic and clear. This not only provides rich and organized data support for subsequent accurate analysis of the dynamic changes in stress, gas, and permeability during protective layer mining, but also helps to understand the mining situation in advance, adjust mining strategies in a timely manner, and effectively improve the safety and efficiency of mining.

[0075] S4: Dynamically monitor the mining of the protective layer to obtain real-time stress distribution data, real-time gas migration data, and real-time permeability data.

[0076] In this embodiment of the application, dynamic monitoring may include: monitoring stress changes in the protected coal seam through stress sensors during the advancement of the protective layer working face; monitoring displacement deformation of the protected coal seam through displacement gauges or inclinometers; periodically monitoring changes in gas pressure and gas content through pre-set pressure testing boreholes and sampling boreholes in the protected coal seam; and monitoring changes in the permeability coefficient of the protected coal seam through a permeability measuring device.

[0077] Dynamic monitoring can also include monitoring surface movement and deformation caused by the mining of protective layers, the morphology of goaf areas, and the development of fractures in the protected coal seams.

[0078] As an optional implementation, S4 can dynamically monitor the mining of the protective layer to obtain real-time stress distribution data, real-time gas migration data, and real-time permeability data in the following ways:

[0079] During the mining process, real-time stress data, real-time gas content, and real-time air permeability values ​​are collected at each monitoring point.

[0080] After the protective layer is mined out, target stress data, target gas content, and target air permeability values ​​are collected at each monitoring point.

[0081] The real-time stress data and target stress data of each monitoring point are determined as real-time stress distribution data;

[0082] The real-time gas content and target gas content at each monitoring point are defined as real-time gas migration data.

[0083] The real-time air permeability values ​​and target air permeability values ​​at each monitoring point are defined as real-time air permeability data.

[0084] This implementation method involves real-time data collection from various monitoring points during the protective layer mining process, enabling timely capture of dynamic changes and the acquisition of first-hand real-time information. Data collection is repeated after mining concludes, providing a comprehensive understanding of the final state. The corresponding data collected at different stages are categorized and integrated into real-time stress distribution, gas migration, and permeability data, encompassing both the dynamic process and the final results. This comprehensive data collection and processing method provides detailed evidence for accurately evaluating the effectiveness of protective layer mining, helping to promptly identify potential problems, optimize subsequent mining plans, and ensure safe and efficient mining operations.

[0085] In this embodiment of the application, after the protective layer mining is completed, representative measuring points (i.e., monitoring points) can be selected in the protected coal seam, and field borehole tests can be conducted on these representative measuring points to collect target stress data, target gas content, and target permeability values ​​for each monitoring point. The selection of representative measuring points can cover different locations within the protective coal seam under the influence of the protective layer mining, including different distances in the strike and dip directions.

[0086] In this embodiment of the application, the on-site drilling test can specifically be:

[0087] The gas pressure (i.e. target stress data) at each monitoring point was determined by the direct pressure measurement method.

[0088] The target methane content at each monitoring point was determined by a combination of desorption method and laboratory analysis.

[0089] The target permeability values ​​at each monitoring point were determined using the borehole flow rate method or well test method.

[0090] As an optional implementation, the following steps may also be included after S4:

[0091] Risk monitoring is conducted on the mining of the protective layer to obtain real-time surface deformation data and real-time fracture data of the protected coal seam; wherein, the real-time surface deformation data includes at least the cumulative surface rise and fall, cumulative surface displacement, surface rise and fall rate, and surface displacement rate, and the real-time fracture data includes at least the fracture length, fracture width, and fracture depth.

[0092] Risk monitoring is performed on the real-time surface deformation data and the real-time crack data based on safety thresholds, and risk monitoring information is output.

[0093] This implementation method involves risk monitoring during protective layer mining, acquiring real-time surface deformation and coal seam fracture data. This data encompasses multi-dimensional surface information, including cumulative surface rise and fall, as well as key fracture indicators such as fracture length. The data is comprehensive and accurate. By monitoring and outputting risk information based on safety thresholds, potential safety hazards during mining, such as excessive surface subsidence or excessively large coal seam fractures, can be detected promptly. Early warnings allow staff to take swift action, effectively preventing accidents, ensuring safe mining operations, reducing economic losses, and improving the overall safety and reliability of the mining process.

[0094] Optionally, risk monitoring can be performed on the real-time surface deformation data and the real-time crack data based on safety thresholds, and the risk monitoring information can be output in the following ways:

[0095] If the cumulative surface elevation or subsidence exceeds a preset elevation or subsidence threshold or the surface elevation or subsidence rate exceeds a preset elevation or subsidence rate, then the risk of surface elevation or subsidence will be added to the risk monitoring information.

[0096] If the cumulative surface displacement is greater than a preset cumulative displacement or the surface displacement rate is greater than a preset displacement rate, then the risk of surface displacement is added to the risk monitoring information.

[0097] If the crack length is greater than a preset length, the crack width is greater than a preset width, or the crack depth is greater than a preset depth, then the risk of surface cracks is added to the risk monitoring information.

[0098] This implementation method, by setting clear safety thresholds, allows for precise comparison and judgment of real-time surface deformation and crack data. Setting corresponding thresholds for cumulative surface rise and fall, rise and fall rate, cumulative displacement, and displacement rate enables timely detection of abnormal surface rise and fall and displacement. Setting thresholds for crack length, width, and depth effectively identifies crack risks. Once the data exceeds the thresholds, the corresponding risk is added to the monitoring information, making risk identification clear and explicit. This helps staff quickly locate problems, take preventative measures in advance, effectively prevent safety accidents such as surface collapse and crack expansion, and ensure mining safety.

[0099] S5: Based on the stress distribution prediction data, the gas migration prediction data, the permeability prediction data, the real-time stress distribution data, the real-time gas migration data, and the real-time permeability data, the mining effect analysis is performed to obtain the mining effect evaluation result.

[0100] As an optional implementation, S5 performs mining effect analysis based on the stress distribution prediction data, the gas migration prediction data, the permeability prediction data, the real-time stress distribution data, the real-time gas migration data, and the real-time permeability data, and obtains the mining effect evaluation result in the following ways:

[0101] Based on the real-time stress data and target stress data at each monitoring point, the current pressure relief range is determined;

[0102] Based on the real-time gas content and target gas content at each monitoring point, the current gas content reduction value is determined;

[0103] Based on the real-time air permeability values ​​and target air permeability values ​​at each monitoring point, the expected improvement factor for current air permeability is determined.

[0104] By comparing the target depressurization range and the predicted stress values ​​at each monitoring point with the current depressurization range and the target stress data at each monitoring point, stress effect evaluation information is obtained.

[0105] The predicted gas content and the predicted reduction value of gas content at each monitoring point are compared with the target gas content and the current reduction value of gas content at each monitoring point to obtain gas content evaluation information.

[0106] The expected improvement factor of air permeability at each monitoring point and the predicted air permeability value are compared with the target air permeability value at each monitoring point and the current expected improvement factor of air permeability to obtain air permeability evaluation information.

[0107] The stress effect evaluation information, the gas content evaluation information, and the permeability evaluation information are determined as the mining effect evaluation results.

[0108] This implementation method, by combining real-time and target data, accurately determines the current pressure relief range, the reduction in gas content, and the expected improvement multiple of permeability, comprehensively reflecting the actual mining situation. A detailed comparison between the predicted and actual data then yields evaluation information on stress, gas content, and permeability. This multi-dimensional, hierarchical comparative analysis method can deeply and accurately assess the effectiveness of protective layer mining, clearly presenting the achievement of various indicators during the mining process. It helps to promptly identify mining problems, providing a strong basis for subsequent optimization of mining strategies, thereby improving mining efficiency and safety, and ensuring the smooth operation of coal mine production.

[0109] Optionally, if the current depressurization range does not completely cover the target depressurization range, the progress information of the protective layer mining and the real-time depressurization range can be obtained. This progress information includes multiple sets of progress data, each containing mining coordinates and mining time. In this case, a dynamic mining-depressurization mapping video can be generated by combining the progress information of the protective layer mining and the real-time depressurization range. This mining-depressurization mapping video can show the correspondence between the mining location and the depressurization range at the corresponding time frame through each frame. Furthermore, this mining-depressurization mapping video can be input into a pre-built depressurization problem analysis model to obtain the depressurization problem cause analysis output by the model.

[0110] Furthermore, it can also identify the first target monitoring point where the current gas content reduction value is less than the predicted gas content reduction value, and perform curve fitting on the real-time gas content of each first target monitoring point to generate the gas content curve corresponding to each first target monitoring point. Additionally, it can input the gas content curve corresponding to each first target monitoring point into a pre-built gas content problem analysis model to obtain the gas content problem cause analysis output by the gas content problem analysis model.

[0111] In addition, it is possible to identify a second target monitoring point where the expected improvement multiple of the current breathability is less than the target breathability value, and to perform curve fitting on the real-time breathability values ​​of each second target monitoring point to generate a breathability curve corresponding to each second target monitoring point. Furthermore, the breathability curves corresponding to each second target monitoring point can be input into a pre-built breathability problem analysis model to obtain the breathability problem cause analysis output by the breathability problem analysis model.

[0112] Then, the causes of pressure relief problems, gas content problems, and permeability problems can be input into the optimization model so that the optimization model can output a targeted optimization scheme for protective layer mining.

[0113] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0114] In this embodiment of the invention, the predicted situation before mining is considered, and the actual dynamic changes during the mining process are also taken into account. This avoids the limitations of a single data source or a single analysis method. It can comprehensively analyze the impact of mining activities on coal seam stress, gas and permeability from multiple key dimensions. In this way, the overall effect of protective layer mining can be comprehensively and accurately evaluated, providing strong support for ensuring mining safety and improving mining efficiency.

[0115] Reference manual attached Figure 2 The diagram shows a structural schematic of a system for finely examining the mining effect of a protective layer, provided in an embodiment of the present invention.

[0116] This invention provides a detailed assessment system 20 for the effectiveness of protective layer mining, comprising: a processor 201 and a memory 202;

[0117] The memory 202 stores programs or instructions that can run on the processor 201. When the program or instructions are executed by the processor 201, they implement the steps of the above-mentioned method for fine investigation of the protective layer mining effect and achieve the same technical effect. To avoid repetition, the present invention will not repeat the above-mentioned steps.

[0118] It should be understood that the processor 201 in this embodiment of the invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0119] It should also be understood that the memory 202 in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DR RAM).

[0120] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0121] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0122] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0123] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0124] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0125] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0126] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0127] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0128] This invention provides a readable storage medium comprising: storing a program or instructions on the readable storage medium, wherein when the program or instructions are executed by a processor, the program or instructions implement the steps of the above-described method for finely examining the mining effect of the protective layer, and can achieve the same technical effect. To avoid repetition, this invention will not elaborate further.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detailed evaluation of the effectiveness of protective layer mining, characterized in that, include: Collect the spatial relationship between the protective layer and the protected coal seam, original geological parameters, gas parameters, and mechanical parameters; Based on the spatial location relationship, the original geological parameters, the gas parameters, and the mechanical parameters, a mining prediction model is constructed. The mining prediction model outputs stress distribution prediction data, gas migration prediction data, and permeability prediction data. Dynamic monitoring is performed on the mining of the protective layer to obtain real-time stress distribution data, real-time gas migration data, and real-time permeability data. Based on the stress distribution prediction data, the gas migration prediction data, the permeability prediction data, the real-time stress distribution data, the real-time gas migration data, and the real-time permeability data, the mining effect is analyzed to obtain the mining effect evaluation result.

2. The method for detailed evaluation of the protective layer mining effect according to claim 1, characterized in that, The original geological parameters include at least the coal seam thickness, burial depth, dip angle, spacing, and lithology of the roof and floor. The gas parameters include at least the original gas pressure, the original gas content, and the gas composition; The mechanical parameters include at least the elastic modulus, Poisson's ratio, compressive strength, and tensile strength of the coal seam and the roof and floor rocks.

3. The method for detailed evaluation of the protective layer mining effect according to claim 1, characterized in that, The mining prediction model, constructed based on the spatial location relationship, the original geological parameters, the gas parameters, and the mechanical parameters, specifically includes: The spatial position relationship, the original geological parameters, the gas parameters, and the mechanical parameters are denoised to obtain the target spatial position relationship, the target original geological parameters, the target gas parameters, and the target mechanical parameters. A three-dimensional geological model of the protective layer and the protected coal seam is constructed based on the spatial relationship of the target. Numerical simulation of the three-dimensional geological model is performed using the original geological parameters of the target, the gas parameters of the target, and the mechanical parameters of the target to obtain a mining prediction model.

4. The method for detailed evaluation of the protective layer mining effect according to claim 1, characterized in that, The output of stress distribution prediction data, gas migration prediction data, and permeability prediction data through the mining prediction model specifically includes: The mining prediction model is used to simulate the mining of the protective layer, and outputs the target pressure relief range, the predicted stress value of each monitoring point, the predicted gas content, the predicted reduction value of gas content, the expected improvement multiple of permeability, and the predicted permeability value. The target depressurization range and the predicted stress values ​​at each monitoring point are determined as stress distribution prediction data; The predicted gas content and the predicted decrease in gas content are determined as gas migration prediction data; The expected improvement factor in breathability and the predicted breathability value are defined as breathability prediction data.

5. The method for detailed evaluation of the protective layer mining effect according to claim 4, characterized in that, The dynamic monitoring of the mining of the protective layer to obtain real-time stress distribution data, real-time gas migration data, and real-time permeability data specifically includes: During the mining process, real-time stress data, real-time gas content, and real-time air permeability values ​​are collected at each monitoring point. After the protective layer is mined out, target stress data, target gas content, and target air permeability values ​​are collected at each monitoring point. The real-time stress data and target stress data of each monitoring point are determined as real-time stress distribution data; The real-time gas content and target gas content at each monitoring point are defined as real-time gas migration data. The real-time air permeability values ​​and target air permeability values ​​at each monitoring point are defined as real-time air permeability data.

6. The method for detailed evaluation of the protective layer mining effect according to claim 1, characterized in that, The method further includes: Risk monitoring is conducted on the mining of the protective layer to obtain real-time surface deformation data and real-time fracture data of the protected coal seam; wherein, the real-time surface deformation data includes at least the cumulative surface rise and fall, cumulative surface displacement, surface rise and fall rate, and surface displacement rate, and the real-time fracture data includes at least the fracture length, fracture width, and fracture depth. Risk monitoring is performed on the real-time surface deformation data and the real-time crack data based on safety thresholds, and risk monitoring information is output.

7. The method for detailed evaluation of the protective layer mining effect according to claim 6, characterized in that, The risk monitoring of the real-time surface deformation data and the real-time crack data based on a safety threshold, and the output of risk monitoring information, specifically includes: If the cumulative surface elevation or subsidence exceeds a preset elevation or subsidence threshold or the surface elevation or subsidence rate exceeds a preset elevation or subsidence rate, then the risk of surface elevation or subsidence will be added to the risk monitoring information. If the cumulative surface displacement is greater than a preset cumulative displacement or the surface displacement rate is greater than a preset displacement rate, then the risk of surface displacement is added to the risk monitoring information. If the crack length is greater than a preset length, the crack width is greater than a preset width, or the crack depth is greater than a preset depth, then the risk of surface cracks is added to the risk monitoring information.

8. The method for detailed evaluation of the protective layer mining effect according to claim 5, characterized in that, The analysis of mining effectiveness based on the stress distribution prediction data, gas migration prediction data, permeability prediction data, real-time stress distribution data, real-time gas migration data, and real-time permeability data yields a mining effectiveness evaluation result, specifically including: Based on the real-time stress data and target stress data at each monitoring point, the current pressure relief range is determined; Based on the real-time gas content and target gas content at each monitoring point, the current gas content reduction value is determined; Based on the real-time air permeability values ​​and target air permeability values ​​at each monitoring point, the expected improvement factor for current air permeability is determined. By comparing the target depressurization range and the predicted stress values ​​at each monitoring point with the current depressurization range and the target stress data at each monitoring point, stress effect evaluation information is obtained. The predicted gas content and the predicted reduction value of gas content at each monitoring point are compared with the target gas content and the current reduction value of gas content at each monitoring point to obtain gas content evaluation information. The expected improvement factor of air permeability at each monitoring point and the predicted air permeability value are compared with the target air permeability value at each monitoring point and the current expected improvement factor of air permeability to obtain air permeability evaluation information. The stress effect evaluation information, the gas content evaluation information, and the permeability evaluation information are determined as the mining effect evaluation results.

9. A system for detailed assessment of the effectiveness of protective layer mining, characterized in that, include: Processor and memory; The memory stores programs or instructions that can run on the processor, which, when executed by the processor, implement the steps of the method for detailed investigation of the protective layer mining effect as described in any one of claims 1 to 8.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the method for detailed investigation of the protective layer mining effect as described in any one of claims 1 to 8.

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