A coal mining face coal seam roof water gushing flow prediction method and system

Abstract

The coal mining face coal seam water inrush flow prediction method and system first clearly proposes and utilizes the physical law that the product of the specific aquifer water inrush flow and the fixed point water pressure is a constant in the stable water inrush state, simplifies the complex water inrush flow prediction problem to the interpretation of the water level data which is easy to monitor, and realizes the real-time and rapid inversion of the water inrush flow. The background parameters are obtained through the early water drainage test, and the dynamic correction is combined with the mining distance, the dynamic characteristics of the mine production are fully considered, and the accuracy and reliability of the inversion result are significantly improved, and the actual mine production is more fitted. The contribution of each main aquifer to the mine water inrush can be accurately quantified, which provides important scientific basis for the mine drainage system capacity determination, the accurate judgment of water disaster risk, the optimization of water protection coal mining scheme and the protection of mine water resources, and is suitable for wide use and popularization in the field of coal mine water prevention and control technology.

Description

Technical Field

[0001] This invention belongs to the field of coal mine water control technology, specifically relating to a method and system for predicting the flow rate of water inflow from the top of the coal seam in a coal mining face. Background Technology

[0002] After coal seam mining, the overlying strata form a "mining-induced water-conducting fracture zone." Once this fracture zone connects multiple overlying aquifers (such as sandstone fracture aquifers, limestone karst aquifers, etc.), groundwater from these aquifers will flow into the underground mining space through this channel, forming mine water inrush. If the water inrush flow is too large, it will not only seriously affect the safe production of coal mines, but may also trigger catastrophic accidents such as water inrush and mine flooding, and cause irreversible damage to the regional groundwater ecological environment. Therefore, accurately predicting and monitoring the mine water inrush flow, especially the water inrush flow from specific key aquifers, is a core aspect of coal mine water hazard prevention and control.

[0003] In existing technologies, the main methods for predicting mine water inflow include:

[0004] Large-well method: This method generalizes the mine pit as a large well and uses steady-flow or unsteady-flow theory for calculation. However, its parameter values ​​are greatly affected by human factors and it is difficult to reflect the dynamic changes in the aquifer structure under the influence of mining, resulting in generally low prediction accuracy.

[0005] Numerical simulation method: This method involves establishing a hydrogeological model of the study area and using computers for numerical simulation. Although theoretically rigorous, this method is complex to establish, requires a large number of geological and hydrogeological parameters, and is costly and time-consuming, making it difficult to meet the needs of mine production for rapid and dynamic assessment of water inflow.

[0006] Hydrochemical analysis: By analyzing the hydrochemical characteristics of mine water and various aquifers, the source of water inflow can be qualitatively or semi-quantitatively determined, but it is difficult to accurately quantify the specific water inflow rate of each aquifer.

[0007] In summary, existing technologies lack a practical method for rapidly, dynamically, and quantitatively inverting the water inflow from a specific aquifer into the mine using readily available monitoring data during the mining process. Summary of the Invention

[0008] The purpose of this invention is to provide a method and system for predicting the flow rate of water inflow from the top of a coal seam in a coal mining face, so as to solve the problems of low prediction efficiency and insufficient accuracy of existing water inflow prediction methods.

[0009] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A method for predicting the flow rate of top water inflow in a coal seam at a coal mining face includes the following steps: Step 1: Determine the target area for water inflow from the top of the coal seam in the coal mining face. Based on the hydrogeological data of the target area, determine the elevation of the bottom plate of the coal seam to be mined, the elevation of the bottom plate of the target water-bearing aquifer, and the boundary of the water-bearing aquifer, and delineate the target water-bearing aquifer. Step 2: Establish the initial characteristic coefficient calculation model; The initial characteristic coefficient calculation model is as follows: K = ΔP + ½ρ(Q / )² + ρgΔh Where K represents the initial characteristic coefficient; ΔP represents water pressure; Q represents the liquid-mass flow rate; This indicates the cross-sectional area of ​​the target aquifer revealed by the pumping hole; ρ represents the density of the liquid mass; g represents gravitational acceleration; Δh represents the height difference between the bottom elevation of the mined coal seam and the bottom elevation of the target water-bearing aquifer; Step 3: Set up at least one hydrological observation well and a pumping well in the target water-filled aquifer, and install hydrological detection devices in the hydrological observation well. Step 4: Conduct multi-depth steady-flow pumping and discharge tests through hydrological observation wells and pumping wells to obtain multiple sets of static parameters of the target water-filled aquifer; Among them, static parameters include the stable water pressure in the hydrological observation well and the stable inflow velocity in the pumping well; Substitute each set of static parameters into the initial feature coefficient calculation model to calculate multiple initial feature coefficients K; The characteristic coefficient K0 is obtained by averaging multiple initial characteristic coefficients K or by performing regression analysis.

[0010] Step 5: Establish a calculation model for predicting water inflow. Coal mining operations are carried out in the target area, and during the coal mining operation, the dynamic parameters of the target water-bearing aquifer are monitored at fixed intervals. Among them, dynamic parameters include the working face advance length and the thickness of the water-bearing aquifer exposed during the coal mining operation. Substitute the dynamic parameters and characteristic coefficient K0 into the water inflow prediction calculation model to calculate the predicted value of the water inflow from the top of the coal seam in the coal mining face. Repeat the above steps until the coal mining operation is completed.

[0011] The present invention also has the following features: Furthermore, in step 3, monitoring points are arranged inside the hydrological observation wells; pressure sensors are installed at the monitoring points.

[0012] Furthermore, step 4 specifically includes the following sub-steps: Step 41: Preset multiple stable inflow values ​​for pumping and draining tests, and conduct stable pumping and draining tests in order of increasing stable inflow values ​​to obtain multiple stable water pressures that correspond one-to-one with the stable inflow values. Step 42: For each steady inflow rate and its corresponding steady water pressure, calculate the corresponding steady inflow velocity using the following formula: v = Q /

[0013] Where v represents the steady-state inflow velocity; This indicates the cross-sectional area of ​​the target aquifer revealed by the pumping hole; in, =2πr M; π represents the mathematical constant pi. r represents the diameter of the pumping hole; M represents the thickness of the aquifer revealed by the pumping hole; Step 43: Substitute each stable inflow rate value and its corresponding stable inflow velocity into the characteristic coefficient calculation model to obtain multiple initial characteristic coefficients K; Step 44: Take the average value of multiple initial characteristic coefficients K or perform regression analysis to obtain the characteristic coefficient K0.

[0014] Furthermore, in step 41, when conducting a stable pumping test in the hydrological observation well, the stable inflow rate in the pumping well is monitored using the flow meter method or the weir measurement method.

[0015] Furthermore, the calculation model for predicting the inflow rate in step 5 is as follows: Q = A

[0016] Where Q represents the inflow rate; A represents the equivalent cross-sectional area of ​​the water passage; This indicates the density of the liquid, 1000 kg / m³. Represents the characteristic coefficients; This indicates the water pressure at the hydrological observation well during coal mining operations; Represents gravitational acceleration; This indicates the height difference between the bottom elevation of the mined coal seam and the bottom elevation of the target water-bearing aquifer.

[0017] Furthermore, the equivalent cross-sectional area A of the water passage is calculated using the following formula: A = Length of the working face advancing in the coal mining operation × Thickness of the aquifer exposed by mining.

[0018] Furthermore, in step 3, pumping holes are specifically set up within a 300m radius around the hydrological observation well.

[0019] A coal seam top inrush water flow prediction system for coal mining faces includes a data acquisition module, a data processing and inversion module, and an early warning module; The data acquisition module is used for: Collect static parameters of the target water-filled aquifer; Dynamic parameters of the target water-bearing aquifer are collected during coal mining operations; The data processing and inversion module is used for: Calculate the characteristic coefficients based on the static parameters; Calculate the corresponding inflow rate based on characteristic parameters and dynamic parameters; The aforementioned early warning module is used to set a water inflow threshold, and to issue an early warning when the water inflow obtained by the data processing and inversion module exceeds the threshold.

[0020] Compared with the prior art, the present invention has the following technical effects: The coal seam top water inflow prediction method and system of the present invention is the first to clearly propose and utilize the physical law that "under stable water inflow conditions, the product of the water inflow of a specific aquifer and the water pressure at a fixed point is a constant value", which simplifies the complex water inflow prediction problem to the interpretation of easily monitored water level data, and realizes real-time and rapid inversion of water inflow.

[0021] By obtaining baseline parameters through preliminary water drainage tests and combining them with dynamic correction based on mining distance, the dynamic characteristics of mine production are fully considered, significantly improving the accuracy and reliability of the inversion results and making them more consistent with actual mine production.

[0022] There is no need to build complex numerical models, nor to conduct direct flow measurements at underground water inflow points, which are difficult to implement. It only requires the use of conventional hydrogeological exploration boreholes and automated water level monitoring equipment. The method is simple, low-cost, and can be easily promoted and applied in major mining areas.

[0023] It can accurately quantify the contribution of each major aquifer to mine water inflow, providing crucial scientific basis for mine drainage system capacity verification, accurate assessment of water hazard risk, optimization of water-conserving coal mining schemes, and protection of water resources in mining areas. It is suitable for widespread use and promotion in the field of coal mine water control technology. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of a conceptual model in the embodiment of mining to connect water-conducting fractures to form aquifers and generate mine water inflow. Detailed Implementation

[0025] It should be noted that, unless otherwise specified, all components in this invention are known in the prior art. For example, the pressure sensor uses a commonly used pressure sensor.

[0026] The following are specific embodiments of the present invention. It should be noted that the present invention is not limited to the following specific embodiments. All equivalent modifications made based on the technical solutions of this application fall within the protection scope of the present invention.

[0027] A method for predicting the flow rate of top water inflow in a coal seam at a coal mining face includes the following steps: Step 1: Determine the target area for water inflow from the top of the coal seam in the coal mining face. Based on the hydrogeological data of the target area, determine the elevation of the bottom plate of the coal seam to be mined, the elevation of the bottom plate of the target water-bearing aquifer, and the boundary of the water-bearing aquifer, and delineate the target water-bearing aquifer. This water-filled aquifer is the main reason that threatens the safety of production at the working face.

[0028] Step 2: Establish the initial characteristic coefficient calculation model; The initial characteristic coefficient calculation model is as follows: K = ΔP + ½ρ(Q / )² + ρgΔh Where K represents the initial characteristic coefficient; ΔP represents water pressure; Q represents the liquid-mass flow rate; This indicates the cross-sectional area of ​​the target aquifer revealed by the pumping hole; ρ represents the density of the liquid mass; g represents gravitational acceleration; Δh represents the height difference between the bottom elevation of the mined coal seam and the bottom elevation of the target water-bearing aquifer; Step 3: Set up at least one hydrological observation well and a pumping well in the target water-filled aquifer, and install hydrological detection devices in the hydrological observation well. As an optional implementation, in step 3, at least one hydrological observation hole and a pumping hole are set in the target water-filled aquifer. It should be noted that the pumping hole here is just a specific name. In actual use, it can be used as both a pumping hole and a draining hole to realize the functions of pumping and draining water.

[0029] Hydrological monitoring devices are installed inside the hydrological observation wells. As a preferred option, multiple monitoring points are set up inside the hydrological monitoring borehole, and a pressure sensor is installed at each monitoring point.

[0030] Those skilled in the art can conventionally select and arrange the transmission system. This embodiment provides a specific setup method, including a data acquisition host and a remote data transmission unit based on GPRS, 4G or 5G networks, to realize the automatic transmission and storage of monitoring data to the cloud or server management platform.

[0031] Specifically, pumping holes are installed within a 300m radius around the hydrological observation wells.

[0032] Step 4: Conduct multi-depth steady-flow pumping and discharge tests through hydrological observation wells and pumping wells to obtain multiple sets of static parameters of the target water-filled aquifer; Among them, static parameters include the stable water pressure in the hydrological observation well and the stable inflow velocity in the pumping well; Substitute each set of static parameters into the initial feature coefficient calculation model to calculate multiple initial feature coefficients K; The characteristic coefficient K0 is obtained by averaging multiple initial characteristic coefficients K or by performing regression analysis.

[0033] Step 5: Establish a calculation model for predicting water inflow. The following provides a further explanation of the inflow rate prediction calculation model constructed in this embodiment. The construction idea of ​​this embodiment is as follows: The coal face and the main water-bearing aquifer are considered as an energy exchange system. During the coal mining process, the mining directly exposes the aquifer, or the aquifer is connected through geological structures and mining-induced water-conducting fractures. This causes groundwater from the aquifer to enter the mine mining system directly or indirectly, thus forming a new groundwater discharge point, namely, mine water inrush. Mine water inrush is the external manifestation of groundwater leakage from the aquifer. Groundwater leakage from the aquifer is accompanied by a response of the aquifer water level (pressure). This process approximately follows Bernoulli's law of conservation of energy. The sum of the pressure energy, kinetic energy, and gravitational potential energy of a unit volume of fluid is a constant. That is, the energy (the sum of pressure potential energy, kinetic energy, and gravitational potential energy) of the groundwater system formed in a certain aquifer is relatively constant (the following formula).

[0034] ΔP + ½ρv² + ρgΔh = K (constant) In other words, pressure potential energy, kinetic energy, and gravitational potential energy can be interconverted, but the total energy remains constant. Bernoulli's equation can be seen as the manifestation of the law of conservation of energy in fluid motion. It describes the relationship between fluid pressure, velocity, and height under ideal fluid and steady flow conditions; that is, the higher the pressure, the higher the relative position, and the faster the velocity, the greater the energy contained.

[0035] In engineering applications, flow rates are more commonly measured and used. Their relationship with flow velocity v and cross-sectional area A is v = Q / A, which therefore follows Bernoulli's energy conservation equation: ΔP + ½ρ(Q / A)² + ρgΔh =K Based on this, the inflow rate Q = A is derived.

[0036] In the case of mine water inflow caused by coal mining, the groundwater density ρ, gravitational acceleration g, and K are all constants and are known at a certain time point on the water-passing cross section A (the product of the perimeter of the mining area and the thickness of the water-bearing aquifer exposed by the mining). The reference plane height Δh of the groundwater at the contact zone between the aquifer and the mining area is also known (at the contact zone between the aquifer and the mining area, the groundwater level drops to the bottom of the aquifer, the location of which is known before mining, and h is the distance between the bottom of the coal seam and the bottom of the water-bearing aquifer). Therefore, the change in water inflow Q is only related to the dynamically expanding water-passing cross section A and the change in the static pressure P of the fluid at a certain point. It can be seen from the above formula that the larger the water-passing cross section, the lower the water inflow location Δh, and the lower the pressure ΔP at the outlet, the greater the amount of water lost by the system.

[0037] Next, coal mining operations were carried out in the target area. During the coal mining operation, the dynamic parameters of the target water-bearing aquifer were monitored at fixed intervals. Among them, dynamic parameters include the water pressure of hydrological observation wells during coal mining operations, the advance length of the working face during coal mining operations, and the thickness of the water-bearing aquifer exposed during mining. Based on the dynamic parameters and characteristic coefficient K0, the predicted value of the water inflow at the top of the coal seam in the coal mining face is calculated using the water inflow prediction calculation model. Repeat the above steps until the coal mining operation is completed.

[0038] More specifically, step 4 includes the following sub-steps: Step 4 specifically includes the following sub-steps: Step 41: Preset multiple stable inflow values ​​for pumping and releasing tests, and conduct stable pumping and releasing tests in the hydrological observation well in order of stable inflow values ​​from small to large, to obtain multiple stable water pressures that correspond one-to-one with the stable inflow values. Both the current meter method and the weir method are well-known methods in this field. The current meter method involves selecting a section of the drainage ditch or canal with a regular cross-section and smooth water flow, and using a current meter to measure the velocity at multiple measuring points within the cross-section. Combined with the measured water depth and width, the average velocity and cross-sectional area are calculated to determine the flow rate. This method is suitable for scenarios with relatively stable water flow and large flow rates, and the measurement results are relatively reliable. However, the operation is relatively complex, requiring the deployment of measuring lines and multiple velocity measurements on-site.

[0039] The weir method involves installing a weir with a specific shaped notch (such as a triangle or rectangle) over a channel through which the inrush water flows. This allows the water to flow steadily out of the weir opening, forming a free flow. By accurately measuring the head height from the water surface upstream of the weir to the bottom of the weir opening, and then calculating the inrush flow rate based on empirical relationships corresponding to different weir types, this method is simple in structure, easy to operate, and particularly suitable for situations where the initial inrush flow rate is small. It offers high measurement accuracy and is a commonly used method for obtaining initial inrush flow rate data in underground coal mines.

[0040] Step 42: For each steady inflow rate and its corresponding steady water pressure, calculate the corresponding steady inflow velocity using the following formula: v = Q /

[0041] Where v represents the steady-state inflow velocity; This indicates the cross-sectional area of ​​the target aquifer revealed by the pumping hole; in, =2πr M; π represents the mathematical constant pi. r represents the diameter of the pumping hole; M represents the thickness of the aquifer revealed by the pumping hole; Step 43: Substitute each stable inflow rate value and its corresponding stable inflow velocity into the characteristic coefficient calculation model to obtain multiple initial characteristic coefficients K corresponding to the monitoring point. Step 44: Take the average value of multiple initial characteristic coefficients K or perform regression analysis to obtain the characteristic coefficient K0.

[0042] As an optional implementation method, a manually controlled steady flow pumping (draining) volume (no less than 3 steady flow rates) is designed, and steady flow pumping (draining) tests are carried out in sequence from small to large, and the steady water pressure corresponding to the steady flow rate is measured. The corresponding design process involves iterating through all steady-flow pumping (draining) volumes to obtain multiple initial characteristic coefficients K. In this step, specifically, regression analysis is a statistical method used to process multiple sets of experimental data and establish quantitative relationships between variables. In this embodiment, multiple sets of data on different pumping flows and their corresponding stable water pressures were obtained based on multiple water pressures and inflow rates. The role of regression analysis is to fit these discrete experimental data points to determine the characteristic coefficient K value that best reflects the energy state of the aquifer system.

[0043] In practice, the flow rate and corresponding water pressure obtained from each set of experiments are used as a set of data points. Regression algorithms such as the least squares method are used to fit these data points, ultimately yielding a K value that comprehensively reflects the entire pumping experiment process. This effectively eliminates the influence of outlier data points caused by measurement errors or local disturbances, and fully utilizes the system information contained in multiple sets of experimental data. This makes the calibrated characteristic coefficient K more representative and stable, providing a more reliable model foundation for subsequent experiments.

[0044] Specifically, in step 42, the equivalent cross-sectional area of ​​the water passage is calculated using the following formula: A = Length of the working face advancing in the coal mining operation × Thickness of the aquifer exposed by mining.

[0045] A coal seam top inflow water flow prediction system corresponding to the above-mentioned method for predicting coal seam top inflow water flow in a coal mining face includes a data acquisition module, a data processing and inversion module, and an early warning module. The data acquisition module is used for: When the water-conducting fracture zone communicates with the target water-filled aquifer, the static parameters of the target water-filled aquifer are collected. Dynamic parameters of the target water-bearing aquifer are collected during coal mining operations; The data processing and inversion module is used for: Calculate the characteristic coefficients based on the static parameters; Calculate the corresponding inflow rate based on characteristic parameters and dynamic parameters; The early warning module is used to set the inflow rate threshold and issue an early warning when the inflow rate obtained by the data processing and inversion module exceeds the threshold.

[0046] The following is a more specific implementation method, with the specific steps as follows: Step S10: Geological exploration and determination of the target aquifer.

[0047] Before the N2105 working face in the case study mine was mined, a comprehensive analysis of geological exploration data determined that the K8 sandstone aquifer, located approximately 45m above the immediate roof, was the main water source. Using drilling and geophysical data, the average thickness of this aquifer was found to be 12m, the floor elevation to be +325m, and the initial hydrostatic pressure to be 2.5 MPa. The floor elevation of the coal seam at the working face was +280m.

[0048] Step S20: Establish a theoretical model for predicting water inrush.

[0049] The goaf of the N2105 working face and the K8 sandstone aquifer are considered as a connected hydraulic system. Based on Bernoulli's law of conservation of energy, the following theoretical model for predicting water inrush is established: K = ΔP + ½ρ(Q / A)² + ρgΔh This leads to the derivation of the inflow inversion model: Q = A

[0050] Where ρ is taken as 1000 kg / m³, and g is taken as 9.8 m / s².

[0051] Step S30: Deploy the monitoring system and collect initial data.

[0052] A long-range hydrological observation borehole was constructed on the surface in the middle of the N2105 working face, reaching the bottom of the K8 aquifer. A pressure sensor was inserted and connected to an automatic data acquisition and transmission device to establish a real-time monitoring system. When the working face advanced 80m, microseismic monitoring showed that the water-conducting fracture zone had developed to the bottom of the K8 aquifer, and continuous water inflow (approximately 5 m³ / h) occurred in the lower roadway of the goaf. Initial calibration was then performed. Record the water pressure at hole ZK01 at this time as P0 = 2.38 MPa.

[0053] The initial water inflow rate Q0 = 0.00139 m³ / s (5 m³ / h) was accurately measured in the lower roadway of the goaf using the weir measurement method.

[0054] At this point, the cross-section A0 = working face advance length 80m × aquifer exposed thickness 12m = 960 m².

[0055] The elevation difference Δh = 280m - 325m = -45m.

[0056] Step S40: Determine the characteristic coefficient K0.

[0057] Substitute P0, Q0, A0, and Δh obtained in step S30 into the above equation: K0 = 2.38e6 + 0.5 1000 (0.00139 / 960)² + 1000 9.8 (-45) The calculated value is K0 ≈ 1.93e6Pa. This value is the characteristic coefficient of this hydrogeological unit at the location of the observation well.

[0058] Step S50: Construct a real-time inversion system for water inflow.

[0059] In subsequent mining operations, the following cycle is executed: Real-time data input: Obtain the real-time water pressure P(t) of borehole ZK01; obtain the real-time advance distance from the production scheduling system, and calculate the current water passage section A(t) = advance distance × 12m; Δh is fixed at -45m.

[0060] Water inflow calculation: Substitute Pmeasured(t), A(t), Δh, and K0 into the above formula to calculate and predict the water inflow in real time.

[0061] Example: When the working face advanced to 200m, P was monitored. 测 =2.15 MPa, A=2400 m².

[0062] Substituting into the calculation, we get Q 预 ≈ 0.025 m³ / s (90 m³ / h).

[0063] Step S60: Model validation and fine-tuning (optional).

[0064] In this step, Qactual is compared with the predicted inflow Qpredicted(t) calculated in the same step S6. If the error exceeds the preset range, the feature coefficient K will be recalibrated using the latest measured data to update the model parameters.

[0065] Specifically, at a depth of 200m, the measured inflow using the weir method was 85 m³ / h (0.0236 m³ / s). The relative error between the predicted value of 90 m³ / h and the measured value of 85 m³ / h was approximately 5.9%, meeting the accuracy requirements. If the error increases later, new measured data sets can be added, and the K value can be recalculated and updated.

[0066] Step S70: Warning of water inrush risk.

[0067] Based on the drainage system capacity development during the mining process at the N2105 working face, an early warning threshold for the increase in water inflow ΔQ is set. 阈 = 50 m³ / h (0.0556 m³ / s), continuously compare the increase ΔQ_predict(t) between the current monitoring Q_predict(t) and the previous monitoring Q_predict(t-1), ΔQ_predict(t-1) and ΔQ_predict(t-1). 阈 .

[0068] When ΔQprevious(t-1)<50 m³ / h, the display shows "normal".

[0069] When 50 m³ / h ≤ ΔQprevious(t-1) < 100 m³ / h, a Level 1 warning is issued: "Blue Warning", indicating that the drainage system capacity needs to be increased.

[0070] When ΔQprevious(t-1) ≥ 100m³ / h, a Level II warning is issued: "Red Warning", and an alarm message is automatically sent to the person in charge of water prevention and control, prompting that measures such as increasing temporary drainage capacity, controlling mining speed, or implementing advance dredging must be taken.

[0071] Compared with existing technologies, the system in this embodiment deeply integrates theoretical models, real-time monitoring and information technology, realizing the automation, intelligence and dynamism of water inflow prediction, significantly improving the initiative and scientific nature of mine water hazard prevention and control. It clearly proposes and utilizes the physical law that "under stable water inflow conditions, the product of water inflow of a specific aquifer and water pressure at a fixed point is a constant value", simplifying the complex water inflow prediction problem into the interpretation of easily monitored water level data, and realizing real-time and rapid inversion of water inflow.

[0072] By obtaining baseline parameters through preliminary water drainage tests and combining them with dynamic correction based on mining distance, the dynamic characteristics of mine production are fully considered, significantly improving the accuracy and reliability of the inversion results and making them more consistent with actual mine production.

[0073] No complex numerical models are required, nor is it necessary to perform direct flow measurements at underground water inflow points, which are difficult to implement. The method is simple, low-cost, and easily applicable in major mining areas, utilizing only conventional hydrogeological survey drilling and automated water level monitoring equipment.

[0074] It can accurately quantify the contribution of each major aquifer to mine water inflow, providing crucial scientific basis for verifying the capacity of mine drainage systems, accurately assessing water hazard risks, optimizing water-conserving coal mining schemes, and protecting water resources in mining areas.

Claims

1. A method for predicting the flow rate of water inflow from the top of a coal seam in a coal mining face, characterized in that, Includes the following steps: Step 1: Determine the target area for water inflow from the top of the coal seam in the coal mining face. Based on the hydrogeological data of the target area, determine the elevation of the bottom plate of the coal seam to be mined, the elevation of the bottom plate of the target water-bearing aquifer, and the boundary of the water-bearing aquifer, and delineate the target water-bearing aquifer. Step 2: Establish the initial characteristic coefficient calculation model; The initial characteristic coefficient calculation model is as follows: K=ΔP + ½ρ(Q / )² + ρgΔh Where K represents the initial characteristic coefficient; ΔP represents water pressure; Q represents the liquid-mass flow rate; This indicates the cross-sectional area of ​​the target aquifer revealed by the pumping hole; ρ represents the density of the liquid mass; g represents gravitational acceleration; Δh represents the height difference between the bottom elevation of the mined coal seam and the bottom elevation of the target water-bearing aquifer; Step 3: Set up at least one hydrological observation well and a pumping well in the target water-filled aquifer, and install hydrological detection devices in the hydrological observation well. Step 4: Conduct multi-depth steady-flow pumping and discharge tests through hydrological observation wells and pumping wells to obtain multiple sets of static parameters of the target water-filled aquifer; Among them, static parameters include the stable water pressure in the hydrological observation well and the stable inflow velocity in the pumping well; Substitute each set of static parameters into the initial feature coefficient calculation model to calculate multiple initial feature coefficients K; The characteristic coefficient K0 is obtained by averaging multiple initial characteristic coefficients K or by performing regression analysis. Step 5: Establish a calculation model for predicting water inflow. Coal mining operations are carried out in the target area, and during the coal mining operation, the dynamic parameters of the target water-bearing aquifer are monitored at fixed intervals. Among them, dynamic parameters include the working face advance length and the thickness of the water-bearing aquifer exposed during the coal mining operation. Substitute the dynamic parameters and characteristic coefficient K0 into the water inflow prediction calculation model to calculate the predicted value of the water inflow from the top of the coal seam in the coal mining face. Repeat the above steps until the coal mining operation is completed.

2. The method for predicting the flow rate of water inflow from the top of the coal seam in a coal mining face as described in claim 1, characterized in that, In step 3, monitoring points are set up inside the hydrological observation wells; pressure sensors are installed at the monitoring points.

3. The method for predicting the flow rate of water inflow from the top of the coal seam in a coal mining face as described in claim 2, characterized in that, Step 4 specifically includes the following sub-steps: Step 41: Preset multiple stable inflow values ​​for pumping and draining tests, and conduct stable pumping and draining tests in order of increasing stable inflow values ​​to obtain multiple stable water pressures that correspond one-to-one with the stable inflow values. Step 42: For each steady inflow rate and its corresponding steady water pressure, calculate the corresponding steady inflow velocity using the following formula: v = Q / Where v represents the steady-state inflow velocity; This indicates the cross-sectional area of ​​the target aquifer revealed by the pumping hole; in, =2πr M; π represents the mathematical constant pi. r represents the diameter of the pumping hole; M represents the thickness of the aquifer revealed by the pumping hole; Step 43: Substitute each stable inflow rate value and its corresponding stable inflow velocity into the characteristic coefficient calculation model to obtain multiple initial characteristic coefficients K; Step 44: Take the average value of multiple initial characteristic coefficients K or perform regression analysis to obtain the characteristic coefficient K0.

4. The method for predicting the flow rate of water inflow from the top of the coal seam in a coal mining face as described in claim 3, characterized in that, In step 41, when conducting a stable pumping test in the hydrological observation well, the stable inflow rate in the pumping well is monitored using the flow velocity meter method or the weir measurement method.

5. The method for predicting the flow rate of water inflow from the top of the coal seam in a coal mining face as described in claim 3, characterized in that, The calculation model for predicting the inflow rate in step 5 is as follows: Q = A Where Q represents the inflow rate; A represents the equivalent cross-sectional area of ​​the water passage; This indicates the density of the liquid, 1000 kg / m³. Represents the characteristic coefficients; This indicates the water pressure at the hydrological observation well during coal mining operations; Represents gravitational acceleration; This indicates the height difference between the bottom elevation of the mined coal seam and the bottom elevation of the target water-bearing aquifer.

6. The method for predicting the flow rate of water inflow from the top of the coal seam in a coal mining face as described in claim 5, characterized in that, Calculate the equivalent cross-sectional area A of the water passage using the following formula: A = Perimeter of the working face advancing in the coal mining operation × Thickness of the aquifer exposed by mining; Specifically, the perimeter of the working face advancement range in coal mining operations refers to the perimeter of the rectangular goaf formed by coal mining, which is twice the sum of the working face advancement length and the width of the coal mining working face.

7. The method for predicting the flow rate of water inflow from the top of the coal seam in a coal mining face as described in claim 6, characterized in that, In step 3, pumping holes are set up within a 300m radius around the hydrological observation well.

8. A coal seam top inflow water flow prediction system for a coal mining face, corresponding to the method for predicting coal seam top inflow water flow in claim 5, characterized in that, It includes a data acquisition module, a data processing and inversion module, and an early warning module; The data acquisition module is used for: Collect static parameters of the target water-filled aquifer; Dynamic parameters of the target water-bearing aquifer are collected during coal mining operations; The data processing and inversion module is used for: Calculate the characteristic coefficients based on the static parameters; Calculate the corresponding inflow rate based on characteristic parameters and dynamic parameters; The aforementioned early warning module is used to set a water inflow threshold, and to issue an early warning when the water inflow obtained by the data processing and inversion module exceeds the threshold.

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