Well wall damage critical condition early warning system and method based on deep rock and soil mechanics

Abstract

The invention relates to the technical field of mine intelligent early warning, and discloses a well wall damage critical condition early warning system and method based on deep rock-soil mechanics, and the method comprises the steps: obtaining initial rock stratum parameters to establish a rock stratum structure grading system, obtaining initial gas drilling geometric parameters, and arranging a three-level displacement monitoring array to construct a three-level displacement characteristic data flow; a displacement-stress mapping model is constructed according to the data, mining trigger parameters and real-time displacement data are collected, dynamically-corrected borehole wall instability critical stress is generated, a borehole wall instability critical safety coefficient is calculated in combination with the mining trigger parameters, and graded early warning criteria are established; and finally, based on the real-time well wall instability judgment data and the grading early warning criterion, generating a well wall instability early warning result and a risk dynamic decision scheme. According to the method, multiple factors are comprehensively considered, accurate early warning of borehole wall instability is achieved, and the safety of mine mining is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent early warning of mines, and more specifically, to an early warning system and method for critical conditions of well wall failure based on deep rock and soil mechanics. Background Art

[0002] In gas drilling operations, the stability of the well wall is crucial to ensure the safe and efficient gas extraction. As a key channel for gas extraction, once the well wall of the gas drilling is unstable, it will not only lead to the interruption of gas extraction and affect the safe production of coal mines, but also may cause gas leakage, explosion and other safety accidents, causing economic losses and affecting personnel safety.

[0003] The patent application with publication number CN115680775A proposes an intelligent early warning system and method for coal and gas outburst based on multi-sensor fusion, which obtains multi-source data through microseismic sensors, ground stress sensors, laser methane sensors and wind speed sensors to solve the problem of high noise and inaccuracy in predicting the level of coal and gas outburst with single data. However, this prior art focuses on coal and gas outburst early warning, which is different from the wellbore instability early warning scenario that the present invention focuses on. Wellbore instability involves many complex factors such as rock structure, gas drilling geometry, displacement changes, and mining effects. Coal and gas outburst early warning technology cannot be directly applied to wellbore instability early warning. Coal and gas outburst early warning mainly revolves around gas-related parameters and geological dynamic phenomena, ignoring the wellbore's own structural characteristics and mechanical response, making it difficult to effectively evaluate the wellbore stability.

[0004] The patent application with publication number CN110985125A provides a deep well soft coal rock burst disaster monitoring and early warning system and its early warning method. It monitors the borehole pressure and tunnel wall deformation through stress sensors and displacement sensors, and issues early warnings based on comprehensive data, thereby improving the accuracy of early warnings. However, this existing technology is mainly aimed at deep well soft coal rock burst disasters, and there are differences in monitoring objects and early warning principles from wellbore instability early warnings. Rock burst disasters mainly focus on the dynamic phenomena caused by the changes in internal stress of the coal body and the sudden release, while wellbore instability is closely related to the gas drilling support structure, the speed of the working face advancement during mining, and the pressure changes of hydraulic supports, in addition to being affected by rock formation stress. This technical solution does not take into account these key factors of wellbore instability and cannot meet the needs of wellbore instability early warnings.

[0005] When dealing with the problem of instability of gas drilling shaft walls under deep geotechnical conditions, the existing mine disaster warning technology does not adequately consider the deep geotechnical mechanical properties, rock formation structure, and various influencing factors during gas drilling construction and operation. It lacks technical means to accurately monitor the status of gas drilling shaft walls, provide early warning of instability risks, and formulate effective response strategies. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a critical condition early warning system and method for wellbore damage based on deep rock and soil mechanics. By comprehensively considering the deep rock and soil mechanics properties, gas drilling geometric parameters, displacement monitoring data and mining trigger parameters, an accurate displacement-stress mapping model is constructed, and dynamic assessment and graded early warning of wellbore instability risks are realized, thereby improving the accuracy of wellbore instability early warning, reducing the false alarm and missed alarm rates, and providing a scientific basis and technical support for wellbore stability management in gas drilling operations.

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

[0008] The critical condition early warning method of wellbore failure based on deep rock and soil mechanics includes:

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

[0010] Collect mining trigger parameter set P1 and real-time three-level displacement characteristic data stream, generate dynamically corrected critical stress of wellbore instability based on displacement-stress mapping model, mining trigger parameter set P1 and real-time three-level displacement characteristic data stream; calculate critical safety factor of wellbore instability in combination with mining trigger parameter set P1, and establish graded early warning criteria; obtain real-time wellbore instability judgment data, generate wellbore instability warning results based on real-time wellbore instability judgment data and graded early warning criteria, and generate risk dynamic decision-making plan based on wellbore instability warning results.

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

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

[0013] Extract the initial rock formation parameters of each structural layer, including the initial compressive strength σ 0,i and the initial strain ε 0,i , forming a complete description set of formation mechanical properties; among them, σ 0,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;

[0014] Locate the soft rock interlayers at all levels of structural layers, obtain the spatial distribution law of the soft rock interlayers, and generate the soft rock interlayer distribution matrix D0;

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

[0016] Furthermore, the obtaining of the initial gas drilling geometric parameter group G0 includes: performing geometric measurement on the inside of the gas drilling to obtain the well wall spatial surface point cloud data; performing denoising and normalization on the well wall spatial surface point cloud data to extract the well wall curvature radius R0 and the casing wall thickness δ0; and constructing the gas drilling initial geometric parameter group G0 according to the well wall curvature radius R0 and the casing wall thickness δ0.

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

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

[0019] The delamination displacement ΔS'1 of the roof rock layer is obtained by using a distributed optical fiber sensor array; the three-dimensional shear deformation angle θ'(t) generated by the soft rock interlayer is measured by using a tilt sensor group; the strain distribution data εr'(t) of the casing wall is obtained by using strain rosettes arranged along the casing annulus;

[0020] The delamination 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; based on the primary data, the secondary data and the tertiary data, a tertiary displacement characteristic data stream is constructed.

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

[0022] Describe the initial compressive strength σ of the concentration according to the formation mechanical properties 0,i and the initial strain ε 0,i , as well as the wellbore curvature radius R0 and casing wall thickness δ0 in the initial gas drilling geometric parameter group G0, the fourth-order tensor form is used to describe the wellbore displacement-stress constitutive relationship, and the initial mapping model M0 is established;

[0023] The three-dimensional rock formation partition matrix, the soft rock interlayer distribution matrix D0, the delamination displacement ΔS'1 in the three-level displacement characteristic data stream and the three-dimensional shear deformation angle θ'(t) are introduced into the initial mapping model M0. The stress-displacement field of the rock formation around the wellbore is solved by the finite element numerical simulation method to obtain the coupling law between different levels of structural layers, soft rock interlayer distribution, ΔS'1 and θ'(t) and gas drilling deformation.

[0024] According to the coupling law between different levels of structural layers, soft rock interlayer distribution, ΔS'1 and θ'(t) and gas drilling deformation, the constitutive parameters in the initial mapping model M0 are calibrated using an iterative optimization algorithm to form the final displacement-stress mapping model.

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

[0026] Furthermore, the real-time three-level displacement characteristic data stream includes the real-time delamination 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;

[0027] The generating of the dynamically corrected critical stress for wellbore instability comprises:

[0028] According to 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 wellbore instability critical stress.

[0029] Further, obtaining the third corrected borehole critical stress includes:

[0030] The real-time delamination 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 borehole critical stress σ 1,i ; σ 1,i represents the first modified borehole critical stress of the i-th structural layer;

[0031] Retrieve the historical database of wellbore damage cases and obtain the historical critical stress σ from the historical database of wellbore damage cases c,hist ;

[0032] According to the first modified borehole critical stress σ 1,i 、Historical instability critical stress σ c,hist The second modified borehole critical stress σ is calculated by the ratio of the real-time delamination displacement to the real-time three-dimensional shear deformation angle ΔS1 / θ(t). 2,i ; σ 2,i represents the second modified borehole critical stress of the i-th structural layer;

[0033] According to the hydraulic support pressure time series data F(t) and the working face advancement speed v in the mining trigger parameter set P1, the second modified wellbore critical stress σ 2,i After correction, the third corrected borehole critical stress σ is obtained 3,i ; σ 3,i It represents the third modified borehole critical stress of the i-th structural layer.

[0034] Furthermore, the calculation of the critical safety factor of wellbore instability includes: obtaining the yield strength σ of the current wellbore steel casing y , based on the dynamic correction of the critical stress of wellbore instability σ 3,i , the yield strength of the current wellbore steel casing σ 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 .

[0035] Furthermore, the critical stress σ for wellbore instability based on dynamic correction 3,i , the yield strength of the current wellbore steel casing σ 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 include:

[0036] Get the thickness h of the i-th level structure layer i , according to h i and initial compressive strength σ 0,i , calculate the weight of the i-th level structure layer ;according to and σ 3,i Calculate the composite critical stress value ;

[0037] According to the comprehensive critical stress value , the yield strength of the current wellbore steel casing σ 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 .

[0038] The critical condition early warning system for well wall damage based on deep rock and soil mechanics is used to implement the above-mentioned critical condition early warning method for well wall damage based on deep rock and soil mechanics. The system includes:

[0039] Data acquisition module: used to obtain initial rock formation parameters and 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 building module: constructs a displacement-stress mapping model based on the rock structure classification system, the initial gas drilling geometry parameter group G0 and the three-level displacement characteristic data stream;

[0041] Graded warning module: used to collect mining trigger parameter set P1 and real-time three-level displacement characteristic data stream, generate dynamically corrected critical stress of wellbore instability based on displacement-stress mapping model, mining trigger parameter set P1 and real-time three-level displacement characteristic data stream; calculate critical safety factor of wellbore instability in combination with mining trigger parameter set P1, and establish graded warning criteria; obtain real-time wellbore instability judgment data, generate wellbore instability warning results based on real-time wellbore instability judgment data and graded warning criteria, and generate risk dynamic decision-making plan based on wellbore instability warning results.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention realizes dynamic assessment and accurate early warning of the risk of wellbore instability by constructing a rock structure classification system and a displacement-stress mapping model, combined with real-time displacement monitoring data and mining trigger parameters. This method overcomes the static nature and limitations of traditional wellbore stability analysis, and can reflect the dynamic changes of the wellbore under complex geological conditions and mining disturbances in real time. At the same time, the establishment of graded early warning criteria makes the early warning more scientific and reasonable, and can take corresponding countermeasures according to the severity of the wellbore instability risk, effectively avoiding the occurrence of wellbore instability accidents and ensuring the safety and efficiency of gas drilling operations. In addition, the generation of risk dynamic decision-making plans provides a scientific basis for mine managers, which helps to achieve the rational allocation and efficient use of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 It is a principle flow chart of the critical condition early warning method of well wall failure based on deep rock and soil mechanics in the present invention;

[0046] Figure 2 A flow chart of a method for establishing a rock formation structure classification system in a critical condition early warning method for wellbore failure based on deep rock and soil mechanics of the present invention;

[0047] Figure 3 It is a flow chart of a method for obtaining an initial gas drilling geometric parameter group G0 in a critical condition early warning method for wellbore damage based on deep rock and soil mechanics of the present invention;

[0048] Figure 4A flow chart of a method for deploying a three-level displacement monitoring array and constructing a three-level displacement characteristic data stream in a critical condition early warning method for wellbore damage based on deep rock and soil mechanics of the present invention;

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

[0050] Figure 6 It is a flow chart of a method for obtaining the third corrected wellbore critical stress in the wellbore failure critical condition early warning method based on deep rock and soil mechanics of the present invention;

[0051] Figure 7 It is a functional module diagram of the critical condition early warning system for well wall failure based on deep rock and soil mechanics in the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Example 1

[0054] See also Figure 1 As shown, this embodiment provides a critical condition early warning method for well wall failure based on deep rock and soil mechanics, including:

[0055] Step S1000, obtaining initial rock formation parameters and establishing a rock formation structure classification system; obtaining an initial gas drilling geometric parameter group G0, deploying 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 group G0 and the three-level displacement characteristic data stream;

[0056] Furthermore, step S1000 includes:

[0057] Step S1100, obtaining initial rock formation parameters and establishing a rock formation structure classification system;

[0058] Furthermore, if Figure 2 As shown, step S1100 includes:

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

[0060] Step S1120, extracting the initial rock formation parameters of each structural layer, the initial rock formation parameters including the initial compressive strength σ0,i and the initial strain ε 0,i , forming a complete description set of formation mechanical properties; among them, σ 0,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;

[0061] 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;

[0062] Step S1140, a rock formation structure classification system is constructed by the three-dimensional rock formation partition matrix, the formation mechanical property description set and the soft rock interlayer distribution matrix D0.

[0063] Specifically, step S1100 aims to obtain initial rock formation parameters and establish a rock formation structure classification system, which is an important geological basis for subsequent wellbore stability analysis. In step S1110, the roof rock formation is divided into n-level structural layers (n≥3) to form a three-dimensional rock formation partition matrix. Here, n represents the number of layers of rock formation division, and its value is flexibly adjusted according to the complexity of the formation. For example, in areas with relatively simple geological conditions, n can be 3, and the roof rock formation is divided into a relatively simple three-layer structure, representing rock formations with different mechanical properties; while in areas with complex geological conditions and diverse rock formations, n may take a value of 5 or even more to divide the rock formation in more detail. This division method is to simplify the description of complex formations and highlight the main rock formation structural factors that have a significant impact on wellbore stability. Its beneficial effect is that the complex rock formation structure is orderly classified, making the subsequent analysis of rock formation mechanical properties and wellbore stability more targeted and organized. From the reasoning process, due to the huge differences in the distribution and properties of rock formations in different strata, if we do not classify them, we will face a large amount of messy data when analyzing the stability of the wellbore, and it will be difficult to determine the key influencing factors. Through classification, complex problems can be disassembled, the difficulty of analysis can be reduced, and the research efficiency can be improved.

[0064] Initial compressive strengthσ 0,i It refers to the ability index of the i-th level structure layer to resist pressure damage without subsequent mining and other disturbances; 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 partition 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 integrates the structural 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, this system can clearly see the distribution of different structural layers, the mechanical parameters of each layer and the location of the soft rock interlayer, providing engineers with intuitive and comprehensive geological information. Its beneficial effect is that it provides a comprehensive and systematic geological basis for the stability analysis of the gas drilling well wall, which helps to more deeply understand the interaction between the rock formation and the gas drilling well wall. From the reasoning process, the stability of the gas drilling well wall is affected by many factors, and the separate structural division, mechanical parameters or soft rock interlayer information cannot fully reflect these influences. By constructing this classification system and organically combining various information, it is possible to grasp the influence of the rock formation on the stability of the gas drilling well wall as a whole, and lay a solid foundation for subsequent gas drilling well wall stability research, instability warning and ensuring the safety of gas drilling operations.

[0068] Step S1200, obtaining an initial gas drilling geometric parameter group G0;

[0069] Furthermore, if Figure 3 As shown, step S1200 includes:

[0070] Step S1210, performing high-precision geometric measurement on the inside of the gas well to obtain spatial surface point cloud data of the well wall;

[0071] Step S1220, denoising and normalizing the wellbore spatial surface point cloud data, extracting the wellbore curvature radius R0 and the casing wall thickness δ0; constructing the gas drilling initial geometric parameter group G0 according to the wellbore curvature radius R0 and the casing wall thickness δ0.

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

[0073] De-noising is to remove noise points in point cloud data caused by measurement errors, environmental interference and other factors, which will affect the accuracy of subsequent parameter extraction. For example, during the measurement process, surrounding dust, slight vibration of equipment, etc. may cause abnormal fluctuations in the measurement data. These abnormal points can be removed by the denoising algorithm to make the point cloud data smoother and more accurate. Normalization is to perform a unified scale transformation on the point cloud data so that data at different locations and under different measurement conditions are comparable. In order to calculate the radius of curvature of the well wall, 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 the principle of differential geometry.

[0074] To extract the casing wall thickness, the point clouds belonging to the inner and outer walls of the casing must be segmented out from the wellbore point cloud data. Segmentation can be performed based on the spatial distribution characteristics of the point cloud, intensity information, etc. If the laser scanner used to collect the point cloud data has an intensity measurement function, since the reflection intensity of the laser on the inner and outer walls 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, the inner and outer wall point clouds of the casing can also be extracted separately based on the spatial position relationship of the point cloud through the regional growing algorithm, etc. After the inner and outer wall point clouds of the casing are successfully segmented, the distance between each pair of corresponding inner and outer wall points is calculated, and the average value of these distances is the casing wall thickness δ0.

[0075] The extracted wellbore curvature radius R0 reflects the curvature of the wellbore surface. The smaller the curvature radius, the more severe the wellbore curvature and the greater the possibility of stress concentration. The casing wall thickness δ0 is an important parameter to measure the bearing capacity of the casing. The thicker the wall thickness, the 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 that of other areas, then stress concentration is more likely to occur in this area, causing deformation or even damage of the wellbore; while areas with thicker casing walls are less likely to deform when subjected to the same pressure. The initial geometric parameter group G0 of gas drilling is constructed, and the wellbore curvature radius R0 and the casing wall thickness δ0 are integrated together, providing key geometric parameters for the 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 subsequently established model more consistent with the actual situation, and can obtain more reliable results when analyzing the stress and deformation of the wellbore, thus providing a more accurate basis for wellbore instability warning.

[0076] Step S1300, deploying a three-level displacement monitoring array and constructing a three-level displacement feature data stream;

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

[0078] Step S1310, deploying a distributed optical fiber sensor array along the axial direction of the gas drilling, deploying a tilt sensor group in the soft rock interlayer, and arranging strain rosettes along the annular direction of the casing to form a three-level displacement monitoring array;

[0079] Step S1320, using a distributed optical fiber sensor array to obtain the delamination displacement ΔS'1 of the roof rock layer;

[0080] Step S1330, using the inclination sensor group, measuring the three-dimensional shear deformation angle θ'(t) generated by the soft rock interlayer;

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

[0082] Step S1350, defining the delamination displacement ΔS'1 as primary data, defining the three-dimensional shear deformation angle θ'(t) as secondary data, and defining the strain distribution data εr'(t) as tertiary data;

[0083] Step S1360, constructing a three-level displacement feature data stream based on the primary data, the secondary data and the tertiary 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 low mechanical strength, soft rock interlayers are prone to shear deformation during the stress process of the wellbore. The inclination sensor group can accurately obtain the three-dimensional shear deformation angle θ'(t) by real-time monitoring the angle changes of the soft rock interlayer in three directions of space. For example, when the soft rock interlayer is squeezed or stretched by the surrounding rock layer, its angle will change. The inclination sensor group can capture these changes in time and convert them into corresponding electrical signals for recording and transmission. The soft rock interlayer is the weak link of wellbore instability. θ'(t) reveals the temporal characteristics of interlayer strength attenuation and destruction. With the progress of coal mining activities, the stress on the soft rock interlayer is constantly changing, 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 the beginning of deformation to the gradual destruction can be clearly observed, and the time sequence and rate of its strength decay can be understood, thereby providing key information for assessing the possibility and time node of wellbore instability. If θ'(t) increases rapidly in a short period of time, it means that the destruction process of the soft rock interlayer is accelerating and the risk of wellbore instability is also increasing sharply.

[0087] Strain gauges are devices used to measure surface strain of objects. By arranging strain gauges at different positions around the casing, the strain of the casing in different directions can be measured, and the strain distribution data εr'(t) can be obtained. When the wellbore wall is subjected to external pressure, rock deformation, etc., the casing will produce corresponding strain. For example, in a gas drilling, due to the uneven settlement of the surrounding rock formations, the casing will be subjected to uneven pressure. At this time, the strain gauge can measure the strain difference in different parts of the casing. εr'(t) is a direct representation of the stress state of the wellbore wall and is highly sensitive to stress concentration and instability cracking. By analyzing the strain distribution data, the stress distribution of the wellbore wall can be directly understood, and the stress concentration area can be discovered in time. Because stress concentration is often the starting point of wellbore instability cracking, once the stress concentration area is found, targeted reinforcement measures can be taken or the mining plan can be adjusted to avoid the occurrence of wellbore instability accidents. According to the principle of material mechanics, the strain of an object is closely related to the stress it is subjected to. By measuring the strain distribution of the casing, the stress state of the wellbore wall can be inferred, providing direct and critical data support for evaluating the stability of the wellbore wall.

[0088] The delamination 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 data classification is based on the different levels of the instability characteristics of the wellbore. The primary data, namely the delamination displacement ΔS'1, reflects the macroscopic deformation trend of the wellbore instability. Because the delamination of the roof rock layer is a macroscopic deformation phenomenon, the change in its displacement can reflect whether the rock layer environment in which the wellbore is located is stable and whether there is an instability trend. The secondary data, namely the three-dimensional shear deformation angle θ'(t), corresponds to the degradation process of the local rock formation strength. As a local weak rock layer, the change of the three-dimensional shear deformation angle of the soft rock interlayer directly reflects the change of the rock formation strength in the local area. By monitoring θ'(t), the attenuation process of the local rock formation strength can be understood. The tertiary data, namely the strain distribution data εr'(t), represents the load response of the wellbore itself. As an important support structure of the wellbore, the strain distribution data of the casing can directly reflect the actual response of the wellbore under the current stress state. Through this classification method, the complex evolution mechanism of wellbore instability can be perceived at multiple scales, from global to local, from rock mass to support. It provides a clear data framework for comprehensive and systematic analysis of the wellbore instability process, allowing researchers to gain an in-depth understanding of the mechanism of wellbore instability from different levels. Wellbore instability is a complex process involving multiple factors and multiple levels, and a single data cannot fully reflect its essence. By classifying the data and conducting collaborative analysis of the data at all levels, the macroscopic deformation of the rock mass, the strength changes of the local rock formations, and the loading conditions of the wellbore support structure can be comprehensively considered, so as to more accurately grasp the evolution process of wellbore instability and lay the foundation for accurate early warning.

[0089] In step S1360, a three-level displacement characteristic data stream is constructed based on the primary data, the secondary data and the tertiary data. This step integrates the previously acquired and graded data to form an orderly data stream. By arranging the delamination 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 fully reflect the change of the wellbore displacement characteristics over time is constructed. For example, data of 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 a three-level displacement characteristic data stream is to provide a unified and standardized data input for the subsequent construction of a displacement-stress mapping model and the analysis of the wellbore instability state. Its beneficial effect is to make the data more systematic and coherent, which is convenient for subsequent data analysis and processing. From the reasoning process, in the subsequent model construction and analysis, a data set that can accurately reflect the history and trend of wellbore displacement changes is required. The three-level displacement characteristic data stream integrates scattered data at all levels, which can fully present the displacement characteristics of the wellbore at different time points, help researchers discover potential correlations and patterns between data, improve the accuracy and reliability of analysis, and provide strong support for accurately assessing the risk of wellbore instability.

[0090] Step S1400, constructing 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.

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

[0092] Step S1410, based on the initial compressive strength σ in the formation mechanical property description set 0,i and the initial strain ε 0,i , as well as the wellbore curvature radius R0 and casing wall thickness δ0 in the initial gas drilling geometric parameter group G0, the fourth-order tensor form is used to describe the wellbore displacement-stress constitutive relationship, and the initial mapping model M0 is established;

[0093] Step S1420, introducing the three-dimensional rock formation partition matrix, the soft rock interlayer distribution matrix D0, the delamination 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, 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 levels of structural layers, soft rock interlayer distribution, ΔS'1 and θ'(t) and gas drilling deformation;

[0094] Step S1430, according to the coupling law between different levels of structural layers, soft rock interlayer distribution, ΔS'1 and θ'(t) and gas drilling deformation, an iterative optimization algorithm is used to calibrate the constitutive parameters in the initial mapping model M0 to form a 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 rock formation structure, gas drilling geometric parameters, displacement data and wellbore stress, and provide a key tool for subsequent accurate analysis of wellbore stability and instability warning. The fourth-order tensor is a mathematical tool used to describe the complex mechanical properties of materials in continuous medium mechanics. It can comprehensively characterize the relationship between stress and displacement of wellbore materials in different directions. In actual application scenarios, such as in deep rock and soil gas extraction environments, the wellbore is subjected to multi-directional pressure 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, so that the model can 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 is that it provides a standardized and mathematical model basis for wellbore mechanical analysis, so that subsequent research can be quantitatively analyzed based on this model. Accurately describing the displacement-stress constitutive relationship of the wellbore is a key prerequisite for analyzing wellbore stability. By integrating multiple key parameters in the form of a fourth-order tensor, the deformation and stress change laws of the well wall under different stress conditions can be fully reflected, which helps researchers to deeply understand the mechanical properties of the well wall and provide strong support for subsequent model optimization and practical application.

[0096] Finite element numerical simulation method is a numerical calculation technology widely used in the field of engineering. It discretizes complex continua into finite units, performs mechanical analysis on each unit, and then combines these units 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 three-dimensional rock stratum partition matrix established previously, and the information contained in the soft rock interlayer distribution matrix D0 is integrated 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 calculation, the stress and displacement distribution of the rock strata around the well wall at different positions can be obtained. The purpose of this step is to reveal the intrinsic relationship between multiple factors and gas drilling deformation. Its beneficial effect is that through simulation analysis, the influence of different factors on the deformation of gas drilling can be intuitively observed, which provides a basis for further optimizing the model and formulating a reasonable early warning strategy. The stability of the well wall is affected by a variety of factors, and the interaction between these factors is complex. Through finite element simulation, these factors can be incorporated into a unified model framework for analysis, and the coupling law between them and gas drilling deformation can be grasped as a whole, laying the foundation for the subsequent accurate evaluation of wellbore stability.

[0097] Iterative optimization algorithm is a type of algorithm that gradually approaches the optimal solution through continuous iterative calculation. In this step, its role is to adjust the constitutive parameters in the initial mapping model M0 so that the model can more accurately reflect the actual situation. For example, in the process of multiple simulation calculations, the parameters in the model, such as the elastic modulus and Poisson's ratio of the material, are automatically adjusted using the iterative optimization algorithm according to the difference between each simulation result and the actual monitoring data. After multiple iterations, when the error between the simulation results 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 wellbore under different conditions, providing a more reliable basis for wellbore instability warning. Due to the uncertainty of geological conditions and wellbore 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 more effectively serve the wellbore stability analysis and early warning work.

[0098] Step S2000, collect the mining trigger parameter set P1 and the real-time three-level displacement characteristic data stream, generate the dynamically corrected critical stress of wellbore instability based on the displacement-stress mapping model, the mining trigger parameter set P1 and the real-time three-level displacement characteristic data stream; calculate the critical safety factor of wellbore instability in combination with the mining trigger parameter set P1, and establish a graded early warning criterion; obtain real-time wellbore instability judgment data, generate wellbore instability warning results based on the real-time wellbore instability judgment data and the graded early warning criterion, and generate a risk dynamic decision-making plan based on the wellbore instability warning results.

[0099] Furthermore, step S2000 includes:

[0100] Step S2100, real-time acquisition of the trigger parameter set P1;

[0101] Furthermore, step S2100 includes:

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

[0103] Step S2120, collecting the working face advancement speed v in real time;

[0104] In step S2130, v and F(t) together constitute the trigger parameter set P1.

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

[0106] In step S2110, the hydraulic support is an important device for supporting the roof rock layer in the coal mining face, and its pressure time series data F(t) can intuitively reflect the change of the support force of the roof rock layer. In the gas drilling project, since the gas drilling is often arranged near the coal mining face, the stress state of the roof rock layer will change due to the coal mining operation. By collecting the hydraulic support pressure time series data F(t) in real time, the dynamic change process of the roof rock layer pressure can be monitored. For example, in a gas drilling project, when the coal mining face advances to the area close to the gas drilling, the hydraulic support pressure time series data F(t) will fluctuate significantly, and this fluctuation reflects the stress adjustment of the roof rock layer 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 layer. Because the change of the roof rock layer pressure will affect its force on the gas drilling well wall, if the roof rock layer pressure is too large or changes too fast, it may lead to the aggravation of the stress redistribution of the surrounding rock of the well wall, thereby threatening the stability of the well wall. By continuously monitoring the hydraulic support pressure time series data F(t), abnormal changes in the roof rock pressure can be discovered in time, providing a basis for subsequent corresponding measures. From the reasoning process, the change in the hydraulic support pressure will be transmitted to the surrounding area of ​​the gas drilling through the rock formation, affecting the stress environment of the well wall. When the hydraulic support pressure increases, the pressure of the roof rock formation on the well wall may also increase, increasing the risk of well wall instability; conversely, when the pressure decreases, although it may reduce the well wall pressure, it may also mean that the roof rock formation is loose and unstable. Therefore, accurately obtaining the hydraulic support pressure time series data F(t) is crucial to evaluating the well wall stability.

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

[0108] In step S2130, the two parameters are combined into the mining trigger parameter set P1 because they reflect the mining stress disturbance from different aspects. The hydraulic support pressure time series data F(t) reflects the change of the current support force of the roof rock layer, while the working face advancement speed v reflects the dynamic process of mining stress change. The combination of the two can more comprehensively describe the influencing factors of mining stress on the stability of the gas drilling wellbore. For example, in actual engineering, when the hydraulic support pressure time series data F(t) shows that the roof rock layer pressure continues to increase, and the working face advancement speed v is fast, it means that the mining stress has a strong impact on the gas drilling wellbore, and the risk of wellbore instability increases significantly. The beneficial effect of this combination is that it provides a rich data basis for the subsequent establishment of a more accurate wellbore stability analysis model. By comprehensively analyzing v and F(t) in the mining trigger parameter set P1, the stability of the wellbore under the influence of mining can be more accurately evaluated, 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 advancement speed v can only reflect one aspect of the mining stress disturbance, while the combination of the two can more comprehensively present the changing characteristics of mining stress. Different combinations of F(t) and v will produce different mining stress states, and the impact on wellbore stability is also different. Therefore, combining v and F(t) to form the mining trigger parameter set P1 helps to more accurately grasp the law of wellbore stability changes and provide a more reliable basis for ensuring the safety of gas drilling.

[0109] Step S2200, obtaining a real-time three-level displacement characteristic data stream, and generating a dynamically corrected critical stress for wellbore instability based on a displacement-stress mapping model, a mining trigger parameter set P1 and the real-time three-level displacement characteristic data stream;

[0110] Further, step S2200 includes:

[0111] Step S2210, obtaining a real-time three-level displacement characteristic data stream, wherein the real-time three-level displacement characteristic data stream includes a real-time delamination displacement ΔS1 of the roof rock layer, a real-time three-dimensional shear deformation angle θ(t) generated by the soft rock interlayer, and a real-time strain distribution data εr(t) of the casing wall;

[0112] Specifically, the real-time delamination displacement ΔS1 reflects the interlayer displacement of the roof rock at the current moment, which is an important manifestation of the macroscopic deformation trend of the wellbore; the real-time three-dimensional shear deformation angle θ(t) focuses on the soft rock interlayer, a key weak area of ​​wellbore instability, and shows the real-time progress of its strength attenuation and destruction; the real-time strain distribution data εr(t) of the casing wall directly reflects the current load state of the wellbore support structure. The purpose of obtaining these real-time data is to provide the latest displacement-related information for the subsequent accurate analysis of the wellbore stability, and closely track the real-time deformation and force response of the wellbore during gas drilling. The stability of the wellbore is in dynamic change and is affected by many factors such as mining. Real-time acquisition of these displacement data can timely discover the abnormal deformation trend of the wellbore and provide first-hand information for predicting the risk of wellbore instability.

[0113] Step S2220, obtaining a third corrected borehole critical stress according to the real-time three-level displacement characteristic data stream and the displacement-stress mapping model;

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

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

[0116] Step S2222, retrieve the wellbore damage case history database, and obtain the historical instability critical stress σ from the wellbore damage case history database c,hist ;

[0117] Step S2223, based on the first modified wellbore critical stress σ 1,i 、Historical instability critical stress σ c,hist The second modified borehole critical stress σ is calculated by the ratio of the real-time delamination displacement to the real-time three-dimensional shear deformation angle ΔS1 / θ(t). 2,i ; σ 2,i represents the second modified borehole critical stress of the i-th structural layer;

[0118] Step S2224: According to the hydraulic support pressure time series data F(t) and the working face advancement speed v in the mining trigger parameter set P1, the second modified wellbore critical stress σ 2,i After correction, the third corrected borehole critical stress σ is obtained 3,i ; σ 3,i It represents the third modified borehole critical stress of the i-th structural layer.

[0119] Specifically, step S2220 aims to obtain the third corrected borehole critical stress 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 assessment of borehole stability. During gas drilling operations, the stability of the borehole is dynamically affected by multiple factors, and obtaining accurate borehole critical stress is crucial for early warning of borehole instability.

[0120] In step S2221, the displacement-stress mapping model is constructed based on the rock formation structure, gas drilling geometric parameters and displacement data obtained in the early stage, which can reflect the intrinsic relationship between the wellbore displacement and stress. The real-time delamination displacement ΔS1 reflects the interlayer displacement of the roof rock layer at the current moment, and is an important manifestation of the macroscopic deformation trend of the wellbore; the real-time three-dimensional shear deformation angle θ(t) focuses on the soft rock interlayer, which is a key weak area of ​​wellbore instability, and shows the real-time progress of its strength attenuation and destruction. Inputting these two real-time monitored displacement data into the model is to use the latest displacement information to preliminarily adjust the estimate of the critical stress of the wellbore. In an actual gas drilling scenario, for example, during the exploitation of a gas drilling, the real-time delamination displacement ΔS1 obtained by the monitoring system suddenly increases, and 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 to obtain the first corrected wellbore critical stress σ 1,i Compared with the initial setting value, it is lower. This shows that under the current displacement state, the stress on the wellbore has changed, and the possibility of instability has increased. The purpose of this step is to make an initial correction to the critical stress of the wellbore based on the real-time displacement data to make it more in line with the actual situation. Its beneficial effect is that it fully considers the current actual displacement changes of the wellbore. The displacement change of the wellbore directly affects its stress distribution. The changes in the real-time delamination displacement and the three-dimensional shear deformation angle reflect the changes in the mechanical state of the wellbore and the surrounding rock formations. By inputting these data into the model for calculation, it is possible to more accurately predict the critical stress value of the wellbore when it is unstable under the influence of the current displacement, providing important basic data for subsequent more accurate analysis. From the reasoning process, the stability of the wellbore is closely related to the displacement. The real-time displacement data can timely reflect the deformation trend of the wellbore. Based on this, the initial correction of the critical stress can make the evaluation results more in line with the actual mechanical state, which helps to discover the potential risk of wellbore instability in advance.

[0121] In step S2222, the wellbore failure case history database stores a large amount of relevant data on wellbore failures in the past, including the historical instability critical stress σ c,histThe stress conditions when the shaft wall reaches the unstable state under the influence of various factors such as different geological conditions and mining conditions are recorded. In actual gas drilling projects, different mines may have different geological structures, rock formation characteristics and mining methods, all of which will lead to different critical stresses when the shaft wall is unstable. For example, in the gas drilling area of ​​a coal mine, due to the complex geological conditions and the presence of 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 is unstable is relatively high. The purpose of retrieving these historical data is to provide empirical references for the current critical stress calculation. Due to the complex and changeable geological conditions and mining processes faced by actual gas drilling projects, it may not be possible to fully and accurately evaluate the stability of the shaft wall by relying solely on current monitoring data and model calculations. Historical data covers the instability information of the shaft wall under various different conditions, providing more reference dimensions for the current analysis. Its beneficial effect is that with the help of historical data, it is possible to make full use of previous engineering experience and effectively make up for the limitations of current monitoring data and model calculations. By comparing the historical critical stress of instability with the currently calculated stress value, the stability of the wellbore can be comprehensively evaluated from multiple angles to improve the reliability of early warning. For example, if the currently calculated first corrected wellbore critical stress σ 1,i The historical critical stress of instability σ under similar geological and mining conditions c,hist When approaching, it is necessary to pay special attention to the stability of the wellbore and take corresponding preventive measures in advance to avoid the occurrence of wellbore instability accidents. From the perspective of the reasoning process, historical data is the accumulation of previous engineering practices, which contains information on wellbore instability under various complex situations. Introducing it into the current calculation process can make the evaluation more comprehensive and accurate, and provide a more reliable basis for wellbore stability analysis.

[0122] In step S2223, after obtaining the historical instability critical stress σ c,hist After that, it is necessary to compare it with the first modified borehole critical stress σ 1,i This is because the first modified borehole critical stress σ 1,i It is calculated based on the current real-time displacement data through the displacement-stress mapping model, reflecting the critical stress under the current wellbore displacement state; while the historical instability critical stress σ c,hist It is the stress data of the wellbore instability under similar circumstances in the past, which includes the combined influence of multiple complex factors. In actual operation, the two can be processed by weighted average and other methods. For example, a weight coefficient α is set, and its value range is [0,1], which can be determined according to the similarity between the current working condition and the historical case. If the current working condition is highly similar to the historical case, α can take a larger value, such as 0.8; when the similarity is low, α can take a smaller value, such as 0.2. Then the second modified wellbore critical stress σ 2,iThe calculation formula is: 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 real-time displacement in combination with historical experience, so that the result is more accurate and reliable. Its beneficial effect is that by comprehensively considering historical data and current real-time displacement data, it can more comprehensively reflect the actual stress conditions of the wellbore under complex working conditions. Historical data provides empirical information on wellbore instability under different conditions, while real-time displacement data reflects the actual deformation state of the current wellbore. Combining the two can avoid the evaluation bias that may be caused by relying solely on a single data source. From the reasoning process, the complexity of geological conditions and mining conditions makes the stability of the wellbore affected by multiple factors. Historical data and real-time displacement data reflect these influencing factors from different angles. Through reasonable weighted processing, the advantages of the two can be combined to make the corrected critical stress more in line with the actual situation, thereby improving the accuracy of the wellbore stability assessment.

[0123] In gas drilling operations, the mining process will have a significant impact on the stress on the well wall, and the hydraulic support pressure time series data F(t) and the working face advancement speed v are two key influencing factors in the mining process. As an important device for supporting the roof rock layer, the fluctuation of the pressure of the hydraulic support directly reflects the change of the roof rock layer pressure. During the coal mining operation, as the working face advances, the stress distribution of the roof rock layer continues to change, causing the pressure on the hydraulic support to fluctuate. This pressure change will be transmitted to the gas drilling well wall through the rock layer, thereby affecting the stress condition of the well wall. For example, when the roof rock layer collapses or moves locally, the pressure of the hydraulic support will increase rapidly. This sudden change in pressure will propagate to the well wall in the form of stress waves, causing the well wall to be subjected to additional stress. The working face advancement speed v directly determines the frequency and intensity of the mining stress acting on the gas drilling well wall. When the working face advancement speed increases, the number of mining stress changes on the well 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 mining stress, posing a greater threat to the stability of the wellbore. For example, at a gas drilling site, when the working face advancement speed increased from 5 meters per day to 8 meters per day, monitoring found that the mining stress on the wellbore increased significantly, and the wellbore displacement and strain also changed significantly.

[0124] In the second modified borehole critical stress σ 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 advancement speed v can be established. This function can be fitted through a large amount of experimental data and field monitoring data, and its form can be determined according to the specific situation, for example, it can be a linear function, a nonlinear function, etc. Assume that the correction function is a linear function, in the form of 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 the least squares method and other methods. Then the third corrected wellbore critical stress σ 3,i The calculation formula is: 3,i =σ 2,i +f(F(t),v).

[0125] The purpose of step S2224 is to make a final correction to the critical stress of the well wall in combination with the key influencing factors in the mining process, so that the corrected critical stress is more consistent with the stress state of the well wall under the actual mining conditions. Its beneficial effect is that the influence of mining disturbance on the stability of the well wall is fully considered. Mining disturbance is an important external factor that causes well wall instability. By incorporating the hydraulic support pressure and the working face advancement speed into the correction process, the stability of the well wall in the actual gas drilling process can be more accurately evaluated, and the timeliness and accuracy of the early warning can be improved. From the reasoning process, the changes in the hydraulic support pressure and the working face advancement speed during the mining process will directly affect the stress of the well 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 well wall instability in advance, and provide a more reliable basis for taking corresponding preventive measures.

[0126] Step S2230: Using the third corrected wellbore critical stress as the dynamically corrected wellbore instability critical stress.

[0127] Specifically, in step S2230, after calculation and correction in the previous multiple steps, the third corrected critical stress of the wellbore integrates multiple information such as real-time displacement monitoring data, historical instability laws, and mining disturbance status. For example, in a mine under complex geological conditions, the dynamically corrected critical stress of wellbore instability obtained by this method can more accurately reflect the risk of wellbore instability during the actual drilling process compared to the critical stress obtained based solely on theoretical calculations or single monitoring data. Using it as the dynamically corrected critical stress of wellbore instability provides key basic data for subsequent calculation of the critical safety factor of wellbore instability and establishment of early warning criteria. Its beneficial effect is that it provides a core basis for accurately evaluating wellbore stability and issuing reliable early warnings. From the perspective of the reasoning process, wellbore instability is the result of the combined effect of multiple factors. The dynamically corrected critical stress of wellbore instability takes these factors into full consideration. Based on this, the subsequent safety factor calculation and early warning judgment can more accurately predict the risk of wellbore instability, so that corresponding measures can be taken in time to ensure mine safety.

[0128] Step S2300, combining the mining trigger parameter set P1, calculating the critical safety factor of wellbore instability and establishing a graded early warning criterion;

[0129] Further, step S2300 includes:

[0130] Step S2310, obtaining the yield strength σ of the current wellbore steel casing y , based on the dynamic correction of the critical stress of wellbore instability σ 3,i , the yield strength of the current wellbore steel casing σ 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 ;

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

[0132] according to and σ 3,i Calculate the composite critical stress value ;

[0133] According to the comprehensive critical stress value , the yield strength of the current wellbore steel casing σ 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 ;

[0134]

[0135] in, is an empirical coefficient used to consider the influence of strain on the safety factor; It is an index used to evaluate the stability of the wellbore under mining disturbance conditions. , can be combined with early warning criteria to achieve graded early warning and take measures in advance; the formula introduces real-time monitoring of strain data , which can dynamically reflect the actual stress state of the well wall under mining disturbance.

[0136] When increasing, Increase, wellbore stability improves; When decreasing, Reduced, the wellbore stability When increasing, Reduced, the wellbore stability decreases; When decreasing, Increased, the wellbore stability is improved. When increases, the denominator increases, Reduced, the wellbore stability decreases; When it decreases, the denominator decreases, The formula combines material strength, real-time monitored strain data and corrected critical stress, which can more comprehensively reflect the actual stress state of the wellbore. It is suitable for complex working conditions such as mining disturbance and can effectively evaluate the stability of the wellbore under dynamic conditions.

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

[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 When the first level warning is activated, is the pressure mutation threshold, is the first safety threshold;

[0139] when and ,at the same time When the second level warning is activated, is the first threshold of the working face advancement speed, is the second safety threshold, is the displacement threshold of roof separation;

[0140] when and ,at the same time When the third level warning is activated, is the second threshold of the working face advancement speed, is the third safety threshold, is the shear deformation angle threshold of soft rock interlayer, > > , > .

[0141] Specifically, the yield strength σ of the wellbore steel casing y It refers to the stress value when the steel casing begins to undergo plastic deformation, which represents the strength characteristics of the steel casing material itself. Dynamically corrected critical stress for wellbore instability σ 3,i It is obtained through a series of correction processes, taking into account multiple factors such as rock structure, displacement monitoring data, historical experience, and mining disturbance, and can more accurately reflect the stress situation when the wellbore is unstable. In actual wellbore engineering, different structural layers have different effects on wellbore stability. The thicker the structural layer, the larger its proportion in the overall wellbore structure, and the greater its impact on wellbore stability; the structural layer with higher initial compressive strength is more capable of withstanding external pressure and maintaining wellbore stability, and also contributes more to wellbore stability. By dividing the thickness of the structural layer h i and initial compressive strength σ 0,i Combined with the calculation weights, the geometric characteristics and mechanical properties of the structural layer itself are comprehensively considered. In this way, in the subsequent analysis, the weights of different structural layers can be used to more accurately evaluate their impact on the wellbore stability, avoiding ignoring important structural layers or over-emphasizing secondary structural layers, making the wellbore stability analysis more scientific and reasonable.

[0142] The wellbore wall is a complex structure composed of multiple structural layers, and the mechanical properties and actual stresses of each structural layer are different. If only the critical stress of a single structural layer is considered, the overall instability risk of the wellbore wall cannot be accurately assessed. The calculated structural layer weights can be used to correct the wellbore wall critical stress σ according to the relative importance of each structural layer. 3,i The weighted sum is then divided by the total weight to obtain the comprehensive critical stress value. The comprehensive critical stress value calculated in this way takes into account both the mechanical properties of each structural layer (reflected in the weight through the initial compressive strength) and the correction of the critical stress of each structural layer by factors such as mining (i.e. σ 3,i ), which fully reflects the critical stress state of the wellbore as a whole under the current working conditions. Compared with analyzing the critical stress of each structural layer separately, it can more accurately assess the instability risk of the wellbore as a whole, and provide more reliable data support for the subsequent calculation of the critical safety factor of wellbore instability and early warning.

[0143] The real-time strain distribution data εr(t) of the casing wall reflects the stress and deformation state of the wellbore steel casing under the current working conditions in real time. Step S2310 provides a comprehensive multi-factor quantitative indicator that can more comprehensively and accurately evaluate the stability of the wellbore. The stability of the wellbore is affected by a variety of factors such as the strength of the steel casing material, the current stress and strain. It is impossible to accurately determine whether the wellbore is in a stable state by considering only a single factor. By incorporating these factors into a calculation formula, the safety factor K c It can fully reflect the actual stress and stability of the well wall 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 When the pressure of the hydraulic support changes, the first-level warning is activated. The change rate of the hydraulic support pressure reflects the change trend of the top rock pressure. It is a preset pressure mutation threshold. When the pressure change rate reaches or exceeds this threshold, it indicates that the roof rock pressure has undergone abnormal changes. is the first safety threshold, when K c When the value is less than or equal to the threshold, it indicates that the stability of the wellbore has been threatened to a certain extent. When the first-level warning is activated, it reminds the staff that the wellbore may begin to show signs of instability.

[0145] when and ,at the same time When the working face advances, the secondary warning is activated; the working face advancement speed v directly affects the magnitude and frequency of the mining stress on the well wall. This is the pre-set first threshold value of the working face advancement speed. When the advancement speed reaches or exceeds this threshold, the mining stress on the well wall will increase significantly. It is the second safety threshold, which is smaller than the first safety threshold. At this time, the wellbore stability faces greater challenges. is the displacement threshold of the roof separation. When the displacement of the roof separation reaches or exceeds the threshold, it indicates that the stability of the roof stratum has further deteriorated. When the second-level warning is activated, the staff is reminded to pay close attention to the wellbore condition and be prepared to take corresponding measures.

[0146] when and ,at the same time When the fire breaks out, the third-level warning is activated; It is the second threshold of the working face advancement speed, which is greater than the first threshold, representing a faster advancement speed and stronger mining stress. It is the third safety threshold, which is greater than the second safety threshold. At this time, the wellbore wall 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 means that the soft rock interlayer has been severely deformed and the wellbore may become unstable at any time. When the third-level warning is activated, the staff is required to take emergency measures quickly to prevent the wellbore instability accident.

[0147] Step S2320 establishes a comprehensive and accurate graded warning system, which combines the mining trigger parameters with the quantitative indicators of wellbore stability, and can more accurately judge the risk of wellbore instability. From the reasoning process, wellbore instability is the result of the combined effect of multiple factors, and a single parameter cannot fully reflect the risk of instability. By comprehensively considering multiple parameters such as the pressure change rate of the hydraulic support, the advancement speed of the working face, the delamination displacement of the roof, the shear deformation angle of the soft rock interlayer, and the critical safety factor of wellbore instability, and setting three levels of warning according to different parameter thresholds, it is possible to carry out graded warnings according to the severity of the wellbore instability risk, so that the staff can take corresponding and reasonable countermeasures according to the warning level, thereby improving the effectiveness and pertinence of the warning and ensuring the safety of gas extraction to the greatest extent.

[0148] Step S2400, obtaining real-time wellbore instability determination data, generating wellbore instability warning results based on the real-time wellbore instability determination data and graded warning criteria, and generating a risk dynamic decision-making plan based on the wellbore instability warning results.

[0149] Specifically, the purpose of step S2400 is to obtain real-time wellbore instability judgment data and generate wellbore instability warning results in combination with graded warning criteria, and then formulate a risk dynamic decision-making plan based on the warning results to deal with possible instability of the wellbore in gas drilling. First, real-time wellbore instability judgment data are obtained. These data come from various previous monitoring links, including real-time collected mining trigger parameter sets (such as real-time hydraulic support pressure data, real-time working face advancement speed), real-time three-level displacement characteristic data streams (real-time delamination displacement ΔS1 of the top rock layer, real-time three-dimensional shear deformation angle θ(t) generated by the soft rock interlayer, and real-time strain distribution data εr(t) of the casing wall), and calculated real-time wellbore instability critical safety factors, etc. These data reflect the stress, deformation and stability state of the wellbore in real time from different aspects, and are the key basis for judging whether the wellbore is unstable and what degree of instability risk it is in. Wellbore instability warning results are generated based on real-time wellbore instability judgment data and graded warning criteria. According to the three-level warning criteria established previously, when each parameter meets the corresponding threshold conditions, different levels of warnings are triggered.

[0150] Different levels of warnings correspond to different degrees of risk and require different response strategies. For the first-level warning, measures such as increasing the monitoring frequency and checking whether the existing support measures are intact may be taken; for the second-level warning, in addition to strengthening monitoring, temporary reinforcement of the well wall may be required, such as adding support materials; and for the third-level warning, the situation is more critical, and it may be necessary to immediately stop related operations, organize personnel to evacuate, and formulate detailed repair or rescue plans. By dynamically adjusting decisions based on the warning results, the losses caused by well wall instability can be minimized. For example, in a gas drilling project, when the monitoring system issued a second-level warning, the staff quickly reinforced the well wall in accordance with the risk dynamic decision-making plan, successfully avoiding the occurrence of well wall instability accidents and ensuring the safety of drilling equipment and personnel.

[0151] By acquiring and analyzing various types of data in real time, and quickly generating early warning results based on graded early warning criteria, the staff can detect problems at the early stage of wellbore instability, and buy precious time for taking measures. Real-time monitoring data can reflect the real-time status of the wellbore and surrounding rock formations. Once these data show abnormal changes and meet the set conditions of the early warning criteria, the early warning system can respond in time, monitor the safety of the wellbore at all times, and immediately issue an alarm once dangerous signs are found. In addition, the formulation of risk dynamic decision-making plans has achieved targeted treatment of the risk of wellbore instability. Different levels of early warnings correspond to different response strategies, making the response measures more scientific and reasonable. In actual projects, the situation of wellbore 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 level. For example, for the low-risk first-level early warning, relatively simple enhanced monitoring measures are taken, which can effectively monitor the wellbore status without wasting resources; and for the high-risk third-level early warning, decisive measures such as stopping operations and evacuating personnel can be taken to maximize the safety of personnel life and equipment property. This differentiated treatment method based on risk levels has greatly improved the efficiency and effectiveness of dealing with the risk of wellbore instability, and provided a strong guarantee for the safe operation of gas drilling.

[0152] Example 2

[0153] This embodiment provides, on the basis of Embodiment 1, a critical condition early warning method for wellbore damage based on deep rock and soil mechanics, including:

[0154] Step S2223, based on the first modified wellbore critical stress σ 1,i 、Historical instability critical stress σ c,hist The second modified borehole critical stress σ is calculated by the ratio of the delamination displacement to the three-dimensional shear deformation angle ΔS1 / θ(t). 2,i ; σ 2,i represents the second modified borehole critical stress of the i-th structural layer;

[0155]

[0156] in, is the dynamic coupling factor, is the shear deformation nonlinear index, is the attenuation coefficient, and These are empirical coefficients that need to be obtained through experiments or historical data fitting. These coefficients reflect the influence of the historical critical stress of instability, the ratio of the delamination displacement and the three-dimensional shear deformation angle on the critical stress of the borehole wall.

[0157] Considering the impact of current real-time monitoring data on the critical stress of the wellbore, The introduction of historical experience data has improved the applicability and accuracy of the early warning model. It reflects the relative relationship between the delamination displacement of the roof rock layer and the shear deformation of the soft rock interlayer, and is sensitive and indicative for the early warning of wellbore instability.

[0158] when When it increases (i.e. the displacement of the roof stratum increases), the logarithmic part in the formula increases, resulting in the second modified borehole critical stress This reflects the influence of the top rock layer displacement on the wellbore stability. When the shear deformation of the soft rock interlayer increases, the logarithmic part of the formula also increases, resulting in the second modified borehole critical stress This reflects the contribution of shear deformation of soft rock interlayer to wellbore instability. The change of will affect the weight of the empirical coefficient in the formula and the sensitivity of the function form, thereby adjusting the second modified borehole critical stress The degree of response to different factors. In summary, this formula comprehensively considers the influence of multiple factors on the critical stress of the wellbore by introducing complex functional forms and empirical coefficients, providing a more scientific and accurate basis for wellbore instability warning. This formula comprehensively considers the relative relationship between real-time monitoring data, historical empirical data and different factors, and improves the accuracy and reliability of wellbore instability warning. By introducing empirical coefficients and complex functional forms, this formula can more comprehensively reflect the complex evolution mechanism of wellbore instability and provide a more scientific basis for risk dynamic decision-making solutions.

[0159] Example 3

[0160] This embodiment provides, on the basis of Embodiment 1, a critical condition early warning method for wellbore damage based on deep rock and soil mechanics, including:

[0161] Step S2224: According to the hydraulic support pressure time series data F(t) and the working face advancement speed v in the mining trigger parameter set P1, the second modified wellbore critical stress σ 2,i After correction, the third corrected borehole critical stress σ is obtained 3,i ; σ 3,i represents the third modified borehole critical stress of the i-th structural layer;

[0162]

[0163] in, It is a dynamic adjustment coefficient used to quantify the influence of mining trigger parameters on the critical stress of the wellbore. Its value can be calibrated according to historical data and actual site conditions. It is the rate of change of hydraulic support pressure, indicating how fast the hydraulic support pressure changes over time. This value can be obtained by numerically differentiating F(t).

[0164] Its significance lies in that it integrates the displacement-stress mapping model and the three-level displacement monitoring data, providing a more accurate benchmark value for the correction of the critical stress of the wellbore. The introduction of takes into account the influence of mining trigger parameters on the critical stress of the wellbore, making the corrected critical stress of the wellbore more in line with the actual situation. F(t)‌ and v‌ are mining trigger parameters. Their real-time monitoring provides important input conditions for the early warning model, which helps to accurately judge the stability state of the wellbore.

[0165] When the change rate of hydraulic support pressure When it increases, it means that the pressure of the hydraulic support changes rapidly, which may have a greater impact on the well wall. 3,i will increase accordingly, reflecting that the critical stress of the wellbore wall is increased by the influence of mining disturbance. When the working face advancement speed v increases, it means that the working face advances faster, and the magnitude and frequency of the mining stress on the wellbore wall may increase. However, since the exponent of v in the formula is -0.5, σ 3,i The trend of decreasing with the increase of v will be relatively gentle. However, it should be noted that the increase of v here may actually mean the increase of mining disturbance. Therefore, in practical applications, it is necessary to comprehensively consider multiple factors to judge the stability state of the wellbore. The existence of , the formula can adaptively adjust σ 3,i to reflect the actual influence of mining trigger parameters on the critical stress of wellbore.

[0166] The formula comprehensively considers the displacement-stress mapping model, three-level displacement monitoring data and mining trigger parameters, realizes the dynamic correction of the critical stress of the wellbore, and improves the accuracy and applicability of the early warning model. , quantified the influence of mining trigger parameters on the critical stress of the wellbore, making the early warning conditions more comprehensive and accurate. This formula can provide a scientific basis for wellbore instability early warning, and help to take corresponding measures in time to avoid wellbore instability accidents.

[0167] Example 4

[0168] This embodiment provides, on the basis of Embodiment 1, a critical condition early warning method for wellbore damage based on deep rock and soil mechanics, including:

[0169] According to the comprehensive critical stress value , the yield strength of the current wellbore steel casing σ 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 ;

[0170]

[0171] in, It 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. It 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. It is an exponential function with the base e of the natural logarithm as its base; t represents time and is used to determine the upper limit of the integral. It represents the cumulative effect of casing wall strain from the initial time (t=0) to the current time t.

[0172] It reflects the relative size of the casing material strength and the critical stress of wellbore instability, and is one of the key factors in evaluating wellbore stability. The introduction of μ‌ takes into account the safety margin of material strength, making the calculation results more conservative and reliable. The integral of represents the accumulated strain of the casing wall over a period of time, reflecting the degree of deformation of the casing material. When the accumulated strain is large, it means that the casing may have approached or reached the yield state, and the stability of the wellbore wall is reduced. As the yield strain of the casing material, it provides a benchmark value for evaluating casing deformation.

[0173] when Increase or When it decreases, it means that the strength of the casing material is relatively improved or the critical stress of wellbore instability is relatively reduced. will increase, reflecting the enhanced stability of the wellbore. When the absolute value of ‌ increases, it means that the strain of the casing wall increases. At this time, the value of the integral term will increase, resulting in The decrease reflects the decrease in wellbore stability. Especially when the cumulative strain approaches or exceeds the yield strain hour, It will decrease rapidly, and the early warning system should issue an alarm in time.

[0174] The 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 the deformation of the wellbore over a period of time, which improves the accuracy and reliability of the early warning. The formula provides a scientific basis for the early warning of wellbore instability, which helps to take corresponding measures in a timely manner to ensure the safety and stability of the wellbore.

[0175] Example 5

[0176] This embodiment provides, on the basis of the first embodiment, a critical condition early warning system for well wall failure based on deep rock and soil mechanics, such as Figure 7 As shown, including:

[0177] Data acquisition module: used to obtain initial rock formation parameters and 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;

[0178] Mapping model building module: constructs a displacement-stress mapping model based on the rock structure classification system, the initial gas drilling geometry parameter group G0 and the three-level displacement characteristic data stream;

[0179] Graded warning module: used to collect mining trigger parameter set P1 and real-time three-level displacement characteristic data stream, generate dynamically corrected critical stress of wellbore instability based on displacement-stress mapping model, mining trigger parameter set P1 and real-time three-level displacement characteristic data stream; calculate critical safety factor of wellbore instability in combination with mining trigger parameter set P1, and establish graded warning criteria; obtain real-time wellbore instability judgment data, generate wellbore instability warning results based on real-time wellbore instability judgment data and graded warning criteria, and generate risk dynamic decision-making plan based on wellbore instability warning results.

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

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

[0182] Step S1120, extracting the initial rock formation parameters of each structural layer, the initial rock formation parameters including the initial compressive strength σ 0,i and the initial strain ε 0,i , forming a complete description set of formation mechanical properties; 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, a rock formation structure classification system is constructed by the three-dimensional rock formation partition matrix, the formation mechanical property description set and the soft rock interlayer distribution matrix D0.

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

[0186] Step S1310, deploying a distributed optical fiber sensor array along the axial direction of the gas drilling, deploying a tilt sensor group in the soft rock interlayer, and arranging strain rosettes along the annular direction of the casing to form a three-level displacement monitoring array;

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

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

[0189] Step S1340, using 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, defining the delamination displacement ΔS'1 as primary data, defining the three-dimensional shear deformation angle θ'(t) as secondary data, and defining the strain distribution data εr'(t) as tertiary data;

[0191] Step S1360, constructing a three-level displacement feature data stream based on the primary data, the secondary data and the tertiary data.

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

[0193] Step S1410, based on the initial compressive strength σ in the formation mechanical property description set 0,i and the initial strain ε 0,i , as well as the wellbore curvature radius R0 and casing wall thickness δ0 in the initial gas drilling geometric parameter group G0, the fourth-order tensor form is used to describe the wellbore displacement-stress constitutive relationship, and the initial mapping model M0 is established;

[0194] Step S1420, introducing the three-dimensional rock formation partition matrix, the soft rock interlayer distribution matrix D0, the delamination 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, 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 levels of structural layers, soft rock interlayer distribution, ΔS'1 and θ'(t) and gas drilling deformation;

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

[0196] In the graded warning module, the generation of dynamically corrected critical stress for wellbore instability includes:

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

[0198] Step S2220, obtaining a third corrected borehole critical stress according to the real-time three-level displacement characteristic data stream and the displacement-stress mapping model;

[0199] Step S2230: Using the third corrected wellbore critical stress as the dynamically corrected wellbore instability critical stress.

[0200] The step S2220 includes:

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

[0202] Step S2222, retrieve the wellbore damage case history database, and obtain the historical instability critical stress σ from the wellbore damage case history database c,hist ;

[0203] Step S2223, based on the first modified wellbore critical stress σ 1,i 、Historical instability critical stress σ c,hist The second modified borehole critical stress σ is calculated by the ratio of the real-time delamination displacement to the real-time three-dimensional shear deformation angle ΔS1 / θ(t). 2,i ; σ 2,i represents the second modified borehole critical stress of the i-th structural layer;

[0204] Step S2224: According to the hydraulic support pressure time series data F(t) and the working face advancement speed v in the mining trigger parameter set P1, the second modified wellbore critical stress σ 2,i After correction, the third corrected borehole critical stress σ is obtained 3,i ; σ 3,i represents the third modified borehole critical stress of the i-th structural layer;

[0205] In the graded warning module, the establishment of graded warning criteria includes:

[0206] Step S2310, obtaining the yield strength σ of the current wellbore steel casing y , based on the dynamic correction of the critical stress of wellbore instability σ 3,i , the yield strength of the current wellbore steel casing σ 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 ;

[0207] Step S2320, based on the critical safety factor K of wellbore instability c , establish a three-level early warning criteria.

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

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

[0210] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A critical condition early warning method for wellbore failure based on deep rock and soil mechanics, characterized in that: The method comprises: Obtain initial rock formation parameters and establish a rock formation structure classification system; obtain an initial gas drilling geometric parameter group G0, deploy a three-level displacement monitoring array, and construct a three-level displacement characteristic data stream; 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 characteristic data stream; Collect mining trigger parameter set P1 and real-time three-level displacement characteristic data stream, generate dynamically corrected critical stress of wellbore instability based on displacement-stress mapping model, mining trigger parameter set P1 and real-time three-level displacement characteristic data stream; calculate critical safety factor of wellbore instability in combination with mining trigger parameter set P1, and establish graded early warning criteria; obtain real-time wellbore instability judgment data, generate wellbore instability warning results based on real-time wellbore instability judgment data and graded early warning criteria, and generate risk dynamic decision-making plan based on wellbore instability warning results.

2. The critical condition early warning method for well wall failure based on deep rock and soil mechanics according to claim 1 is characterized in that: The obtaining of initial rock formation parameters and establishing a rock formation structure classification system comprises: Divide the roof rock layer into n-level structural layers to form a three-dimensional rock layer partition matrix, n ≥ 3; Extract the initial rock formation parameters of each structural layer, including the initial compressive strength σ 0,i and the initial strain ε 0,i , forming a complete description set of formation mechanical properties; among them, σ 0,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; Locate the soft rock interlayers at all levels of structural layers, obtain the spatial distribution law of the soft rock interlayers, and generate the soft rock interlayer distribution matrix D0; The rock structure classification system is composed of the three-dimensional rock stratum partition matrix, the stratum mechanical property description set and the soft rock interlayer distribution matrix D0.

3. The critical condition early warning method for wellbore failure based on deep rock and soil mechanics according to claim 2 is characterized in that: The method of obtaining the initial gas drilling geometric parameter group G0 includes: performing geometric measurement on the inside of the gas drilling well to obtain the well wall spatial surface point cloud data; performing denoising and normalization processing on the well wall spatial surface point cloud data to extract the well wall curvature radius R0 and the casing wall thickness δ0; and constructing the gas drilling initial geometric parameter group G0 according to the well wall curvature radius R0 and the casing wall thickness δ0.

4. The critical condition early warning method for well wall failure based on deep rock and soil mechanics according to claim 3 is characterized in that: The three-stage displacement monitoring array is arranged by arranging a distributed optical fiber sensor array along the axial direction of the gas drilling, deploying a dip sensor group in the soft rock interlayer, and arranging strain rosettes along the casing annular direction to form a three-stage displacement monitoring array.

5. The critical condition early warning method for wellbore failure based on deep rock and soil mechanics according to claim 4 is characterized in that: The constructing of the three-level displacement feature data stream comprises: The delamination displacement ΔS'1 of the roof rock layer is obtained by using a distributed optical fiber sensor array; the three-dimensional shear deformation angle θ'(t) generated by the soft rock interlayer is measured by using a tilt sensor group; the strain distribution data εr'(t) of the casing wall is obtained by using strain rosettes arranged along the casing annulus; The delamination 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; based on the primary data, the secondary data and the tertiary data, a tertiary displacement characteristic data stream is constructed.

6. The critical condition early warning method for well wall failure based on deep rock and soil mechanics according to claim 5 is characterized in that: The construction of the displacement-stress mapping model comprises: Describe the initial compressive strength σ of the concentration according to the formation mechanical properties 0,i and the initial strain ε 0,i , as well as the wellbore curvature radius R0 and casing wall thickness δ0 in the initial gas drilling geometric parameter group G0, the fourth-order tensor form is used to describe the wellbore displacement-stress constitutive relationship, and the initial mapping model M0 is established; The three-dimensional rock formation partition matrix, the soft rock interlayer distribution matrix D0, the delamination displacement ΔS'1 in the three-level displacement characteristic data stream and the three-dimensional shear deformation angle θ'(t) are introduced into the initial mapping model M0. The stress-displacement field of the rock formation around the wellbore is solved by the finite element numerical simulation method to obtain the coupling law between different levels of structural layers, soft rock interlayer distribution, ΔS'1 and θ'(t) and gas drilling deformation. According to the coupling law between different levels of structural layers, soft rock interlayer distribution, ΔS'1 and θ'(t) and gas drilling deformation, the constitutive parameters in the initial mapping model M0 are calibrated using an iterative optimization algorithm to form the final displacement-stress mapping model.

7. The critical condition early warning method for well wall failure based on deep rock and soil mechanics according to claim 1 is characterized in that: The acquisition of the mining trigger parameter set P1 includes: real-time acquisition of hydraulic support pressure time series data F(t) and working face advancement speed v; v and F(t) together constitute the mining trigger parameter set P1.

8. The critical condition early warning method for wellbore failure based on deep rock and soil mechanics according to claim 2 is characterized in that: The real-time three-level displacement characteristic data stream includes the real-time delamination 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; The generating of the dynamically corrected critical stress for wellbore instability comprises: According to the real-time three-level displacement characteristic data stream and displacement-stress mapping model, the third corrected borehole critical stress is obtained; The third corrected wellbore critical stress is used as the dynamically corrected wellbore instability critical stress.

9. The critical condition early warning method for well wall failure based on deep rock and soil mechanics according to claim 8 is characterized in that: The obtaining of the third modified borehole critical stress comprises: The real-time delamination 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 borehole critical stress σ 1,i ; σ 1,i represents the first modified borehole critical stress of the i-th structural layer; Retrieve the historical database of wellbore damage cases and obtain the historical critical stress σ from the historical database of wellbore damage cases c,hist ; According to the first modified borehole critical stress σ 1,i 、Historical instability critical stress σ c,hist The second modified borehole critical stress σ is calculated by the ratio of the real-time delamination displacement to the real-time three-dimensional shear deformation angle ΔS1 / θ(t). 2,i ; σ 2,i represents the second modified borehole critical stress of the i-th structural layer; According to the hydraulic support pressure time series data F(t) and the working face advancement speed v in the mining trigger parameter set P1, the second modified wellbore critical stress σ 2,i After correction, the third corrected borehole critical stress σ is obtained 3,i ; σ 3,i It represents the third modified borehole critical stress of the i-th structural layer.

10. The critical condition early warning method for wellbore failure based on deep rock and soil mechanics according to claim 9 is characterized in that: The calculation of the critical safety factor of wellbore instability includes: obtaining the yield strength σ of the current wellbore steel casing y , based on the dynamic correction of the critical stress of wellbore instability σ 3,i , the yield strength of the current wellbore steel casing σ 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 .

11. The critical condition early warning method for well wall failure based on deep rock and soil mechanics according to claim 10 is characterized in that: The critical stress σ of wellbore instability based on dynamic correction 3,i , the yield strength of the current wellbore steel casing σ 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 include: Get the thickness h of the i-th level structure layer i , according to h i and initial compressive strength σ 0,i , calculate the weight of the i-th level structure layer ;according to and σ 3,i Calculate the composite critical stress value ; According to the comprehensive critical stress value , the yield strength of the current wellbore steel casing σ 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 .

12. A critical condition early warning system for well wall failure based on deep rock and soil mechanics, which is used to implement the critical condition early warning method for well wall failure based on deep rock and soil mechanics as claimed in any one of claims 1 to 11, characterized in that: The system comprises: Data acquisition module: used to obtain initial rock formation parameters and 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; Mapping model building module: constructs a displacement-stress mapping model based on the rock structure classification system, the initial gas drilling geometry parameter group G0 and the three-level displacement characteristic data stream; Graded warning module: used to collect mining trigger parameter set P1 and real-time three-level displacement characteristic data stream, generate dynamically corrected critical stress of wellbore instability based on displacement-stress mapping model, mining trigger parameter set P1 and real-time three-level displacement characteristic data stream; calculate critical safety factor of wellbore instability in combination with mining trigger parameter set P1, and establish graded warning criteria; obtain real-time wellbore instability judgment data, generate wellbore instability warning results based on real-time wellbore instability judgment data and graded warning criteria, and generate risk dynamic decision-making plan based on wellbore instability warning results.

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