Coal seam gas content measuring method and system fused with advanced gas loss amount correction

Abstract

The invention discloses a coal seam gas content measuring method and system fused with advanced gas loss amount correction, and relates to the technical field of gas content determination.The method comprises the steps that stage division is conducted on a coal seam drilling period, a gas loss mechanism and key monitoring parameters are defined, and a stage element matrix is determined; acquiring a coal seam structure of a pre-drilling area, setting a baseline coal seam state, constructing a numerical simulator according to lightweight structural characteristics, executing dynamic simulation based on a stage element matrix, and determining simulation flow data; and deploying a measuring and calculating unit, and determining gas content data according to the simulated flow data. The technical problems that in the prior art, when the coal seam gas content is measured, the advanced gas loss amount is not considered sufficiently, and the measurement precision is low in different coal seam states are solved, and the technical effects that the coal seam gas content is accurately measured, and the gas assignment amount evaluation precision of a specific coal seam is improved are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gas content determination, in particular to a coal seam gas content determination method and system fusing ahead gas loss amount correction. BACKGROUND

[0002] With the increasingly serious global climate change problem, effectively controlling greenhouse gas emissions has become the focus. In the practice of multi-regional coordinated emission reduction, the existing technology has obvious shortcomings. The traditional management mode often separates each region, lacks a unified coordination mechanism, resulting in that regional emission data cannot be shared, and it is difficult to form a synergistic effect; the analysis of emission sources only stays at the extensive level, and cannot decouple and empower the emission branches based on emission factors, making the management and control lack precision; at the same time, the management decision relies on experience judgment, lacks a scientific decision model and dynamic adjustment mechanism, resulting in low management efficiency, difficult to adapt to complex and changeable emission scenarios, and unable to meet the actual needs of multi-regional greenhouse gas coordinated emission reduction.

[0003] The existing technology has the technical problems of insufficient consideration of the ahead gas loss amount when determining the coal seam gas content, and low determination accuracy in different coal seam states. SUMMARY

[0004] The present application provides a coal seam gas content determination method and system fusing ahead gas loss amount correction, which is used to solve the technical problems of insufficient consideration of the ahead gas loss amount when determining the coal seam gas content in the prior art, and low determination accuracy in different coal seam states.

[0005] In view of the above problems, the present application provides a coal seam gas content determination method and system fusing ahead gas loss amount correction.

[0006] In a first aspect, the present application provides a coal seam gas content determination method fusing ahead gas loss amount correction, which comprises:

[0007] Divide the coal seam drilling period into stages, define the gas loss mechanism and the key monitoring parameters, and determine the stage element matrix; obtain the coal seam structure of the pre-drilling area, construct a numerical simulator with lightweight structure characteristics by setting a baseline coal seam state, perform dynamics simulation based on the stage element matrix, and determine the simulation flow data, wherein the baseline coal seam state is any one of hard and complete coal seam, soft and broken coal seam, and high gas coal seam; deploy a measurement unit, determine the gas content data according to the simulation flow data, wherein the first-order measurement is performed with the stage ahead loss amount and the later desorption superposition, and the second-order compensation is performed based on the migration coefficients of the baseline coal seam state and the non-baseline coal seam state.

[0008] In a second aspect of the present application, a coal seam gas content determination system is provided, which comprises:

[0009] A stage element matrix determination module is configured to divide a coal seam drilling period into stages, define a gas loss mechanism and key monitoring parameters, and determine a stage element matrix. A simulation flow data determination module is configured to obtain a coal seam structure of a pre-drilling area, set a baseline coal seam state, construct a numerical simulator based on lightweight structure characteristics, perform a dynamics simulation based on the stage element matrix, and determine simulation flow data. The baseline coal seam state is any one of a hard and complete coal seam, a soft and broken coal seam, and a high-gas coal seam. A gas content data determination module is configured to deploy a measurement unit, determine gas content data based on the simulation flow data, perform a first-order measurement based on a stage loss and a later desorption, and perform a second-order compensation based on a migration coefficient of the baseline coal seam state and a non-baseline coal seam state.

[0010] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] The coal seam drilling period is divided into stages, the gas loss mechanism and key monitoring parameters are defined, and the stage element matrix is determined. The coal seam structure of the pre-drilling area is obtained, the baseline coal seam state is set, the numerical simulator is constructed based on the lightweight structure characteristics, the dynamics simulation based on the stage element matrix is performed, and the simulation flow data is determined. The measurement unit is deployed, the gas content data is determined based on the simulation flow data, the first-order measurement based on the stage loss and the later desorption is performed, and the second-order compensation based on the migration coefficient of the baseline coal seam state and the non-baseline coal seam state is performed. The technical effect of accurately determining the coal seam gas content and improving the evaluation accuracy of the gas occurrence in the specific coal seam is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0013] Figure 1 A coal seam gas content determination method flowchart is provided for the embodiments of the present application.

[0014] Figure 2 A coal seam gas content determination system structure diagram is provided for the embodiments of the present application.

[0015] Legend of the drawings: stage element matrix determination module 10, simulation flow data determination module 20, gas content data determination module 30. DETAILED DESCRIPTION

[0016] This application provides a method and system for determining coal seam gas content by incorporating corrections for advance gas loss, which addresses the technical problems in existing technologies where the determination of coal seam gas content does not adequately consider advance gas loss and has low accuracy under different coal seam conditions.

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0018] Example 1, as Figure 1 As shown, this application provides a method for determining coal seam gas content by incorporating corrections for advance gas loss, the method comprising:

[0019] Step S100: Divide the coal seam drilling cycle into stages, define the gas loss mechanism and key monitoring parameters, and determine the stage element matrix.

[0020] Specifically, the coal seam drilling cycle is divided according to the evolution of the gas loss mechanism, identifying five stages and integrating them into a stage element matrix. The first stage is the borehole stress disturbance stage, where pre-pressure relief occurs due to fracture initiation, and the key monitoring parameters are drilling pressure and vibration frequency. The second stage is the coal body fracturing stage, where coal granulation increases the desorption surface area, requiring monitoring of drilling speed and coal powder particle size. The third stage is the coal sample exposure stage, where atmospheric pressure difference triggers rapid gas escape, requiring monitoring of exposure time and environmental pressure. The fourth stage is the closed desorption stage, mainly releasing free gas, focusing on desorption rate and accumulation. The fifth stage is the residual gas desorption stage, involving the desorption of adsorbed gas, requiring monitoring of secondary desorption after crushing. Integrating the "stage division - gas loss mechanism - key monitoring parameters" sequence corresponding to these five stages forms the stage element matrix.

[0021] Step S200: Obtain the coal seam structure of the pre-drilling area, construct a numerical simulator with lightweight structural features by setting the baseline coal seam state, execute a dynamic simulation based on the stage element matrix, and determine the simulation flow data. The baseline coal seam state is any one of hard and intact coal seam, broken and soft coal seam, and high-gas coal seam.

[0022] Specifically, the coal seam structure of the pre-drilling area is first obtained. Considering that the coal seam states of different layers may be inconsistent, the distributed construction is complex and the uncertainty in the simulation process is high. Therefore, a baseline coal seam state is uniformly set (any one of hard and intact coal seams, broken and soft coal seams, and high-gas coal seams). Based on this, a numerical simulator is constructed with lightweight structural features. Specifically, the first deployment information is determined by assembling a lightweight structural assembly to perform lightweight geometric deployment of the coal seam structure. At the same time, the baseline mechanical laws are determined as the second deployment information based on the baseline coal and rock state, combined with the mining machinery dynamics (at least based on the mining aperture and rotation speed), coal and rock fracturing, and gas diffusion dynamics. The two deployment information are integrated to build a numerical simulator. Then, a dynamic simulation based on the stage element matrix is ​​executed. According to the measurement requirements (forward requirements are to use the predetermined mining conditions as the simulation quantity and gas diffusion as the output quantity, and reverse requirements are to use the predetermined gas diffusion as the simulation quantity and mining conditions as the output quantity), simulation stream data is output. Subsequently, the simulation results are further adjusted based on the migration coefficient between states.

[0023] Step S300: Deploy the calculation unit to determine the gas content data based on the simulated flow data. The calculation is performed by superimposing the staged advance loss amount with the later desorption, and the second-order compensation is performed by the migration coefficient based on the baseline coal seam state and the non-baseline coal seam state.

[0024] Specifically, a calculation unit consisting of a first calculation node and a second calculation node cascaded together is deployed to determine the gas content data based on simulated flow data. The first calculation node performs phased gas emission superposition, superimposing the advance loss amount of the borehole stress disturbance stage, coal body fracturing stage, and coal sample exposure stage with the later desorption data of the closed desorption stage and residual gas desorption stage to complete the first-order calculation. The second calculation node performs state change migration compensation based on the baseline coal seam state. This migration coefficient is determined by measuring the difference between the clusters of the baseline coal seam state (any type of hard and intact coal seam, broken and soft coal seam, and high-gas coal seam) and the non-baseline coal seam state (by subtracting the smallest unit element of mining machinery power - coal and rock fracturing - gas diffusion power, and measuring it with gas difference). The migration coefficient is used to perform second-order compensation on the first-order calculation results to finally obtain accurate gas content data.

[0025] In one possible implementation, step S100 further includes:

[0026] Step S110: Based on the changes in the gas loss mechanism, determine the stage sequence based on the stage division - gas loss mechanism - key monitoring parameters.

[0027] Step S120: Integrate the stage sequence as the stage element matrix.

[0028] Specifically, the coal seam drilling period is divided into five stages based on the change of gas loss mechanism, forming a stage sequence based on "division stage-gas loss mechanism-key monitoring parameter": the first stage sequence is the drilling stress disturbance stage, the gas loss mechanism is crack initiation leading to pre-discharge, and the key monitoring parameter is drilling pressure and vibration frequency; the second stage sequence is the coal body crushing stage, the gas loss mechanism is particleization increasing desorption surface area, and the key monitoring parameter is drilling speed and coal powder particle size; the third stage sequence is the coal sample exposure stage, the gas loss mechanism is atmospheric pressure difference causing rapid dispersion, and the key monitoring parameter is exposure time and environmental pressure; the fourth stage sequence is the sealed desorption stage, the gas loss mechanism is free state gas release, and the key monitoring parameter is desorption rate and cumulative amount; and the fifth stage sequence is the residual gas desorption stage, the gas loss mechanism is adsorbed state gas desorption, and the key monitoring parameter is secondary desorption amount after crushing.

[0029] The determined five stage sequences are integrated to form a stage element matrix. The integrated stage element matrix includes the following corresponding relationships: the drilling stress disturbance stage corresponds to the gas loss mechanism of crack initiation leading to pre-discharge, the key monitoring parameter is drilling pressure and vibration frequency; the coal body crushing stage corresponds to the gas loss mechanism of particleization increasing desorption surface area, the key monitoring parameter is drilling speed and coal powder particle size; the coal sample exposure stage corresponds to the gas loss mechanism of atmospheric pressure difference causing rapid dispersion, the key monitoring parameter is exposure time and environmental pressure; the sealed desorption stage corresponds to the gas loss mechanism of free state gas release, the key monitoring parameter is desorption rate and cumulative amount; and the residual gas desorption stage corresponds to the gas loss mechanism of adsorbed state gas desorption, the key monitoring parameter is secondary desorption amount after crushing. The matrix system sorts out each division stage, the corresponding gas loss mechanism and the key monitoring parameter, and provides a structured basic data framework for subsequent stage-based kinetic simulation and gas content measurement.

[0030] In a possible implementation manner, the step S110 further includes:

[0031] Step S111: the first stage sequence is the drilling stress disturbance stage-crack initiation leading to pre-discharge-drilling pressure and vibration frequency.

[0032] Step S112: the second stage sequence is the coal body crushing stage-particleization increasing desorption surface area-drilling speed and coal powder particle size.

[0033] Step S113: the third stage sequence is the coal sample exposure stage-atmospheric pressure difference causing rapid dispersion-exposure time and environmental pressure.

[0034] Step S114: the fourth stage sequence is the sealed desorption stage-free state gas release-desorption rate and cumulative amount.

[0035] Step S115: the fifth stage sequence is a residual gas desorption stage - adsorbed gas desorption - secondary desorption amount after crushing.

[0036] Specifically, the first stage sequence is a borehole stress disturbance stage - crack initiation leading to pre-pressure relief - drilling pressure, vibration frequency, that is, this stage belongs to the initial stage in the coal seam drilling period. During the drilling process, mechanical action will cause stress disturbance to the coal seam, which in turn leads to the initiation of cracks in the coal body, causing the gas to be pre-vented before the formal drilling of the coal sample, resulting in gas loss. To accurately grasp the gas loss in this stage, the key parameters to be monitored are drilling pressure and vibration frequency, which can reflect the strength and characteristics of stress disturbance during drilling, providing a basis for quantifying the advanced gas loss in this stage.

[0037] The second stage sequence is a coal body crushing stage - particleization increases desorption surface area - drilling speed, coal powder particle size, that is, this stage is after the borehole stress disturbance stage in the coal seam drilling period. During continuous drilling, the coal body is subjected to mechanical crushing and particleization, which significantly increases the desorption surface area of the coal body, accelerating the desorption and dispersion of gas from the coal body, resulting in gas loss. To accurately assess the gas loss in this stage, the key parameters to be monitored are drilling speed and coal powder particle size, which reflect the rate of coal body crushing and the degree of coal body particleization, respectively, providing data support for quantifying the advanced gas loss caused by particleization in this stage.

[0038] The third stage sequence is a coal sample exposure stage - atmospheric pressure difference leading to rapid dispersion - exposure time, environmental pressure, that is, this stage is after the coal body crushing stage. When the drilled coal sample is separated from the original coal seam environment and exposed to the atmosphere, the pressure difference between the coal seam interior and the external atmosphere (atmospheric pressure difference) will cause the gas in the coal sample to rapidly disperse due to pressure driving, resulting in gas loss. To accurately measure the degree of gas loss in this stage, the key parameters to be monitored are exposure time and environmental pressure, which reflect the length of time the coal sample is in contact with the atmosphere and the pressure state of the external atmosphere, respectively, providing key data support for quantifying the advanced gas loss caused by atmospheric pressure difference in this stage.

[0039] The fourth stage sequence is a closed desorption stage-free gas release-desorption rate and cumulative amount, that is, after the coal sample is exposed and collected and placed in a closed environment, the free gas that has not escaped in the coal body will continue to be released in the closed space; the gas loss in this stage is mainly caused by the natural desorption process of free gas. In order to accurately measure the gas release in this stage, the desorption rate and cumulative amount, which are two key parameters, need to be monitored. The desorption rate reflects the release intensity of free gas per unit time, and the cumulative amount reflects the total amount of free gas released in this stage. Both of them provide the core basis for subsequent calculation of gas loss in this stage and superposition with other stage data.

[0040] The fifth stage sequence is a residual gas desorption stage-adsorbed gas desorption-secondary desorption after crushing, that is, after the gas escape and release in the previous stage, part of the adsorbed gas remains in the coal sample. These adsorbed gases need to be desorbed after the coal sample is further crushed, because the coal structure is completely destroyed and the adsorption balance is broken, forming secondary desorption. The gas loss in this stage is mainly caused by the desorption process of adsorbed gas. In order to accurately quantify the residual gas in this stage, the key parameter of secondary desorption after crushing needs to be monitored. This parameter directly reflects the total amount of residual adsorbed gas in the coal sample, and provides important data support for the final integration of gas loss in each stage and the accurate measurement of gas content.

[0041] In one possible implementation manner, step S200 further includes:

[0042] Step S210: determining the first deployment information by performing lightweight geometry deployment based on the coal seam structure, wherein the lightweight structure assembly is assembled and deployed.

[0043] Step S220: determining the baseline mechanical law as the second deployment information based on the baseline coal rock state, mining mechanical power-coal rock crushing-gas diffusion power, wherein the mining mechanical power is determined based on at least mining aperture and rotating speed conditions.

[0044] Step S230: building the numerical simulator according to the first deployment information and the second deployment information.

[0045] Specifically, by extracting the key geometric features of the coal seam (such as thickness, bedding distribution, fracture development zone, and other core structural parameters), the secondary detail information is removed to simplify the model dimension, and then a modular lightweight structure assembly is used for deployment, that is, the overall structure is disassembled into several independently characterized geometric units (such as different lithology layer units, fracture dense units, etc.) according to the spatial distribution characteristics of the coal seam. Each unit is constructed with a lightweight parameterized model and is logically related through an interface to form an assembly model that can reflect the real structure characteristics of the coal seam and reduce the computational load. The assembly model serves as the first deployment information and provides a basic geometric framework for the subsequent numerical simulator.

[0046] Based on the mining aperture and rotational speed conditions, the mining mechanical power is quantified, that is, by collecting the mechanical output parameters (such as torque, impact force) under different aperture drill bits and different rotational speeds, a correlation model of mining aperture, rotational speed, and mechanical power is established. Then, the mining mechanical power parameters are input into the coal rock breaking simulation module, combined with the physical and mechanical properties of the baseline coal rock (such as hardness, compressive strength), the breaking degree of the coal body under the action of the machine (such as breaking rate, particle distribution characteristics) is analyzed, and the mapping relationship between mechanical power and coal rock breaking is established. Finally, based on the granulation characteristics of the broken coal rock (such as specific surface area, porosity), combined with the gas diffusion dynamics principle, the diffusion coefficient, diffusion rate, and other parameters of gas in the broken coal body are derived, and the correlation law of coal rock breaking and gas diffusion dynamics is constructed. By integrating the correlation relationships of the above three links, the baseline mechanical law is formed as the second deployment information.

[0047] When building a numerical simulator according to the first and second deployment information, a clustering algorithm and a regression model trained based on historical mining data are introduced: first, the K-means clustering algorithm is used to cluster different coal seam states (hard and complete coal seam, soft and broken coal seam, high gas coal seam) in the historical mining data, determine the clustering cluster corresponding to the baseline coal rock state, and extract the minimum unit element of “mining mechanical power-coal rock breaking-gas diffusion dynamics” in the cluster as the training sample; second, the random forest regression algorithm is used, the lightweight geometric parameters (such as coal seam thickness, fracture distribution, etc.) in the first deployment information and the key variables (such as mining aperture, rotational speed, coal rock breaking degree, etc.) in the second deployment information are used as input features, and the measured gas diffusion data is used as output label, and the mapping relationship model is trained; finally, the baseline mechanical law obtained by clustering is coupled with the regression model in the lightweight geometric assembly body framework, and the model parameters are optimized through algorithm iteration, so that the simulator can dynamically output the gas transport dynamics simulation results conforming to the stage element matrix based on the input first and second deployment information, and the numerical simulator is built.

[0048] In one possible implementation, step S220 further includes:

[0049] Step S221: Obtain historical mining data, cluster the coal seam state, and determine X clusters, wherein the maximum inter-class difference is used for partitioning into hard and complete coal seams, soft and broken coal seams, and high gas coal seams, and the inter-class difference is adjustable.

[0050] Step S222: Extract the first cluster of the baseline coal and rock state from the X clusters, and mine the baseline mechanical law based on the minimum unit element of mining mechanical power-coal and rock breaking-gas diffusion power.

[0051] Specifically, when obtaining historical mining data, a historical data set containing coal seam physical and mechanical parameters (such as hardness, compressive strength), gas parameters (such as gas pressure, adsorption constant), and mining process parameters (such as hole diameter, rotational speed) is collected; the coal seam state is used as the clustering basis, an improved K-means clustering algorithm is used, and based on the principle of maximum inter-class difference (the inter-class difference is adjustable by adjusting the inter-class distance weight parameter), the historical data is iteratively clustered through the preset hard and complete coal seam, soft and broken coal seam, and high gas coal seam three core cluster centers: the Euclidean distance between the sample and each cluster center is calculated, the sample is assigned to the nearest cluster, and the cluster center is repeatedly updated through optimization of the objective function (minimization of intra-cluster variance and maximization of inter-cluster distance) until convergence, and finally X clusters are determined, wherein the three core clusters correspond to hard and complete coal seams, soft and broken coal seams, and high gas coal seams, and the remaining clusters are transition types or special state coal seams.

[0052] The decision tree algorithm and random forest regression model are used to mine the baseline mechanical law: after extracting the first cluster corresponding to the baseline coal and rock state from the X clusters, the mining mechanical power parameters (such as hole diameter, rotational speed), coal and rock breaking parameters (such as particle size, breaking rate), and gas diffusion parameters (such as diffusion coefficient, desorption rate) in the cluster are used as sample features and labels, a decision tree algorithm is used to construct the correlation path of "mining mechanical power-coal and rock breaking-gas diffusion power", key influence factors of each link are identified, a random forest regression model is used to train multiple sets of sample data, a minimum unit element is used as an input variable, a nonlinear mapping relationship between the three is fitted, model parameters are optimized through iteration to improve prediction accuracy, and finally the baseline mechanical law under the baseline coal and rock state is mined, that is, the quantitative correlation law of coal and rock breaking and gas diffusion under different mining mechanical power.

[0053] In one possible implementation, step S222 further includes:

[0054] Step S2221: Measure the inter-cluster difference of the X clusters based on the minimum unit element of mining mechanical power-coal and rock breaking-gas diffusion power, and determine the inter-cluster difference sequence, wherein the measurement method is to calculate the difference between the first cluster and the remaining clusters.

[0055] Step S2222: measuring the migration coefficient with the gas difference based on the inter-cluster difference sequence, wherein the migration coefficient represents the gas loss migration amount between the baseline coal seam state and any non-baseline coal seam state based on the state change.

[0056] Specifically, the minimum unit elements (such as mining aperture, rotating speed, coal rock breaking granularity, gas diffusion coefficient, etc.) in the "mining mechanical power-coal rock breaking-gas diffusion power" chain are taken as the measurement benchmark, and the inter-cluster difference of the X clustering clusters is measured. Taking the first clustering cluster representing the baseline coal rock state as a reference, the difference between the first clustering cluster and each of the remaining clustering clusters in each minimum unit element is calculated, such as the mechanical power difference, the average particle size difference after coal rock breaking, the gas diffusion rate difference, etc. under the same mining aperture between the first clustering cluster and the second clustering cluster, and the difference values are arranged in order to form an inter-cluster difference sequence that can reflect the feature difference between different clustering clusters, thereby quantifying the difference degree of the baseline coal seam state and other coal seam state clustering clusters in key elements.

[0057] The multivariate linear regression algorithm is used to measure the migration coefficient: the element difference values (such as mining mechanical power difference, coal rock breaking parameter difference, gas diffusion parameter difference) between the first clustering cluster and each non-baseline clustering cluster in the inter-cluster difference sequence are taken as independent variables, and the corresponding gas loss amount difference (gas difference) is taken as the dependent variable to construct a regression model; by training the correlation between the two in the historical data, the weight coefficients of each element difference value are determined, and then the quantitative calculation formula of the element difference value and the gas difference is obtained; using the formula, the corresponding gas loss migration amount, i.e. the migration coefficient, can be calculated according to the element difference value between any non-baseline coal seam state and the baseline coal seam state, thereby realizing the quantitative representation of the gas loss migration amount between the baseline and non-baseline coal seam states caused by the state change.

[0058] In one possible implementation, step S200 further includes:

[0059] Step S240: determining the measurement requirement, wherein the measurement requirement is a forward requirement and a reverse requirement, the forward requirement is to take the predetermined mining condition as the simulation quantity and take the gas diffusion as the output quantity, and the reverse requirement is to take the predetermined gas diffusion as the simulation quantity and take the mining condition as the output quantity.

[0060] Step S250: performing the stage-based dynamics simulation based on the stage element matrix under the measurement requirement, and outputting the simulation flow data.

[0061] Specifically, the specific implementation means for determining the determination requirement is: for the forward requirement, collect the coal seam geological data of the pre-drilling area, combine the technical parameter range of the mining equipment, preset different mining condition combinations (such as specific parameters of hole diameter φ94mm, rotating speed 800r / min, etc.), input these parameters into the numerical simulator to output corresponding gas diffusion rate, cumulative dispersion amount and other data; for the reverse requirement, according to the limit value requirement (such as minimum gas dispersion amount) of gas dispersion in the coal mine safety regulations, set the predetermined gas diffusion parameters (such as diffusion rate ≤0.5m³ / min) as the constraint conditions of the simulator, and through iterative calculation, the optimal mining condition parameters such as mining hole diameter and rotating speed that meet the conditions are back calculated, so as to determine the determination target in different scenarios.

[0062] According to the determined forward or reverse determination requirement, relying on the five stages of drilling stress disturbance, coal body crushing, coal sample exposure, sealed desorption and residual gas analysis divided in the stage element matrix, combined with the gas loss mechanism and key monitoring parameters corresponding to each stage, the dynamics simulation of each stage is executed in the numerical simulator. For the forward requirement, input the predetermined mining condition parameters into the simulator, and calculate the gas diffusion related data by stage; for the reverse requirement, take the predetermined gas diffusion parameters as the constraint, and back calculate the mining condition parameters required by each stage. By integrating the simulation results of each stage, simulation flow data containing time sequence and spatial distribution characteristics are formed and output.

[0063] In one possible implementation, step S300 further includes:

[0064] Step S310: deploying a first calculation node, wherein the first calculation node performs phased gas dispersion superposition.

[0065] Step S320: deploying a second calculation node, wherein the second calculation node performs state transition migration compensation based on the baseline coal seam state.

[0066] Step S330: cascading the first calculation node and the second calculation node as the calculation unit, and connecting the calculation unit at the back of the numerical simulator.

[0067] Specifically, the first calculation node can use a long short-term memory (LSTM) algorithm to perform the phase gas emission superposition: the gas emission data of five stages of drilling stress disturbance, coal body crushing, coal sample exposure, sealed desorption and residual gas analysis are input into the LSTM model in time sequence, the time sequence dependence of the data of each stage is learned by the model, and the dynamic change characteristics of the gas emission of different stages are captured; through the synergistic effect of the forgetting gate, the input gate and the output gate, the key emission information of each stage is screened and retained, and finally the effective gas emission amounts of the five stages are accumulated and superimposed, the total gas emission amount after superposition is output, and the accurate summary of the phase gas emission is realized.

[0068] The second calculation node can use a support vector regression (SVR) algorithm to perform state transition migration compensation based on the baseline coal seam state: the cluster difference sequence (such as the element difference of the mining mechanical power, the coal rock crushing, and the gas diffusion power) between the baseline coal seam state and the non-baseline coal seam state is taken as the input feature, and the corresponding gas difference (migration coefficient) is taken as the target value, and the SVR model is trained; after receiving the superposition result of the first calculation node, the node extracts the element difference between the current coal seam state and the baseline state and inputs the trained model to obtain the migration coefficient corresponding to the state transition, and then compensates and corrects the superposition result through the coefficient, realizes the gas loss migration compensation based on the state trend, and improves the calculation accuracy.

[0069] The first calculation node and the second calculation node are cascaded through a data interface to form a complete calculation unit, wherein the output end of the first calculation node is connected to the input end of the second calculation node, so that the phase gas emission superposition result output by the first calculation node can be directly transmitted to the second calculation node; at the same time, the calculation unit is connected to the output end of the numerical simulator through a data transmission protocol, the simulation flow data generated by the numerical simulator is first input into the first calculation node, the result is transmitted to the second calculation node after the phase gas emission superposition by the first calculation node, and finally the processed gas content data is output through the calculation unit, forming a complete data flow path of “numerical simulator→first calculation node→second calculation node→final gas content data”, ensuring the real-time and accuracy of data transmission between nodes.

[0070] In a possible implementation manner, the step S330 further includes:

[0071] Step S331: retrieving the first deployment information.

[0072] Step S332: performing four-dimensional identification in the first deployment information according to the gas content data, and determining a determination graph, wherein the four-dimensional identification includes spatial three dimensions and a time dimension.

[0073] Step S333: interface visualization of the determination atlas.

[0074] Specifically, the first deployment information is extracted through an internal data calling mechanism, which is determined in the process of building the numerical simulator based on the lightweight geometry deployment of the coal seam structure, and contains key data such as the lightweight geometry structure of the coal seam assembled by the lightweight structure assembly, etc., providing a basic framework for subsequent four-dimensional identification in it to determine the determination atlas.

[0075] According to the determined gas content data, the four-dimensional identification operation is implemented in the called first deployment information (i.e. lightweight geometry deployment information based on the structure of the coal seam). The spatial three-dimensional dimension accurately locates each specific position in the coal seam through x, y, z coordinates, and the time dimension marks the gas content change at different times. The gas content data is associated with the corresponding space-time coordinates, thereby constructing a determination atlas that can fully reflect the distribution characteristics of the gas content in the coal seam at different spatial positions and different times. The atlas integrates the space-time dynamic information of the gas content.

[0076] The determined determination atlas containing four-dimensional identification (spatial three-dimensional and time dimension) is subjected to interface visualization processing. The space-time distribution characteristics of the gas content in the coal seam are presented in an intuitive graphical form through visualization technology, such as using different colors to identify the high and low of the gas content, combining with a three-dimensional model to show the spatial distribution, and dynamically demonstrating the gas content change at different times through a time axis, so that the staff can clearly and conveniently understand the distribution state and change rule of the coal seam gas content, providing an intuitive reference basis for coal mine gas management.

[0077] Embodiment two, based on the same inventive concept as the coal seam gas content determination method fused with the correction of the advanced gas loss amount in the preceding embodiments, as shown in the following table, the present application provides a coal seam gas content determination system fused with the correction of the advanced gas loss amount, and the system and method embodiments in the present application are based on the same inventive concept. Among them, the system comprises: Figure 2

[0078] The stage element matrix determination module 10 is used to divide the coal seam drilling period into stages, define the gas loss mechanism and key monitoring parameters, and determine the stage element matrix.

[0079] The simulation flow data determination module 20 is used to obtain the coal seam structure of the pre-drilling area, construct a numerical simulator with lightweight structure characteristics by setting a baseline coal seam state, execute dynamics simulation based on the stage element matrix, and determine the simulation flow data, wherein the baseline coal seam state is any one of hard and complete coal seam, soft and broken coal seam, and high gas coal seam.

[0080] ​The gas content data determination module 30 is configured to deploy a calculation unit to determine gas content data according to the simulated flow data, wherein a first-order calculation is performed by superimposing a phased advanced loss amount and a later desorption, and a second-order compensation is performed based on a migration coefficient of a baseline coal seam state and a non-baseline coal seam state.

[0081] Further, the system is also used for the following functions:

[0082] Based on the transition of the gas loss mechanism, a stage sequence based on the division stage-gas loss mechanism-key monitoring parameter is determined; and the stage sequence is integrated as the stage element matrix.

[0083] Further, the system is also used for the following functions:

[0084] The first stage sequence is a borehole stress disturbance stage-fracture initiation leading to pre-pressure relief-borehole pressure and vibration frequency; the second stage sequence is a coal body crushing stage-particleization increasing desorption surface area-drilling speed and coal powder particle size; the third stage sequence is a coal sample exposure stage-atmospheric pressure difference causing rapid dispersion-exposure time and environmental pressure; the fourth stage sequence is a sealed desorption stage-free state gas release-desorption rate and cumulative amount; and the fifth stage sequence is a residual gas desorption stage-adsorbed state gas desorption-secondary desorption amount after crushing.

[0085] Further, the system is also used for the following functions:

[0086] By performing lightweight geometry deployment based on the coal seam structure, first deployment information is determined, wherein a lightweight structure assembly is assembled and deployed; for a baseline coal rock state, a baseline mechanical law is determined as second deployment information based on mining mechanical power-coal rock crushing-gas diffusion power, wherein the mining mechanical power is determined based on at least mining aperture and rotating speed conditions; and the numerical simulator is built according to the first deployment information and the second deployment information.

[0087] Further, the system is also used for the following functions:

[0088] Historical mining data is acquired to cluster coal seam states to determine X clustering clusters, wherein the division based on the maximum inter-class difference is hard and complete coal seams, soft and broken coal seams, and high-gas coal seams, and the inter-class difference is adjustable; a first clustering cluster of the baseline coal rock state in the X clustering clusters is extracted to mine the baseline mechanical law based on the smallest unit element of mining mechanical power-coal rock crushing-gas diffusion power.

[0089] Further, the system is also used for the following functions:

[0090] The X clustering clusters are measured by inter-cluster difference based on the minimum unit element of mining machinery power-coal rock breaking-gas diffusion power, and an inter-cluster difference sequence is determined, wherein the measurement manner is to perform element difference between the first clustering cluster and the remaining clustering clusters; the migration coefficient is measured based on the inter-cluster difference sequence, wherein the migration coefficient represents the state change-based gas loss migration amount between the baseline coal seam state and any non-baseline coal seam state.

[0091] Further, the system is also used for the following functions:

[0092] Determine the determination requirement, wherein the determination requirement is a forward requirement and a reverse requirement, the forward requirement is a predetermined mining condition as an analog quantity and gas diffusion as an output quantity, and the reverse requirement is a predetermined gas diffusion as an analog quantity and mining conditions as an output quantity; based on the stage element matrix, the stage dynamics simulation is performed based on the determination requirement, and simulation flow data is output.

[0093] Further, the system is also used for the following functions:

[0094] Deploy a first calculation node, wherein the first calculation node performs stage gas diffusion superposition; deploy a second calculation node, wherein the second calculation node performs state transition migration compensation based on the baseline coal seam state; cascade the first calculation node and the second calculation node as the calculation unit, and the calculation unit is connected to the numerical simulator.

[0095] Further, the system is also used for the following functions:

[0096] Retrieve first deployment information; according to the gas content data, perform four-dimensional identification in the first deployment information to determine a determination graph, wherein the four-dimensional identification includes spatial three-dimensional and time dimension; and interface visualization is performed on the determination graph.

[0097] It should be noted that the above sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. Moreover, the above describes a specific embodiment of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0098] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0099] The specification and drawings are, of course, to be regarded in an illustrative rather than a restrictive sense. It is to be understood that any such modifications, variations, combinations or equivalents that fall within the scope of the application are intended to be embraced herein.

Claims

1. A method of determining the gas content of a coal seam by fusing a correction for the amount of gas lost in advance, characterized by, The method comprises: periodically drilling coal seams, defining gas loss mechanisms and key monitoring parameters, and determining stage element matrices; obtaining the structure of the pre-drilling area of the coal seam, setting a baseline coal seam state, constructing a numerical simulator with lightweight structure characteristics, performing dynamic simulation based on the stage element matrix, and determining simulation flow data, wherein the baseline coal seam state is any one of hard and complete coal seam, soft and broken coal seam, and high gas coal seam; deploying a measurement unit to determine gas content data based on the simulation flow data, wherein a first-order measurement is performed based on the loss amount in advance and the desorption in the later stage, and a second-order compensation is performed based on the migration coefficient of the baseline coal seam state and the non-baseline coal seam state.

2. The coal seam gas content measurement method of claim 1, wherein, Determine the stage element matrix, including: taking the transition of the gas loss mechanism as the division standard, determine the stage sequence based on the division stage-gas loss mechanism-key monitoring parameter; integrate the stage sequence as the stage element matrix.

3. The method for determining coal seam gas content by incorporating correction for advanced gas loss as described in claim 2, characterized in that, The first stage sequence is the drilling stress disturbance stage, the pre-pressure relief caused by the crack initiation, the drilling pressure and vibration frequency; The second stage sequence is the coal body crushing stage, the particle size increase to increase the desorption surface area, the drilling speed and coal powder particle size; The third stage sequence is the coal sample exposure stage, the atmospheric pressure difference causing rapid dispersion, the exposure time and environmental pressure; The fourth stage sequence is the sealed desorption stage, the release of free gas, the desorption rate and cumulative amount; The fifth stage sequence is the residual gas desorption stage, the desorption of adsorbed gas, and the secondary desorption amount after crushing.

4. The coal bed gas content measurement method of claim 1, wherein the gas loss amount before fusion is corrected by the amount of gas loss after fusion. Constructing a numerical simulator with lightweight structure characteristics, including: determine the first deployment information by performing lightweight geometry deployment based on the structure of the coal seam, wherein the lightweight structure assembly body is assembled and deployed; for the baseline coal rock state, determine the baseline mechanical law as the second deployment information based on the mining mechanical power-coal rock crushing-gas diffusion power, wherein the mining mechanical power is determined based on at least the mining aperture and the speed condition; build the numerical simulator according to the first deployment information and the second deployment information.

5. The method for determining coal seam gas content by incorporating correction for advanced gas loss as described in claim 4, characterized in that, Determine the baseline mechanical law based on the mining mechanical power-coal rock crushing-gas diffusion power, including: obtain historical mining data, cluster the coal seam state, and determine X clusters, wherein the division based on the maximum inter-class difference is hard and complete coal seam, soft and broken coal seam, and high gas coal seam, and the inter-class difference is adjustable; extract the first cluster of the baseline coal rock state from the X clusters, and mine the baseline mechanical law based on the minimum unit element of the mining mechanical power-coal rock crushing-gas diffusion power.

6. The method for determining coal seam gas content by incorporating correction for advanced gas loss as described in claim 5, characterized in that, After mining the baseline mechanical law, including: based on the minimum unit element of the mining mechanical power-coal rock crushing-gas diffusion power, measure the cluster difference between the X clusters to determine the cluster difference sequence, wherein the measurement method is to calculate the difference between the first cluster and the remaining clusters respectively; based on the cluster difference sequence, measure the migration coefficient based on the gas difference, wherein the migration coefficient represents the state trend-based gas loss migration amount between the baseline coal seam state and any non-baseline coal seam state.

7. The coal bed gas content measurement method of claim 6, wherein the gas loss amount before fusion is corrected by the amount of gas loss after fusion. The system is used for implementing the coal seam gas content determination method of claim 1-9, and the system comprises: A stage element matrix determination module is configured to divide a coal seam drilling period into stages, define a gas loss mechanism and a key monitoring parameter, and determine a stage element matrix. A simulation flow data determination module is configured to obtain a coal seam structure of a pre-drilling area, construct a numerical simulator with a lightweight structure feature by setting a baseline coal seam state, perform a dynamic simulation based on the stage element matrix, and determine simulation flow data, wherein the baseline coal seam state is any one of a hard and complete coal seam, a soft and broken coal seam, and a high-gas coal seam.

8. The coal bed gas content measurement method of claim 1, wherein the gas loss amount before fusion is corrected by the amount of gas loss after fusion. A gas content data determination module is configured to deploy a calculation unit and determine gas content data according to the simulation flow data, wherein a first-order calculation is performed based on a stage-by-stage loss and a later desorption superposition, and a second-order compensation is performed based on a migration coefficient of the baseline coal seam state and a non-baseline coal seam state. After determining the gas content data, the method comprises: Retrieving first deployment information; According to the gas content data, performing four-dimensional identification in the first deployment information to determine a determination atlas, wherein the four-dimensional identification includes spatial three dimensions and a time dimension; 9. The coal bed gas content measurement method of claim 1, wherein the gas loss amount before fusion is corrected by the amount of gas loss after fusion. Interface visualization is performed on the determination atlas. The system is used for implementing the coal seam gas content determination method of claim 1-9, and the system comprises: A stage element matrix determination module is configured to divide a coal seam drilling period into stages, define a gas loss mechanism and a key monitoring parameter, and determine a stage element matrix. A simulation flow data determination module is configured to obtain a coal seam structure of a pre-drilling area, construct a numerical simulator with a lightweight structure feature by setting a baseline coal seam state, perform a dynamic simulation based on the stage element matrix, and determine simulation flow data, wherein the baseline coal seam state is any one of a hard and complete coal seam, a soft and broken coal seam, and a high-gas coal seam.

10. A coal seam gas content measurement system fused with a gas loss amount correction, characterized by, A gas content data determination module is configured to deploy a calculation unit and determine gas content data according to the simulation flow data, wherein a first-order calculation is performed based on a stage-by-stage loss and a later desorption superposition, and a second-order compensation is performed based on a migration coefficient of the baseline coal seam state and a non-baseline coal seam state. ​ ​ ​

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