Real-time dynamic monitoring system for horizontal directional drilling track

Abstract

The invention relates to the technical field of horizontal directional drilling monitoring, and particularly discloses a horizontal directional drilling track real-time dynamic monitoring system which comprises a drilling data acquisition module, a self-adaptive data transmission module, a geological layer analysis module, a three-dimensional track monitoring module and an automatic control module. According to the invention, the geological layer analysis module applies an advanced intelligent analysis technology to carry out deep excavation and accurate identification on dynamic data, and the converted drilling data is further converted into a three-dimensional trajectory diagram, so that an operator of a ground control center can master the full view of the drilling operation in real time, the visual monitoring of the operation process is realized, and the working efficiency is improved. The automatic control module fuses a deep learning algorithm, can intelligently identify route deviation or abnormal conditions in the three-dimensional trajectory diagram, quickly generates a visual adjustment instruction, can find and correct potential problems in the first time, and effectively avoids operation delay or resource waste caused by factors such as trajectory deviation.

Description

Technical Field

[0001] The invention belongs to the technical field of horizontal directional drilling monitoring, and in particular relates to a real-time dynamic monitoring system for horizontal directional drilling trajectories. Background Art

[0002] In the field of geological exploration and engineering construction, horizontal directional drilling technology is widely used in operations such as crossing projects and pipeline laying. However, the traditional horizontal directional drilling trajectory monitoring system has exposed some obvious shortcomings in practical applications. The existing control system has a lag in response to trajectory deviation or abnormal conditions and lacks an automated adjustment mechanism. Once the trajectory is found to deviate from the predetermined path, the operator is often required to manually adjust the drill bit parameters. This process is not only time-consuming and labor-intensive, but also has limited adjustment accuracy, making it difficult to achieve fine control of the drilling trajectory.

[0003] In view of this, the inventor proposes a real-time dynamic monitoring system for horizontal directional drilling trajectory to solve the above problems. Summary of the invention

[0004] The object of the present invention is to provide a real-time dynamic monitoring system for horizontal directional drilling trajectory to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A horizontal directional drilling trajectory real-time dynamic monitoring system, comprising:

[0007] A drilling data acquisition module is used to collect status data in drilling;

[0008] An adaptive data transmission module is used to transmit the state data to a ground control center and obtain dynamic data after screening;

[0009] A geological layer analysis module, used for intelligently analyzing and identifying the dynamic data to obtain drilling data;

[0010] A three-dimensional trajectory monitoring module is used to convert the drilling data into a three-dimensional trajectory map at the ground control center, predict future drilling trajectories based on historical drilling data and geological models, and assist in formulating an optimal drilling path;

[0011] The automated control module is used to use a machine learning algorithm to determine whether the route in the three-dimensional trajectory map has an abnormal deviation and obtain route trajectory data; when a trajectory deviation or other anomalies are found, a visual adjustment instruction is generated and returned to the ground control center to adjust the drill bit parameters.

[0012] Preferably, the drilling environment information acquisition module includes a multi-sensor array unit, a miniature optical fiber camera unit and a high-frequency electromagnetic wave signal transmitter unit;

[0013] The multi-sensor array unit includes at least one of an accelerometer, a gyroscope, a magnetometer and a pressure sensor, and is used to capture the position information data of the drill bit in real time, wherein the position information data includes the inclination angle data, the depth change data, and the pressure environment data in the borehole;

[0014] A micro fiber optic camera unit is used to collect images inside the borehole in real time and obtain image data;

[0015] The status data includes location information data and image data;

[0016] The high-frequency electromagnetic wave signal transmitter unit is used to transmit status data to the adaptive data transmission module.

[0017] Preferably, the position information data is calculated using a combined navigation algorithm, and the combined navigation algorithm includes a position calculation formula and a posture calculation formula, and its expression is:

[0018] Position calculation formula:

[0019]

[0020]

[0021] in:

[0022] p(t): drill bit position vector at time t, including x, y, and z coordinates;

[0023] v(t): drill bit velocity vector at time t;

[0024] a(t): drill bit acceleration measured by the acceleration sensor;

[0025] The posture calculation formula uses the quaternion integration of the angular velocity ω:

[0026]

[0027] in:

[0028] q(t): drill head attitude expressed by quaternion;

[0029] ω(t): angular velocity measured by the gyroscope;

[0030] Quaternion multiplication symbol;

[0031] By integrating the acceleration and angular velocity, the real-time position and attitude change of the drill bit can be obtained.

[0032] Preferably, the adaptive data transmission module includes a data priority transmission unit;

[0033] The data priority transmission unit receives the status data, prioritizes and compresses the status data, and transmits it to the ground control center, where dynamic data is obtained after screening.

[0034] Preferably, the geological layer analysis module includes a geological layer analysis unit and a borehole image analysis unit;

[0035] The geological layer analysis unit uses a deep learning algorithm to analyze dynamic data, automatically classify different geological layers, and obtain geological layer image data;

[0036] The drilling image analysis unit analyzes the geological layer image data to obtain drilling data including the posture, angle and speed of the drill bit.

[0037] Preferably, the three-dimensional trajectory monitoring module is used to convert the drilling data into a three-dimensional trajectory diagram at the ground control center, and the conversion expression f(x) is:

[0038] f(x)=a i +b i (xx i )+c i (xx i ) 2 +d i (xx i ) 3 ,x∈[x i ,x i+1 ]

[0039] x: drilling data;

[0040] f(x): three-dimensional trajectory function interpolated in the interval;

[0041] ai,bi,ci,di: coefficients of cubic spline interpolation;

[0042] xi: known trajectory point position.

[0043] Preferably, the automation control module includes a real-time diagnosis unit and a path adjustment unit.

[0044] The real-time diagnosis unit uses a deep learning algorithm to determine whether the route in the three-dimensional trajectory map has abnormal deviations and obtain route trajectory data; when trajectory deviations or other abnormalities are found, visual adjustment instructions are generated;

[0045] The path adjustment unit returns the visual adjustment instructions to the ground control center to implement the drill bit parameter adjustment.

[0046] Preferably, the drill bit parameter adjustment adopts PID control algorithm to perform real-time direction and angle fine-tuning, and the expression of PID control algorithm is:

[0047]

[0048] u(t): control input, used to adjust the direction and angle of the drill;

[0049] e(t): trajectory deviation error, the difference between the actual position and the target position;

[0050] Kp, Ki, Kd: proportional, integral and differential gain coefficients.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The geological layer analysis module in the present invention uses advanced intelligent analysis technology to deeply mine and accurately identify dynamic data. The converted drilling data is further converted into a three-dimensional trajectory map, so that the operators in the ground control center can instantly grasp the overall picture of the drilling operation and realize visual monitoring of the operation process. The automation control module integrates deep learning algorithms, which can intelligently identify route deviations or abnormal conditions in the three-dimensional trajectory map and quickly generate visual adjustment instructions. It can discover and correct potential problems in the first time, effectively avoiding operation delays or waste of resources due to factors such as trajectory deviation. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A block diagram of a horizontal directional drilling trajectory real-time dynamic monitoring system of the present invention;

[0054] Figure 2 It is a schematic diagram of drilling a guide hole of the present invention;

[0055] Figure 3 It is a schematic diagram of auxiliary positioning of the drill bit of the present invention;

[0056] Figure 4 This is a schematic diagram of a mud circulation system in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0057] 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.

[0058] Embodiment 1:

[0059] See also Figure 1 As shown, a horizontal directional drilling trajectory real-time dynamic monitoring system comprises:

[0060] A drilling data acquisition module, including a multi-sensor array unit, a micro-fiber camera unit and a high-frequency electromagnetic wave signal transmitter unit, is used to collect status data in the drilling;

[0061] An adaptive data transmission module, including a signal repeater network unit and a data priority transmission unit, is used to transmit the state data to a ground control center, and obtain dynamic data after screening;

[0062] A geological layer analysis module, including a geological layer feature recognition unit, a real-time risk warning unit and a drilling image analysis unit, is used to intelligently analyze and recognize the dynamic data to obtain drilling data;

[0063] A three-dimensional trajectory monitoring module, including a three-dimensional real-time trajectory modeling unit, for converting the drilling data into a three-dimensional trajectory map at the ground control center, predicting future drilling trajectories based on historical drilling data and geological models, and assisting in formulating an optimal drilling path;

[0064] The automated control module is used to use a machine learning algorithm to determine whether the route in the three-dimensional trajectory map has an abnormal deviation and obtain route trajectory data; when a trajectory deviation or other anomalies are found, a visual adjustment instruction is generated and returned to the ground control center to adjust the drill bit parameters. The adjustment is made by marking the recommended adjustment direction and angle on the three-dimensional trajectory map for the operator to make fine adjustments.

[0065] As can be seen from the above, the geological layer analysis module uses advanced intelligent analysis technology to deeply mine and accurately identify dynamic data. The converted drilling data is further converted into a three-dimensional trajectory map, so that the operators in the ground control center can instantly grasp the overall picture of the drilling operation and realize visual monitoring of the operation process. The automation control module integrates deep learning algorithms, which can intelligently identify route deviations or abnormal conditions in the three-dimensional trajectory map and quickly generate visual adjustment instructions. It can discover and correct potential problems in the first time, effectively avoiding operation delays or waste of resources due to factors such as trajectory deviation.

[0066] Embodiment 2:

[0067] refer to Figure 1 As shown, the drilling environment information acquisition module includes a multi-sensor array unit, a micro-fiber camera unit and a high-frequency electromagnetic wave signal transmitter unit;

[0068] The multi-sensor array unit includes an accelerometer, a gyroscope, a magnetometer and a pressure sensor, which are used to capture the position information data of the drill bit in real time, and the position information data includes the inclination angle data, the depth change data, and the pressure environment data in the borehole;

[0069] The micro optical fiber camera unit is used to collect images inside the borehole in real time to obtain image data to assist in determining changes in geological layers;

[0070] The state data includes the position information data and the image data;

[0071] The high-frequency electromagnetic wave signal transmitter unit is used to transmit the status data to the adaptive data transmission module to ensure stable transmission of signals in complex geological layers.

[0072] Specifically, the position information data is calculated using a combined navigation algorithm, and the combined navigation algorithm includes a position calculation formula and a posture calculation formula, and its expression is:

[0073] The position calculation formula is:

[0074]

[0075]

[0076] in:

[0077] p(t): drill bit position vector at time t, including x, y, and z coordinates;

[0078] v(t): drill bit velocity vector at time t;

[0079] a(t): drill bit acceleration measured by the acceleration sensor;

[0080] The posture calculation formula uses quaternion integration of angular velocity ω:

[0081]

[0082] in:

[0083] q(t): drill head attitude expressed by quaternion;

[0084] ω(t): angular velocity measured by the gyroscope;

[0085] Quaternion multiplication symbol;

[0086] By integrating the acceleration and angular velocity, the real-time position and posture changes of the drill bit can be obtained. This formula can be used to draw the drilling trajectory of the drill bit in real time in three-dimensional space, providing accurate data support for three-dimensional modeling.

[0087] Specifically, the adaptive data transmission module includes a data priority transmission unit;

[0088] The data priority transmission unit receives the status data, prioritizes and compresses the status data, and transmits it to the ground control center, where dynamic data is obtained after screening, ensuring efficient transmission of camera images, sensor data, and location information.

[0089] Specifically, the geological layer analysis module includes a geological layer analysis unit and a borehole image analysis unit;

[0090] The geological layer analysis unit adopts a deep learning algorithm to analyze the dynamic data, automatically classify different geological layers, and obtain geological layer image data;

[0091] The drilling image analysis unit analyzes the geological layer image data to obtain the drilling data including the posture, angle and speed of the drill bit, identifies the geological layer detail information, and prevents the drill bit from deviating from the preset trajectory.

[0092] Specifically, the expression of the deep learning algorithm is:

[0093] P(y=1X)=σ(w·X+b)

[0094]

[0095] Where P(y=1|X): the probability that the current geological layer belongs to a certain type (such as sand layer);

[0096] X: feature vector, including data measured by density, humidity, and pressure sensors;

[0097] w: weight vector of the model;

[0098] B: bias term;

[0099] σ(z): activation function, usually the Sigma function;

[0100] By inputting geological layer characteristic data, the model calculates the probability that the geological layer belongs to a specific stratum (such as rock layer, sand layer, etc.), thereby realizing automatic identification and classification. The algorithm enables the system to intelligently identify different strata and mark them on the display interface.

[0101] Specifically, the machine learning algorithm is linear regression, and its expression is:

[0102]

[0103] in:

[0104] is a predicted value, indicating a certain dimension of the drill bit position or a prediction of whether a deviation has occurred;

[0105] β0 is the intercept term;

[0106] β1, β2, …, βn are regression coefficients, which represent the influence of various features, including x, y, z coordinates, velocity, and acceleration of the trajectory on the predicted value;

[0107] x1,x2,…,xn are input features.

[0108] Specifically, the three-dimensional trajectory monitoring module is used to convert the drilling data into the three-dimensional trajectory graph at the ground control center, and the conversion expression f(x) is:

[0109] f(x)=a i +b i (xx i )+c i (xx i ) 2 +d i (xx i ) 3 ,x∈[x i ,x i+1 ]

[0110] x: drilling data;

[0111] f(x): three-dimensional trajectory function interpolated in the interval;

[0112] ai,bi,ci,di: coefficients of cubic spline interpolation;

[0113] xi: known trajectory point position;

[0114] The path of the drill bit is displayed in three-dimensional space. The system uses a three-dimensional interpolation algorithm to smooth the trajectory to intuitively display the running trajectory of the drill bit.

[0115] The interpolation algorithm is used to generate a continuous trajectory curve, avoiding the uneven effect caused by discrete sampling points, so that the three-dimensional path of the drill bit presents a smooth continuous curve. This visualization helps the operator to intuitively understand the real-time position and posture of the drill bit.

[0116] Specifically, the automation control module includes a real-time diagnosis unit and a path adjustment unit.

[0117] The real-time diagnosis unit uses a deep learning algorithm to determine whether the route in the three-dimensional trajectory map has an abnormal deviation, and obtains route trajectory data; when a trajectory deviation or other abnormalities are found, a visual adjustment instruction is generated;

[0118] The path adjustment unit returns the visual adjustment instruction to the ground control center to implement the drill bit parameter adjustment.

[0119] Specifically, the drill bit parameter adjustment uses a PID control algorithm to perform real-time direction and angle fine-tuning to ensure that the drill bit drills according to the planned path. The expression of the PID control algorithm is:

[0120]

[0121] u(t): control input, used to adjust the direction and angle of the drill;

[0122] e(t): trajectory deviation error, the difference between the actual position and the target position;

[0123] Kp, Ki, Kd: proportional, integral and differential gain coefficients;

[0124] The PID control algorithm adjusts the angle of the drill bit through real-time feedback of the offset error, ensuring that the drill bit remains stably and accurately on the planned path, reducing the risk of deviation.

[0125] As can be seen from the above, the system calculates and corrects the spatial position of the drill bit in real time through the combined navigation and position tracking algorithms, ensuring that the drilling path is highly consistent with the designed trajectory, effectively avoiding the risk of deviation from the predetermined trajectory. Based on the PID control algorithm, the system can automatically correct the drill bit that deviates from the trajectory and respond quickly when the deviation reaches the set threshold, reducing the risk of deviation caused by human error. The system's automatic monitoring and control function reduces the reliance on manual monitoring, allowing construction personnel to focus on higher-value decisions during drilling operations, thereby improving operational efficiency.

[0126] Based on real-time data analysis and geological identification functions, the system can dynamically adjust the drilling speed for different geological layers, thereby speeding up the drilling process. Through the images collected by the built-in camera and the AI ​​geological layer recognition algorithm, the system can analyze the geological information around the drill bit in real time, and accurately identify different geological types such as sand layers, clay layers, and rock layers. When the system recognizes changes in the geological layer, it will automatically adjust the drilling speed and pressure to reduce drill bit wear, avoid damage to the drill bit or pipe caused by improper drilling speed, and increase the service life of the equipment.

[0127] The system's adaptive frequency regulation function dynamically adjusts the transmission frequency according to depth and signal strength, ensuring stable transmission of images and sensor data even in deep environments, greatly reducing signal interference and loss during data transmission. Efficient data transmission enables operators to grasp the drill bit status and drilling environment changes in real time, shortens decision-making feedback time, and improves construction response speed. The system dynamically displays the actual path of the drill bit through a three-dimensional model and generates a real-time updated three-dimensional trajectory map to help operators intuitively grasp the drill bit position and drilling direction during the construction process.

[0128] Embodiment three:

[0129] This design is specifically used in the field of geological exploration and engineering construction. The construction project uses horizontal directional drilling technology to lay pipelines underground in the city. In order to avoid obstacles such as underground pipe networks and building foundations, it is necessary to strictly control the trajectory of the drill bit and accurately cross the geological layer. In order to achieve precise drilling, the construction team introduced a real-time dynamic monitoring system for the trajectory of horizontal directional drilling;

[0130] Furthermore, a construction project uses horizontal directional drilling technology to lay pipelines underground in the city. In order to avoid obstacles such as underground pipe networks and building foundations, it is necessary to strictly control the trajectory of the drill bit and accurately cross the geological layer. In order to achieve precise drilling, the construction team introduced a real-time dynamic monitoring system for the trajectory of horizontal directional drilling.

[0131] System composition and specific parameter settings

[0132] Sensor Module:

[0133] Accelerometer and gyroscope: used for real-time position and attitude measurement of the drill head.

[0134] Sampling frequency: 500Hz

[0135] Accuracy: ±0.05° (gyro) and ±0.01m / s 2 (Accelerometer)

[0136] Built-in camera:

[0137] Type: HD industrial camera

[0138] Resolution: 1280x720

[0139] Sampling frequency: 60fps

[0140] Data transmission module:

[0141] Adaptive data transmission frequency, ranging from 5 to 50MHz (dynamically adjusted based on underground environment)

[0142] Maximum transmission distance: 500 meters

[0143] Data processing and AI-assisted geological identification:

[0144] Using Convolutional Neural Networks (CNN) for Geological Strata Analysis and Identification

[0145] Model training parameters: 5000 geological sample images, data enhancement ratio of 1.2

[0146] Control and visualization module:

[0147] Use 3D models to dynamically display the real-time position of the drill bit

[0148] Provides real-time deviation warning and uses PID control algorithm to correct deviation

[0149] Implementation steps and real-time monitoring data

[0150] 1. Drill start and attitude calibration

[0151] Initial position calibration: Before drilling begins, the system calibrates the drill head posture to ensure zero offset error.

[0152] In order to ensure the accuracy of crossing and docking, in addition to using geomagnetic guidance, offshore positioning magnetic targets will also be used to guide the detector;

[0153] Assisted positioning at sea

[0154] Real-time position tracking: the drill bit’s starting coordinates are (0,0,0) and the initial angle is 0°

[0155] Initial speed: 0m / s

[0156] Accelerometer data: a x =0.02m / s 2

[0157] a y =0.01m / s 2

[0158] a z =-9.81m / s 2

[0159] Attitude angle (initial): 0°

[0160] Drilling process:

[0161] 1) Installation of offshore casing and its supporting piles

[0162] Support pile installation

[0163] The supporting piles are installed using a pile hammer. The size and spacing of the supporting piles are determined based on calculations. To facilitate later removal, the tops of the supporting piles are about 5m above the water surface.

[0164] Casing installation

[0165] The drilling rig on the drilling rig support platform provides thrust, and with the cooperation of the floating crane, the casing is installed section by section from top to bottom. The length of the casing end entering the mud is 50m;

[0166] 2) If Figure 2 After the casing is installed, the drilling rig support platform is in place and the pilot hole drilling begins. In order to ensure the accuracy of crossing and docking, in addition to using geomagnetic guidance, offshore positioning magnetic targets will be used to assist the positioning of the guidance detector at sea;

[0167] 3) If Figure 3As shown in the figure, after the drill bit is unearthed from the seabed, the diver connects the drill bit to the cable and recycles the cable to pull the drill rod from the seabed through the unearthed

[0168] The side drilling rod gantry support is salvaged to the auxiliary work boat, the pilot drill bit is removed, the reamer is connected, and the hole reaming work begins. The hole reaming auxiliary work boat is moored at the excavation point, responsible for the installation of the reamer and the connection of the drill rod;

[0169] The hole expansion difference is shown in Table 1 below:

[0170]

[0171] Table 1

[0172] 4) Pipeline pullback

[0173] There are two ways to tow back submarine pipelines: prefabrication as a whole and towing back at one time, and towing back pipelines one by one while welding.

[0174] 1) Prefabrication of the whole and one-time pullback

[0175] The implementation steps of the program are:

[0176] (1) Before the directional drilling and hole expansion is completed, the entire section of submarine pipeline is pre-laid near the excavation end to be towed back.

[0177] (2) After hole expansion is completed, the support vessel at the excavation end is evacuated and the drill pipe is pushed forward to the starting pipe-laying head position.

[0178] (3) Connect the drill pipe to the starting pipe-laying head on the water surface and lower it to the seabed.

[0179] (4) Use the drilling rig at the underground end to pull the entire section of pipeline back at one time.

[0180] The advantages of this solution are: short offshore towing time and low construction risk of towing operations.

[0181] The disadvantages of this solution are: (1) the pre-laid pipeline has a long residence time and requires auxiliary pipe stabilization measures; (2) the starting resistance of the backhaul is large and the requirements for the backhaul equipment are high.

[0182] like Figure 4 Shown: 5) Mud treatment

[0183] In directional drilling construction projects, freshwater mud is used, the main components of which are: water + bentonite (6% to 8%). It is non-toxic and harmless to the environment.

[0184] Directional drilling mud system consists of mud mixing tank, mud pump, mud reverse circulation slurry pump, and mud recovery and processing system. The returned mud is processed by the mud processor and then recycled;

[0185] 2. Real-time drill trajectory monitoring

[0186] After entering the drill, the system collects 500 position and attitude data points per second to draw a three-dimensional trajectory.

[0187] The acceleration is integrated to obtain the displacement and update the three-dimensional coordinates of the drill.

[0188] Example of trajectory data (first 5 seconds):

[0189] 1 second: coordinates (0.15, 0.1, -0.2), attitude angle 0.5°

[0190] 2 seconds: coordinates (0.3, 0.25, -0.4), attitude angle 1.0°

[0191] 5 seconds: coordinates (0.75, 0.65, -1.2), attitude angle 2.5°

[0192] 3. AI-assisted geological layer identification

[0193] When the drill bit penetrates deep into the earth, the camera collects images of the surrounding geology and identifies the current geological layer.

[0194] The system determined that the current geological layer type was a sand layer, and the drill bit encountered a clay layer with obvious density changes. The system's recognition accuracy was 92%.

[0195] Example of geological data:

[0196] Image feature vector: contains color, texture, and granularity information

[0197] Geological classification: Sand layer (identification confidence 92%)

[0198] 4. Intelligent adaptive data transmission

[0199] As the drill bit goes deeper, the system automatically adjusts the transmission frequency to optimize signal strength.

[0200] The transmission frequency adjustment at different depths is as follows:

[0201]

[0202] 5. Deviation warning and PID automatic control

[0203] When the system detects that the drill bit deviates from the predetermined trajectory (more than 5 cm), it automatically triggers the PID control algorithm to correct the deviation.

[0204] The deviation threshold is set at 5 cm, and the system adjusts the direction and angle of the drill bit through PID to correct its position in real time.

[0205] Example of offset data:

[0206] Offset: 0.06 meters (above the deviation threshold)

[0207] PID correction: Δx = -0.03, Δy = -0.02

[0208] System application effect

[0209] Precise position control: During the entire drilling process, the final position deviation of the drill bit is controlled within 1.5 cm, and the attitude angle error is less than 1°, which greatly improves the accuracy of drilling.

[0210] Geological layer identification effect: Based on sensor data and AI image recognition technology, the system accurately identifies different geological layers and dynamically adjusts the drilling speed, avoiding drill bit loss caused by changes in geological layers.

[0211] Data transmission stability: The adaptive transmission module ensures stable signal transmission in deep geological environments, reduces data loss, and improves the real-time performance of drill bit position and geological data.

[0212] Offset correction effect: The PID control algorithm achieves rapid automatic correction, controlling the trajectory offset within 5 cm and successfully preventing the risk of drill bit deviation.

[0213] Improved construction efficiency: Through real-time monitoring, early warning and adaptive regulation, the system shortens the traditional drilling time by 20%, effectively reducing the manual monitoring and control costs during the drilling process.

[0214] From the above, we can see that the system realizes precise control and real-time dynamic monitoring of the drill bit trajectory in complex geological environments, and ensures the efficiency, safety and precision of drilling construction through intelligent means.

[0215] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time dynamic monitoring system for horizontal directional drilling trajectory, characterized in that: include: A drilling data acquisition module is used to collect status data in drilling; An adaptive data transmission module is used to transmit the state data to a ground control center and obtain dynamic data after screening; A geological layer analysis module, used for intelligently analyzing and identifying the dynamic data to obtain drilling data; A three-dimensional trajectory monitoring module is used to convert the drilling data into a three-dimensional trajectory map at the ground control center, predict future drilling trajectories based on historical drilling data and geological models, and assist in formulating an optimal drilling path; The automated control module is used to use a machine learning algorithm to determine whether the route in the three-dimensional trajectory map has an abnormal deviation and obtain route trajectory data; when a trajectory deviation or other anomalies are found, a visual adjustment instruction is generated and returned to the ground control center to adjust the drill bit parameters.

2. A horizontal directional drilling trajectory real-time dynamic monitoring system according to claim 1, characterized in that: The drilling environment information acquisition module includes a multi-sensor array unit, a micro-fiber camera unit and a high-frequency electromagnetic wave signal transmitter unit; The multi-sensor array unit includes at least one of an accelerometer, a gyroscope, a magnetometer and a pressure sensor, and is used to capture the position information data of the drill bit in real time, wherein the position information data includes tilt angle data, depth change data, and pressure environment data in the borehole; The micro optical fiber camera unit is used to collect the internal image of the borehole in real time to obtain image data; The state data includes the position information data and the image data; The high-frequency electromagnetic wave signal transmitter unit is used to transmit the status data to the adaptive data transmission module.

3. A horizontal directional drilling trajectory real-time dynamic monitoring system according to claim 2, characterized in that: The position information data is calculated using a combined navigation algorithm, which includes a position calculation formula and a posture calculation formula, and its expression is: The position calculation formula is: in: p(t): drill bit position vector at time t, including x, y, and z coordinates; v(t): drill bit velocity vector at time t; a(t): drill bit acceleration measured by the acceleration sensor; The posture calculation formula uses quaternion integration of angular velocity ω: in: q(t): drill head attitude expressed by quaternion; ω(t): angular velocity measured by the gyroscope; : quaternion multiplication symbol; By integrating the acceleration and angular velocity, the real-time position and attitude change of the drill bit can be obtained.

4. A horizontal directional drilling trajectory real-time dynamic monitoring system according to claim 1, characterized in that: The adaptive data transmission module includes a data priority transmission unit; The data priority transmission unit receives the status data, prioritizes and compresses the status data, and transmits the status data to a ground control center to obtain dynamic data after screening.

5. A horizontal directional drilling trajectory real-time dynamic monitoring system according to claim 1, characterized in that: The geological layer analysis module includes a geological layer analysis unit and a drilling image analysis unit; The geological layer analysis unit adopts a deep learning algorithm to analyze the dynamic data, automatically classify different geological layers, and obtain geological layer image data; The drilling image analysis unit analyzes the geological layer image data to obtain the drilling data including the posture, angle and speed of the drill bit.

6. A horizontal directional drilling trajectory real-time dynamic monitoring system according to claim 5, characterized in that: The expression of the deep learning algorithm is: P(y=1X)=σ(w·X+b) Where P(y=1|X): the probability that the current geological layer belongs to a certain type; X: feature vector, including data measured by density, humidity, and pressure sensors; w: weight vector of the model; B: bias term; σ(z): activation function, usually the Sigmoid function.

7. A horizontal directional drilling trajectory real-time dynamic monitoring system according to claim 1, characterized in that: The machine learning algorithm is linear regression, which is expressed as: in: is a predicted value, indicating a certain dimension of the drill bit position or a prediction of whether a deviation has occurred; β0 is the intercept term; β1, β2, …, βn are regression coefficients, which represent the influence of various features, including x, y, z coordinates, velocity, and acceleration of the trajectory on the predicted value; x1,x2,…,xn are input features.

8. A horizontal directional drilling trajectory real-time dynamic monitoring system according to claim 7, characterized in that: The three-dimensional trajectory monitoring module is used to convert the drilling data into the three-dimensional trajectory diagram at the ground control center, and the conversion expression f(x) is: f(x)=a i +b i (x-x i )+c i (x-x i ) 2 +d i (x-x i ) 3 ,x∈[x i ,x i+1 ] Where x: drilling data; f(x): three-dimensional trajectory function interpolated in the interval; ai,bi,ci,di: coefficients of cubic spline interpolation; xi: known trajectory point position.

9. A horizontal directional drilling trajectory real-time dynamic monitoring system according to claim 1, characterized in that: The automation control module includes a real-time diagnosis unit and a path adjustment unit. The real-time diagnosis unit uses a deep learning algorithm to determine whether the route in the three-dimensional trajectory map has an abnormal deviation, and obtains route trajectory data; when a trajectory deviation or other abnormalities are found, a visual adjustment instruction is generated; The path adjustment unit returns the visual adjustment instruction to the ground control center to implement drill bit parameter adjustment.

10. A horizontal directional drilling trajectory real-time dynamic monitoring system according to claim 9, characterized in that: The drill bit parameter adjustment uses a PID control algorithm to perform real-time direction and angle fine-tuning. The expression of the PID control algorithm is: Where u(t): control input, used to adjust the direction and angle of the drill; e(t): trajectory deviation error, the difference between the actual position and the target position; Kp, Ki, Kd: proportional, integral and differential gain coefficients.

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