An intelligent drilling rig operation system and a method for deviation correction control
The smart drilling rig system addresses inefficiencies in traditional drilling by integrating multi-dimensional signal fusion and virtual-real interaction for real-time, adaptive control, enhancing stuck drill detection and trajectory accuracy while reducing equipment wear.
Patent Information
- Authority / Receiving Office
- HK · HK
- Patent Type
- Applications
- Current Assignee / Owner
- CHINA RAILWAY NO 2 ENG GROUP CO LTD
- Filing Date
- 2026-04-29
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional drilling rig operations rely heavily on operator experience and lack the ability to predict progressive stuck drill incidents, leading to inefficient construction processes and equipment wear due to delayed responses and inaccurate trajectory corrections.
A smart drilling rig operation system that integrates multi-dimensional signal fusion, including mechanical vibration, thermal field, and acoustic signals, with a virtual-real interaction mechanism to dynamically assess and optimize drilling strategies, using sensors and machine learning for real-time decision-making and obstacle detection.
The system significantly reduces stuck drill response time, enhances trajectory accuracy, and reduces equipment wear by providing adaptive control strategies that overcome single-sensor limitations and improve formation adaptability.
Smart Images
Figure 00000000_0000_ABST
Abstract
Description
(19) State Intellectual Property Office (12) Invention Patent Application (10) Application Publication Number (43) Application Publication Date (21) Application Number 202511470392.2 (22) Application Date 2025.10.15 (71) Applicant China Railway No. 2 Engineering Group Co., Ltd. Address 16, Tongjin Road, Jinniu District, Chengdu, Sichuan Province 610000 Applicant National Engineering Research Center for High-Speed Railway Construction Technology, Central South University (72) Inventors Zhao Fei, Zhou Yinghua, Yang Weichao, Xu Tianliang, Ma Hui, Xiao Kai, He Hong, Zhao Lun, Hu Houshan (74) Patent Agency Sichuan Liju Law Firm 51221 Patent Attorney Fan Wenyuan (51) Int.Cl. E21B 44 / 00 (2006.01) G06F 30 / 17 (2020.01) G06F 18 / 25 (2023.01) G06F 18 / 10 (2023.01) G06T 17 / 00(2006.01) G06V 10 / 141(2022.01) G06V 10 / 26(2022.01) G06V 10 / 764(2022.01) G06V 10 / 74(2022.01) G06N 3 / 0464(2023.01) G06N 3 / 045(2023.01) G06N 3 / 092(2023.01) G06N 20 / 00(2019.01) G06F 119 / 08(2020.01) G06F 119 / 14(2020.01) (54) Invention Title: A Smart Drilling Rig Operation System and Correction Control Method (57) Abstract: This invention relates to the field of mechanical automation, specifically to a smart drilling rig operation system and correction control method. The system significantly shortens the response time for stuck drill identification and handling through multi-dimensional signal fusion and dynamic risk assessment, avoiding efficiency losses caused by traditional shutdown protection. Imaging and obstacle positioning technologies are used to replace manual drill bit lifting and inspection, reducing equipment idle time and mechanical wear. Multi-physics field signal fusion technology breaks through the limitations of single sensor perception, which is conducive to accurately identifying the spatial distribution of underground obstacles and the characteristics of geological interface abrupt changes. Acoustic spectrum and thermal field distribution analysis can enhance the analytical ability of drill bit-formation interaction dynamics, providing multi-dimensional input for intelligent decision-making. Combined with a virtual-real interaction mechanism, the control strategy is dynamically optimized to adapt to the changing needs of different formation conditions and construction stages. Claims 2 pages, Description 7 pages, Drawings 1 page, CN 120946306 A 2025.11.14 CN 1 20 94 63 06 A 1. A drilling rig intelligent operation system, characterized in that it includes: a data acquisition module, used to acquire mechanical vibration signals, torque signals, spatial displacement signals, thermal field signals, acoustic signals and image signals during the drilling process;The analysis module processes the signals acquired by the acquisition module to obtain the temporal evolution law of torque-displacement, displacement stagnation characteristics, vibration spectrum, thermal field distribution map, and acoustic time-frequency map. It then fuses these with a dynamic filtering algorithm and a machine learning model to generate a borehole condition feature vector. A stuck drill risk classification mechanism is established based on a historical working condition model library, and a stuck drill risk level is generated. Based on the abrupt change law of the vibration spectrum, the attenuation law of the thermal field distribution map, and the acoustic time-frequency map, the formation type and interface location are generated. A three-dimensional model of the borehole interior is constructed based on image signals. The spatial coordinates of hard obstacles in the borehole path are located based on the energy reflection characteristics of the acoustic signal and the local temperature anomaly of the thermal field signal. A time-series prediction model of the borehole trajectory is constructed based on the nonlinear relationship between the torque growth rate and the displacement stagnation duration. Borehole trajectory deviation characteristics are identified. The virtual simulation module establishes a coupled dynamic model of the drill pipe and geological environment. Through simulation, it optimizes the handling strategy and feeds it back to the decision module. It pre-simulates the equipment response under extreme working conditions, generates safety protection strategies, and feeds them back to the decision module. The decision module determines whether to correct the deviation based on the stuck drill risk level, generates a handling strategy, and forms adjustment instructions. 2. The intelligent drilling rig operation system according to claim 1, characterized in that the acquisition module includes: a vibration detection sensor, for acquiring mechanical vibration signals including axial vibration, radial vibration, and circumferential vibration signals of the drill rod; a laser displacement sensor and inertial measurement unit, for acquiring spatial displacement signals of the drill bit; a thermal field detection unit, for acquiring temperature field distribution data of the borehole inner wall; an acoustic signature acquisition unit, for acquiring acoustic signals during the drilling process; and an imaging component, for acquiring image signals. 3. The intelligent drilling rig operation system according to claim 2, characterized in that: a vibration spectrum is obtained by performing wavelet packet decomposition on the axial vibration, radial vibration, and circumferential vibration signals of the drill rod; the energy ratio of the low-frequency band (0-200Hz) and the high-frequency band (greater than 500Hz) is extracted to calculate the spectral kurtosis, thereby obtaining the vibration spectrum abrupt change law; the borehole inner wall temperature field is divided into regions, and the radial temperature gradient is calculated to obtain a thermal field distribution map; the formation resonance peak is extracted from the acoustic time-frequency map using Mel-spectrum cepstral coefficients, and the sound wave energy attenuation rate is calculated to obtain the attenuation rate variation law. 4. The intelligent drilling rig operation system according to claim 3, characterized in that a feature weight allocation model based on an attention mechanism is constructed to perform cross-dimensional signal fusion of vibration spectrum energy, thermal field distribution, and acoustic energy attenuation rate to generate a multi-dimensional working condition feature vector. 5. The intelligent drilling rig operation system according to claim 4, characterized in that the dynamic weight range of vibration spectrum energy is 0.4-0.6, the dynamic weight range of thermal field distribution is 0.3-0.5, and the dynamic weight range of acoustic energy attenuation rate is 0.2-0.4. 6. The intelligent drilling rig operation system according to claim 3, characterized in that, based on the vibration spectrum abrupt change intensity...A three-tiered (low, medium, and high) stuck-drill risk classification mechanism is established by combining the temperature, area of the abnormal thermal field region, and acoustic signal energy attenuation rate with a historical working condition pattern library. 7. A drilling rig intelligent operation system according to any one of claims 1-6, characterized in that a convolutional neural network is used to perform joint feature learning on the vibration spectrum diagram, thermal field distribution diagram, and acoustic time-frequency diagram to obtain the formation type and its interface location. 8. A drilling operation correction control method, characterized in that it employs a drilling rig intelligent operation system according to any one of claims 1-7, comprising: real-time acquisition of mechanical vibration signals, torque signals, spatial displacement signals, thermal field signals, and acoustic signals during drilling operations; spatiotemporal fusion processing of the above signals to extract working condition feature vectors; determination of the stuck-drill risk level; when a preset risk level is reached, acquisition of image signals to construct a three-dimensional model of the borehole interior, determining the offset of the borehole trajectory; simulation of different handling strategies in a virtual environment to obtain the optimal solution, and generation of adjustment instructions. 9. A drilling operation correction control method according to claim 8, characterized in that the spatiotemporal fusion processing includes signal noise reduction and feature enhancement operations; the virtual environment simulation includes dynamic response prediction of geological conditions. 10. A drilling operation correction control method according to claim 8, characterized in that the execution effect data of the adjustment command is fed back to the virtual simulation module to form learning samples, and the decision weight parameters of the machine learning model are updated periodically. Claims 2 / 2 Page 3 CN 120946306 A A Drilling Rig Intelligent Operation System and Correction Control Method Technical Field
[0001] This invention relates to the field of mechanical automation, and in particular to a drilling rig intelligent operation system and correction control method. Background Art
[0002] As the core equipment for foundation engineering construction, auger drilling equipment is widely used in building pile foundation construction, geological exploration and other fields. Traditional drilling rig operations rely heavily on the experience and judgment of operators, and there are generally some technical bottlenecks when encountering complex strata (such as hard rock strata and high cohesive soil strata). Existing equipment mostly adopts a single torque threshold alarm mechanism, which can only trigger shutdown protection after a stuck drill occurs. It lacks the ability to predict progressive stuck drill in advance, resulting in a delayed response to stuck drill. Manual handling requires repeated drill lifting and inspection, leading to decreased construction efficiency and increased wear and tear on drill bits. Conventional laser guidance systems are limited by the accuracy of straightness measurement and are easily affected by drill rod deflection and hole wall collapse during deep hole operations, making it difficult to achieve millimeter-level trajectory correction. Correction operations mostly rely on trial and error to adjust drilling parameters, which can easily cause secondary deviations.
[0003] Current monitoring systems usually independently collect single-dimensional signals such as vibration and pressure, lacking spatiotemporal correlation analysis of multi-physics field data, making it difficult to accurately identify complex working conditions such as abrupt changes in soil interfaces and underground obstacles, resulting in one-sided working condition perception. ExistingMost intelligent drilling rig control systems adopt fixed control strategies and lack a dynamic interaction mechanism between physical equipment and virtual models, resulting in a lack of formation adaptability in the correction strategy and an inability to achieve continuous optimization of the construction process.
[0004] The purpose of this invention is to address the problem that the single-dimensional signals used in the prior art are insufficient to identify complex working conditions, leading to poor effectiveness of stuck drill fault handling and correction strategies. This invention provides a smart drilling rig operation system and a correction control method.
[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: Firstly, the present invention provides a smart drilling rig operation system, comprising: an acquisition module for acquiring mechanical vibration signals, torque signals, spatial displacement signals, thermal field signals, acoustic signals, and image signals during the drilling process; an analysis module for processing the signals acquired by the acquisition module to obtain the temporal evolution law of torque-displacement, displacement stagnation characteristics, vibration spectrum diagram, thermal field distribution diagram, and acoustic time-frequency diagram; generating a drilling condition feature vector by fusing dynamic filtering algorithms with a machine learning model; establishing a stuck drill risk classification mechanism and generating a stuck drill risk level by combining a historical working condition pattern library; generating formation types and interface locations based on the abrupt change law of the vibration spectrum diagram, the attenuation law of the thermal field distribution diagram, and the acoustic time-frequency diagram; constructing a three-dimensional model of the borehole interior based on the image signals; locating the spatial coordinates of hard obstacles in the borehole path based on the energy reflection characteristics of the acoustic signals and the local temperature anomalies of the thermal field signals; constructing a time series prediction model of the borehole trajectory based on the nonlinear relationship between the torque growth rate and the displacement stagnation duration; and identifying borehole trajectory deviation characteristics. The virtual simulation module establishes a coupled dynamic model of the drill pipe and geological environment, optimizes the handling strategy through simulation and feeds it back to the decision module; it pre-simulates the equipment response under extreme working conditions, generates a safety protection strategy and feeds it back to the decision module; the decision module judges whether to correct the deviation based on the risk level of stuck drill; it generates a handling strategy and forms an adjustment instruction.
[0006] Through multi-dimensional signal fusion and dynamic risk assessment, the stuck drill identification and handling response time is significantly shortened, avoiding the efficiency loss caused by traditional shutdown protection. Imaging and obstacle positioning technology is used to replace manual drill lifting inspection, reducing the idle running time and mechanical wear of the equipment. Multi-physics field signal fusion technology breaks through the limitations of single sensor perception, which is conducive to accurately identifying the spatial distribution of underground obstacles and the characteristics of geological interface abrupt changes; acoustic spectrum and thermal field distribution analysis can enhance the analytical ability of drill bit-formation interaction dynamics, providing multi-dimensional input for intelligent decision-making. Combined with the virtual-real interaction mechanism, the control strategy is dynamically optimized to adapt to the changing needs of different formation conditions and construction stages.
[0007] Further, the acquisition module includes: a vibration detection sensor, which acquires mechanical vibration signals including drill pipe axial vibration, radial vibration and circumferential vibration signals;A laser displacement sensor and inertial measurement unit are used to acquire spatial displacement signals of the drill bit; a thermal field detection unit is used to acquire temperature field distribution data of the borehole inner wall; an acoustic signature acquisition unit is used to acquire acoustic signals during the drilling process; and an imaging component is used to acquire image signals.
[0008] Further, the vibration spectrum is obtained by wavelet packet decomposition of the axial vibration, radial vibration, and circumferential vibration signals of the drill rod. The energy ratio of the low-frequency band (0-200Hz) and the high-frequency band (greater than 500Hz) is extracted to calculate the spectral kurtosis and obtain the vibration spectrum abrupt change law; the borehole inner wall temperature field is divided into regions, the radial temperature gradient is calculated, and a thermal field distribution map is obtained; the formation resonance peak is extracted from the acoustic time-frequency map through the Mel spectrum cepstral coefficients, and the acoustic energy attenuation rate is calculated to obtain the attenuation rate change law.
[0009] Further, a feature weight allocation model based on an attention mechanism is constructed to perform cross-dimensional signal fusion of vibration spectrum energy, thermal field distribution, and acoustic energy attenuation rate to generate a multi-dimensional working condition feature vector.
[0010] Further, the dynamic weight range of vibration spectrum energy is 0.4-0.6, the dynamic weight range of thermal field distribution is 0.3-0.5, and the dynamic weight range of acoustic energy attenuation rate is 0.2-0.4. Further, based on the vibration spectrum abrupt change intensity, the area of the thermal field anomaly region, and the acoustic signal energy attenuation rate combined with the historical working condition pattern library, a three-level stuck drill risk classification mechanism is established.
[0011] Further, a convolutional neural network is used to perform joint feature learning on the vibration spectrum map, thermal field distribution map, and acoustic time-frequency map to obtain the formation type and its interface location.
[0012] In a second aspect, the present invention also provides a method for correcting deviations in drilling operations, employing a drilling rig intelligent operation system as described above, comprising: real-time acquisition of mechanical vibration signals, torque signals, spatial displacement signals, thermal field signals, and acoustic signals during drilling operations; spatiotemporal fusion processing of the above signals to extract working condition feature vectors; determination of the risk level of stuck drill bit; when a preset risk level is reached, acquisition of image signals to construct a three-dimensional model of the borehole interior, determining the deviation of the borehole trajectory; simulation of different handling strategies in a virtual environment to obtain the optimal solution, and generation of adjustment instructions.
[0013] Further, the spatiotemporal fusion processing includes signal denoising and feature enhancement operations; the virtual environment simulation includes dynamic response prediction of geological conditions.
[0014] Further, the execution effect data of the adjustment instructions is fed back to the virtual simulation module to form learning samples, and the decision weight parameters of the machine learning model are updated periodically. Instruction manual, pages 2 / 7, CN 120946306 A
[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: breaking through the limitations of traditional single torque threshold alarm, realizing dynamic identification and prevention of progressive stuck drill risk.This method reduces construction interruptions and equipment damage caused by human intervention; improves the accuracy of dynamic correction of borehole trajectory and enhances the ability to track straight trajectories in complex strata; integrates multi-physical field signals such as mechanical vibration, thermal field distribution, and acoustic spectrum to facilitate accurate identification of abrupt soil interface changes, underground obstacles, and geological anomaly areas; constructs a dynamic interaction mechanism between physical equipment and virtual models to form intelligent decision optimization through virtual-real linkage, realizes adaptive optimization of control strategies and continuous improvement of the construction process, and successfully constructs a closed-loop collaborative control system of "multi-source perception - intelligent analysis - prevention and control autonomous correction - virtual simulation".
[0016] Figure 1 is a flowchart illustrating a correction control method for borehole operations.
[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0018] Unless otherwise specified, in the description of specific embodiments of the present invention, terms such as "upper," "lower," "left," "right," "center," "inner," and "outer" indicating orientation or positional relationships are expressions based on the orientation or positional relationships shown in the accompanying drawings, or the orientation or positional relationship in which the product / equipment / device is usually placed during use. These terms of orientation or positional relationships are merely for the purpose of facilitating the description of the present invention or simplifying the description in specific embodiments, and for enabling those skilled in the art to quickly understand the solution, and do not indicate or imply that a specific device / component / element must have a specific orientation, or be constructed and operated in a specific positional relationship, and therefore should not be construed as a limitation of the present invention.
[0019] Furthermore, the appearance of terms such as "horizontal," "vertical," "suspended," and "parallel" does not indicate that the corresponding device / component / element is required to be absolutely horizontal, vertical, suspended, or parallel, but rather that it may be slightly tilted or have deviations. For example, "horizontal" merely means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted. Alternatively, it can be simplified to mean that the corresponding device / component / element is arranged in a "horizontal", "vertical", "suspended", "parallel" or other orientations, and can have an error / deviation of ±10% relative to the corresponding orientation, more preferably within ±8%, more preferably within ±6%, more preferably within ±5%, and more preferably within ±4%. As long as the corresponding device / component / element is within the error / deviation range, it can still achieve its function in the present invention.
[0020] In addition, the use of terms such as "first", "second", and "third" in the terminology is only used to distinguish the same or similar components.The description should not be construed as emphasizing or implying the relative importance of a particular component.
[0021] Furthermore, in the description of the embodiments of the present invention, "several", "multiple", and "several" represent at least two. It can be any number of components, such as 2, 3, 4, 5, 6, 7, 8, 9, or even more than 9.
[0022] Furthermore, in the description of the technical solutions of the present invention, unless otherwise explicitly specified / limited / restricted, the terms "set", "install", "connect", "link", "provided with", "lay", and "arrange" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to common connection methods in the art, such as welding, riveting, bolting, and threaded connections. Such connections can be mechanical, electrical, or communication connections; they can be direct connections or indirect connections through an intermediate medium; they can refer to the internal communication of two components. Instruction Manual Page 3 / 7 6 CN 120946306 A
[0023] Example 1 The intelligent drilling system of the present invention includes: an acquisition module for acquiring mechanical vibration signals, torque signals, spatial displacement signals, thermal field signals, acoustic signals and image signals during the drilling process; an analysis module for processing the signals acquired by the acquisition module to obtain the time-series evolution law of torque-displacement, displacement stagnation characteristics, vibration spectrum diagram, thermal field distribution diagram and acoustic time-frequency diagram, and generating a drilling condition feature vector by fusing dynamic filtering algorithm and machine learning model; establishing a stuck drill risk classification mechanism and generating a stuck drill risk level by combining historical working condition mode library; generating formation type and interface position based on the abrupt change law of vibration spectrum diagram, thermal field distribution diagram and acoustic time-frequency diagram; constructing a three-dimensional model inside the borehole based on image signals; locating the spatial coordinates of hard obstacles in the borehole path based on the energy reflection characteristics of acoustic signals and the local temperature anomaly of thermal field signals; constructing a time series prediction model of the borehole trajectory based on the nonlinear relationship between torque growth rate and displacement stagnation time; and identifying borehole trajectory deviation characteristics. The virtual simulation module establishes a coupled dynamic model of the drill pipe and geological environment, optimizes the handling strategy through simulation and feeds it back to the decision-making module; it pre-simulates the equipment response under extreme working conditions, generates a safety protection strategy and feeds it back to the decision-making module; the decision-making module determines whether to correct the deviation based on the risk level of stuck drill; it generates a handling strategy and forms an adjustment instruction.
[0024] Specifically, the acquisition module includes: a vibration detection sensor, which collects mechanical vibration signals including axial vibration, radial vibration and circumferential vibration signals of the drill pipe; a laser displacement sensor and inertial measurement unit, used to collect spatial displacement signals of the drill bit; a thermal field detection unit, used to collect temperature field distribution data of the borehole inner wall; an acoustic feature acquisition unit, used to collect acoustic signals during the drilling process; and an imaging component, used to collect image signals.
[0025] Specifically, for example, triaxial accelerometers are arranged at key nodes of the drill rod to collect axial, radial, and circumferential vibration signals of the drill rod in real time, capturing the dynamic interaction characteristics between the drill bit and the formation; a combination of laser displacement sensor and inertial measurement unit is used to monitor the three-dimensional spatial displacement changes of the drill bit, and Kalman filtering algorithm is used to eliminate measurement errors caused by drill rod deflection; an infrared thermal imager array is integrated behind the drill bit to acquire the temperature field distribution data of the borehole inner wall in real time, and the friction abnormality area is identified by the change of thermal radiation intensity; an acoustic sensor group is arranged along the drill rod to collect acoustic signals during the drilling process, and the acoustic spectrum characteristics of different formations are extracted by wavelet transform; the imaging component can use a retractable optical imaging component, which is easy to extend into the borehole and can also adapt to the low-light environment inside the borehole.
[0026] By performing wavelet packet decomposition on the axial, radial, and circumferential vibration signals of the drill rod to obtain the vibration spectrum map, the energy ratio of the low frequency band of 0-200Hz and the high frequency band of greater than 500Hz is extracted to calculate the spectrum kurtosis and obtain the vibration spectrum change law.
[0027] The borehole inner wall temperature field is divided into regions, the radial temperature gradient is calculated, and a thermal field distribution map is obtained; the formation resonance peak is extracted from the acoustic time-frequency map using the Mel spectrum cepstral coefficients, the acoustic energy attenuation rate is calculated, and the attenuation rate variation law is obtained.
[0028] The processing of various acquired signals also includes: for example, using an adaptive band-stop filter to suppress interference in specific frequency bands for mechanical noise in vibration signals; performing environmental noise compensation on acoustic signals to enhance the significance of formation characteristic frequencies; and eliminating signal delays caused by differences in sampling frequencies of different sensors through a timestamp synchronization mechanism to ensure the spatiotemporal consistency of vibration, displacement, thermal field, and acoustic signals. Instruction manual, page 4 / 7, CN 120946306 A
[0029] Multi-sensor timestamp synchronization achieves signal alignment through Lagrange interpolation: , where: represents the sampled value of the k-th sensor at time , represents the target time, represents the time of the i-th known data point, represents the time of the j-th known data point; n represents the interpolation order, which can be selected according to the actual situation, such as 3-5.
[0030] Based on the feature weight allocation mechanism, cross-dimensional correlation features such as vibration spectrum characteristics, thermal radiation distribution patterns, and acoustic energy attenuation laws are extracted to generate a high-information-density working condition characterization vector. For example, the vibration spectrum characteristics are extracted by performing wavelet packet decomposition on the triaxial acceleration signal to extract the energy ratio of the 0-200Hz low-frequency band and the >500Hz high-frequency band, and the spectral kurtosis index is used to identify impact vibration anomalies. The thermal radiation distribution pattern uses the data input from the infrared thermal imager to segment the high-temperature area of the borehole wall and calculate the radial temperature gradient to judge the heat diffusion anomaly. The acoustic energy attenuation feature was extracted by using Mel-spectrum cepstral coefficients to extract the formation resonance peaks and calculating the acoustic energy attenuation rate.
[0031] Feature fusion vector obtained by dynamic weight allocation:
[0032] Among them: represents the dynamic weight of vibration spectrum characteristics; represents the vibration spectrum kurtosis; represents the dynamic weight of thermal field distribution; represents the maximum radial temperature gradient; represents the dynamic weight of acoustic attenuation rate; represents the acoustic attenuation rate; The dynamic weight allocation table is shown in Table 1: Table 1 Dynamic weight allocation table
[0033] Use the long short-term memory network to analyze the time-series evolution law of torque and displacement data, and identify the composite abnormal pattern of sudden torque increase accompanied by displacement stagnation; according to the vibration spectrum mutation intensity, the area of the thermal field abnormal area and the acoustic signal energy attenuation rate, combined with the historical working condition pattern library, establish a low, medium and high three-level sticking risk classification mechanism. The sticking risk assessment function is expressed as; Among them: represents the vibration spectrum kurtosis; represents the maximum kurtosis value; represents the area of the thermal anomaly area; represents the maximum area of the thermal anomaly; represents the acoustic attenuation rate; represents the maximum attenuation rate; The risk level can be set as: R < 0.4, judged as low risk; 0.4 < R < 0.7, judged as medium risk; R > 0.7, judged as high risk.
[0034] As shown in Table 2: Table 2 Sticking risk classification assessment and parameter boundaries DESCRIPTION 5 / 7 pages 8 CN 120946306 A
[0035] Monitor the non-linear relationship between the torque growth rate and the displacement stagnation duration through the sliding window mechanism, and early warn of the progressive sticking risk. By constructing a time-series prediction model, combined with the current drilling pressure, rotation speed and formation type, predict the resistance change curve in the future drilling depth interval; generate multiple sets of potential risk evolution paths through Monte Carlo simulation, and evaluate the sticking probability and trajectory deviation risk under different control strategies.
[0036] Trajectory dynamic deviation correction calculates the azimuth deviation and deflection distance between the actual trajectory of the drill bit and the target axis according to the displacement sensor data and the hole wall deformation characteristics output by the three-dimensional modeling module; constructs a drill pipe-stratum coupling dynamics model, and takes minimizing the trajectory deviation as the objective function to roll and optimize the multi-degree-of-freedom adjustment instructions of the hydraulic actuator (including drilling pressure distribution, azimuth fine-tuning, rotational speed adaptation); The virtual simulation module can perform digital twin modeling and optimization: establish a drill pipe flexible body model based on the finite element method, and generate a stratified stratum mechanics parameter database in combination with geological exploration data; synchronize the sensor data of the physical system and the virtual model state in real time, and simulate the drill bit-stratum interaction force, drill pipe bending stress distribution and hole wall stability evolution. Execute multiple sets of deviation correction schemes (such as different drilling pressure gradients, deviation correction angle combinations) in parallel in the virtual environment, and evaluate the trajectory convergence speed and energy consumption under each scheme; feedback the actual execution effect of the optimal strategy to the virtual model, and reversely correct the estimated value of the stratum mechanics parameters to improve the model prediction accuracy. Store historical construction data (including formation characteristics, deviation correction strategies, execution effects), and optimize the decision weights of control strategies through reinforcement learning algorithms. The virtual simulation module can also be used to establish safety protection and abnormal responseIn response, the equipment response under extreme working conditions is rehearsed in a virtual environment, and safety protection strategies are generated in advance. For sudden situations such as borehole wall collapse and abnormal drill string vibration, similar historical cases are called to generate emergency protection commands (such as progressive decompression and drill retraction, grouting to reinforce the borehole wall) to reduce the risk of equipment damage under abnormal working conditions.
[0037] According to the borehole trajectory deviation characteristics, a multi-degree-of-freedom hydraulic actuator can be used to adjust the drill rod. The drilling pressure, rotation speed and azimuth angle are dynamically adjusted through model predictive control algorithms to achieve dynamic trajectory correction with sub-degree accuracy. For large-angle deviation conditions, an adaptive control strategy can be used to dynamically adjust the hydraulic system parameters to suppress overshoot oscillation and secondary deviation risks during the correction process.
[0038] The stratum type (such as clay, rock, and gravel) and interface position can be obtained by using a convolutional neural network to perform joint feature learning on the vibration spectrum diagram, thermal field distribution diagram and acoustic time-frequency diagram. Based on the energy reflection characteristics of the acoustic signal and the local temperature anomaly of the thermal field, the spatial coordinates of hard obstacles (such as boulders and concrete blocks) in the borehole path are located.
[0039] A drilling operation correction control method using a drilling rig intelligent operation system according to this embodiment, as shown in Figure 1, includes the following steps: Real-time acquisition of mechanical vibration signals, torque signals, spatial displacement signals, thermal field signals, and acoustic signals during the drilling operation; Spatiotemporal fusion processing of the above signals to extract working condition feature vectors; Determination of the risk level of stuck drill bit; When the preset risk level is reached, acquisition of image signals to construct a three-dimensional model inside the borehole and determination of the offset of the borehole trajectory; Simulation of different handling strategies in a virtual environment to obtain the optimal solution and generate adjustment instructions.
[0040] When the preset risk level is reached, refer to Table 2 for processing. For example, when a high-risk stuck drill warning is triggered, the drive mechanism pushes the telescopic high-definition camera to the stuck drill position, and at the same time turns on the active supplementary light source to adapt to the low-light environment inside the borehole; Obstacle point cloud data is obtained based on binocular vision and structured light scanning technology, and the obstacle type (such as rock blocks, metal foreign objects) is distinguished by semantic segmentation algorithm, and its geometric size and embedding depth are calculated; Preset processing schemes are matched for obstacle types (such as adjusting the drill bit speed for impact crushing, and starting lateral vibration to assist in getting out of trouble), and the feasibility of the strategy is verified by the virtual simulation module.
[0041] Low-risk and medium-risk cases are handled according to Table 2.
[0042] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention. Instruction manual 7 / 7 pages 10 CN 120946306 A Figure 1 Instruction manual drawing 1 / 1 page 11 CN 120946306 A AN INTELLIGENT DRILLING RIGOPERATION SYSTEM AND A METHOD FOR DEVIATION CORRECTION CONTROL Abstract A drilling rig intelligent operation system and deviation control method in the field of mechanical automation. The system significantly reduces the response time for identifying and resolving stuck-pipe incidents through multi-dimensional signal fusion and dynamic risk assessment, thereby avoiding efficiency losses caused by traditional shutdown protection measures. By employing imaging and obstacle localization technologies to replace manual drill-out inspections, the system reduces equipment idle time and mechanical wear. Multi-physics signal fusion technology overcomes the limitations of single-sensor perception, facilitating the precise identification of the spatial distribution of underground obstacles and sudden changes in geological interfaces. Analysis of acoustic spectra and thermal field distributions enhances the ability to analyze the dynamics of bit-formation interaction, providing multi-dimensionalinput for intelligent decision-making. Combined with a virtual-physical interaction mechanism, this enables the dynamic optimization of control strategies to adapt to changing requirements across different formation conditions and construction phases. .
Claims
1. A drilling rig intelligent operation system, characterized in that, include: The acquisition module is used to acquire mechanical vibration signals, torque signals, spatial displacement signals, thermal field signals, acoustic signals, and image signals during the drilling process; The analysis module processes the signals acquired by the acquisition module to obtain the temporal evolution law of torque-displacement, displacement stagnation characteristics, vibration spectrum, thermal field distribution map, and acoustic time-frequency map. It then fuses these with a dynamic filtering algorithm and a machine learning model to generate a borehole condition feature vector. A stuck drill risk classification mechanism is established based on a historical working condition model library, and a stuck drill risk level is generated. Based on the abrupt change law of the vibration spectrum, the attenuation law of the thermal field distribution map, and the acoustic time-frequency map, the formation type and interface location are generated. A three-dimensional model of the borehole interior is constructed based on the image signals. The spatial coordinates of hard obstacles in the borehole path are located based on the energy reflection characteristics of the acoustic signals and the local temperature anomalies of the thermal field signals. A time-series prediction model of the borehole trajectory is constructed based on the nonlinear relationship between the torque growth rate and the displacement stagnation duration. Finally, borehole trajectory deviation characteristics are identified. The virtual simulation module establishes a coupled dynamic model of the drill pipe and the geological environment, optimizes the handling strategy through simulation and feeds it back to the decision-making module; it also simulates the equipment response under extreme working conditions, generates safety protection strategies and feeds them back to the decision-making module. The decision-making module determines whether to correct the deviation based on the risk level of the stuck drill; it generates a handling strategy and forms an adjustment instruction.
2. The intelligent drilling rig operation system according to claim 1, characterized in that, The data acquisition module includes: Vibration detection sensors collect mechanical vibration signals, including axial vibration, radial vibration, and circumferential vibration signals of the drill pipe. A laser displacement sensor and an inertial measurement unit are used to acquire spatial displacement signals of the drill bit; The thermal field detection unit is used to collect data on the temperature field distribution inside the borehole wall; The acoustic signature acquisition unit is used to acquire acoustic signals during the drilling process; Imaging components are used to acquire image signals.
3. The intelligent drilling rig operation system according to claim 2, characterized in that, The vibration spectrum is obtained by wavelet packet decomposition of the axial, radial and circumferential vibration signals of the drill pipe. The energy ratio of the low frequency band of 0-200Hz and the high frequency band of greater than 500Hz is extracted to calculate the spectral kurtosis and obtain the vibration spectrum change law. The temperature field inside the borehole wall is divided into regions, the radial temperature gradient is calculated, and a thermal field distribution map is obtained. By extracting the formation resonance peak from the acoustic time-frequency diagram using the Mel spectrum cepstral coefficients, the acoustic energy attenuation rate is calculated, and the attenuation rate variation law is obtained.
4. The intelligent drilling rig operation system according to claim 3, characterized in that, A feature weight allocation model based on an attention mechanism is constructed to fuse vibration spectrum energy, thermal field distribution, and acoustic energy attenuation rate across dimensions to generate a multi-dimensional working condition feature vector.
5. The intelligent drilling rig operation system according to claim 4, characterized in that, The dynamic weighting range for vibration spectrum energy is 0.4-0.6, the dynamic weighting range for thermal field distribution is 0.3-0.5, and the dynamic weighting range for acoustic energy attenuation rate is 0.2-0.
4.
6. The intelligent drilling rig operation system according to claim 3, characterized in that, Based on the intensity of vibration spectrum mutation, the area of abnormal thermal field region, and the acoustic signal energy attenuation rate, combined with a historical working condition pattern database, a three-level stuck drill risk classification mechanism is established, consisting of low, medium, and high risk levels.
7. A drilling rig intelligent operation system according to any one of claims 1-6, characterized in that, A convolutional neural network was used to perform joint feature learning on the vibration spectrum, thermal field distribution map, and acoustic time-frequency map to obtain the stratigraphic type and its interface location.
8. A method for correcting deviations during drilling operations, characterized in that, The drilling rig intelligent operation system as described in any one of claims 1-7 includes: Real-time acquisition of mechanical vibration signals, torque signals, spatial displacement signals, thermal field signals, and acoustic signals during drilling operations; The above signals are subjected to spatiotemporal fusion processing to extract the operating condition feature vector; Determine the risk level of a stuck drill bit; When the preset risk level is reached, image signals are acquired to construct a three-dimensional model of the borehole interior and determine the offset of the borehole trajectory. The optimal solution is obtained by simulating different handling strategies in a virtual environment, and adjustment instructions are generated.
9. The drilling operation correction control method according to claim 8, characterized in that, Spatiotemporal fusion processing includes signal denoising and feature enhancement operations; virtual environment simulation includes dynamic response prediction of geological conditions.
10. The drilling operation correction control method according to claim 8, characterized in that, The execution effect data of the adjustment instructions is sent back to the virtual simulation module to form learning samples, and the decision weight parameters of the machine learning model are updated regularly.