Vertical shaft monitoring method and shaft monitoring device

Abstract

The present invention relates to the technical field of mine shaft monitoring, and discloses a shaft monitoring method and a shaft monitoring device. The shaft monitoring method comprises: obtaining an original model and original image of the shaft; controlling a three-dimensional laser scanning device and an image acquisition device to move axially along the shaft to obtain point cloud data and monitoring images of the shaft, and determining a monitoring model based on the point cloud data; and comparing the original model with the monitoring model, and comparing the original image with the monitoring image, to determine the deformation data of the shaft. By modeling the point cloud data collected by laser to macroscopically analyze the deformation of the shaft around the entire circumference, and performing microscopic strain and crack analysis of the shaft using digital imaging technology, global monitoring of the shaft is achieved, breaking through the limitations of traditional point sensor monitoring, achieving real-time analysis of deformation distribution across the entire cross-section, expanding the monitoring coverage, reducing monitoring blind spots, and improving the accuracy of monitoring results, thus providing an effective basis for predicting shaft instability and damage.

Description

Technical Field

[0001] The present application relates to the technical field of mine shaft monitoring, and in particular to a shaft monitoring method and a shaft monitoring device. Background Art

[0002] As the intensity of mineral resource mining increases year by year, shallow mineral resources are being depleted, forcing countries to increase mining depths to obtain mineral resources. As a key hub connecting surface ore processing facilities with underground ore mining and transportation, vertical shafts play a vital role in deep mineral resource mining.

[0003] As mining resources are buried deeper, vertical shaft construction depths increase, leading to complex and variable forces acting on the shaft. Ultra-deep shafts traverse numerous strata, and their surrounding rock, in addition to varying lithologies, is susceptible to expansion and significant deformation under the influence of high ground pressure, intense excavation disturbance, and high rock temperatures. These factors can lead to shaft deformation and damage, ultimately posing a serious threat to mine safety and causing significant economic losses.

[0004] Wellbore deformation is a slow process, necessitating long-term monitoring to prevent deformation and damage. Existing monitoring technologies, such as strain gauges and displacement meters embedded in the surrounding rock, can capture data at discrete points along the wellbore. However, these measurements are inaccurate, have limited coverage, and are difficult to install. Summary of the Invention

[0005] This application aims to solve at least one of the technical problems existing in the prior art or related art.

[0006] The first aspect of the technical solution of the present application proposes a vertical shaft borehole monitoring method, which includes: obtaining an original model and an original image of the borehole; controlling a three-dimensional laser scanning device and an image acquisition device to move axially along the borehole to obtain point cloud data and a monitoring image of the borehole, and determining a monitoring model based on the point cloud data; comparing the original model and the monitoring model, and comparing the original image and the monitoring image to determine the deformation data of the borehole.

[0007] In some technical solutions provided in this application, before obtaining the original model and original image of the wellbore, it also includes: determining the key monitoring area of ​​the wellbore based on the surrounding rock information of the wellbore; setting marks in the key monitoring area; wherein the acquisition targets of the original image and the monitoring image include the marks.

[0008] In some technical solutions provided in this application, a three-dimensional laser scanning device and an image acquisition device are arranged on a cabin platform, and the steps of controlling the axial movement of the three-dimensional laser scanning device and the image acquisition device along the wellbore specifically include: controlling the cabin platform to move downward, and controlling the three-dimensional laser scanning device to continuously acquire point cloud data during operation; controlling the cabin platform to pause operation when it reaches the height of any key monitoring area, and controlling the image acquisition device to acquire monitoring images; controlling the cabin platform to move downward to the bottom of the wellbore.

[0009] In some technical solutions provided in this application, the steps for determining the key monitoring area of ​​the wellbore specifically include: determining the fault distribution information of the surrounding rock based on the core cataloging of the wellbore; determining the fault position of the surrounding rock; and determining the circumferential area of ​​the wellbore corresponding to the fault position as the key monitoring area.

[0010] In some technical solutions provided in this application, the steps for determining the key monitoring area of ​​the wellbore specifically include: determining the minimum principal stress direction of the wellbore based on the ground stress information of the surrounding rock; dividing the rock quality grade of the surrounding rock based on the geological information and rock mechanical parameters of the surrounding rock; determining the location of low-quality surrounding rock; and determining the location of the low-quality surrounding rock corresponding to the wellbore in the minimum principal stress direction as the key monitoring area.

[0011] In some technical solutions provided in the present application, the steps for determining the direction of the minimum principal stress of the wellbore specifically include: obtaining ground stress information based on the hydraulic fracturing method; obtaining formation information based on geological exploration holes to determine the conditional area where the surrounding rock is intact and composed of brittle rock; using the stress relief method to correct the ground stress information in the conditional area to determine the direction of the minimum principal stress.

[0012] In some technical solutions provided in this application, the steps for determining the deformation data of the wellbore specifically include: spatially aligning the monitoring model and the monitoring image through the key monitoring area; calculating the monitoring model according to the ICP algorithm to determine the radial displacement of the wellbore; determining the strain information, displacement information and crack information of the key monitoring area based on sub-pixel analysis of the monitoring image; using a convolutional neural network to perform pixel-level segmentation of the cracks in the monitoring image and calculate the expansion rate of the cracks.

[0013] In some technical solutions provided in the present application, before obtaining the original model and original image of the wellbore, it also includes: controlling the image acquisition device to obtain calibration images of multiple calibration plates; determining the position of the feature points of the calibration plate in any calibration image; and optimizing the internal and external parameters of the image acquisition device.

[0014] In some technical solutions provided in the present application, after the step of determining the deformation data of the wellbore, it also includes: determining a threshold based on the monitoring object of the wellbore, the monitoring objects include: strain, displacement and cracks, and the number of thresholds corresponding to any category of monitoring objects is at least three; the area where the monitoring results of the monitoring object exceed the threshold is determined as a risk area; and the coordinates of the risk area are determined through the monitoring model.

[0015] The second aspect of the technical solution of the present application proposes a wellbore monitoring device, which is used for the vertical shaft monitoring method provided by any of the above-mentioned technical solutions. The wellbore monitoring device includes: a cabin platform, a three-dimensional laser scanning device and multiple image acquisition devices. The cabin platform is used for axial movement along the wellbore. The three-dimensional laser scanning device and multiple image acquisition devices are arranged on the cabin platform. The horizontal field of view ranges of the multiple image acquisition devices are connected in sequence and cover the inner wall of the wellbore along the circumferential direction. The vertical field of view of the multiple image acquisition devices has the same height.

[0016] Compared with the related art, the present invention has at least the following beneficial effects:

[0017] By using laser-collected point cloud data modeling to macro-analyze the deformation of the entire circumference of the wellbore, and using digital imaging technology to perform microscopic strain and crack analysis of the wellbore, global monitoring of the wellbore is achieved. This breaks through the limitations of traditional point sensor monitoring and enables real-time analysis of deformation distribution across the entire cross-section. This expands the monitoring coverage, reduces monitoring blind spots, and improves the accuracy of monitoring results, providing an effective basis for predicting wellbore instability and damage. Furthermore, by constructing a non-contact monitoring system, the monitoring equipment is protected from long-term operation in high-pressure, high-temperature, and highly corrosive environments. Its damage resistance is significantly improved compared to traditional point-line monitoring, ensuring long-term and stable monitoring in extreme environments. This avoids the risk of repeated drilling and equipment replacement caused by zero-point drift and failure of point sensors due to environmental interference, significantly reducing maintenance costs. Furthermore, the impact of buried construction on the progress of shaft construction is avoided, achieving the goal of shortening the construction period. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of some embodiments below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference numerals are used throughout the accompanying drawings to denote the same components. In the accompanying drawings:

[0019] Figure 1 A schematic structural diagram of a wellbore monitoring device according to an embodiment of the present application;

[0020] Figure 2 A partial structural diagram of a wellbore monitoring device according to an embodiment of the present application;

[0021] Figure 3 A comparison diagram of an original model and a monitoring model of an embodiment provided in this application;

[0022] Figure 4 A schematic diagram of a key monitoring area according to an embodiment of the present application;

[0023] Figure 5 A speckle and strain cloud diagram of an embodiment provided in this application;

[0024] Figure 6 A schematic diagram of a key monitoring area according to an embodiment of the present application;

[0025] Figure 7 (a) is a top view of a key monitoring area of ​​an embodiment provided by this application;

[0026] Figure 7 (b) is a main view of a key monitoring area of ​​an embodiment provided by this application;

[0027] Figure 8 A schematic flow chart of a vertical shaft monitoring method according to an embodiment of the present application;

[0028] Figure 9 A risk warning flow chart for a vertical shaft provided in accordance with an embodiment of the present application.

[0029] in, Figures 1 to 4 、 Figure 6 and Figure 7 The corresponding relationship between the reference numerals and component names is as follows:

[0030] 10 wellbore monitoring devices; 100 cabin platform; 200 three-dimensional laser scanning device; 300 image acquisition device; 310 series shielded wire; 320 terminal shielded wire; 400 wire rope; 500 monitoring cabin; 600 computer equipment; 20 wellbores. DETAILED DESCRIPTION

[0031] In order to better understand the above technical solution, the technical solution of the embodiment of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiment of the present application and the specific features in the embodiment are detailed descriptions of the technical solution of the embodiment of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiment of the present application and the technical features in the embodiment can be combined with each other.

[0032] The first aspect of the embodiment of the present application provides a vertical shaft monitoring method, such as Figure 8 As shown, the vertical shaft monitoring method includes:

[0033] Step 101, obtaining an original model and an original image of the wellbore;

[0034] Step 102: Control the 3D laser scanning device and the image acquisition device to move along the axial direction of the wellbore to obtain point cloud data and monitoring images of the wellbore, and determine a monitoring model based on the point cloud data;

[0035] Step 103 : Compare the original model with the monitoring model, and compare the original image with the monitoring image to determine the deformation data of the wellbore.

[0036] In this embodiment, the vertical shaft can be an ultra-deep shaft. To monitor shaft deformation, a 3D laser scanning device and an image acquisition device are placed within the shaft. The 3D laser scanning device can be a high-frequency 3D laser scanner (≥1 million points per second). It emits laser beams and receives reflected signals to acquire 3D point cloud data circumferentially around the shaft's inner wall, and builds a monitoring model based on this point cloud data. The image acquisition device can be a high-resolution, industrial-grade HD camera. Multiple image acquisition devices can be arranged to form a camera array based on the circumference of the shaft's cross-section, and equipped with a ring-shaped fill light to capture monitoring images covering the shaft's circumference.

[0037] The original model and original image are used as the original data for comparing the wellbore deformation. They can be collected by 3D laser scanning device and image acquisition device in the early stage of well construction. When the wellbore deforms, the monitoring model or monitoring image deviates from the original data, such as Figure 3 As shown, the monitoring model A1 deviates from the original model A0. By comparing the specific shape and location of the deviation, the deformation amount and location of the wellbore can be determined. Based on 3D laser scanning and DIC (Digital Image Correlation) technology, the global deformation of the wellbore and micro-deformations in key areas such as shear zones and hidden fractures can be accurately captured, thereby monitoring the wellbore deformation.

[0038] For example, after data collection is completed, the point cloud data and monitoring images are transmitted to the ground data processing platform via wireless transmission. The wireless transmission method can use the computer 5G private network to avoid signal attenuation caused by wired transmission.

[0039] By using laser-collected point cloud data modeling to macro-analyze the deformation of the entire circumference of the wellbore, and using digital imaging technology to perform microscopic strain and crack analysis of the wellbore, global monitoring of the wellbore is achieved. This breaks through the limitations of traditional point sensor monitoring and enables real-time analysis of deformation distribution across the entire cross-section. This expands the monitoring coverage, reduces monitoring blind spots, and improves the accuracy of monitoring results, providing an effective basis for predicting wellbore instability and damage. Furthermore, by constructing a non-contact monitoring system, the monitoring equipment is protected from long-term operation in high-pressure, high-temperature, and highly corrosive environments. Its damage resistance is significantly improved compared to traditional point-line monitoring, ensuring long-term and stable monitoring in extreme environments. This avoids the risk of repeated drilling and equipment replacement caused by zero-point drift and failure of point sensors due to environmental interference, significantly reducing maintenance costs. Furthermore, the impact of buried construction on the progress of shaft construction is avoided, achieving the goal of shortening the construction period.

[0040] In some embodiments provided in this application, Figure 4 and Figure 5 As shown, before obtaining the original model and original image of the wellbore, it also includes: determining the key monitoring area of ​​the wellbore based on the surrounding rock information of the wellbore; setting marks in the key monitoring area; wherein the acquisition targets of the original image and the monitoring image include the marks.

[0041] In this embodiment, key monitoring areas of the wellbore are determined based on surrounding rock quality. These areas are those with poor surrounding rock quality and are prone to deformation and cracking. Using these key monitoring areas as acquisition area B for the image acquisition device enables coordinated global and local diagnosis, improving the monitoring efficiency of DIC technology. Furthermore, simultaneous laser scanning and image acquisition in these key monitoring areas enhances the accuracy of monitoring results and makes the determination of wellbore deformation more reliable.

[0042] First, pretreatment work is carried out in the key monitoring area to clean the surface of the well wall, remove dust from the well wall with a high-pressure air gun, and polish the wall surface in the key monitoring area to make it smooth. Then, a mark is set in the key monitoring area B to provide traceable feature points on the inner wall of the wellbore to facilitate the calculation of the displacement field using the DIC algorithm. For example, the mark can be a speckle. A layer of white matte paint is first sprayed on the surface of the well wall. After drying, a professional speckle production tool is used to print a black circular pattern on the well wall to form a mark. The density of the speckle should be 45% to 55%, and it should be randomly distributed so that the corresponding speckle can be accurately matched before and after deformation. For example, the mark can be a Poisson disk distribution generated by MATLAB (minimum spacing ≥ 0.7d, d is the diameter of the disk).

[0043] In some embodiments provided in this application, Figure 1As shown, the three-dimensional laser scanning device and the image acquisition device are arranged on the cabin platform, and the steps of controlling the axial movement of the three-dimensional laser scanning device and the image acquisition device along the wellbore specifically include: controlling the cabin platform to move downward, and controlling the three-dimensional laser scanning device to continuously acquire point cloud data during operation; controlling the cabin platform to pause when reaching the height of any key monitoring area, and controlling the image acquisition device to acquire monitoring images; controlling the cabin platform to move downward to the bottom of the wellbore.

[0044] In this embodiment, the cabin platform is a liftable platform that moves downward at operating speed, and the three-dimensional laser scanning device continuously collects point cloud data during operation. The cabin platform stops when it passes through a designated key monitoring area, and the image acquisition device takes pictures to collect monitoring images. After the pictures are completed, the cabin platform continues to move downward and collect point cloud data. When the cabin platform reaches the next key monitoring area, it stops again and takes pictures. This process is repeated until the cabin platform reaches the bottom of the well, allowing the image acquisition device to obtain monitoring images of all key monitoring areas. The three-dimensional laser scanning device collects complete point cloud data of the wellbore, completing the collection of this monitoring data.

[0045] In some embodiments provided in the present application, the steps of determining the key monitoring area of ​​the wellbore specifically include: determining the fault distribution information of the surrounding rock based on the core logging of the wellbore; determining the fault position of the surrounding rock; and determining the circumferential area of ​​the wellbore corresponding to the fault position as the key monitoring area.

[0046] In this embodiment, a fault is a structure where the earth's crust breaks under stress, resulting in significant relative displacement of rock blocks along the fracture surface. Because the fault weakens the rock strength, the wellbore is more susceptible to damage. By core logging the geological survey boreholes of the ultra-deep vertical shaft during well construction, the distribution of faults at different depths of the shaft was obtained, such as Figure 6 As shown in the figure, the fault location is determined based on the fault distribution, the axial position of the key monitoring area on the wellbore is determined based on the height of the fault location, and the circumferential annular area where the axial location is located is determined as the key monitoring area B. In this way, the key monitoring area is delineated based on the fault information of the surrounding rock, making the key monitoring range more reasonable and accurate.

[0047] In some embodiments provided in the present application, the steps of determining the key monitoring area of ​​the wellbore specifically include: determining the minimum principal stress direction of the wellbore based on the ground stress information of the surrounding rock; dividing the rock quality grade of the surrounding rock based on the geological information and rock mechanical parameters of the surrounding rock; determining the location of low-quality surrounding rock; and determining the location of the low-quality surrounding rock corresponding to the wellbore in the minimum principal stress direction as the key monitoring area.

[0048] In this embodiment, since there are many joints and fissures distributed inside the rock, the intact rock has greater strength and is less susceptible to damage than rocks containing joints. Therefore, the joints weaken the rock strength, making the wellbore more susceptible to damage. The lithology of the formations corresponding to different depths of the vertical shaft is obtained through core logging, and geological surveys of the vertical shaft surrounding rock are conducted during well construction to obtain information such as the joints, structural occurrence, density, and water content of the surrounding rock. Rock samples are processed by on-site sampling, and rock mechanics tests are performed on the rock samples to obtain the rock mechanics parameters of the surrounding rock, and the rock mass mechanics parameters of the vertical shaft surrounding rock are obtained by calculation. The surrounding rock grade of the vertical shaft is divided based on the joint information and rock mass mechanics parameters. For example, the rock mass quality grade of the surrounding rock can be divided by RMR (Rockmassrating, rock mass geomechanics classification index value), and areas with poor and very poor grading results are determined to be the locations of low-quality surrounding rock.

[0049] It should be noted that even if the quality of the rock mass is poor, that is, the rock mass quality grade is low, but it is not in the direction of the minimum principal stress, the wellbore is usually not easily damaged. Figure 7 As shown in (a), when dividing the key monitoring area, the minimum principal stress direction is used as the basis, and the radial direction R on the wellbore is determined by the minimum principal stress, and then the radial position B1 is determined. For example, the radial position B1 is within the preset width range on the left and right sides of the radial direction. Figure 7 As shown in (b), the axial position B2 of the low-quality surrounding rock on the wellbore is determined by its location, and the overlapping range of the radial and axial positions is defined as the key monitoring area B. In this way, the key monitoring area is delineated based on joint information, rock mechanical parameters, and in-situ stress distribution, making the key monitoring range more reasonable and accurate.

[0050] In some embodiments provided in the present application, the step of determining the direction of the minimum principal stress of the wellbore specifically includes: obtaining ground stress information based on the hydraulic fracturing method; obtaining formation information based on geological exploration holes to determine a conditional area where the surrounding rock is intact and composed of brittle rock; and using the stress relief method to correct the ground stress information in the conditional area to determine the direction of the minimum principal stress.

[0051] In this embodiment, hydraulic fracturing is used in the early stages of well construction to obtain a preliminary understanding of the distribution of ground stresses in the strata through which the vertical shaft passes. Using hydraulic fracturing, large-scale preliminary exploration can be carried out, and ground stress information can be quickly obtained, with low detection costs and high efficiency. Ground stress information includes the magnitude and direction of action of the maximum and minimum principal stresses in the wellbore. During well construction, based on the formation information obtained from the geological exploration borehole, a conditional area with intact surrounding rock and composed of brittle rock is found, and the stress relief method is used to obtain the accurate stress conditions in this conditional area. The stress relief method has high detection accuracy, efficiency, and accuracy. By calibrating the stress relief method at key locations (conditional areas), local accuracy can be ensured. By combining the two methods, the direction of the minimum principal stress is made more accurate.

[0052] In some embodiments provided in the present application, the steps for determining the deformation data of the wellbore specifically include: spatially aligning the monitoring model and the monitoring image through the key monitoring area; calculating the monitoring model according to the ICP algorithm to determine the radial displacement of the wellbore; determining the strain information, displacement information and crack information of the key monitoring area based on the sub-pixel analysis of the monitoring image; using a convolutional neural network to perform pixel-level segmentation of the cracks in the monitoring image and calculate the expansion rate of the cracks.

[0053] In this embodiment, a data processing platform based on point cloud data and monitoring images is established. The laser point cloud data and the monitoring image, which serves as a DIC image, are spatially aligned by matching key monitoring areas. An overall monitoring model of the wellbore is extracted based on the point cloud data and compared with the original model. The radial displacement of the wellbore is calculated using the ICP (Iterative Closest Point) algorithm. Sub-pixel analysis is performed on the DIC image to extract the maximum principal strain and shear strain distribution and displacement changes. Local strain, displacement field, and crack information are obtained from the DIC image to determine strain, displacement, and crack information in key monitoring areas. Crack information includes crack location and size. A convolutional neural network (CNN) is used to perform pixel-level segmentation on cracks in the DIC image, enabling intelligent crack identification and calculating crack propagation rates. Multiple measurements are then compared and analyzed to determine the crack's growth.

[0054] By combining multimodal data fusion with intelligent algorithms, a multi-parameter fusion early warning model based on displacement-strain-cracks is developed in shaft surrounding rock monitoring to improve the accuracy of wellbore deformation analysis results.

[0055] In some embodiments provided in the present application, before obtaining the original model and original image of the wellbore, it also includes: controlling the image acquisition device to obtain calibration images of multiple calibration plates; determining the position of the feature points of the calibration plate in any calibration image; and optimizing the internal parameters and external parameters of the image acquisition device.

[0056] In this embodiment, the calibration plate can be a high-precision dot array plane, such as a chessboard or dot array. The calibration plate is placed in the field of view of the image acquisition device, and at least 50 sets of calibration images with different postures are taken from different angles and positions to improve the calibration accuracy. In any calibration image, the position of the feature points on the calibration plate is detected and extracted. An optimization algorithm is used to calculate the intrinsic and extrinsic parameters of the camera. The optimization algorithm can be the least squares method. The internal parameters include: focal length, principal point coordinates, distortion coefficient, and the external parameters include: rotation matrix and translation vector. The calibration image is used to optimize the image acquisition device, improve the acquisition performance of the image acquisition device, enable the image acquisition device to establish an accurate imaging model, eliminate optical distortion, improve pixel-level accuracy, unify the coordinate system, realize dynamic posture tracking and large scene measurement, and lay the foundation for the accuracy of subsequent acquisition of monitoring images.

[0057] In some embodiments provided in the present application, after the step of determining the deformation data of the wellbore, it also includes: determining a threshold based on the monitoring object of the wellbore, the monitoring objects include: strain, displacement and cracks, and the number of thresholds corresponding to any category of monitoring objects is at least three; the area where the monitoring result of the monitoring object exceeds the threshold is determined as a risk area; and the coordinates of the risk area are determined through the monitoring model.

[0058] In this embodiment, a wellbore deformation early warning function is implemented by acquiring radial deformation data of the wellbore, strain change cloud maps of key monitoring areas, and crack images, combined with visual recognition methods. Based on long-term monitoring data and actual conditions, thresholds for strain, displacement, and crack extension in different vertical wellbores are defined to ensure the effectiveness and reliability of the early warning system. Three levels of warning thresholds can be set, corresponding to mild, moderate, and structural failure, and the coordinates of risk areas are located using a 3D laser scanning model.

[0059] For example, Figure 9 As shown, point cloud data, monitoring images, geological information, concrete parameters and rock mass parameters are learned through the concrete deformation and cracking database to obtain a shaft damage prediction model and a shaft risk prediction result.

[0060] The second aspect of the embodiment of the present application proposes a wellbore monitoring device 10, such as Figure 1 and Figure 2 As shown, the wellbore monitoring device 10 is used for the vertical shaft monitoring method provided by any of the above embodiments, and the wellbore monitoring device 10 includes: a cabin platform 100, a three-dimensional laser scanning device 200 and a plurality of image acquisition devices 300, the cabin platform 100 is used for axial movement along the wellbore 20, the three-dimensional laser scanning device 200 and the plurality of image acquisition devices 300 are arranged on the cabin platform 100, the horizontal field of view ranges of the plurality of image acquisition devices 300 are connected in sequence and cover the inner wall of the wellbore 20 along the circumferential direction, and the vertical field of view heights of the plurality of image acquisition devices 300 are the same.

[0061] In this embodiment, a 3D laser scanning device 200 is mounted on a cabin platform 100, which is located within a monitoring cabin 500 and secured to a steel wire rope 400 that controls the operation of the monitoring cabin 500. Multiple balancing platforms are connected to the multiple image acquisition devices 300 in a one-to-one correspondence. The multiple balancing platforms are mounted on the cabin platform 100 so that the horizontal fields of view of the multiple image acquisition devices 300 are sequentially connected and circumferentially cover the inner wall of the shaft 20, ensuring that the acquisition range covers the entire circumference of the shaft 20. The multiple image acquisition devices 300 are connected via a series shielded conductor 310. The vertical fields of view of the multiple image acquisition devices 300 are at the same height, ensuring that the acquisition fields of the multiple image acquisition devices 300 are at the same level and that the captured images are not misaligned when stitched together. The balancing platforms are used to determine the spatial relationship between the shaft wall and the 3D laser scanning device 200 and image acquisition devices 300, compensate for vibration errors in the lifting platform, and determine the positional relationship between the point cloud coordinates and the captured monitoring images in the global coordinate system.

[0062] The wellbore monitoring device 10 also includes an inertial navigation system and a computer 600. The image acquisition device 300 is connected to the computer 600 via a shielded terminal conductor 320. The computer 600 controls the inertial navigation system, the 3D laser scanning device 200, and the image acquisition device 300. It controls the operating posture of the wellbore monitoring device 10 and collects, stores, and uploads point cloud data and monitoring images.

[0063] By using laser-collected point cloud data modeling to macro-analyze the full-circumference deformation of wellbore 20, and using digital imaging technology to perform microscopic strain and crack analysis of wellbore 20, global monitoring of wellbore 20 is achieved. This overcomes the limitations of traditional point-based monitoring, enabling real-time analysis of deformation distribution across the entire cross-section, expanding monitoring coverage, reducing blind spots, and improving the accuracy of monitoring results, thus providing an effective basis for predicting instability and failure of wellbore 20. Furthermore, by constructing a non-contact monitoring system, monitoring equipment is protected from long-term operation in high-pressure, high-temperature, and highly corrosive environments. Its damage resistance is significantly improved compared to traditional point-based monitoring, ensuring long-term, stable monitoring in extreme environments. This avoids the risk of repeated drilling and replacement of equipment due to zero-point drift and failure of point-based sensors caused by environmental interference, significantly reducing maintenance costs. Furthermore, the impact of buried construction on the progress of shaft construction is avoided, achieving the goal of shortening the construction period.

[0064] In a specific embodiment, a non-contact ultra-deep vertical shaft monitoring method is provided, comprising:

[0065] Step 1: Based on geological information and rock mechanics parameters, define the key monitoring areas of the vertical shaft.

[0066] Step 2: Construct and arrange a non-contact monitoring system including a 3D laser scanning device and an image acquisition device.

[0067] Step 3: Based on the division results of key areas of the shaft, pre-processing work such as speckle spraying is carried out.

[0068] Step 4: Collect and transmit point cloud data and speckle image data.

[0069] Step 5: Build a data processing platform based on point cloud data and speckle images.

[0070] Step 6: Risk warning for ultra-deep vertical shafts.

[0071] This invention breaks away from the traditional monitoring mode of vertical shaft point sensor wiring. It adopts three-dimensional laser scanning modeling to macro-analyze the deformation of the entire circumference of the shaft, and adopts digital image correlation technology to perform microscopic strain and crack analysis on the entire circumference of the vertical shaft. It constructs a non-contact monitoring system, optimizes its layout, and builds a point cloud data and speckle image data processing platform on the surface for risk warning of ultra-deep vertical shafts.

[0072] In the present invention, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The term "plurality" refers to two or more, unless expressly limited otherwise. Terms such as "installed," "connected," "connected," and "fixed" should be interpreted broadly. For example, "connected" can mean a fixed connection, a detachable connection, or an integral connection; "connected" can mean a direct connection or an indirect connection through an intermediary. Those skilled in the art will understand the specific meanings of these terms in the present invention based on specific circumstances.

[0073] In the description of the present invention, it should be understood that the directions or positional relationships indicated by terms such as "up", "down", "left", "right", "front" and "back" are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or unit referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0074] Throughout this specification, terms such as "one embodiment," "some embodiments," and "specific embodiments" mean that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0075] The above are merely some embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A vertical shaft monitoring method, characterized in that: include: Determining a key monitoring area of ​​the wellbore based on surrounding rock information of the wellbore; Setting up markers within the key monitoring areas; obtaining an original model and an original image of the wellbore; Controlling a three-dimensional laser scanning device and an image acquisition device to move along the axial direction of the wellbore to obtain point cloud data and monitoring images of the wellbore, and determining a monitoring model based on the point cloud data; Comparing the original model with the monitoring model, and comparing the original image with the monitoring image, to determine deformation data of the wellbore; Wherein, the acquisition targets of the original image and the monitoring image include the mark; The step of determining the key monitoring area of ​​the wellbore specifically includes: Determining fault distribution information of the surrounding rock based on core logging of the wellbore; determining a fault location in the surrounding rock; Determining a circumferential area of ​​the wellbore corresponding to the fault position as the key monitoring area; The step of determining the key monitoring area of ​​the wellbore specifically includes: Determining the minimum principal stress direction of the wellbore based on the ground stress information of the surrounding rock; Classifying the rock mass quality grade of the surrounding rock according to the geological information and rock mass mechanical parameters of the surrounding rock; Identify the location of low-quality surrounding rock; The position of the low-quality surrounding rock corresponding to the wellbore in the direction of the minimum principal stress is determined as the key monitoring area.

2. The vertical shaft monitoring method according to claim 1, characterized in that: The three-dimensional laser scanning device and the image acquisition device are arranged on the cabin platform, and the step of controlling the three-dimensional laser scanning device and the image acquisition device to move along the axial direction of the wellbore specifically includes: Controlling the cabin platform to move downward, and controlling the three-dimensional laser scanning device to continuously acquire the point cloud data during operation; Controlling the cabin platform to suspend operation when reaching the height of any of the key monitoring areas, and controlling the image acquisition device to acquire the monitoring image; The cabin platform is controlled to move downward to the bottom of the shaft.

3. The vertical shaft monitoring method according to claim 1, characterized in that: The step of determining the minimum principal stress direction of the wellbore specifically includes: obtaining the ground stress information according to a hydraulic fracturing method; Obtain stratigraphic information based on geological exploration holes to determine areas where the surrounding rock is intact and composed of brittle rock; The ground stress information in the conditional area is corrected using a stress relief method to determine the minimum principal stress direction.

4. The vertical shaft monitoring method according to claim 1, characterized in that: The step of determining the deformation data of the wellbore specifically includes: Spatially aligning the monitoring model and the monitoring image through the key monitoring area; Calculating the monitoring model according to the ICP algorithm to determine the radial displacement of the wellbore; Analyzing the monitoring image according to sub-pixels to determine strain information, displacement information, and crack information of the key monitoring area; A convolutional neural network is used to perform pixel-level segmentation on the cracks in the monitoring image and calculate the expansion rate of the cracks.

5. The vertical shaft monitoring method according to any one of claims 1 to 4, characterized in that: Before obtaining the original model and original image of the wellbore, the method further includes: Controlling the image acquisition device to acquire calibration images of multiple calibration plates; Determining the position of the feature points of the calibration plate in any one of the calibration images; Optimize the intrinsic and extrinsic parameters of the image acquisition device.

6. The vertical shaft monitoring method according to any one of claims 1 to 4, characterized in that: After the step of determining the deformation data of the wellbore, the method further includes: Determining a threshold value based on monitoring objects of the wellbore, wherein the monitoring objects include strain, displacement, and crack, and the number of threshold values ​​corresponding to any category of the monitoring objects is at least three; The area where the monitoring result of the monitoring object exceeds the threshold is determined as a risk area; The coordinates of the risk area are determined using the monitoring model.

7. A wellbore monitoring device, characterized in that: The vertical shaft monitoring method according to any one of claims 1 to 6, wherein the shaft monitoring device comprises: A cabin platform, used for axial movement along the wellbore; A three-dimensional laser scanning device and multiple image acquisition devices are installed on the cabin platform. The horizontal fields of view of the multiple image acquisition devices are connected in sequence and cover the inner wall of the wellbore along the circumferential direction. The vertical fields of view of the multiple image acquisition devices have the same height. The acquisition area of ​​the image acquisition device is the key monitoring area of ​​the wellbore.

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