Real-time monitoring and early warning system for stability of underground coal gasification combustion area

Abstract

The invention relates to the field of combustion area stability monitoring, in particular to a coal underground gasification combustion area stability real-time monitoring and early warning system. According to the method, all the fracture points on the inner wall of the surrounding rock in the combustion space area are monitored in real time, the adjacent fracture points are divided to obtain the real-time area of each risk area, and the area diffusivity is calculated by tracking the area variation of each risk area; the method comprises the following steps: determining a stress abrupt change point, carrying out correlation analysis on the stress abrupt change point and a risk region to obtain a stress influence coefficient, and calculating a risk coefficient by combining the real-time area and the area diffusivity of each risk region; combining the risk coefficient of each risk area with a spatial distribution density characteristic value and a correlation influence characteristic value to obtain stability levels of the gasification combustion space area, and sending out corresponding early warning signals according to different stability levels; professionals can take differential prevention and control measures according to the early warning level, safety accidents such as collapse of the combustion space area are effectively avoided, and the stability of the surface ecological environment is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of combustion space stability monitoring, and relates to a coal underground gasification combustion space stability real-time monitoring and early warning system. BACKGROUND

[0002] The combustion space refers to a complex underground space that is consumed, hollowed out and physically and chemically changed after coal is combusted in situ and converted into combustible gas through coal underground gasification technology in the underground coal seam. The existence of the combustion space will destroy the original stress balance of the underground rock mass, and risks such as surrounding rock rupture, deformation and even collapse are prone to occur. Therefore, real-time and accurate monitoring and early warning of the stability of the combustion space is a key requirement for ensuring the safe application of coal underground gasification technology.

[0003] However, the prior art has the following problems: the prior art mainly compares the stress values, displacement or stress mutation data of each point with the preset safety threshold to analyze the stability of the combustion space, without considering analyzing the risk area from the rupture which is the most essential factor affecting stability and analyzing the risk area area diffusion degree, and without analyzing the point-surface-space through the point-surface-space, which is difficult to meet the fine and forward-looking requirements of risk prediction in coal underground gasification engineering.

[0004] Even if the stress mutation is calculated, the prior art does not consider classifying the stress mutation points and analyzing the risk area with the existing risks, which deviates from the actual situation of the risk area actual safety state, and further reduces the reliability of the subsequent stability evaluation results. SUMMARY

[0005] In order to overcome the shortcomings of the prior art, the present application provides a coal underground gasification combustion space stability real-time monitoring and early warning system.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical solution: a coal underground gasification combustion space stability real-time monitoring and early warning system, comprising: a rupture point monitoring module, a risk area analysis module, a risk coefficient calculation module, a risk correlation analysis module, and a monitoring and early warning module. The connection relationship between the modules is: the rupture point monitoring module is connected with the risk area analysis module, the risk coefficient calculation module is connected with the risk area analysis module and the risk correlation analysis module, and the monitoring and early warning module is connected with the risk correlation analysis module.

[0007] The rupture point monitoring module: uniformly arranging a sensor group in the inner wall of the combustion space surrounding rock, receiving the seismic wave in the continuous time through the sensor group, and locating all the rupture point positions.

[0008] Risk area analysis module: the real-time area of each risk area is obtained by dividing adjacent fracture points, and the area diffusion degree is calculated by tracking the area change of each risk area.

[0009] Risk coefficient calculation module: stress mutation points are determined by real-time acquisition of stress data of the inner wall of surrounding rock, and the risk coefficient is calculated by combining the real-time area and the area diffusion degree of each risk area through the correlation analysis of the stress mutation points and the risk area.

[0010] Risk correlation analysis module: according to the spatial position of each risk area of the inner wall of the surrounding rock of the combustion air area, the spatial distribution density characteristic value and the correlation influence characteristic value of the combustion air area are obtained.

[0011] Monitoring and early warning module: the stability level of the combustion air area is analyzed by combining the risk coefficient of each risk area with the spatial distribution density characteristic value and the correlation influence characteristic value, and the corresponding early warning signal is sent according to different stability levels.

[0012] Compared with the prior art, the present application has the following beneficial effects: (1) the present application obtains the real-time area of each risk area by dividing adjacent fracture points, and calculates the area diffusion degree by tracking the area change of each risk area, which realizes the conversion of discrete fracture point monitoring into continuous and quantifiable risk area dynamic evaluation, and provides a data basis for subsequent analysis of the risk coefficient of the risk area.

[0013] (2) the present application determines stress mutation points by real-time acquisition of stress data of the inner wall of surrounding rock, and calculates the risk coefficient by combining the real-time area and the area diffusion degree of each risk area through the correlation analysis of the stress mutation points and the risk area, which considers the potential influence of the stress mutation points outside the area on the risk area, and improves the scientificity and pertinence of risk judgment.

[0014] (3) the present application obtains the spatial distribution density characteristic value and the correlation influence characteristic value of the combustion air area according to the spatial position of each risk area of the inner wall of the surrounding rock of the combustion air area, which considers the overall distribution of the risk area and the linkage influence between the risk areas, captures the potential correlation between risks, avoids sudden accidents caused by ignoring the coupling effect between areas, and provides core technical support for long-term safe and stable operation of the combustion air area.

[0015] (4) the present application analyzes the stability level of the combustion air area by combining the risk coefficient of each risk area with the spatial distribution density characteristic value and the correlation influence characteristic value, and sends the corresponding early warning signal according to different stability levels, which realizes comprehensive evaluation of the stability of the combustion air area, avoids resource waste caused by indiscriminate early warning, and improves the early warning efficiency. DETAILED DESCRIPTION

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the system module connections of the present invention.

[0018] Figure 2 This is a schematic diagram of the method for obtaining the real-time area of ​​each risk area in this invention.

[0019] Figure 3 This is a schematic diagram illustrating the specific steps of the risk coefficient calculation module in this invention. Detailed Implementation

[0020] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. Furthermore, it should be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale.

[0021] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.

[0022] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0023] This invention monitors all fracture points on the inner wall of the gasification combustion zone in real time, divides adjacent fracture points to obtain the real-time area of ​​each risk zone, and calculates the area diffusion by tracking the area changes of each risk zone. By identifying stress mutation points, it performs correlation analysis between stress mutation points and risk zones to obtain stress influence coefficients. Combining the real-time area and area diffusion of each risk zone, it calculates the risk coefficients. By combining the risk coefficients of each risk zone with spatial distribution density characteristics and correlation influence characteristics, it obtains the stability level of the gasification combustion zone. Corresponding early warning signals are issued according to different stability levels. This allows professionals to take differentiated prevention and control measures according to the early warning level, effectively avoiding safety accidents such as combustion zone collapse and ensuring the stability of the surface ecological environment.

[0024] Please see Figure 1As shown, this invention provides a real-time monitoring and early warning system for the stability of the combustion zone in underground coal gasification, comprising: a fracture point monitoring module, a risk area analysis module, a risk coefficient calculation module, a risk correlation analysis module, and a monitoring and early warning module. The connections between the modules are as follows: the fracture point monitoring module is connected to the risk area analysis module; the risk coefficient calculation module is connected to both the risk area analysis module and the risk correlation analysis module; and the monitoring and early warning module is connected to the risk correlation analysis module.

[0025] Fracturing point monitoring module: Sensor groups are evenly distributed on the inner wall of the surrounding rock in the combustion zone. The sensor groups receive seismic waves over a continuous period of time to locate the positions of all fracture points.

[0026] Considering that the fracturing of the surrounding rock in the combustion zone is essentially a process in which the stored elastic potential energy is rapidly released after the internal stress of the rock mass exceeds its strength threshold, and this process will excite microseismic waves, seismic waves are real-time signals that directly reflect the fracturing behavior. By capturing this signal, the occurrence and location of the fracturing can be traced back. Considering that the fracturing point is difficult to monitor in real time by visual observation or surface detection, microseismic sensors can be deployed in all planar surrounding rock areas of the combustion zone to receive seismic waves and trace the location of the fracturing point.

[0027] Furthermore, considering that the propagation speed of longitudinal waves is faster than that of transverse waves when seismic waves propagate in rock media, the longitudinal wave signal arrives at the microseismic sensor first. Therefore, this invention analyzes the arrival time of longitudinal waves in microseismic waves.

[0028] Based on this, in a specific embodiment of the present invention, the specific contents of the fracture point monitoring module include: firstly, uniformly dividing all planar surrounding rock areas in the combustion zone into grids, taking the intersections of each grid as monitoring points, and deploying a sensor group containing microseismic sensors and stress sensors at each monitoring point.

[0029] Secondly, waveform signals appearing over a continuous time period are recorded from the microseismic sensors at each monitoring point.

[0030] Next, the waveform morphology and dominant frequency of the waveform signals at each monitoring point are compared with each other. Waveform signals with consistent waveform morphology and similar dominant frequency are taken as waveform signals of the same rupture point. All monitoring points that are the same as the waveform signals of each rupture point are counted. The first arrival time of the P-wave is extracted from the waveform signals of each monitoring point corresponding to each rupture point. The monitoring points corresponding to each rupture point are grouped into pairs to obtain each group of monitoring points. The first arrival time difference of the P-wave corresponding to each group of monitoring points is calculated.

[0031] In a preferred embodiment of the present invention, the waveform morphology features include peak value and rising edge slope; waveform signals with the same peak value and the same rising edge slope are categorized as having consistent waveform morphology features, and waveform signals with a main frequency difference less than a set deviation threshold are categorized as having similar main frequencies. In the present invention, the set deviation threshold can be 10%.

[0032] Then, based on the time difference of the first arrival of the longitudinal wave at each monitoring point at each fracture point and the longitudinal wave velocity in the surrounding rock medium, the distance difference between each monitoring point is calculated.

[0033] One method is to calibrate through experiments, collect representative test rock samples on-site, and use an ultrasonic detector to emit ultrasonic longitudinal waves into the test rock to measure the propagation speed of the longitudinal waves in the test rock.

[0034] Finally, the distance difference between each set of monitoring points at each rupture point and the location of each set of monitoring points are substituted into the Euclidean distance difference calculation formula to output the specific location of each rupture point.

[0035] The Euclidean distance difference calculation formula is as follows: .

[0036] in The Z, Y, and Z coordinates represent the specific location of the rupture point, respectively. and These represent the Z, Y, and Z coordinates of two of the sensor positions, respectively. This represents the distance difference from the rupture point to the locations of two of the sensors.

[0037] in This represents the Euclidean distance from the desired break point to one of the sensors. This represents the Euclidean distance from the desired break point to another sensor.

[0038] This invention uses a sensor array evenly distributed on the inner wall of the surrounding rock in the combustion zone to receive seismic waves over a continuous period of time, thereby locating all rupture points. This provides a foundation for subsequent risk area analysis and reliable data anchors for all subsequent risk analysis stages, ensuring the authenticity and effectiveness of the analysis results of the entire monitoring and early warning system from the root.

[0039] Risk area analysis module: The real-time area of ​​each risk area is obtained by dividing adjacent rupture points, and the area diffusion degree is calculated by tracking the area change of each risk area.

[0040] Given that the risks and hazards of the combustion zone are directly related to the size of the fractured area, a larger risk area means a wider range of surrounding rock fracturing, more severe damage to the integrity of the rock mass structure, and a greater probability and impact of subsequent deformation and collapse. Judging the risk solely by the number of fracture points cannot accurately reflect the actual coverage of the fractured area.

[0041] Based on this, such as Figure 2 As shown, the method for obtaining the area of ​​each risk area is as follows: W1. Calculate the Euclidean distance between each rupture point and other rupture points, and record two points with a distance less than a set distance threshold as adjacent points. Take a certain rupture point as the initial rupture point and include its adjacent points into the initial rupture point cluster.

[0042] The distance threshold is set to define the adjacent range of the rupture point. In this invention, its value is 0.2m. Implementers can set other specific values ​​themselves.

[0043] W2. Sequentially add the neighboring points of each other rupture point in the initial rupture point cluster to the initial rupture point cluster until all neighboring points of all rupture points in the initial rupture point cluster are in the corresponding cluster.

[0044] W3. Count all rupture point clusters, and denote the smallest bounding polygon containing all points in each rupture point cluster as each risk region.

[0045] W4. Calculate the real-time area of ​​each risk area by multiplying the number of grids covered by each risk area by the area of ​​a single grid.

[0046] Considering that the fracturing of the surrounding rock in the combustion zone has dynamic evolution characteristics, the risk area is not static and its area diffusion directly reflects the development trend of the surrounding rock instability. At the same time, the area diffusion of the risk area is positively correlated with the stability risk of the combustion zone. The higher the area diffusion, the faster the propagation rate of rock mass fracturing and the higher the degree of stress imbalance. The probability and scope of subsequent surrounding rock collapse and deformation also increase.

[0047] Therefore, it is necessary to quantify the dynamic diffusion process of the risk area into specific indicators by calculating the area diffusion degree, so as to ensure that the assessment of the risk of the combustion zone is consistent with the actual evolution law and avoid early warning delays or misjudgments caused by ignoring the diffusion trend.

[0048] The method for calculating the area diffusion of the risk area is as follows: monitor the area of ​​each risk area after a set time period, calculate the difference between the area of ​​each risk area after the set time period and the real-time area, and record the ratio of the difference to the real-time area as the area diffusion.

[0049] This invention obtains the real-time area of ​​each risk region by dividing adjacent rupture points, and calculates the area diffusion degree by tracking the area changes of each risk region. This realizes the transformation of discrete rupture point monitoring into continuous and quantifiable dynamic assessment of risk regions, providing a data foundation for subsequent analysis of the risk coefficient of risk regions.

[0050] Risk coefficient calculation module: By collecting stress data of the inner wall of the surrounding rock in real time, the stress mutation point is determined, the stress mutation point is correlated with the risk area, and the risk coefficient is calculated by combining the real-time area and area diffusion of each risk area.

[0051] Given that changes in surrounding rock stress are an intrinsic cause of risk, and that sudden stress changes are a direct precursor to accelerated rock fracturing or even collapse, neglecting to consider sudden stress changes may lead to the overlooking of the collapse precursors they cause.

[0052] Based on this, such as Figure 3 As shown, the specific contents of the risk coefficient calculation module include: S1, extracting recent historical stress data for a set period from the historical stress data monitored by the stress sensors corresponding to each monitoring point, constructing a recent stress time series, comparing the stress at adjacent time points in the recent stress time series to obtain the stress change, and forming a recent stress change sequence based on the stress change.

[0053] S2. Based on the stress data of each monitoring point monitored in real time by the stress sensor, calculate the current stress change of each monitoring point, and analyze whether there is a stress mutation by combining the recent stress change sequence of each monitoring point. The monitoring point with stress mutation is taken as the stress mutation point.

[0054] In a preferred embodiment of the present invention, the method for determining the stress abrupt change point includes: firstly, calculating the recent standard deviation and recent average value of all stress changes in the recent stress change sequence of each monitoring point.

[0055] Secondly, a corresponding deviation threshold is set based on the recent standard deviation of the recent stress change sequence at each monitoring point. In this invention, the deviation threshold is based on... The principle is to use three times the recent standard deviation as the deviation threshold.

[0056] Finally, when the deviation between the current stress change at a certain monitoring point and the recent average value is greater than the corresponding deviation threshold, it is determined that there is a stress mutation at the monitoring point, and the monitoring point is recorded as a stress mutation point.

[0057] S3. Analyze the positional relationship between each stress mutation point and each risk area, and obtain the stress influence coefficient based on the positional relationship analysis.

[0058] Considering that stress abrupt changes are a direct manifestation of the imbalance in the mechanical equilibrium of the surrounding rock in the combustion zone, and that the impact of stress abrupt change points on the risk area is related to the number and spatial distance of these points, the more stress abrupt change points there are around the same risk area, the wider the area is affected by mechanical anomalies. Simultaneously, the closer the abrupt change point is to the risk area, the easier it is for the stress disturbance it induces to be transmitted to the risk area, accelerating the spread of the risk or rock mass instability. Therefore, the stress influence coefficient is an important indicator for analyzing risk levels.

[0059] Based on this, the method for calculating the stress influence coefficient includes: first, comparing the location of each stress mutation point with all risk areas, recording the stress mutation points located within the risk areas as mutation points within the area, and counting the number of mutation points within the area of ​​each risk area.

[0060] Secondly, mutation points located outside all risk areas are designated as external mutation points. Each external mutation point is then associated with the risk area that has the shortest straight-line distance to it, and this distance is recorded as the association distance. The straight-line distance refers to the shortest distance from the external mutation point to the boundary of the risk area.

[0061] Next, the number of external mutation points associated with each risk area and the associated distance of each external mutation point are counted.

[0062] Then, the ratios of the number of intra-regional mutation points and the number of external mutation points in each risk zone to the total number of stress mutation points in the combustion zone are denoted as the intra-regional mutation point ratio and the external mutation point ratio, respectively.

[0063] Finally, the stress influence coefficient is obtained by comprehensively analyzing the ratio of mutation points within the region, the ratio of mutation points outside the region, and the correlation distances corresponding to the mutation points outside the region.

[0064] The formula for calculating the stress influence coefficient is as follows: .

[0065] in, Represents the stress influence coefficient. The ratio of mutation points in the representative region Represents the ratio of external mutation points. Represents the number of external mutation points. The number representing each external mutation point. Representing the The association distance corresponding to each external mutation point This represents the maximum distance from the plane boundary of the risk area. ,in These represent the weights corresponding to internal and external mutation points, respectively. .

[0066] From the perspective of rock mechanics, the impact of abrupt changes within a region on the risk area is the most direct and severe; therefore, the weighting settings must meet certain conditions. The specific values ​​can be determined by inviting relevant experts who study the stability of the combustion zone to analyze them, having the experts submit their respective weighted results, and then statistically analyzing the results to calculate the mean and determine the final weight.

[0067] in The term representing the impact of external stress abrupt changes is expressed as the product of the ratio of external abrupt changes to the associated distance, reflecting their coupling relationship. This demonstrates that the impact of external stress abrupt changes on the risk area is based on the combined effect of their quantity and distance attenuation efficiency. This represents the correlation distance expression, showing that the reaction stress influence coefficient is inversely proportional to the correlation distance. This represents the normalized average of the correlation distances between each risk area, and the denominator is avoided by adding it to the natural number 1.

[0068] S4. The risk coefficient of each risk area is obtained by comprehensively calculating the area, area diffusion degree and stress influence coefficient of each risk area.

[0069] Preferably, the present invention obtains the area ratio of each risk area by analyzing the ratio of the area of ​​the risk area to the overall area of ​​the inner wall of the surrounding rock of the combustion zone, and obtains the risk coefficient of each risk area by weighted summing of the area ratio, area diffusion degree and stress influence coefficient of each risk area.

[0070] The specific weights corresponding to the area ratio, area diffusion degree, and stress influence coefficient can be extracted from the historical collapse data of similar combustion zones. The area ratio, diffusion degree, stress influence coefficient, and corresponding risk coefficient are extracted, and the area ratio, diffusion degree, and stress influence coefficient are used as independent variables, and the risk coefficient is used as the dependent variable. A multiple linear regression model is constructed for quantitative analysis, and the regression coefficients and constant terms corresponding to the area ratio, diffusion degree, and stress influence coefficient are fitted. Then, the regression coefficients corresponding to the area ratio, diffusion degree, and stress influence coefficient are normalized and summed to 1. The normalized regression coefficients corresponding to the area ratio, diffusion degree, and stress influence coefficient are used as their respective weights.

[0071] This invention identifies stress mutation points by real-time acquisition of stress data from the inner wall of the surrounding rock, performs correlation analysis between stress mutation points and risk areas, calculates risk coefficients by combining the real-time area and area diffusion of each risk area, and considers the potential impact of external stress mutation points on risk areas, thereby improving the scientific nature and pertinence of risk assessment.

[0072] Risk correlation analysis module: Based on the spatial location of each risk area on the inner wall of the surrounding rock of the combustion zone, obtain the spatial distribution density characteristic value and correlation influence characteristic value of the combustion zone.

[0073] Considering that the stability of the combustion zone is not determined solely by the size of a single risk area, the spatial distribution of risk areas also significantly affects the overall structural stability. For example, when multiple risk areas are densely distributed, they are prone to forming through-flow fracture channels, which greatly reduces the bearing capacity of the surrounding rock. On the other hand, the threat of scattered risk areas to overall stability is relatively controllable. Therefore, the spatial distribution characteristics of risk areas can be quantified to obtain distribution density characteristic values ​​to supplement the evaluation dimensions.

[0074] Based on this, the method for obtaining the spatial distribution density characteristic value is as follows: according to the spatial location of each risk area in the inner wall of the surrounding rock of the combustion zone, determine all risk areas in each planar surrounding rock area, analyze the ratio of the average geometric center distance between all risk areas in each planar surrounding rock area to the maximum boundary distance of the corresponding plane, and use this ratio as the dispersion of each planar surrounding rock area.

[0075] The area coverage is defined as the ratio of the sum of the risk areas in each planar surrounding rock region to the total area of ​​the corresponding planar surrounding rock region.

[0076] Considering that a larger area coverage value indicates a larger area of ​​risk zone coverage in each planar surrounding rock region, and a higher distribution density, the distribution density is directly proportional to the area coverage value. On the other hand, considering that a smaller dispersion value indicates a closer geometric center distance between the risk zones in each planar surrounding rock region, and a denser distribution, and a higher distribution density, the distribution density is inversely proportional to the dispersion value. Analyzing the ratio between the two can accurately map the correspondence between the planar risk level and the actual instability probability, and accurately reflect the synergistic effect of risk space occupancy and clustering effect.

[0077] Based on this, the ratio of area coverage to dispersion of each plane surrounding rock area is used as the distribution density.

[0078] The spatial density characteristic value of the combustion zone is obtained by weighted summation of the distribution density of all planar surrounding rock areas.

[0079] Considering that the distribution density of the top, bottom, and surrounding rocks of the combustion zone has an inconsistent impact on the stability of the combustion zone, different weights are set to reflect the overall density characteristics of the combustion zone.

[0080] Considering that the surrounding rock plays a supporting role, its collapse has the greatest impact on the overall stability. Therefore, the weight of the plane corresponding to the surrounding rock should be the highest, while the weight of the bottom surrounding rock has the least impact on the overall stability. The specific weight values ​​can be set by the implementer, but the sum of the weights of all planes is 1.

[0081] Considering that the proximity of risk areas within the same plane may lead to related effects, the rupture and expansion of one area can transmit stress disturbances to surrounding areas, accelerating the instability of adjacent risk areas and thus increasing the safety risks of the overall structure of the combustion zone, thereby affecting the overall stability of the combustion zone. Ignoring related effects will result in a lower calculation of subsequent risk values, affecting the judgment of the overall stability of the combustion zone.

[0082] Based on this, the method for obtaining the correlation influence characteristic value is to obtain the shortest straight-line distance between the boundaries of each risk area and other risk areas within each plane surrounding rock area.

[0083] When other risk areas whose shortest straight-line distance to the corresponding boundary of each risk area is less than the set boundary distance threshold are selected, they are associated and combined to obtain each area association group.

[0084] The regional boundary distance threshold can be obtained by extracting the collapse range of adjacent risk areas caused by each collapse risk area from historical collapse events in the combustion zone, and using the average of the collapse ranges of all adjacent risk areas as the regional boundary distance threshold.

[0085] Considering that the risk of collapse in the combustion zone is not the result of a single risk area acting independently, but is often caused by multiple risk areas being interconnected and influencing each other, and considering that the overall instability of the combustion zone is increasing, it is essentially the result of the combined effect of the increase in the number of risk areas within the associated group and the increase in the total number of such associated groups. If calculations such as addition are used, the amplification effect of this dual influence will be weakened. Multiplication calculations can avoid the misjudgment that the advantage of one dimension cancels out the disadvantage of another dimension, and ensure the accurate quantification of the associated impact.

[0086] Based on this, the sum of the number of risk areas in each regional association group is used as the product of the sum of the number of risk areas in each regional association group and the total number of regional association groups as the characteristic value of the association impact.

[0087] This invention obtains the spatial distribution density and correlation influence characteristics of the combustion zone based on the spatial location of each risk area on the inner wall of the combustion zone surrounding rock. It takes into account the overall distribution of risk areas and the linkage influence between risk areas, captures the potential correlation between risks, avoids sudden accidents caused by ignoring the coupling effect between areas, and provides core technical support for the long-term safe and stable operation of the combustion zone.

[0088] Monitoring and early warning module: Combines the risk coefficients of each risk area with the spatial distribution density characteristics and the associated impact characteristics to analyze the stability level of the combustion zone, and issues corresponding early warning signals according to different stability levels.

[0089] In a preferred embodiment of the present invention, the specific method for analyzing the stability level of the gasification combustion zone includes: normalizing the correlation influence characteristic value and the spatial distribution density characteristic value respectively.

[0090] The normalization process involves extracting historical monitoring data of the combustion zone from the combustion zone monitoring database, selecting the maximum value of the associated impact characteristic value and the maximum value of the spatial distribution density characteristic value, and performing a ratio analysis between the associated impact characteristic value and the spatial distribution density characteristic value and the corresponding maximum value. When the ratio result is greater than 1, the normalization result is recorded as 1. The normalization process makes the associated impact characteristic value, the spatial distribution density characteristic value, and the risk level dimension the same.

[0091] The risk value is the product of the correlation impact characteristic value, the spatial distribution density characteristic value, and the maximum value of the risk coefficient of each risk area within the combustion zone.

[0092] Among them, the three factors of correlation influence characteristic value, spatial distribution density characteristic value and risk coefficient of each risk area in the combustion air zone do not affect stability independently, but have a risk transmission relationship that is linked one after another. Only through multiplication can the actual effect of this chain amplification be reflected: if other forms such as addition are used, there will be a misjudgment that the low-risk term cancels out the high-risk term, while multiplication can make the influence of any one term fully highlighted, which is consistent with the actual law of combustion air zone instability.

[0093] The risk value is matched with the risk value range corresponding to each set stability level to obtain the stability level corresponding to the combustion zone.

[0094] The risk value is a value between [0, 1], with a larger value indicating a higher risk and a lower stability level. In a specific example of this invention, three stability levels are set: medium, high, and low. The risk value range for the high stability level is [0, M1], for the medium stability level it is (M1, M2], and for the low stability level it is (M2, 1]. The specific value range is determined by extracting historical collapse data from the combustion zone monitoring database and tracing the risk value of each collapse event before the collapse. Considering that the stability level of the combustion zone is generally considered to be higher in the months before the collapse, this invention calculates the average risk value of the three months before the collapse and records it as the maximum value of the high stability level risk value range, i.e., M1. Considering that the closer to the collapse time, the lower the stability level, this invention calculates the average risk value of the week before the collapse and records it as the minimum value of the medium stability level risk value range, i.e., M2. The specific time can also be set by the implementer.

[0095] This invention analyzes the stability level of the combustion zone by combining the risk coefficients of each risk area with the spatial distribution density characteristics and the correlation influence characteristics. Based on different stability levels, corresponding early warning signals are issued to achieve a comprehensive assessment of the stability of the combustion zone. By classifying early warnings for different stability levels, the waste of resources caused by indiscriminate early warnings is avoided, and the early warning efficiency is improved.

[0096] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0097] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0098] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0099] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0100] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A real-time monitoring and early warning system for the stability of the combustion chamber in underground coal gasification, characterized in that, include: Fracturing point monitoring module: Sensor groups are evenly distributed on the inner wall of the surrounding rock in the combustion zone. The sensor groups receive seismic waves over a continuous period of time to locate the positions of all fracture points. Risk area analysis module: It obtains the real-time area of ​​each risk area by dividing adjacent rupture points, and calculates the area diffusion degree by tracking the area change of each risk area; Risk coefficient calculation module: By collecting stress data of the inner wall of the surrounding rock in real time, the stress mutation point is determined, the stress mutation point is correlated with the risk area, and the risk coefficient is calculated by combining the real-time area and area diffusion of each risk area. Risk correlation analysis module: Based on the spatial location of each risk area on the inner wall of the surrounding rock of the combustion zone, obtain the spatial distribution density characteristic value and correlation influence characteristic value of the combustion zone; Monitoring and early warning module: Combines the risk coefficients of each risk area with the spatial distribution density characteristics and the associated impact characteristics to analyze the stability level of the combustion zone, and issues corresponding early warning signals according to different stability levels.

2. The real-time monitoring and early warning system for the stability of the underground coal gasification combustion zone according to claim 1, characterized in that, The specific contents of the fracture point monitoring module include: In the combustion zone, all planar surrounding rock areas are divided into uniform grids, and the intersections of each grid are used as monitoring points. Sensor groups containing microseismic sensors and stress sensors are deployed at each monitoring point. Waveform signals appearing over a continuous time period are recorded from the microseismic sensors at each monitoring point; The waveform morphology and main frequency of the waveform signals at each monitoring point are compared with each other. Waveform signals with consistent waveform morphology and similar main frequency are taken as waveform signals of the same rupture point. All monitoring points that are the same as the waveform signals of each rupture point are counted. The first arrival time of the P-wave is extracted from the waveform signals of each monitoring point corresponding to each rupture point. The monitoring points corresponding to each rupture point are grouped in pairs to obtain each group of monitoring points. The first arrival time difference of the P-wave corresponding to each group of monitoring points is calculated. The distance difference between each set of monitoring points is calculated based on the time difference of the first arrival of the longitudinal wave at each set of monitoring points at each fracture point, combined with the longitudinal wave velocity under the surrounding rock medium. Substitute the distance difference between each set of monitoring points at each rupture point and the location of each set of monitoring points into the Euclidean distance difference calculation formula to output the specific location of each rupture point.

3. The real-time monitoring and early warning system for the stability of the underground coal gasification combustion zone according to claim 1, characterized in that, The method for obtaining the real-time area of ​​each risk zone includes: Calculate the Euclidean distance between each rupture point and other rupture points. Record two points whose distance is less than a set distance threshold as adjacent points. Take a certain rupture point as the initial rupture point and include its adjacent points into the initial rupture point cluster. The adjacent points of each other rupture point in the initial rupture point cluster are successively included in the initial rupture point cluster until all adjacent points of all rupture points in the initial rupture point cluster are in the corresponding cluster. Collect all rupture point clusters, and denote the smallest bounding polygon containing all points in each rupture point cluster as each risk region; The real-time area of ​​each risk zone is calculated by multiplying the number of grids covered by each risk zone by the area of ​​a single grid.

4. The real-time monitoring and early warning system for the stability of the underground coal gasification combustion zone according to claim 3, characterized in that, The method for calculating the area diffusion of the risk zone is as follows: Monitor the area of ​​each risk area after a set time period, calculate the difference between the area of ​​each risk area after the set time period and the real-time area, and record the ratio of the difference to the real-time area as the area diffusion degree.

5. A real-time monitoring and early warning system for the stability of the underground coal gasification combustion zone according to claim 2, characterized in that, The specific contents of the risk coefficient calculation module include: The stress data for the recent historical set period is extracted from the historical stress data monitored by the stress sensors at each monitoring point, and a recent stress time series is constructed. The stress at adjacent time points in the recent stress time series is compared by difference to obtain the stress change. The recent stress change series is composed based on the stress change. Based on the stress data of each monitoring point monitored in real time by the stress sensor, the current stress change of each monitoring point is calculated. Combined with the recent stress change sequence of each monitoring point, it is analyzed whether there is a stress mutation. The monitoring point with stress mutation is taken as the stress mutation point. Analyze the positional relationship between each stress abrupt change point and each risk area, and obtain the stress influence coefficient based on the positional relationship analysis; The risk coefficient of each risk area is obtained by comprehensively calculating the area, area diffusion degree, and stress influence coefficient of each risk area.

6. A real-time monitoring and early warning system for the stability of the underground coal gasification combustion zone according to claim 5, characterized in that, The method for determining stress abrupt change points includes: Calculate the recent standard deviation and recent mean of all stress changes in the recent stress change sequence for each monitoring point; Set the corresponding deviation threshold based on the recent standard deviation of the recent stress change sequence at each monitoring point; When the deviation between the current stress change at a monitoring point and the recent average value is greater than the corresponding deviation threshold, it is determined that there is a stress mutation at the monitoring point, and the monitoring point is recorded as a stress mutation point.

7. A real-time monitoring and early warning system for the stability of the underground coal gasification combustion zone according to claim 6, characterized in that, The method for calculating the stress influence coefficient includes: The location of each stress mutation point is compared with that of all risk areas. Stress mutation points located within risk areas are recorded as mutation points within the area. The number of mutation points within the area of ​​each risk area is counted. Mutation points located outside all risk areas are designated as external mutation points. Each external mutation point is associated with the risk area that is closest to it by a straight line, and the distance between them is designated as the association distance. Count the number of external mutation points associated with each risk area and the associated distance of each external mutation point; The ratios of the number of intra-regional mutation points and the number of external mutation points in each risk zone to the total number of stress mutation points in the combustion zone are denoted as the intra-regional mutation point ratio and the external mutation point ratio, respectively. The stress influence coefficient is obtained by comprehensively analyzing the ratio of mutation points within the region, the ratio of mutation points outside the region, and the correlation distances corresponding to the mutation points outside the region.

8. The real-time monitoring and early warning system for the stability of the underground coal gasification combustion zone according to claim 1, characterized in that, The method for obtaining the spatial distribution density feature value: Based on the spatial location of each risk area on the inner wall of the surrounding rock in the combustion zone, all risk areas in each planar surrounding rock area are determined. The ratio of the average geometric center distance between all risk areas in each planar surrounding rock area to the maximum boundary distance of the corresponding plane is analyzed, and the ratio is used as the dispersion of each planar surrounding rock area. The ratio of the sum of the risk areas in each planar surrounding rock region to the total area of ​​the corresponding planar surrounding rock region is used as the area coverage. The ratio of area coverage to dispersion of each plane surrounding rock region is used as the distribution density; The spatial density characteristic value of the combustion zone is obtained by weighted summation of the distribution density of all planar surrounding rock areas.

9. A real-time monitoring and early warning system for the stability of the underground coal gasification combustion zone according to claim 1, characterized in that, The method for obtaining the correlation influence feature values: Obtain the shortest straight-line distance between each risk zone and other risk zones within each plane surrounding rock area; Filter out other risk areas whose shortest straight-line distance to the boundary of each risk area is less than the set boundary distance threshold, and associate and combine them to obtain each area association group; The sum of the number of risk areas in each regional association group is counted, and the product of this sum and the total number of regional association groups is used as the characteristic value of the association impact.

10. A real-time monitoring and early warning system for the stability of the underground coal gasification combustion zone according to claim 9, characterized in that, Specific methods for analyzing the stability level of the combustion zone include: The correlation influence feature value and the spatial distribution density feature value are normalized respectively; The risk value is the product of the correlation impact characteristic value, the spatial distribution density characteristic value, and the maximum value of the risk coefficient of each risk area within the combustion zone. The risk value is matched with the risk value range corresponding to each set stability level to obtain the stability level corresponding to the combustion zone.

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