Early warning system and method based on critical conditions for shaft wall failure in deep geomechanics

Abstract

The present invention relates to the field of mine intelligent early warning technology, and discloses an early warning system and method based on the critical conditions of shaft wall failure in deep geomechanics. The method includes obtaining initial rock formation parameters to establish a rock formation structure classification system, obtaining initial gas drilling geometric parameters and arranging a three-level displacement monitoring array to construct a three-level displacement characteristic data stream; constructing a displacement-stress mapping model based on these data, collecting mining-induced trigger parameters and real-time displacement data, generating a dynamically corrected critical stress for shaft wall instability, and calculating the critical safety factor for shaft wall instability in combination with the mining-induced trigger parameters to establish a hierarchical early warning criterion; finally, based on the real-time shaft wall instability determination data and the hierarchical early warning criterion, generating an early warning result for shaft wall instability and a risk dynamic decision-making plan; by comprehensively considering various factors, the present invention realizes accurate early warning of shaft wall instability and significantly improves the safety of mine exploitation.

Description

Technical Field

[0001] The present invention relates to the technical field of mine intelligent early warning, and more specifically, to an early warning system and method based on the critical conditions of shaft wall failure in deep geomechanics. Background Art

[0002] In gas drilling operations, the stability of the shaft wall is crucial for ensuring the safe and efficient progress of gas drainage work. As a key passage for gas drainage, once the shaft wall of a gas well loses stability, it will not only lead to the interruption of gas drainage, affecting the safe production of coal mines, but may also trigger safety accidents such as gas leakage and explosion, causing economic losses and endangering personnel safety.

[0003] The patent application with the publication number CN115680775A proposes an intelligent early warning system and method for coal and gas outbursts based on multi-sensor fusion. By obtaining multi-source data through microseismic sensors, in-situ stress sensors, laser methane sensors, and wind speed sensors, it aims to solve the problem of large noise and inaccuracy in predicting the level of coal and gas outbursts with single data. However, this existing technology focuses on the early warning of coal and gas outbursts, which is different from the shaft wall instability early warning scenario concerned by the present invention. Shaft wall instability involves many complex factors such as rock formation structure, the geometry of gas wells, displacement changes, and mining-induced effects. The coal and gas outburst early warning technology cannot be directly applied to shaft wall instability early warning. Coal and gas outburst early warning mainly focuses on gas-related parameters and geological dynamic phenomena, ignoring the structural characteristics and mechanical responses of the shaft wall itself, and it is difficult to effectively evaluate the stability of the shaft wall.

[0004] The patent application with the publication number CN110985125A provides a deep well soft coal rockburst disaster monitoring and early warning system and its early warning method. By monitoring the borehole pressure and roadway wall deformation through stress sensors and displacement sensors, and releasing early warnings based on comprehensive data, the accuracy of early warnings is improved. However, this existing technology mainly targets deep well soft coal rockburst disasters, and there are differences in the monitoring objects and early warning principles from shaft wall instability early warning. Rockburst disasters mainly focus on the stress changes inside the coal body and the dynamic phenomena caused by sudden release. In addition to being affected by rock formation stress, shaft wall instability is also closely related to the support structure of gas wells, the advancing speed of the working face during the mining process, and the pressure changes of hydraulic supports. This technical solution does not consider these key factors of shaft wall instability and cannot meet the requirements of shaft wall instability early warning.

[0005] When the existing mine disaster early warning technology deals with the problem of shaft wall instability of gas wells under deep geomechanical conditions, it insufficiently considers the deep geomechanical characteristics, rock formation structure, and various influencing factors during the construction and operation of gas wells. There is a lack of technical means to accurately monitor the state of the shaft wall of gas wells, early warn of instability risks, and formulate effective countermeasures. Summary of the Invention

[0006] To overcome the above-mentioned defects of the prior art, the present invention provides a warning system and method for the critical conditions of shaft wall failure based on deep geomechanics. By comprehensively considering deep geomechanical properties, gas drilling geometric parameters, displacement monitoring data, and mining-induced triggering parameters, an accurate displacement-stress mapping model is constructed, realizing the dynamic assessment and hierarchical warning of the risk of shaft wall instability, improving the accuracy of shaft wall instability warning, reducing the false alarm and missed alarm rates, and providing a scientific basis and technical support for the shaft wall stability management in gas drilling operations.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A warning method for the critical conditions of shaft wall failure based on deep geomechanics, comprising:

[0009] Obtain initial rock formation parameters and establish a rock formation structure classification system; obtain an initial gas drilling geometric parameter group G0, arrange a three-level displacement monitoring array, and construct a three-level displacement characteristic data stream; construct a displacement-stress mapping model according to the rock formation structure classification system, the initial gas drilling geometric parameter group G0, and the three-level displacement characteristic data stream;

[0010] Collect a mining-induced triggering parameter set P1 and a real-time three-level displacement characteristic data stream, generate a dynamically corrected critical stress for shaft wall instability based on the displacement-stress mapping model, the mining-induced triggering parameter set P1, and the real-time three-level displacement characteristic data stream; calculate the critical safety factor for shaft wall instability in combination with the mining-induced triggering parameter set P1, and establish a hierarchical warning criterion; obtain real-time shaft wall instability determination data, generate a shaft wall instability warning result based on the real-time shaft wall instability determination data and the hierarchical warning criterion, and generate a risk dynamic decision-making plan based on the shaft wall instability warning result.

[0011] Furthermore, the obtaining of the initial rock formation parameters and establishing the rock formation structure classification system includes:

[0012] Divide the roof rock formation into n structural layers to form a three-dimensional rock formation partition matrix, where n≥3;

[0013] Extract the initial rock formation parameters of each structural layer, and the initial rock formation parameters include the initial compressive strength σ 0,i and the initial strain ε 0,i , constituting a complete set of descriptions of the formation mechanical properties; where σ 0,i represents the initial compressive strength of the i-th structural layer, and ε 0,i represents the initial strain of the i-th structural layer, and 1≤i≤n;

[0014] Locate the soft rock interlayers of each structural layer, obtain the spatial distribution law of the soft rock interlayers, and generate a soft rock interlayer distribution matrix D0;

[0015] The rock stratum structure classification system is composed of a three-dimensional rock stratum zoning matrix, a formation mechanical property description set, and a soft rock interlayer distribution matrix D0.

[0016] Furthermore, the acquisition of the initial gas drilling geometric parameter group G0 includes: performing geometric measurements inside the gas drilling to obtain the point cloud data of the wellbore spatial surface; denoising and normalizing the point cloud data of the wellbore spatial surface, and extracting the wellbore curvature radius R0 and the casing wall thickness δ0; constructing the initial geometric parameter group G0 of the gas drilling according to the wellbore curvature radius R0 and the casing wall thickness δ0.

[0017] Furthermore, the layout of the three-level displacement monitoring array includes: arranging a distributed optical fiber sensor array along the axial direction of the gas drilling, deploying an inclination sensor group in the soft rock interlayer, and arranging strain rosettes along the circumferential direction of the casing to form a three-level displacement monitoring array.

[0018] Furthermore, the construction of the three-level displacement characteristic data stream includes:

[0019] Using the distributed optical fiber sensor array to obtain the separation displacement ΔS'1 of the roof rock stratum; using the inclination sensor group to measure the three-dimensional shear deformation angle θ'(t) generated by the soft rock interlayer; using the strain rosettes arranged along the circumferential direction of the casing to obtain the strain distribution data εr'(t) of the casing wall;

[0020] Defining the separation displacement ΔS'1 as the first-level data, the three-dimensional shear deformation angle θ'(t) as the second-level data, and the strain distribution data εr'(t) as the third-level data; constructing a three-level displacement characteristic data stream according to the first-level data, the second-level data, and the third-level data.

[0021] Furthermore, the construction of the displacement-stress mapping model includes:

[0022] According to the initial compressive strength σ 0,i and the initial strain ε 0,i in the formation mechanical property description set, as well as the wellbore curvature radius R0 and the casing wall thickness δ0 in the initial gas drilling geometric parameter group G0, describe the wellbore displacement-stress constitutive relationship in the form of a fourth-order tensor, and establish an initial mapping model M0;

[0023] Introduce the three-dimensional rock stratum zoning matrix, the soft rock interlayer distribution matrix D0, the separation displacement ΔS'1 and the three-dimensional shear deformation angle θ'(t) in the three-level displacement characteristic data stream into the initial mapping model M0, solve the stress-displacement field of the rock stratum around the wellbore by the finite element numerical simulation method, and obtain the coupling law between different-level structural layers, the soft rock interlayer distribution, ΔS'1 and θ'(t) and the deformation of the gas drilling;

[0024] According to the coupling laws among different-level structural layers, the distribution of soft rock interlayers, ΔS'1, and θ'(t) and the deformation of gas drilling, an iterative optimization algorithm is used to calibrate the constitutive parameters in the initial mapping model M0 to form the final displacement-stress mapping model.

[0025] Furthermore, the set P1 of mining-induced trigger parameters collected includes: real-time collection of the time-series data F(t) of the hydraulic support pressure and the working face advance speed v; v and F(t) together constitute the set P1 of mining-induced trigger parameters.

[0026] Furthermore, the real-time three-level displacement characteristic data stream includes the real-time separation displacement ΔS1 of the roof rock stratum, the real-time three-dimensional shear deformation angle θ(t) generated by the soft rock interlayer, and the real-time strain distribution data εr(t) of the casing wall;

[0027] The generation of the dynamically corrected critical stress for wellbore instability includes:

[0028] Based on the real-time three-level displacement characteristic data stream and the displacement-stress mapping model, the third corrected wellbore critical stress is obtained; the third corrected wellbore critical stress is used as the dynamically corrected critical stress for wellbore instability.

[0029] Furthermore, the obtaining of the third corrected wellbore critical stress includes:

[0030] The real-time separation displacement ΔS1 and the real-time three-dimensional shear deformation angle θ(t) are input into the displacement-stress mapping model to obtain the first corrected wellbore critical stress σ 1,i ; σ 1,i represents the first corrected wellbore critical stress of the i-th level structural layer;

[0031] The historical database of wellbore failure cases is retrieved, and the historical critical stress for instability σ c,hist ;

[0032] Based on the first corrected wellbore critical stress σ 1,i , the historical critical stress for instability σ c,hist , and the ratio ΔS1 / θ(t) of the real-time separation displacement to the real-time three-dimensional shear deformation angle, the second corrected wellbore critical stress σ 2,i ; σ 2,i represents the second corrected wellbore critical stress of the i-th level structural layer;

[0033] Based on the time-series data F(t) of the hydraulic support pressure and the working face advance speed v in the set P1 of mining-induced trigger parameters, the second corrected wellbore critical stress σ 2,i is corrected to obtain the third corrected wellbore critical stress σ 3,i ; σ 3,i represents the third corrected wellbore critical stress of the i-th level structural layer.

[0034] Further, calculating the critical safety factor for borehole wall instability includes: obtaining the yield strength σ of the current steel casing of the borehole wall y , based on the dynamically corrected critical stress σ for borehole wall instability 3,i , the yield strength σ of the current steel casing of the borehole wall y and the real-time strain distribution data εr(t) of the casing wall, calculating the critical safety factor K for borehole wall instability c .

[0035] Further, based on the dynamically corrected critical stress σ for borehole wall instability 3,i , the yield strength σ of the current steel casing of the borehole wall y and the real-time strain distribution data εr(t) of the casing wall, calculating the critical safety factor K for borehole wall instability c includes:

[0036] Obtaining the thickness h of the i-th structural layer i , according to h i and the initial compressive strength σ 0,i , calculating the weight of the i-th structural layer ; according to and σ 3,i calculating the comprehensive critical stress value ;

[0037] According to the comprehensive critical stress value , the yield strength σ of the current steel casing of the borehole wall y and the real-time strain distribution data εr(t) of the casing wall, calculating the critical safety factor K for borehole wall instability c .

[0038] Based on the early warning system for the critical conditions of borehole wall failure in deep geomechanics, which is used to implement the above-mentioned early warning method for the critical conditions of borehole wall failure in deep geomechanics, the system includes:

[0039] Data acquisition module: used to obtain the initial rock formation parameters, establish a rock formation structure classification system; obtain the initial gas drilling geometric parameter group G0, deploy a three-level displacement monitoring array, and construct a three-level displacement characteristic data stream;

[0040] Mapping model construction module: According to the rock formation structure classification system, the initial gas drilling geometric parameter group G0 and the three-level displacement characteristic data stream, construct a displacement-stress mapping model;

[0041] Hierarchical early warning module: It is used to collect the mining-induced trigger parameter set P1 and the real-time three-level displacement characteristic data stream. Based on the displacement-stress mapping model, the mining-induced trigger parameter set P1 and the real-time three-level displacement characteristic data stream, it generates the dynamically corrected critical stress of shaft wall instability; combined with the mining-induced trigger parameter set P1, it calculates the critical safety factor of shaft wall instability, establishes a hierarchical early warning criterion; obtains the real-time shaft wall instability determination data, and based on the real-time shaft wall instability determination data and the hierarchical early warning criterion, it generates a shaft wall instability early warning result, and generates a risk dynamic decision-making plan based on the shaft wall instability early warning result.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] By constructing a hierarchical system of rock strata structures and a displacement-stress mapping model, and combining real-time displacement monitoring data and mining-induced trigger parameters, the present invention realizes the dynamic assessment and accurate early warning of the risk of shaft wall instability. This method overcomes the static nature and limitations in traditional shaft wall stability analysis, and can reflect the dynamic changes of the shaft wall under complex geological conditions and mining disturbances in real time. At the same time, the establishment of a hierarchical early warning criterion makes the early warning more scientific and reasonable, and corresponding countermeasures can be taken according to the severity of the risk of shaft wall instability, effectively avoiding the occurrence of shaft wall instability accidents, and ensuring the safety and efficiency of gas drilling operations. In addition, the generation of a risk dynamic decision-making plan provides a scientific basis for mine managers, which helps to achieve the rational allocation and efficient utilization of resources. Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0045] Figure 1 It is the principle flow chart of the early warning method for the critical condition of shaft wall failure based on deep geomechanics in the present invention;

[0046] Figure 2 It is the method flow chart for establishing a hierarchical system of rock strata structures in the early warning method for the critical condition of shaft wall failure based on deep geomechanics in the present invention;

[0047] Figure 3 It is the method flow chart for obtaining the initial geometric parameter group G0 of gas drilling in the early warning method for the critical condition of shaft wall failure based on deep geomechanics in the present invention;

[0048] Figure 4It is the method flow chart for arranging a three - level displacement monitoring array and constructing a three - level displacement characteristic data stream in the early warning method for the critical condition of shaft wall failure based on deep geomechanics of the present invention;

[0049] Figure 5 It is the method flow chart for constructing a displacement - stress mapping model in the early warning method for the critical condition of shaft wall failure based on deep geomechanics of the present invention;

[0050] Figure 6 It is the method flow chart for obtaining the third - corrected critical stress of the shaft wall in the early warning method for the critical condition of shaft wall failure based on deep geomechanics of the present invention;

[0051] Figure 7 It is the functional module diagram of the early warning system for the critical condition of shaft wall failure based on deep geomechanics in the present invention. Detailed implementation mode

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] Embodiment 1

[0054] Please refer to Figure 1 As shown, this embodiment provides an early warning method for the critical condition of shaft wall failure based on deep geomechanics, including:

[0055] Step S1000, obtain initial rock formation parameters, establish a rock formation structure classification system; obtain the initial gas drilling geometric parameter group G0, arrange a three - level displacement monitoring array, and construct a three - level displacement characteristic data stream; according to the rock formation structure classification system, the initial gas drilling geometric parameter group G0 and the three - level displacement characteristic data stream, construct a displacement - stress mapping model;

[0056] Further, step S1000 includes:

[0057] Step S1100, obtain initial rock formation parameters, establish a rock formation structure classification system;

[0058] Further, as Figure 2 shown, step S1100 includes:

[0059] Step S1110, divide the roof rock formation into n - level structural layers to form a three - dimensional rock formation partition matrix, where n≥3;

[0060] Step S1120, extract the initial rock formation parameters of each level of structural layer, and the initial rock formation parameters include the initial compressive strength σ0,i and the initial strain ε 0,i , constituting a complete set of descriptions of the strata mechanical properties; where σ 0,i represents the initial compressive strength of the i-th structural layer, and ε 0,i represents the initial strain of the i-th structural layer, 1 ≤ i ≤ n;

[0061] Step S1130: Precisely locate the soft rock interlayers of each level of structural layer, obtain the spatial distribution law of the soft rock interlayers, and generate the soft rock interlayer distribution matrix D0;

[0062] Step S1140: Form a rock stratum structure classification system from the three-dimensional rock stratum partition matrix, the set of descriptions of the strata mechanical properties, and the soft rock interlayer distribution matrix D0.

[0063] Specifically, step S1100 aims to obtain the initial rock stratum parameters and establish a rock stratum structure classification system, which is an important geological basis for subsequent analysis of shaft wall stability. In step S1110, the roof rock stratum is divided into n levels of structural layers (n ≥ 3) to form a three-dimensional rock stratum partition matrix. Here, n represents the number of layers of rock stratum division, and its value is flexibly adjusted according to the complexity of the strata. For example, in areas with relatively simple geological conditions, n can be taken as 3, and the roof rock stratum is divided into three relatively simple layers of structure, respectively representing rock strata with different mechanical properties; while in areas with complex geological conditions and diverse rock stratum changes, n may take a value of 5 or more to divide the rock strata more carefully. This division method is to simplify the description of complex strata and highlight the main rock stratum structure factors that have a significant impact on shaft wall stability. Its beneficial effect is to classify the complex rock stratum structure in an orderly manner, making the subsequent analysis of the mechanical properties of the rock stratum and shaft wall stability more targeted and systematic. From the reasoning process, due to the huge differences in the distribution and properties of rock strata in different strata, if no hierarchical division is carried out, a large amount of messy data will be faced in the analysis of shaft wall stability, and it is difficult to determine the key influencing factors. Through hierarchical division, complex problems can be disassembled, the analysis difficulty can be reduced, and the research efficiency can be improved.

[0064] The initial compressive strength σ 0,i refers to the ability index of the i-th structural layer to resist pressure failure without being disturbed by subsequent mining and other disturbances; the initial strain ε 0,iIt reflects the initial characteristics of the deformation of the i-th structural layer when it is subjected to stress. For example, for a specific i-th structural layer, if its initial compressive strength is high, it means that the layer is relatively more stable when under pressure; if the initial strain is large, it means that it is more likely to deform when subjected to stress. These parameters provide data support for accurately describing the mechanical properties of the formation, and are key data for the subsequent construction of displacement-stress mapping models and analysis of wellbore stability. The beneficial effect is that by accurately obtaining the mechanical parameters of each structural layer, the behavior of the rock formation under different stress conditions can be more accurately simulated, thereby more accurately evaluating the stability of the wellbore. The reasoning process is that the stability of the wellbore is closely related to the mechanical properties of the surrounding rock formations. Only by accurately mastering these parameters can the effect of the rock formation on the wellbore be truly reflected in the subsequent model construction and analysis, providing a reliable basis for early warning.

[0065] Soft rock interlayers refer to rock layers with lower strength and poorer stability between relatively harder rock layers. Since soft rock interlayers have weaker mechanical properties, they are potential weak areas for wellbore instability in gas drilling. For example, in some strata involved in gas drilling, soft rock interlayers may be composed of mudstone, shale, etc. These rocks have low compressive strength and are easily softened and deformed when exposed to water. During gas drilling operations, factors such as pressure changes in the well and gas flow will affect the wellbore wall, and soft rock interlayers are more likely to deform and break under these effects, which in turn affects the stability of the gas drilling wellbore wall.

[0066] By precisely locating the soft rock interlayer and obtaining its spatial distribution law, the key locations where problems may occur in the gas drilling wellbore wall can be identified. The generated soft rock interlayer distribution matrix D0 can intuitively display the location, thickness and other information of the soft rock interlayer in the rock formation, providing an important basis for subsequent targeted monitoring and analysis. Its beneficial effect is to identify the potential weak links of the gas drilling wellbore wall in advance, making monitoring and early warning more targeted, and effectively improving the accuracy and timeliness of the gas drilling wellbore wall instability early warning. The reasoning process is that the existence of soft rock interlayers increases the risk of wellbore instability in gas drilling. Only by accurately locating and mastering its distribution law can we focus on these areas during the monitoring and early warning process. Once abnormal changes occur, such as deformation of soft rock interlayers leading to wellbore wall displacement and stress changes, early warnings can be issued in time to avoid the occurrence of wellbore wall instability accidents in gas drilling and ensure the safety of gas drilling work.

[0067] Step S1140 integrates the three-dimensional rock formation zoning matrix, the formation mechanical property description set, and the soft rock interlayer distribution matrix D0 to form a rock formation structure classification system. This system comprehensively combines the structure division, mechanical properties, and weak link information of the rock formation, and comprehensively describes the characteristics of the rock formation. For example, in a specific gas drilling project, through this system, the distribution of different structural layers, the mechanical parameters of each layer, and the position of the soft rock interlayer can be clearly seen, providing intuitive and comprehensive geological information for engineers. Its beneficial effect is that it provides a comprehensive and systematic geological basis for the analysis of the stability of the gas drilling wellbore, which helps to more deeply understand the interaction relationship between the rock formation and the gas drilling wellbore. From the reasoning process, the stability of the gas drilling wellbore is affected by various factors, and separate structure division, mechanical parameters, or soft rock interlayer information cannot comprehensively reflect these effects. By constructing this classification system and organically combining various information, the impact of the rock formation on the stability of the gas drilling wellbore can be grasped as a whole, laying a solid foundation for subsequent research on the stability of the gas drilling wellbore, instability warning, and ensuring the safety of gas drilling operations.

[0068] Step S1200: Obtain the initial gas drilling geometric parameter set G0;

[0069] Furthermore, as Figure 3 shown, Step S1200 includes:

[0070] Step S1210: Conduct high-precision geometric measurement inside the gas drilling to obtain the point cloud data of the wellbore spatial curved surface;

[0071] Step S1220: Denoise and normalize the point cloud data of the wellbore spatial curved surface, and extract the wellbore curvature radius R0 and the casing wall thickness δ0; Based on the wellbore curvature radius R0 and the casing wall thickness δ0, construct the initial gas drilling geometric parameter set G0.

[0072] Specifically, 3D laser scanning technology is an advanced measurement technology. It determines the distance between the measurement point and the scanner by emitting laser beams and measuring the time it takes for the laser to reflect back, thereby obtaining the three-dimensional coordinate information of the object's surface. In gas drilling measurement, this technology can quickly and accurately obtain a large number of discrete point data on the wellbore surface. The point cloud composed of these data can truly reflect the spatial curved surface shape of the wellbore. For example, in a certain gas drilling measurement project, a 3D laser scanning device is used to scan along the axial direction of the gas drilling. Thousands of measurement point data can be obtained per second. These point cloud data can be accurate to the millimeter level, fully presenting the concave and convex conditions and irregular areas of the wellbore. The purpose of obtaining the point cloud data of the wellbore spatial curved surface is to provide raw data support for the subsequent extraction of key geometric parameters of wellbore deformation. Its beneficial effect is that, compared with traditional measurement methods, the data obtained by 3D laser scanning technology is more comprehensive and accurate, and can capture the subtle geometric changes of the wellbore. From the reasoning process, the deformation of the wellbore is often complex and subtle, and traditional measurement methods may not be able to accurately obtain this change information. The high-precision measurement characteristics of 3D laser scanning technology can obtain richer detailed data, laying a foundation for accurately analyzing wellbore deformation.

[0073] Denoising processing is to remove the noise points in the point cloud data caused by factors such as measurement errors and environmental interference. These noise points will affect the accuracy of subsequent parameter extraction. For example, during the measurement process, dust in the surrounding environment and slight vibrations of the equipment may cause abnormal fluctuations in the measurement data. Through denoising algorithms, these abnormal points can be removed, making the point cloud data smoother and more accurate. Normalization processing is to perform a unified scale transformation on the point cloud data, making the data under different positions and different measurement conditions comparable. To calculate the curvature radius of the wellbore, it is necessary to select a suitable local area in the point cloud data to construct a surface model. The moving least squares method (MLS) can be used to fit a local surface in the neighborhood of each point. Based on the constructed local surface model, the curvature is calculated using differential geometry principles.

[0074] To extract the casing wall thickness, it is necessary to first segment the point cloud belonging to the inner wall and outer wall of the casing from the wellbore point cloud data. The segmentation can be carried out according to the spatial distribution characteristics, intensity information, etc. of the point cloud. If the laser scanner used to collect the point cloud data has an intensity measurement function, since the reflection intensity of the laser by the inner wall and outer wall of the casing may be different, this intensity difference can be used in combination with the threshold segmentation algorithm to separate the inner and outer wall point clouds. In addition, according to the spatial position relationship of the point cloud, the inner and outer wall point clouds of the casing can also be extracted separately through algorithms such as region growing. After successfully segmenting the inner and outer wall point clouds of the casing, for each pair of corresponding inner wall points and outer wall points, calculate the distance between them. The average value of these distances is the casing wall thickness δ0.

[0075] The extracted wellbore curvature radius \(R_0\) reflects the degree of bending of the wellbore surface. The smaller the curvature radius, the more severe the bending of the wellbore, and the greater the possibility of stress concentration. The casing wall thickness \(\delta_0\) is an important parameter to measure the bearing capacity of the casing. The greater the wall thickness, the relatively stronger the bearing capacity of the casing. For example, in a gas drilling, if the curvature radius of a certain section of the wellbore is significantly smaller than other areas, stress concentration is more likely to occur in this area, leading to deformation or even failure of the wellbore. While in the area with a thicker casing wall, it is less likely to deform under the same pressure. The initial geometric parameter group \(G_0\) of the gas drilling is constructed by integrating the wellbore curvature radius \(R_0\) and the casing wall thickness \(\delta_0\), providing key geometric parameters for subsequent analysis of the stress state and deformation characteristics of the wellbore. The beneficial effect is that by accurately extracting and integrating these geometric parameters, the stability of the wellbore under different working conditions can be more accurately evaluated. From the reasoning process, the geometric shape of the wellbore and the thickness of the casing directly affect the mechanical properties and bearing capacity of the wellbore. Accurate geometric parameters can make the subsequent established model more in line with the actual situation, and more reliable results can be obtained when analyzing the stress and deformation of the wellbore, thus providing a more accurate basis for wellbore instability warning.

[0076] Step S1300, arrange a three - level displacement monitoring array and construct a three - level displacement characteristic data stream;

[0077] Furthermore, as Figure 4 shown, step S1300 includes:

[0078] Step S1310, arrange a distributed fiber optic sensor array along the axial direction of the gas drilling, deploy an inclinometer sensor group in the soft rock interlayer, and arrange strain rosettes along the circumferential direction of the casing to form a three - level displacement monitoring array;

[0079] Step S1320, use the distributed fiber optic sensor array to obtain the separation displacement \(\Delta S'_1\) of the roof rock formation;

[0080] Step S1330, use the inclinometer sensor group to measure the three - dimensional shear deformation angle \(\theta'(t)\) generated by the soft rock interlayer;

[0081] Step S1340, use the strain rosettes arranged along the circumferential direction of the casing to obtain the strain distribution data \(\varepsilon_r'(t)\) of the casing wall;

[0082] Step S1350, define the separation displacement \(\Delta S'_1\) as the first - level data, the three - dimensional shear deformation angle \(\theta'(t)\) as the second - level data, and the strain distribution data \(\varepsilon_r'(t)\) as the third - level data;

[0083] Step S1360, construct a three - level displacement characteristic data stream according to the first - level data, the second - level data, and the third - level data.

[0084] Specifically, the main purpose of step S1300 is to obtain multi-dimensional displacement data by reasonably arranging monitoring equipment, and to construct a data stream that can reflect the characteristics of wellbore instability, so as to provide data support for the subsequent accurate analysis of wellbore instability. The distributed optical fiber sensor array is a monitoring device based on optical fiber sensing technology. It can use the characteristic changes of light in the optical fiber to perceive the changes of external physical quantities, and can continuously measure the small strain changes at different axial positions of gas drilling, so as to obtain the delamination displacement information of the top rock layer. The inclination sensor group is specially used to measure the three-dimensional shear deformation angle of the soft rock interlayer. Its principle is to perceive the inclination angle changes of the soft rock interlayer in different directions through the internal sensitive elements. The strain rosette is composed of multiple strain gauges at a certain angle. After being arranged along the casing circumferentially, the strain of the casing wall in different directions can be measured, and the strain distribution data can be obtained. Through this collaborative arrangement of multiple types of sensors, the physical quantities related to the wellbore can be fully monitored from different dimensions and different positions. Its beneficial effect is that a comprehensive and multi-level monitoring system is constructed to ensure that sufficient and accurate wellbore displacement related data can be obtained. From the reasoning process, wellbore instability is a complex process, and a single type of sensor cannot fully capture its changing characteristics. The distributed fiber optic sensor array can monitor the delamination displacement of the rock formation, the inclination sensor group focuses on the deformation of the weak link of the soft rock interlayer, and the strain rosette directly reflects the stress and strain of the casing. The combination of the three can cover key information from different aspects of the wellbore instability process and provide sufficient data basis for subsequent analysis. For example, in a deep mine, through such a three-level displacement monitoring array, the gradual deformation of the soft rock interlayer during the mining process and the corresponding strain changes of the casing wall were successfully monitored, providing key data for early warning of wellbore instability.

[0085] Delamination displacement refers to the displacement caused by the relative separation between different rock layers under the action of force on the roof rock layer. Because the internal stress state of the rock layer will change under the influence of mining and other activities, when this stress change exceeds a certain degree, delamination will occur between the rock layers. The distributed optical fiber sensor array can accurately capture the changes in this delamination displacement due to its high sensitivity and continuous monitoring characteristics. Delamination displacement can sensitively capture the interlayer dislocation of the roof rock layer and finely depict the dynamic process of wellbore deformation. From the reasoning process, the interlayer dislocation of the roof rock layer is one of the important early manifestations of wellbore instability, and the change trend of delamination displacement can intuitively reflect the stability change of the roof rock layer. By continuously monitoring ΔS'1, once it is found that its change rate is accelerating or reaches a certain threshold, it indicates that the wellbore may be at risk of instability, which provides an important basis for taking timely measures.

[0086] Due to its relatively low mechanical strength, the soft rock interlayer is prone to shear deformation during the stress-bearing process of the shaft wall. The inclination sensor group can accurately obtain the three-dimensional shear deformation angle θ'(t) by real-time monitoring of the angle changes of the soft rock interlayer in three spatial directions. For example, when the soft rock interlayer is subjected to extrusion or tension from the surrounding rock strata, its angle will change. The inclination sensor group can promptly capture these changes and convert them into corresponding electrical signals for recording and transmission. The soft rock interlayer is a weak link in the instability of the shaft wall, and θ'(t) reveals the temporal characteristics of the strength attenuation and failure of the interlayer. As coal mining activities progress, the stress on the soft rock interlayer continuously changes, and its strength will gradually decay. By monitoring the three-dimensional shear deformation angle θ'(t), the entire process of the soft rock interlayer from initial deformation to gradual failure can be clearly observed, and the time sequence and rate of its strength attenuation can be understood, thus providing key information for evaluating the possibility and time node of shaft wall instability. If θ'(t) rapidly increases within a short period, it indicates that the failure process of the soft rock interlayer is accelerating, and the risk of shaft wall instability is also increasing sharply.

[0087] A strain rosette is a device used to measure the surface strain of an object. By arranging strain gauges at different circumferential positions of the casing, the strain conditions of the casing in different directions can be measured, and then the strain distribution data εr'(t) can be obtained. When the shaft wall is subjected to external pressure, rock stratum deformation, etc., the casing will generate corresponding strains. For example, in a certain gas drilling, due to the uneven settlement of the surrounding rock strata, the casing will be subjected to uneven pressure, and at this time, the strain rosette can measure the strain differences at different parts of the casing. εr'(t) is a direct characterization of the stress-bearing state of the shaft wall and is highly sensitive to stress concentration and instability cracking. By analyzing the strain distribution data, the stress distribution condition borne by the shaft wall can be directly understood, and stress concentration areas can be promptly discovered. Since stress concentration is often the starting point of shaft wall instability cracking, once a stress concentration area is discovered, targeted reinforcement measures or mining plan adjustments can be taken to avoid the occurrence of shaft wall instability accidents. According to the principles of material mechanics, the strain of an object is closely related to the stress it bears. By measuring the strain distribution of the casing, the stress-bearing state of the shaft wall can be deduced, providing direct and key data support for evaluating the stability of the shaft wall.

[0088] The separation displacement ΔS'1 is defined as the primary data, the three-dimensional shear deformation angle θ'(t) is defined as the secondary data, and the strain distribution data εr'(t) is defined as the tertiary data. Such a classification of data is based on different levels of the characteristics of shaft wall instability they reflect. The primary data, i.e., the separation displacement ΔS'1, reflects the macroscopic deformation trend of shaft wall instability. Since the separation of the roof rock stratum is a macroscopic deformation phenomenon, the change in its displacement can overall reflect whether the rock stratum environment where the shaft wall is located is stable and whether there is a tendency of instability. The secondary data, namely the three-dimensional shear deformation angle θ'(t), corresponds to the degradation process of the strength of local rock strata. As a local weak rock stratum, the change in the three-dimensional shear deformation angle of the soft rock interlayer directly reflects the change in the strength of the rock strata in this local area. By monitoring θ'(t), the attenuation process of the strength of local rock strata can be understood. The tertiary data, i.e., the strain distribution data εr'(t), represents the load response of the shaft wall itself. As an important support structure of the shaft wall, the strain distribution data of the casing can directly reflect the actual response of the shaft wall under the current stress state. Through this classification method, the complex evolution mechanism of shaft wall instability is perceived from multiple scales from the global to the local and from the rock mass to the support. It provides a clear data framework for comprehensively and systematically analyzing the process of shaft wall instability, enabling researchers to deeply understand the mechanism of shaft wall instability from different levels. Shaft wall instability is a complex process involving multiple factors and multiple levels, and single data cannot comprehensively reflect its essence. By classifying the data and conducting collaborative analysis on each level of data, the macroscopic deformation of the rock mass, the change in the strength of local rock strata, and the load-bearing situation of the shaft wall support structure can be comprehensively considered, so as to more accurately grasp the evolution process of shaft wall instability and lay a foundation for realizing accurate early warning.

[0089] In step S1360, a three - level displacement feature data stream is constructed based on the primary data, secondary data, and tertiary data. This step integrates the previously obtained and classified data to form an ordered data stream. By arranging the separation displacement ΔS'1, the three - dimensional shear deformation angle θ'(t), and the strain distribution data εr'(t) in a certain time sequence and logical relationship, a data stream that can comprehensively reflect the variation of the shaft wall displacement characteristics over time is constructed. For example, data at each level can be collected at a certain time interval (such as every minute or every hour) and recorded in sequence to form a continuous data stream. The purpose of constructing the three - level displacement feature data stream is to provide unified and standardized data input for subsequent construction of the displacement - stress mapping model and analysis of the shaft wall instability state. Its beneficial effect is that the data becomes more systematic and coherent, facilitating subsequent data analysis and processing. From the reasoning process, in subsequent model construction and analysis, a data set that can accurately reflect the historical changes and trends of the shaft wall displacement is required. The three - level displacement feature data stream integrates the scattered data at each level, can completely present the displacement characteristics of the shaft wall at different time points, helps researchers discover potential correlations and laws between the data, improves the accuracy and reliability of the analysis, and thus provides strong support for accurately assessing the risk of shaft wall instability.

[0090] Step S1400: Construct a displacement - stress mapping model based on the rock formation structure classification system, the initial gas drilling geometric parameter group G0, and the three - level displacement feature data stream.

[0091] Furthermore, as Figure 5 shown, step S1400 includes:

[0092] Step S1410: According to the initial compressive strength σ 0,i and the initial strain ε 0,i in the description of the formation mechanical properties, and the shaft wall curvature radius R0 and the casing wall thickness δ0 in the initial gas drilling geometric parameter group G0, describe the constitutive relationship between the shaft wall displacement and stress in the form of a fourth - order tensor, and establish the initial mapping model M0;

[0093] Step S1420: Introduce the three - dimensional rock formation partition matrix, the soft rock interlayer distribution matrix D0, the separation displacement ΔS'1 and the three - dimensional shear deformation angle θ'(t) in the three - level displacement feature data stream into the initial mapping model M0, solve the stress - displacement field of the rock formation around the shaft wall by the finite - element numerical simulation method, and obtain the coupling law between different - level structural layers, soft rock interlayer distribution, ΔS'1, θ'(t) and the deformation of the gas drilling;

[0094] Step S1430: According to the coupling laws between different-level structural layers, the distribution of soft rock interlayers, ΔS'1, and θ'(t) and the deformation of gas drilling, an iterative optimization algorithm is used to calibrate the constitutive parameters in the initial mapping model M0 to form the final displacement-stress mapping model.

[0095] Specifically, the core purpose of step S1400 is to construct a model that can accurately reflect the relationship between the rock formation structure, the geometric parameters of gas drilling, displacement data, and the wellbore stress, providing a key tool for subsequent accurate analysis of wellbore stability and instability warning. The fourth-order tensor is a mathematical tool used in continuum mechanics to describe the complex mechanical properties of materials. It can comprehensively characterize the relationship between stress and displacement of wellbore materials in different directions. In practical application scenarios, such as in the deep geotechnical gas drainage environment, the wellbore is subjected to multi-directional pressures and constraints from the surrounding rock formations, and its mechanical behavior is complex and diverse. Through the fourth-order tensor form, these complex mechanical relationships can be mathematically expressed, enabling the model to more accurately simulate the actual mechanical response of the wellbore. The purpose of establishing the initial mapping model M0 in this step is to provide a basic framework for subsequent in-depth analysis. Its beneficial effect lies in providing a standardized and mathematical model basis for wellbore mechanical analysis, enabling subsequent research to conduct quantitative analysis based on this model. Accurately describing the displacement-stress constitutive relationship of the wellbore is the key prerequisite for analyzing wellbore stability. By integrating various key parameters through the fourth-order tensor form, the deformation and stress change laws of the wellbore under different loading conditions can be comprehensively reflected, helping researchers deeply understand the mechanical properties of the wellbore and providing strong support for the optimization and practical application of subsequent models.

[0096] The finite element numerical simulation method is a numerical calculation technology widely used in the engineering field. It discretizes a complex continuum into a finite number of elements, conducts mechanical analysis on each element, and then combines these elements to simulate the mechanical behavior of the entire structure. Taking a specific deep mine gas drilling as an example, the rock strata around the gas drilling are discretized according to the previously established three-dimensional rock strata zoning matrix, and the information contained in the soft rock interlayer distribution matrix D0 is incorporated into the model. At the same time, data such as the separation displacement ΔS'1 and the three-dimensional shear deformation angle θ'(t) are input. Through finite element simulation calculations, the stress and displacement distribution of the rock strata around the shaft wall at different positions can be obtained. The purpose of this step is to reveal the internal relationship between various factors and the deformation of the gas drilling. The beneficial effect is that through simulation analysis, the influence mode and degree of different factors on the deformation of the gas drilling can be intuitively observed, providing a basis for further optimizing the model and formulating a reasonable early warning strategy. The stability of the shaft wall is affected by a combination of various factors, and the interaction relationship between these factors is complex. Through finite element simulation, these factors can be analyzed within a unified model framework to grasp the coupling law between them and the deformation of the gas drilling as a whole, laying a foundation for accurately evaluating the shaft wall stability subsequently.

[0097] The iterative optimization algorithm is a class of algorithms that gradually approaches the optimal solution through continuous iterative calculations. In this step, its role is to adjust the constitutive parameters in the initial mapping model M0 to make the model more accurately reflect the actual situation. For example, during multiple simulation calculations, according to the difference between each simulation result and the actual monitoring data, the iterative optimization algorithm automatically adjusts the parameters in the model, such as the elastic modulus and Poisson's ratio of the material. After multiple iterations, when the error between the simulation result of the model and the actual monitoring data reaches an acceptable range, it is considered that the optimal constitutive parameters are obtained, and then the final displacement-stress mapping model is formed. The purpose of this step is to improve the accuracy and reliability of the model. The calibrated model can more accurately predict the stress and displacement changes of the shaft wall under different conditions, providing a more reliable basis for shaft wall instability early warning. Due to the uncertainty of the geological conditions and shaft wall mechanical behavior in actual engineering, the initial model may not be able to fully and accurately reflect the actual situation. By continuously adjusting the model parameters through the iterative optimization algorithm, the model can better fit the actual data, improve the accuracy and applicability of the model, and thus serve the shaft wall stability analysis and early warning work more effectively.

[0098] Step S2000: Collect the mining-induced trigger parameter set P1 and the real-time three-level displacement feature data stream. Based on the displacement-stress mapping model, the mining-induced trigger parameter set P1, and the real-time three-level displacement feature data stream, generate the dynamically corrected critical stress for shaft wall instability. Combine the mining-induced trigger parameter set P1, calculate the critical safety factor for shaft wall instability, and establish a hierarchical early warning criterion. Obtain the real-time shaft wall instability determination data, and based on the real-time shaft wall instability determination data and the hierarchical early warning criterion, generate the shaft wall instability early warning result, and generate a risk dynamic decision-making plan based on the shaft wall instability early warning result.

[0099] Further, step S2000 includes:

[0100] Step S2100: Collect the mining-induced trigger parameter set P1 in real time;

[0101] Further, step S2100 includes:

[0102] Step S2110: Collect the hydraulic support pressure time series data F(t) in real time;

[0103] Step S2120: Collect the working face advance speed v in real time;

[0104] Step S2130: v and F(t) together constitute the mining-induced trigger parameter set P1.

[0105] Specifically, the purpose of step S2100 is to collect the mining-induced trigger parameter set P1 in real time, which plays a key role in accurately evaluating the stability of the gas drilling shaft wall under mining influence. The mining-induced trigger parameter set P1 contains key information that can reflect the mining stress disturbance situation, and this information is an important basis for subsequent shaft wall stability analysis and early warning.

[0106] In step S2110, the hydraulic support is an important device for supporting the roof rock formation in the coal mining face, and its pressure time series data F(t) can intuitively reflect the change of the supporting force on the roof rock formation. In the gas drilling project, since gas wells are often arranged near the coal mining face, the stress state of the roof rock formation will change due to coal mining operations. By collecting the pressure time series data F(t) of the hydraulic support in real time, the dynamic change process of the roof rock formation pressure can be monitored. For example, in a certain gas drilling project, when the coal mining face advances to the area near the gas well, the pressure time series data F(t) of the hydraulic support will show obvious fluctuations, and these fluctuations reflect the stress adjustment of the roof rock formation caused by mining. The beneficial effect of this data collection step is that it provides direct data support for analyzing the stability of the roof rock formation. Because the change of the roof rock formation pressure will affect its acting force on the gas wellbore wall. If the roof rock formation pressure is too large or changes too fast, it may lead to an aggravation of the stress redistribution of the wellbore surrounding rock, thereby threatening the stability of the wellbore wall. By continuously monitoring the pressure time series data F(t) of the hydraulic support, abnormal changes in the roof rock formation pressure can be detected in time, providing a basis for taking corresponding measures subsequently. From the reasoning process, the change of the hydraulic support pressure will be transmitted to the surrounding of the gas well through the rock formation, affecting the stress environment of the wellbore wall. When the hydraulic support pressure increases, the pressure of the roof rock formation on the wellbore wall may also increase, increasing the risk of wellbore instability; on the contrary, when the pressure decreases, although it may relieve the wellbore pressure, it may also mean that the roof rock formation has become loose or in other unstable conditions. Therefore, accurately obtaining the pressure time series data F(t) of the hydraulic support is crucial for evaluating the stability of the wellbore wall.

[0107] In step S2120, the advancing speed v of the working face is a key parameter in the coal mining operation process, and it is closely related to the intensity and scope of mining-induced stress disturbance. In the environment where the gas drilling is located, the speed of the advancing speed v of the working face directly affects the speed and intensity of the mining-induced stress propagating to the surrounding rock strata of the gas drilling. For example, in different gas drilling projects, when the advancing speed of the working face is relatively fast, the mining-induced stress is quickly transmitted to the surrounding of the gas drilling in a short time, causing the stress of the shaft wall surrounding rock to change rapidly, increasing the possibility of shaft wall instability; while when the advancing speed is slow, the stress change is relatively gentle. The purpose of collecting this parameter is that by mastering the advancing speed v of the working face, the influence law of mining-induced stress on the stability of the gas drilling shaft wall can be better analyzed. Its beneficial effect is that it can provide an important reference for predicting the risk of shaft wall instability. When the advancing speed v of the working face is known, combined with other parameters (such as the time series data F(t) of the hydraulic support pressure), the influence degree of mining-induced stress on the shaft wall can be judged more accurately. From the analysis of the reasoning process, the advancing speed v of the working face determines the frequency and amplitude of the mining-induced stress change. The faster the advancing speed, the greater the change amount of the mining-induced stress per unit time, and the greater the impact on the shaft wall stability; the slower the advancing speed, the more time the shaft wall has to adapt to the stress change, and relatively the risk of instability is lower. Therefore, real-time collection of the advancing speed v of the working face is of great significance for ensuring the stability of the gas drilling shaft wall.

[0108] In step S2130, the reason for combining these two parameters into the mining-induced trigger parameter set P1 is that they reflect the situation of mining-induced stress disturbance from different aspects. The hydraulic support pressure time-series data F(t) reflects the change in the current support force on the roof rock formation, while the working face advancing speed v reflects the dynamic process of mining-induced stress change. The combination of the two can more comprehensively describe the influencing factors of mining-induced stress on the stability of the gas drilling shaft wall. For example, in actual engineering, when the hydraulic support pressure time-series data F(t) shows that the roof rock formation pressure continues to increase and the working face advancing speed v is relatively fast, it indicates that the influence of mining-induced stress on the gas drilling shaft wall is relatively strong, and the risk of shaft wall instability increases significantly. The beneficial effect of this combination method is that it provides a rich data basis for establishing a more accurate shaft wall stability analysis model in the follow-up. By comprehensively analyzing v and F(t) in the mining-induced trigger parameter set P1, the stability status of the shaft wall under mining influence can be evaluated more accurately, thereby providing strong support for taking effective early warning and protection measures. From the reasoning process, the individual hydraulic support pressure time-series data F(t) or the working face advancing speed v can only reflect one aspect of mining-induced stress disturbance, while the combination of the two can more comprehensively present the change characteristics of mining-induced stress. Different combinations of F(t) and v will result in different mining-induced stress states, and the influence on shaft wall stability is also different. Therefore, jointly constituting the mining-induced trigger parameter set P1 with v and F(t) helps to more accurately grasp the change law of shaft wall stability and provide a more reliable basis for ensuring the safety of gas drilling.

[0109] In step S2200, obtain the real-time three-level displacement characteristic data stream, and generate the dynamically corrected critical stress of shaft wall instability based on the displacement-stress mapping model, the mining-induced trigger parameter set P1, and the real-time three-level displacement characteristic data stream.

[0110] Furthermore, step S2200 includes:

[0111] In step S2210, obtain the real-time three-level displacement characteristic data stream, where the real-time three-level displacement characteristic data stream includes the real-time separation displacement amount ΔS1 of the roof rock formation, the real-time three-dimensional shear deformation angle θ(t) generated by the soft rock interlayer, and the real-time strain distribution data εr(t) of the casing wall.

[0112] Specifically, the real-time separation displacement ΔS1 reflects the interlayer dislocation of the roof rock strata at the current moment and is an important manifestation of the macroscopic deformation trend of the shaft wall; the real-time three-dimensional shear deformation angle θ(t) focuses on the key weak area of the shaft wall instability, namely the soft rock interlayer, and shows the real-time progress of its strength attenuation and failure; the real-time strain distribution data εr(t) of the casing wall directly reflects the current load-bearing state of the shaft wall support structure. The purpose of obtaining these real-time data is to provide the latest displacement-related information for subsequent accurate analysis of the shaft wall stability and closely track the real-time deformation and stress response of the shaft wall during the gas drilling process. The stability of the shaft wall is in dynamic change and is affected by various factors such as mining-induced activities. Obtaining these displacement data in real time can timely detect the abnormal deformation trend of the shaft wall and provide first-hand information for predicting the risk of shaft wall instability.

[0113] Step S2220: Obtain the third corrected critical stress of the shaft wall according to the real-time three-level displacement characteristic data stream and the displacement-stress mapping model;

[0114] Furthermore, as Figure 6 shown, step S2220 includes:

[0115] Step S2221: Input the real-time separation displacement ΔS1 and the real-time three-dimensional shear deformation angle θ(t) into the displacement-stress mapping model to obtain the first corrected critical stress σ 1,i ; σ 1,i represents the first corrected critical stress of the i-th structural layer;

[0116] Step S2222: Retrieve the historical database of shaft wall failure cases and obtain the historical critical stress of instability σ c,hist ;

[0117] Step S2223: Calculate the second corrected critical stress σ 1,i according to the first corrected critical stress σ c,hist , the historical critical stress of instability σ 2,i and the ratio of the real-time separation displacement to the real-time three-dimensional shear deformation angle ΔS1 / θ(t); σ 2,i represents the second corrected critical stress of the i-th structural layer;

[0118] Step S2224: Correct the second corrected critical stress σ 2,i according to the hydraulic support pressure time series data F(t) and the working face advance speed v in the mining-induced trigger parameter set P1 to obtain the third corrected critical stress σ 3,i ; σ 3,i represents the third corrected critical stress of the i-th structural layer.

[0119] Specifically, step S2220 aims to obtain the third corrected critical stress of the shaft wall based on the real-time three-level displacement characteristic data stream and the displacement-stress mapping model, providing key data support for the subsequent accurate evaluation of shaft wall stability. During the gas drilling operation, the stability of the shaft wall is dynamically affected by various factors, and obtaining accurate critical stress of the shaft wall is crucial for warning of shaft wall instability.

[0120] In step S2221, the displacement-stress mapping model is constructed based on the previously obtained rock formation structure, gas drilling geometric parameters, and displacement data. It can reflect the internal relationship between the displacement and stress of the shaft wall. The real-time separation displacement ΔS1 reflects the interlayer dislocation of the roof rock formation at the current moment and is an important manifestation of the macroscopic deformation trend of the shaft wall. The real-time three-dimensional shear deformation angle θ(t) focuses on the key weak area of the shaft wall instability, namely the soft rock interlayer, and shows the real-time progress of its strength attenuation and failure. Inputting these two real-time monitored displacement data into the model is to use the latest displacement information to preliminarily adjust the estimation of the critical stress of the shaft wall. In an actual gas drilling scenario, for example, during the mining process of a certain gas drilling, it is obtained through the monitoring system that the real-time separation displacement ΔS1 suddenly increases, and at the same time, the real-time three-dimensional shear deformation angle θ(t) also exceeds the normal range. After inputting these data into the displacement-stress mapping model, the model calculates according to the established relationship and obtains the first corrected critical stress σ of the shaft wall 1,i which is lower than the initial set value. This indicates that under the current displacement state, the stress borne by the shaft wall has changed, and the possibility of instability increases. The purpose of this step is to initially correct the critical stress of the shaft wall based on the real-time displacement data to make it more in line with the actual situation. Its beneficial effect lies in fully considering the current actual displacement change of the shaft wall. The displacement change of the shaft wall directly affects its stress distribution, and the changes in the real-time separation displacement and the three-dimensional shear deformation angle reflect the change in the mechanical state of the shaft wall and the surrounding rock formations. By inputting these data into the model for calculation, the critical stress value when the shaft wall is unstable under the influence of the current displacement can be predicted more accurately, providing important basic data for subsequent more accurate analysis. From the reasoning process, the stability of the shaft wall is closely related to the displacement. The real-time displacement data can timely reflect the deformation trend of the shaft wall. Based on this, the initial correction of the critical stress can make the evaluation result more in line with the actual mechanical state and help to detect the potential risk of shaft wall instability in advance.

[0121] In step S2222, the historical database of shaft wall failure cases stores a large amount of relevant data when shaft wall failures occurred in the past, including the historical instability critical stress σ c,histRecorded the stress conditions when the shaft wall reaches the unstable state under the influence of various factors such as different geological conditions and mining working conditions. In actual gas drilling projects, different mines may have different geological structures, rock layer characteristics, and mining methods, and these factors will all lead to differences in the critical stress when the shaft wall becomes unstable. For example, in the gas drilling area of a certain coal mine, due to complex geological conditions and many soft rock interlayers, the historical critical stress of instability when the shaft wall is unstable is relatively low; while in another area with relatively stable geological conditions, the historical critical stress of instability when the shaft wall of the gas drilling is relatively high. The purpose of retrieving these historical data is to provide an empirical reference for the current critical stress calculation. Since the geological conditions and mining processes faced by actual gas drilling projects are complex and changeable, relying solely on current monitoring data and model calculations may not be able to comprehensively and accurately evaluate the stability of the shaft wall. Historical data covers the instability information of the shaft wall in a variety of different situations, providing more reference dimensions for the current analysis. Its beneficial effect is that by relying on historical data, the engineering experience of the past can be fully utilized to effectively make up for the limitations of current monitoring data and model calculations. By comparing the historical critical stress of instability with the stress value calculated currently, the stability of the shaft wall can be comprehensively evaluated from multiple angles, improving the reliability of early warning. For example, if the currently calculated first corrected critical stress of the shaft wall σ 1,i is close to the historical critical stress of instability σ c,hist under a similar geological and mining working condition, special attention needs to be paid to the stability of the shaft wall, and corresponding preventive measures should be taken in advance to avoid the occurrence of shaft wall instability accidents. From the reasoning process, historical data is the accumulation of past engineering practices and contains the instability information of the shaft wall in various complex situations. Introducing it into the current calculation process can make the evaluation more comprehensive and accurate, providing a more reliable basis for the analysis of shaft wall stability.

[0122] In step S2223, after obtaining the historical critical stress of instability σ c,hist , it is necessary to combine and analyze it with the first corrected critical stress of the shaft wall σ 1,i . This is because the first corrected critical stress of the shaft wall σ 1,i is calculated through the displacement-stress mapping model based on the current real-time displacement data, reflecting the critical stress situation under the current shaft wall displacement state; while the historical critical stress of instability σ c,hist is the stress data when the shaft wall was unstable in the past under similar situations, containing the comprehensive influence of various complex factors. In actual operation, methods such as weighted average can be used to process the two. For example, set a weight coefficient α, and its value range is [0,1], which can be determined according to the similarity degree between the current working condition and the historical case. If the current working condition is highly similar to the historical case, α can be set to a larger value, such as 0.8; when the similarity degree is low, α can be set to a smaller value, such as 0.2. Then the second corrected critical stress of the shaft wall σ 2,iThe calculation formula is: σ 2,i =α×σ c,hist +(1 - α)×σ 1,i The purpose of this step is to further correct the critical stress calculated based on the real-time displacement by combining historical experience, making the result more accurate and reliable. Its beneficial effect lies in that by comprehensively considering historical data and current real-time displacement data, it can more comprehensively reflect the true stress-bearing situation of the shaft wall under complex working conditions. Historical data provides empirical information on shaft wall instability under different conditions, while real-time displacement data reflects the actual deformation state of the current shaft wall. Combining the two can avoid evaluation biases that may be caused by relying solely on a single data source. From the reasoning process, the complexity of geological conditions and mining working conditions makes the shaft wall stability affected by multiple factors. Historical data and real-time displacement data respectively reflect these influencing factors from different perspectives. Through reasonable weighting, the advantages of the two can be combined, making the corrected critical stress more in line with the actual situation, thus improving the accuracy of shaft wall stability evaluation.

[0123] During gas drilling operations, the mining process has a significant impact on the stress-bearing of the shaft wall, and the time-series data of hydraulic support pressure F(t) and the working face advance speed v are two key influencing factors during the mining process. As an important device for supporting the roof rock formation, the pressure fluctuation of the hydraulic support directly reflects the change in the roof rock formation pressure. During coal mining operations, as the working face advances, the stress distribution of the roof rock formation continuously changes, resulting in the pressure borne by the hydraulic support also fluctuating. This pressure change is transmitted to the gas drilling shaft wall through the rock formation, thereby affecting the stress-bearing condition of the shaft wall. For example, when local caving or movement occurs in the roof rock formation, the hydraulic support pressure will increase rapidly, and this sudden pressure change will be transmitted to the shaft wall in the form of stress waves, causing the shaft wall to be subjected to additional stress. The working face advance speed v directly determines the frequency and intensity of the mining stress acting on the gas drilling shaft wall. When the working face advance speed increases, the number of times the mining stress changes acting on the shaft wall per unit time increases, and the stress concentration phenomenon becomes more obvious. At the same time, rapid advancement may also lead to an increase in the intensity of the mining stress, posing a greater threat to the shaft wall stability. For example, at a gas drilling site, when the working face advance speed increased from 5 meters per day to 8 meters per day, it was found through monitoring that the mining stress on the shaft wall increased significantly, and obvious changes also occurred in the shaft wall displacement and strain.

[0124] When correcting the critical stress σ of the second corrected shaft wall 2,iWhen making corrections, a correction function f(F(t), v) based on the hydraulic support pressure time-series data F(t) and the working face advance speed v can be established. This function can be obtained by fitting a large amount of experimental data and on-site monitoring data, and its form can be determined according to specific circumstances. For example, it can be a linear function, a non-linear function, etc. Assuming the correction function is a linear function with the form f(F(t), v)=k1×F(t)+k2×v+k3, where k1, k2, and k3 are coefficients to be determined and can be fitted according to historical data by methods such as the least squares method. Then the critical stress σ 3,i of the third corrected shaft wall is calculated as follows: σ 3,i =σ 2,i +f(F(t), v).

[0125] The purpose of step S2224 is to combine the key influencing factors in the mining process to finally correct the critical stress of the shaft wall, so that the corrected critical stress is more in line with the stress state of the shaft wall under the actual mining conditions. Its beneficial effect is that it fully considers the influence of mining disturbance on the stability of the shaft wall. Mining disturbance is an important external factor leading to the instability of the shaft wall. By incorporating the hydraulic support pressure and the working face advance speed into the correction process, the stability of the shaft wall during the actual gas drilling process can be more accurately evaluated, improving the timeliness and accuracy of early warning. From the reasoning process, the changes in the hydraulic support pressure and the working face advance speed during the mining process will directly affect the stress situation of the shaft wall. By establishing a correction function to quantify these factors and incorporate them into the calculation of the critical stress, the evaluation results can more comprehensively reflect the actual working conditions, discover the potential risks of shaft wall instability in advance, and provide a more reliable basis for taking corresponding preventive measures.

[0126] Step S2230: Take the critical stress of the third corrected shaft wall as the critical stress of shaft wall instability for dynamic correction.

[0127] Specifically, in step S2230, after calculations and corrections in multiple previous steps, the third corrected critical shaft wall stress incorporates various information such as real-time displacement monitoring data, historical instability laws, and mining disturbance states. For example, in a mine under certain complex geological conditions, the dynamically corrected critical stress of the shaft wall obtained by this method can more accurately reflect the instability risk of the shaft wall during the actual drilling process compared to the critical stress obtained solely based on theoretical calculations or single monitoring data. Using this as the dynamically corrected critical stress of the shaft wall provides key basic data for subsequent calculations of the critical safety factor of shaft wall instability and the establishment of early warning criteria. Its beneficial effect is that it provides a core basis for accurately evaluating the stability of the shaft wall and issuing reliable early warnings. From the reasoning process, shaft wall instability is the result of the combined action of multiple factors. The dynamically corrected critical stress of the shaft wall comprehensively considers these factors, and subsequent calculations of the safety factor and early warning judgment based on this can more accurately predict the risk of shaft wall instability, thereby taking corresponding measures in a timely manner to ensure the safety of the mine.

[0128] Step S2300: Combine the mining-induced trigger parameter set P1 to calculate the critical safety factor of shaft wall instability and establish a hierarchical early warning criterion.

[0129] Furthermore, step S2300 includes:

[0130] Step S2310: Obtain the yield strength σ of the current shaft wall steel casing y , and based on the dynamically corrected critical stress σ of the shaft wall 3,i , the yield strength σ of the current shaft wall steel casing y and the real-time strain distribution data εr(t) of the casing wall, calculate the critical safety factor K of shaft wall instability c ;

[0131] Obtain the thickness h of the i-th structural layer i , and according to h i and the initial compressive strength σ 0,i , calculate the weight of the i-th structural layer , ;

[0132] According to and σ 3,i calculate the comprehensive critical stress value ;

[0133] According to the comprehensive critical stress value , the yield strength σ of the current shaft wall steel casing y and the real-time strain distribution data εr(t) of the casing wall, calculate the critical safety factor K of shaft wall instability c ;

[0134]

[0135] Among them, is an empirical coefficient used to consider the influence degree of strain on the safety factor; is a stability index for evaluating the shaft wall under mining disturbance conditions. By calculating , hierarchical early warning can be achieved in combination with the early warning criterion, and measures can be taken in advance; the strain data monitored in real time is introduced into the formula , which can dynamically reflect the actual stress state of the shaft wall under mining disturbance.

[0136] When increases, increases, and the stability of the shaft wall improves; When decreases, decreases, and the stability of the shaft wall decreases When increases, decreases, and the stability of the shaft wall decreases; When decreases, increases, and the stability of the shaft wall improves. When increases, the denominator increases, decreases, and the stability of the shaft wall decreases; When decreases, the denominator decreases, increases, and the stability of the shaft wall improves. The formula combines material strength, strain data monitored in real time, and the corrected critical stress, which can more comprehensively reflect the actual stress state of the shaft wall, is applicable to complex working conditions such as mining disturbance, and can effectively evaluate the stability of the shaft wall under dynamic conditions.

[0137] Step S2320, based on the critical safety factor K of shaft wall instability c , establish a three-level early warning criterion.

[0138] Calculate the change rate of the hydraulic support pressure. When the change rate of the hydraulic support pressure is greater than or equal to and , start the first-level early warning; among them, is the pressure mutation threshold, is the first safety threshold;

[0139] When and , and at the same time , start the second-level early warning; among them, is the first threshold of the working face advancing speed, is the second safety threshold, is the roof separation displacement threshold;

[0140] When and , and at the same time , start the third-level early warning; among them, is the second threshold value of the working face advancing speed, is the third safety threshold value, is the shear deformation angle threshold value of the soft rock interlayer, > > , > 。

[0141] Specifically, the yield strength σ of the shaft wall steel casing y refers to the stress value that the steel casing bears when it begins to undergo plastic deformation, which represents the strength characteristics of the steel casing material itself. The dynamically corrected critical stress σ of shaft wall instability 3,i is obtained through a series of correction processes, comprehensively considering various factors such as rock stratum structure, displacement monitoring data, historical experience, and mining disturbance, and can more accurately reflect the stress situation when the shaft wall is unstable. In actual shaft wall engineering, the influence of different structural layers on the shaft wall stability is different. For a structural layer with a larger thickness, its proportion in the overall shaft wall structure is relatively larger, and the influence on the shaft wall stability is greater; a structural layer with a higher initial compressive strength has a stronger ability to bear external pressure and maintain the shaft wall stability, and also makes a greater contribution to the shaft wall stability. By combining the thickness h of the structural layer i and the initial compressive strength σ 0,i to calculate the weight, the geometric characteristics and mechanical properties of the structural layer itself are comprehensively considered. In subsequent analysis, according to the weights of different structural layers, the influence of them on the shaft wall stability can be more accurately evaluated, avoiding ignoring important structural layers or overemphasizing secondary structural layers, and making the shaft wall stability analysis more scientific and reasonable.

[0142] The shaft wall is a complex structure composed of multiple structural layers, and the mechanical properties and actual stresses borne by each structural layer are different. If only considering the critical stress of a single structural layer, it is impossible to accurately evaluate the overall instability risk of the shaft wall. Through the calculated weights of the structural layers, according to the relative importance of each structural layer, the corresponding corrected critical stress σ of the shaft wall 3,i can be weighted and summed, and then the comprehensive critical stress value is obtained by dividing by the total weight. The comprehensive critical stress value calculated in this way not only considers the mechanical properties of each structural layer itself (reflected in the weight through the initial compressive strength), but also considers the correction of the critical stress of each structural layer by factors such as mining (i.e., σ 3,i ), comprehensively reflecting the critical stress state of the overall shaft wall under the current working conditions. Compared with analyzing the critical stress of each structural layer separately, it can more accurately evaluate the overall instability risk of the shaft wall and provide more reliable data support for subsequent calculation of the critical safety factor of shaft wall instability and early warning.

[0143] The real-time strain distribution data εr(t) of the casing wall can reflect the stress and deformation state of the steel casing of the wellbore under the current working conditions in real time. Step S2310 provides a quantitative index that comprehensively considers multiple factors and can more comprehensively and accurately evaluate the stability of the wellbore. The stability of the wellbore is comprehensively affected by various factors such as the material strength of the steel casing, the current stress, and the strain. Considering only a single factor cannot accurately determine whether the wellbore is in a stable state. By incorporating these factors into a calculation formula, the safety factor K is obtained. c It can comprehensively reflect the actual stress and stability status of the wellbore under mining disturbance.

[0144] Calculate the change rate of the hydraulic support pressure. When the change rate of the hydraulic support pressure is greater than or equal to and , initiate a first-level warning. The change rate of the hydraulic support pressure reflects the change trend of the roof rock pressure. is the preset pressure mutation threshold. When the pressure change rate reaches or exceeds this threshold, it indicates that the roof rock pressure has changed abnormally. is the first safety threshold. When K c is less than or equal to this threshold, it indicates that the stability of the wellbore has been threatened to a certain extent. When initiating a first-level warning, it prompts the staff that the wellbore may start to show unstable signs.

[0145] When and , and at the same time , initiate a second-level warning; The advancing speed v of the working face directly affects the magnitude and frequency of the mining stress on the wellbore. is the first preset threshold for the advancing speed of the working face. When the advancing speed reaches or exceeds this threshold, the mining stress on the wellbore will increase significantly. is the second safety threshold, which is less than the first safety threshold. At this time, the stability of the wellbore faces greater challenges. is the roof separation displacement threshold. When the roof separation displacement ΔS1 reaches or exceeds this threshold, it indicates that the stability of the roof rock layer has deteriorated further. When initiating a second-level warning, it reminds the staff to closely monitor the wellbore condition and prepare to take corresponding measures.

[0146] When and , and at the same time , initiate a third-level warning; is the second threshold for the advancing speed of the working face, which is greater than the first threshold, representing a faster advancing speed and stronger mining stress. is the third safety threshold, which is greater than the second safety threshold. At this time, the wellbore is in a highly unstable state. is the shear deformation angle threshold of the soft rock interlayer. When the shear deformation angle θ(t) of the soft rock interlayer reaches or exceeds this threshold, it indicates that the soft rock interlayer has been severely deformed and the shaft wall may become unstable at any time. When the third-level early warning is triggered, it is required that the staff take emergency measures promptly to prevent the occurrence of shaft wall instability accidents.

[0147] Step S2320 establishes a comprehensive and accurate hierarchical early warning system, which combines the mining-induced triggering parameters with the quantitative indicators of shaft wall stability, and can more accurately judge the risk degree of shaft wall instability. From the reasoning process, the instability of the shaft wall is the result of the combined action of multiple factors, and a single parameter cannot fully reflect the instability risk. By comprehensively considering multiple parameters such as the pressure change rate of hydraulic supports, the working face advancing speed, the roof separation displacement, the shear deformation angle of the soft rock interlayer, and the critical safety factor of shaft wall instability, and setting the third-level early warning according to different parameter thresholds, it is possible to conduct hierarchical early warning according to the severity of the shaft wall instability risk, enabling the staff to take corresponding and reasonable countermeasures according to the early warning level, improving the effectiveness and pertinence of the early warning, and ensuring the safety of gas drainage to the greatest extent.

[0148] In step S2400, real-time shaft wall instability determination data is obtained, and based on the real-time shaft wall instability determination data and the hierarchical early warning criterion, a shaft wall instability early warning result is generated, and a risk dynamic decision-making plan is generated based on the shaft wall instability early warning result.

[0149] Specifically, the purpose of step S2400 is to generate a shaft wall instability early warning result by obtaining real-time shaft wall instability determination data and combining the hierarchical early warning criterion, and then formulate a risk dynamic decision-making plan based on the early warning result to deal with the possible instability of the gas drilling shaft wall. First, real-time shaft wall instability determination data is obtained, and these data come from previous monitoring links, including the set of mining-induced triggering parameters collected in real time (such as real-time hydraulic support pressure data, real-time working face advancing speed), the real-time three-level displacement characteristic data stream (the real-time separation displacement ΔS1 of the roof rock layer, the real-time three-dimensional shear deformation angle θ(t) generated by the soft rock interlayer, and the real-time strain distribution data εr(t) of the casing wall), as well as the calculated real-time critical safety factor of shaft wall instability, etc. These data reflect the stress, deformation, and stability state of the shaft wall in real time from different aspects, and are the key basis for judging whether the shaft wall is unstable and at what degree of instability risk. A shaft wall instability early warning result is generated based on the real-time shaft wall instability determination data and the hierarchical early warning criterion. According to the previously established third-level early warning criterion, when each parameter meets the corresponding threshold condition, different levels of early warning are triggered.

[0150] Early warnings at different levels correspond to different degrees of risk and require different coping strategies. For a first-level early warning, measures such as increasing the monitoring frequency and checking the integrity of existing support measures may be taken; for a second-level early warning, in addition to strengthening monitoring, temporary reinforcement of the shaft wall may also be required, such as adding support materials; while for a third-level early warning, the situation is more critical, and it may be necessary to immediately stop the relevant operations, organize the evacuation of personnel, and formulate a detailed repair or emergency plan. By dynamically adjusting decisions based on the early warning results in this way, the losses caused by shaft wall instability can be minimized. For example, in a certain gas drilling project, when the monitoring system issued a second-level early warning, the staff quickly carried out temporary reinforcement of the shaft wall according to the risk dynamic decision-making plan, successfully avoiding the occurrence of shaft wall instability accidents and ensuring the safety of drilling equipment and personnel.

[0151] By obtaining and analyzing various types of data in real time and quickly generating early warning results based on the classification early warning criteria, it enables the staff to detect problems at the initial stage of shaft wall instability and gain valuable time to take measures. Real-time monitoring data can reflect the real-time state of the shaft wall and the surrounding rock strata. Once these data show abnormal changes and meet the set conditions of the early warning criteria, the early warning system can respond in a timely manner, constantly monitor the safety status of the shaft wall, and immediately issue an alarm once dangerous signs are found. In addition, the formulation of the risk dynamic decision-making plan realizes the targeted treatment of the risk of shaft wall instability. Different levels of early warnings correspond to different coping strategies, making the coping measures more scientific and reasonable. In actual projects, the situation of shaft wall instability is complex and diverse. The risk dynamic decision-making plan can reasonably allocate resources and take the most appropriate measures according to the specific risk levels. For example, for a first-level early warning with low risk, relatively simple measures such as strengthening monitoring can be taken, which can effectively monitor the shaft wall state without causing waste of resources; while for a third-level early warning with high risk, decisive measures such as stopping operations and evacuating personnel can be taken to maximize the protection of personnel's lives and the safety of equipment and property. This way of differential treatment according to risk levels greatly improves the efficiency and effect of coping with the risk of shaft wall instability and provides a strong guarantee for the safe operation of gas drilling.

[0152] Example 2

[0153] Based on Example 1, this embodiment provides an early warning method for the critical condition of shaft wall failure based on deep geomechanics, including:

[0154] Step S2223, according to the first corrected critical stress of the shaft wall σ 1,i 、historical critical stress of instability σ c,hist and the ratio of the separation displacement to the three-dimensional shear deformation angle ΔS1 / θ(t), calculate the second corrected critical stress of the shaft wall σ 2,i ; σ 2,i represents the second corrected critical stress of the i-th structural layer;

[0155]

[0156] Among them, is the dynamic coupling factor, is the shear deformation nonlinear index, is the attenuation coefficient, and are empirical coefficients, which need to be obtained by fitting through experiments or historical data. These coefficients reflect the influence degree of the historical critical stress of instability, the ratio of the separation displacement amount to the three-dimensional shear deformation angle on the critical stress of the shaft wall.

[0157] Considering the influence of the current real-time monitoring data on the critical stress of the shaft wall, introducing historical experience data, improving the applicability and accuracy of the early warning model, reflecting the relative relationship between the separation displacement of the roof rock stratum and the shear deformation of the soft rock interlayer, being sensitive and indicative to the early warning of shaft wall instability.

[0158] When increases (i.e., the separation displacement of the roof rock stratum increases), the logarithmic part in the formula increases, resulting in the increase of the second corrected critical stress of the shaft wall This reflects the influence of the separation displacement of the roof rock stratum on the stability of the shaft wall. When decreases (i.e., the shear deformation of the soft rock interlayer increases), the logarithmic part in the formula also increases, resulting in the increase of the second corrected critical stress of the shaft wall This reflects the contribution of the shear deformation of the soft rock interlayer to the instability of the shaft wall. The change of will affect the weight of the empirical coefficient and the sensitivity of the function form in the formula, thereby adjusting the response degree of the second corrected critical stress of the shaft wall to different factors. To sum up, by introducing a complex function form and empirical coefficients, this formula comprehensively considers the influence of various factors on the critical stress of the shaft wall, providing a more scientific and accurate basis for the early warning of shaft wall instability. This formula comprehensively considers real-time monitoring data, historical experience data and the relative relationship between different factors, improving the accuracy and reliability of the early warning of shaft wall instability. By introducing empirical coefficients and complex function forms, this formula can more comprehensively reflect the complex evolution mechanism of shaft wall instability, providing a more scientific basis for the dynamic decision-making plan of risks.

[0159] Example 3

[0160] Based on Example 1, this example provides a method for warning the critical conditions of shaft wall failure based on deep geomechanics, including:

[0161] Step S2224: According to the hydraulic support pressure time series data F(t) and the working face advance speed v in the mining-induced trigger parameter set P1, the critical stress σ of the second corrected shaft wall is corrected to obtain the critical stress σ of the third corrected shaft wall; σ represents the critical stress of the third corrected shaft wall of the i-th structural layer. 2,i Among them, 3,i is a dynamic adjustment coefficient used to quantify the influence degree of mining-induced trigger parameters on the critical stress of the shaft wall, and its value can be calibrated according to historical data and on-site actual conditions. 3,i is the change rate of the hydraulic support pressure, indicating how fast the hydraulic support pressure changes with time. This value can be obtained by numerically differentiating F(t).

[0162]

[0163] The significance of is that it combines the displacement-stress mapping model and the three-level displacement monitoring data, providing a relatively accurate reference value for the correction of the critical stress of the shaft wall. The introduction of

[0164] considers the influence of mining-induced trigger parameters on the critical stress of the shaft wall, making the corrected critical stress of the shaft wall more in line with the actual situation. As mining-induced trigger parameters, F(t) and v, their real-time monitoring provides important input conditions for the early warning model, helping to accurately judge the stability state of the shaft wall. When the change rate

[0165] of the hydraulic support pressure increases, it means that the hydraulic support pressure changes rapidly, which may have a greater impact on the shaft wall. At this time, σ will increase accordingly, reflecting that the critical stress of the shaft wall is affected by mining disturbances and increases. When the working face advance speed v increases, it means that the working face advances faster, and the magnitude and frequency of the mining stress on the shaft wall may increase. However, since the exponent of v in the formula is -0.5, the trend of σ decreasing with the increase of v will be relatively gentle. However, it should be noted that the increase of v here actually may mean the enhancement of mining disturbances. Therefore, in practical applications, multiple factors need to be considered comprehensively to judge the stability state of the shaft wall. At the same time, due to the existence of the formula can adaptively adjust the value of σ to reflect the actual influence of mining-induced trigger parameters on the critical stress of the shaft wall. 3,i 3,i 3,i 3,i 3,i This formula comprehensively considers the displacement-stress mapping model, the three-level displacement monitoring data and the mining-induced trigger parameters, realizes the dynamic correction of the critical stress of the shaft wall, and improves the accuracy and applicability of the early warning model. By introducing the dynamic adjustment coefficient

[0166] , it quantifies the influence of mining-induced triggering parameters on the critical stress of the shaft wall, making the warning conditions more comprehensive and accurate. This formula can provide a scientific basis for the warning of shaft wall instability, helping to take corresponding measures in time to avoid the occurrence of shaft wall instability accidents.

[0167] Example 4

[0168] Based on Example 1, this example provides a warning method for the critical condition of shaft wall failure based on deep geomechanics, including:

[0169] According to the comprehensive critical stress value , the yield strength σ of the current shaft wall steel casing y and the real-time strain distribution data εr(t) of the casing wall, calculate the critical safety factor K of shaft wall instability c ;

[0170]

[0171] Among them, is the material strength safety factor, which is an empirical value used to consider the safety margin of material strength. Its value can be determined according to relevant standards and specifications. is the yield strain of the casing material, which is the strain value of the casing material at the yield point. This parameter can also be obtained through material mechanics tests. is an exponential function with the base e of the natural logarithm; t represents time and is used to determine the upper limit of integration. represents the cumulative effect of the strain of the casing wall from the initial moment (t = 0) to the current moment t.

[0172] reflects the relative magnitude of the strength of the casing material and the critical stress of shaft wall instability, and is one of the key factors for evaluating the stability of the shaft wall. The introduction of μ considers the safety margin of material strength, making the calculation result more conservative and reliable. The integral of represents the cumulative strain of the casing wall over a period of time, reflecting the degree of deformation of the casing material. When the cumulative strain is large, it indicates that the casing may be close to or reach the yield state, and the shaft wall stability decreases. As the yield strain of the casing material, it provides a reference value for evaluating the deformation of the casing.

[0173] When increases or decreases, it indicates that the strength of the casing material is relatively increased or the critical stress of shaft wall instability is relatively decreased. At this time, will increase, reflecting the enhanced stability of the shaft wall. When the absolute value of increases, it indicates that the strain of the casing wall increases. At this time, the value of the integral term will increase, resulting in Decrease, reflecting the reduction of wellbore stability. Especially when the cumulative strain approaches or exceeds the yield strain When it will decrease rapidly, and the warning system should issue an alarm in time.

[0174] This formula comprehensively considers the yield strength of the wellbore steel casing, the comprehensive critical stress value, and the real-time strain distribution data of the casing wall, and can more accurately evaluate the stability state of the wellbore. By introducing the concepts of material strength safety factor and cumulative strain, the formula not only considers the current state of the wellbore, but also takes into account the deformation of the wellbore over a period of time, improving the accuracy and reliability of the warning. This formula provides a scientific basis for wellbore instability warning, helps to take corresponding measures in time, and ensures the safety and stability of the wellbore.

[0175] Example 5

[0176] Based on Example 1, this example provides a warning system for the critical conditions of wellbore failure based on deep geomechanics, as Figure 7 shown, including:

[0177] Data acquisition module: used to obtain initial rock formation parameters, establish a rock formation structure classification system; obtain the initial gas drilling geometric parameter group G0, arrange a three-level displacement monitoring array, and construct a three-level displacement characteristic data stream;

[0178] Mapping model construction module: construct a displacement-stress mapping model according to the rock formation structure classification system, the initial gas drilling geometric parameter group G0, and the three-level displacement characteristic data stream;

[0179] Hierarchical warning module: used to collect the mining-induced trigger parameter set P1 and the real-time three-level displacement characteristic data stream, generate a dynamically corrected critical stress for wellbore instability based on the displacement-stress mapping model, the mining-induced trigger parameter set P1, and the real-time three-level displacement characteristic data stream; combine the mining-induced trigger parameter set P1, calculate the critical safety factor for wellbore instability, establish a hierarchical warning criterion; obtain the real-time wellbore instability determination data, generate a wellbore instability warning result based on the real-time wellbore instability determination data and the hierarchical warning criterion, and generate a risk dynamic decision-making plan based on the wellbore instability warning result.

[0180] In the data acquisition module, the establishment of the rock formation structure classification system includes:

[0181] Step S1110, divide the roof rock formation into n structural layers to form a three-dimensional rock formation partition matrix, where n≥3;

[0182] Step S1120, extract the initial rock formation parameters of each structural layer, and the initial rock formation parameters include the initial compressive strength σ 0,i and the initial strain ε 0,i , to form a complete set of formation mechanical property descriptions; among them, σ0,i represents the initial compressive strength of the i-th structural layer, ε 0,i represents the initial strain of the i-th structural layer, 1 ≤ i ≤ n;

[0183] Step S1130, finely locate the soft rock interlayers of each structural layer, obtain the spatial distribution law of the soft rock interlayers, and generate the soft rock interlayer distribution matrix D0;

[0184] Step S1140, constitute the rock layer structure grading system from the three-dimensional rock layer zoning matrix, the formation mechanics property description set, and the soft rock interlayer distribution matrix D0.

[0185] In the data acquisition module, the construction of the three-level displacement characteristic data stream includes:

[0186] Step S1310, arrange a distributed optical fiber sensor array along the axial direction of the gas well, deploy an inclination sensor group in the soft rock interlayer, and arrange strain rosettes along the circumferential direction of the casing to form a three-level displacement monitoring array;

[0187] Step S1320, use the distributed optical fiber sensor array to obtain the separation displacement ΔS'1 of the roof rock layer;

[0188] Step S1330, use the inclination sensor group to measure the three-dimensional shear deformation angle θ'(t) generated by the soft rock interlayer;

[0189] Step S1340, use the strain rosettes arranged along the circumferential direction of the casing to obtain the strain distribution data εr'(t) of the casing wall;

[0190] Step S1350, define the separation displacement ΔS'1 as the first-level data, define the three-dimensional shear deformation angle θ'(t) as the second-level data, and define the strain distribution data εr'(t) as the third-level data;

[0191] Step S1360, construct a three-level displacement characteristic data stream according to the first-level data, the second-level data, and the third-level data.

[0192] In the mapping model construction module, the construction of the displacement-stress mapping model includes:

[0193] Step S1410, according to the initial compressive strength σ in the formation mechanics property description set 0,i and the initial strain ε 0,i , and the wellbore curvature radius R0 and the casing wall thickness δ0 in the initial gas well geometric parameter group G0, describe the wellbore displacement-stress constitutive relationship in the form of a fourth-order tensor, and establish the initial mapping model M0;

[0194] Step S1420: Introduce the three-dimensional rock stratum zoning matrix, the soft rock interlayer distribution matrix D0, the separation displacement ΔS'1 and the three-dimensional shear deformation angle θ'(t) in the three-level displacement characteristic data stream into the initial mapping model M0, and solve the stress-displacement field of the rock stratum around the shaft wall through the finite element numerical simulation method to obtain the coupling law between different-level structural layers, the soft rock interlayer distribution, ΔS'1 and θ'(t) and the deformation of the gas drilling;

[0195] Step S1430: According to the coupling law between different-level structural layers, the soft rock interlayer distribution, ΔS'1 and θ'(t) and the deformation of the gas drilling, calibrate the constitutive parameters in the initial mapping model M0 by using the iterative optimization algorithm to form the final displacement-stress mapping model.

[0196] In the hierarchical early warning module, the generation of the dynamically corrected critical stress of shaft wall instability includes:

[0197] Step S2210: Obtain the real-time three-level displacement characteristic data stream, which includes the real-time separation displacement ΔS1 of the roof rock stratum, the real-time three-dimensional shear deformation angle θ(t) generated by the soft rock interlayer, and the real-time strain distribution data εr(t) of the casing wall;

[0198] Step S2220: Obtain the third corrected critical stress of the shaft wall according to the real-time three-level displacement characteristic data stream and the displacement-stress mapping model;

[0199] Step S2230: Take the third corrected critical stress of the shaft wall as the dynamically corrected critical stress of shaft wall instability.

[0200] The said Step S2220 includes:

[0201] Step S2221: Input the real-time separation displacement ΔS1 and the real-time three-dimensional shear deformation angle θ(t) into the displacement-stress mapping model to obtain the first corrected critical stress σ 1,i ; σ 1,i represents the first corrected critical stress of the i-th level structural layer;

[0202] Step S2222: Retrieve the historical database of shaft wall failure cases, and obtain the historical critical stress of instability σ c,hist ;

[0203] Step S2223: According to the first corrected critical stress σ 1,i , the historical critical stress of instability σ c,hist and the ratio of the real-time separation displacement to the real-time three-dimensional shear deformation angle ΔS1 / θ(t), calculate and obtain the second corrected critical stress σ 2,i ; σ 2,i represents the second corrected critical stress of the i-th level structural layer;

[0204] Step S2224: According to the hydraulic support pressure time series data F(t) and the working face advancing speed v in the mining-induced trigger parameter set P1, correct the critical stress σ of the second corrected shaft wall 2,i to obtain the critical stress σ of the third corrected shaft wall 3,i ; σ 3,i represents the critical stress of the third corrected shaft wall of the i-th structural layer;

[0205] In the classification early warning module, the establishment of the classification early warning criterion includes:

[0206] Step S2310: Obtain the yield strength σ of the current shaft wall steel casing y , and based on the dynamically corrected critical stress σ of shaft wall instability 3,i , the yield strength σ of the current shaft wall steel casing y and the real-time strain distribution data εr(t) of the casing wall, calculate the critical safety factor K of shaft wall instability c ;

[0207] Step S2320: Based on the critical safety factor K of shaft wall instability c , establish a three-level early warning criterion.

[0208] The methods and systems of the present application may be implemented in many ways. For example, the methods and systems of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of the steps for the method is only for illustration, and the steps of the method of the present application are not limited to the above specific described order unless otherwise specifically stated.

[0209] In addition, the parts of the above technical solutions provided in the embodiments of the present application that are consistent with the corresponding technical solutions in the prior art in terms of implementation principles are not described in detail to avoid excessive elaboration.

[0210] As described in the above specific embodiments, the purpose, technical solutions, and beneficial effects of the present invention are further described in detail. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A warning method based on the critical condition of shaft wall failure in deep geomechanics, characterized in that, The method includes: Obtaining initial rock formation parameters and establishing a rock formation structure classification system; obtaining an initial gas drilling geometric parameter set G0, arranging a three-level displacement monitoring array, and constructing a three-level displacement characteristic data stream; constructing a displacement-stress mapping model based on the rock formation structure classification system, the initial gas drilling geometric parameter set G0, and the three-level displacement characteristic data stream; The method for arranging the three-level displacement monitoring array includes: arranging a distributed optical fiber sensor array along the axial direction of the gas drilling, deploying an inclination sensor group in the soft rock interlayer, and arranging strain rosettes along the circumferential direction of the casing to form a three-level displacement monitoring array; The construction of the three-level displacement characteristic data stream includes: using the distributed optical fiber sensor array to obtain the separation displacement amount ΔS'1 of the roof rock formation; using the inclination sensor group to measure the three-dimensional shear deformation angle θ'(t) generated by the soft rock interlayer; using the strain rosettes arranged along the circumferential direction of the casing to obtain the strain distribution data εr'(t) of the casing wall; defining the separation displacement amount ΔS'1 as the first-level data, defining the three-dimensional shear deformation angle θ'(t) as the second-level data, and defining the strain distribution data εr'(t) as the third-level data; constructing a three-level displacement characteristic data stream based on the first-level data, the second-level data, and the third-level data; Collecting a mining-induced trigger parameter set P1 and a real-time three-level displacement characteristic data stream, generating a dynamically corrected critical stress for shaft wall instability based on the displacement-stress mapping model, the mining-induced trigger parameter set P1, and the real-time three-level displacement characteristic data stream; calculating a critical safety factor for shaft wall instability in combination with the mining-induced trigger parameter set P1, and establishing a hierarchical early warning criterion; obtaining real-time shaft wall instability determination data, generating a shaft wall instability early warning result based on the real-time shaft wall instability determination data and the hierarchical early warning criterion, and generating a risk dynamic decision-making plan based on the shaft wall instability early warning result.

2. The early warning method for the critical condition of shaft wall failure based on deep geomechanics according to claim 1, characterized in that The obtaining of the initial rock formation parameters and the establishment of the rock formation structure classification system include: Dividing the roof rock formation into n structural layers to form a three-dimensional rock formation partition matrix, where n≥3; Extract the initial rock formation parameters of each level of structural layer, where the initial rock formation parameters include the initial compressive strength σ 0,i and the initial strain ε 0,i , constituting a complete set of descriptions of the formation mechanical properties; among them, σ 0,i represents the initial compressive strength of the i-th level of structural layer, and ε 0,i represents the initial strain of the i-th level of structural layer, where 1 ≤ i ≤ n; Locating the soft rock interlayers of each structural layer, obtaining the spatial distribution law of the soft rock interlayers, and generating a soft rock interlayer distribution matrix D0; The rock formation structure classification system is composed of the three-dimensional rock formation partition matrix, the formation mechanics property description set, and the soft rock interlayer distribution matrix D0.

3. The warning method based on the critical condition of shaft wall failure in deep geomechanics according to claim 2, wherein The obtaining of the initial gas drilling geometric parameter set G0 includes: performing geometric measurement inside the gas drilling to obtain the point cloud data of the wellbore spatial curved surface; performing denoising and normalization processing on the point cloud data of the wellbore spatial curved surface, and extracting the wellbore curvature radius R0 and the casing wall thickness δ0; constructing an initial gas drilling geometric parameter set G0 based on the wellbore curvature radius R0 and the casing wall thickness δ0.

4. The warning method based on the critical condition of shaft wall failure in deep geomechanics according to claim 3, characterized in that, The construction of the displacement-stress mapping model includes: According to the initial compressive strength σ 0,i and the initial strain ε 0,i , as well as the wellbore curvature radius R0 and the casing wall thickness δ0 in the initial gas drilling geometric parameter group G0, a constitutive relationship between wellbore displacement and stress is described in the form of a fourth-order tensor, and an initial mapping model M0 is established; Introducing the three-dimensional rock formation partition matrix, the soft rock interlayer distribution matrix D0, the separation displacement amount ΔS'1 and the three-dimensional shear deformation angle θ'(t) in the three-level displacement characteristic data stream into the initial mapping model M0, solving the stress-displacement field of the rock formation around the wellbore by the finite element numerical simulation method, and obtaining the coupling law between different-level structural layers, the soft rock interlayer distribution, ΔS'1 and θ'(t) and the deformation of the gas drilling; According to the coupling laws among different-level structural layers, the distribution of soft rock interlayers, ΔS'1 and θ'(t) and the deformation of gas drilling, an iterative optimization algorithm is used to calibrate the constitutive parameters in the initial mapping model M0 to form the final displacement-stress mapping model.

5. The early warning method for the critical condition of shaft wall failure based on deep geomechanics according to claim 1, characterized in that, The collected mining-induced trigger parameter set P1 includes: real-time collection of the hydraulic support pressure time series data F(t) and the working face advancing speed v; v and F(t) together constitute the mining-induced trigger parameter set P1.

6. The early warning method based on the critical condition of shaft wall failure in deep geomechanics according to claim 2, characterized in that, The real-time three-level displacement characteristic data stream includes the real-time separation displacement ΔS1 of the roof rock stratum, the real-time three-dimensional shear deformation angle θ(t) generated by the soft rock interlayer, and the real-time strain distribution data εr(t) of the casing wall; The generation of the dynamically corrected critical stress of the shaft wall instability includes: According to the real-time three-level displacement characteristic data stream and the displacement-stress mapping model, the third corrected critical stress of the shaft wall is obtained; The third corrected critical stress of the shaft wall is used as the dynamically corrected critical stress of the shaft wall instability.

7. The warning method for the critical condition of shaft wall failure based on deep geomechanics according to claim 6, characterized in that, The obtaining of the third corrected critical stress of the shaft wall includes: Input the real-time separation displacement ΔS1 and the real-time three-dimensional shear deformation angle θ(t) into the displacement-stress mapping model to obtain the first corrected critical stress σ of the shaft wall 1,i ; σ 1,i represents the first corrected critical stress of the i-th structural layer of the shaft wall; Retrieve the historical database of wellbore failure cases, and obtain the historical critical stress of instability σ from the historical database of wellbore failure cases c,hist ; According to the first corrected critical stress of the shaft wall σ 1,i , the historical instability critical stress σ c,hist and the ratio ΔS1 / θ(t) of the real-time separation displacement amount to the real-time three-dimensional shear deformation angle, the second corrected critical stress of the shaft wall σ 2,i is calculated; σ 2,i represents the second corrected critical stress of the shaft wall of the i-th structural layer; According to the hydraulic support pressure time series data F(t) and the working face advancing speed v in the mining-induced trigger parameter set P1, the critical stress σ of the second corrected shaft wall is corrected to obtain the critical stress σ of the third corrected shaft wall. 2,i ; σ 3,i ; σ 3,i represents the critical stress of the third corrected shaft wall of the i-th structural layer.

8. The early warning method for the critical condition of shaft wall failure based on deep geomechanics according to claim 7, characterized in that, The calculation of the critical safety factor for wellbore instability includes: obtaining the yield strength σ of the current wellbore steel casing y , based on the dynamically corrected critical stress σ for wellbore instability 3,i , the yield strength σ of the current wellbore steel casing y and the real-time strain distribution data εr(t) of the casing wall, calculating the critical safety factor K for wellbore instability c .

9. The warning method for the critical condition of shaft wall failure based on deep geomechanics according to claim 8, characterized in that, The critical stress σ of wellbore instability based on dynamic correction 3,i , the yield strength σ of the current wellbore steel casing y and the real-time strain distribution data εr(t) of the casing wall to calculate the critical safety factor K of wellbore instability c including: Obtain the thickness h of the i-th structural layer i , according to h i and the initial compressive strength σ 0,i , calculate the weight of the i-th structural layer ; According to and σ 3,i calculate the comprehensive critical stress value ; According to the comprehensive critical stress value , the yield strength σ of the current wellbore steel casing y and the real-time strain distribution data εr(t) of the casing wall, calculate the critical safety factor K of wellbore instability c .

10. A warning system based on the critical condition of shaft wall failure in deep geomechanics, which is used to implement the warning method for the critical condition of shaft wall failure in deep geomechanics described in any one of claims 1-9, characterized in that, The system includes: Data acquisition module: used to acquire the initial rock stratum parameters and establish a rock stratum structure classification system; acquire the initial gas drilling geometric parameter group G0, arrange a three-level displacement monitoring array, and construct a three-level displacement characteristic data stream; Mapping model construction module: According to the rock stratum structure classification system, the initial gas drilling geometric parameter group G0 and the three-level displacement characteristic data stream, construct a displacement-stress mapping model; Hierarchical early warning module: used to collect the mining-induced trigger parameter set P1 and the real-time three-level displacement characteristic data stream, generate the dynamically corrected critical stress of the shaft wall instability based on the displacement-stress mapping model, the mining-induced trigger parameter set P1 and the real-time three-level displacement characteristic data stream; combine the mining-induced trigger parameter set P1, calculate the critical safety factor of the shaft wall instability, and establish a hierarchical early warning criterion; obtain the real-time shaft wall instability determination data, generate a shaft wall instability early warning result based on the real-time shaft wall instability determination data and the hierarchical early warning criterion, and generate a risk dynamic decision-making plan based on the shaft wall instability early warning result.

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