Intelligent torque control method suitable for high-torque drilling screw rod

Abstract

The application relates to the technical field of drilling engineering, and specifically discloses an intelligent torque control method suitable for a high-torque drilling screw rod, which comprises the following steps: collecting drilling column vibration frequency data and drilling pressure adjustment frequency data in real time during drilling, combining sparse Fourier transformation to extract zero sequence vibration frequency characteristic values, evaluating the stability of drilling column vibration, simultaneously utilizing empirical mode decomposition and Hilbert transformation to analyze differential adjustment frequency, constructing differential adjustment frequency characteristic values to measure the stability of drilling pressure adjustment, fusing the above two characteristic values into a comprehensive characteristic vector, inputting the comprehensive characteristic vector into a risk prediction model trained based on a random forest algorithm, intelligently identifying whether the drilling column is in a high-frequency resonance critical state, and automatically triggering a torque compensation mechanism when it is determined that there is a resonance risk, and dynamically adjusting drilling pressure and torque output.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of drilling engineering, in particular to an intelligent torque control method suitable for high-torque drilling screw rods. BACKGROUND

[0002] In the process of modern oil, gas and unconventional energy exploration and development, drilling operations are facing challenges such as complex formations, increased well depth, and increased demand for drilling pressure and torque, especially in high-torque drilling conditions. The drill string system bears a large torsional stress and periodic vibration. As the core power device in directional drilling, the screw rod is prone to high-frequency resonance due to factors such as geological mutations, improper drilling pressure adjustment, or coupling of excitation frequency and natural frequency, which can cause severe torsional vibration of the drill string, leading to drill fatigue fracture, connection thread failure, downhole tool damage, and even wellbore instability, which seriously affects drilling efficiency and safety.

[0003] The prior art has the following disadvantages:

[0004] In the process of high-torque drilling, the intelligent torque control system may cause high-frequency resonance if the natural frequency of the drill string and the formation or downhole tool is coupled. When the system adjusts the torque in real time, the algorithm response delay or sensor data noise may cause the adjustment frequency of the motor power or drilling pressure to coincide with the natural vibration frequency of the drill string, forming a positive feedback loop. At this time, the drill string will accumulate severe torsional vibration energy in a short time, and the local stress will far exceed the material fatigue limit, especially in weak parts such as drill string joints and threads, which may cause instantaneous torsional load concentration, eventually leading to drill string fracture. If the resonance is not inhibited in time, it may also trigger a chain reaction, such as drill tool falling off, wellbore instability, and even downhole tool damage.

[0005] Therefore, it is urgent to propose an intelligent torque control method suitable for high-torque drilling screw rods, which can integrate multi-source sensor data, realize real-time evaluation and intelligent prediction of the drill string vibration state and drilling pressure adjustment stability through advanced signal processing and machine learning technology, and actively intervene in the control parameters before the high-frequency resonance critical state arrives, to improve the safety, stability and intelligent level of drilling operations. SUMMARY

[0006] The purpose of the present application is to provide an intelligent torque control method suitable for high-torque drilling screw rods to solve the problems in the above background.

[0007] The purpose of the present application can be achieved by the following technical solutions:

[0008] An intelligent torque control method suitable for high-torque drilling screw rods, comprising the following steps:

[0009] S1: collecting vibration frequency data and weight adjustment frequency data of the drill string in real time during drilling;

[0010] S2: extracting zero sequence vibration frequency component of the drill string from the vibration frequency data, calculating zero sequence vibration frequency characteristic value according to the change rate of the zero sequence vibration frequency component, and using the zero sequence vibration frequency characteristic value to evaluate the vibration stability of the drill string;

[0011] S3: calculating differential adjustment frequency based on the weight adjustment frequency data, calculating differential adjustment frequency characteristic value according to the change degree of the differential adjustment frequency, and using the differential adjustment frequency characteristic value to evaluate the stability of the weight adjustment;

[0012] S4: constructing the zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value into a comprehensive feature vector, analyzing the comprehensive feature vector using a machine learning model, and determining whether the drill string is in a high-frequency resonance critical state according to the analysis result;

[0013] S5: if the drill string is determined to be in a high-frequency resonance critical state, automatically triggering a torque compensation mechanism to dynamically adjust the weight to ensure safe and stable operation of the drill string.

[0014] Preferably, the process of obtaining the zero sequence vibration frequency characteristic value is as follows:

[0015] During drilling, vibration frequency data of the drill string is obtained, zero sequence vibration frequency component of the drill string is extracted from the vibration frequency data, and zero sequence vibration frequency characteristic value is calculated according to the change rate of the zero sequence vibration frequency component. Specifically, the process includes:

[0016] The time series of the obtained vibration frequency data of the drill string is applied to sparse Fourier transform to obtain its frequency domain sparse representation: ;

[0017] In the formula, sparse spectrum result, x ( n ) represents the time series of the vibration frequency data, n represents the number of collection points, k is the set sparse degree, which represents the number of main frequency components, represents the frequency component;

[0018] The zero sequence vibration frequency component is identified from the sparse spectrum , and the change rate of the corresponding frequency component over time is calculated: ;

[0019] In the formula, zero sequence vibration frequency component, t j represents the j th collection time point, j represents the number of collection time points, represents the zero sequence vibration frequency value extracted at the j represents the zero sequence vibration frequency value extracted at the j

[0020] According to the change rate of the zero sequence vibration frequency value, the zero sequence vibration frequency characteristic value is calculated, and the calculation expression is:

[0021] In the formula, F zv represents the zero sequence vibration frequency characteristic value, m represents the total number of collection time points.

[0022] Preferably, the evaluation of the vibration stability of the drill string specifically includes:

[0023] In the drilling process, it is judged whether the zero sequence vibration frequency characteristic value is greater than or equal to a preset threshold value. If yes, the vibration of the drill string is unstable, and if no, the vibration of the drill string is stable.

[0024] Preferably, the acquisition process of the differential adjustment frequency characteristic value is:

[0025] In the drilling process, the weight-on-bit adjustment frequency data is acquired, the differential adjustment frequency is calculated based on the weight-on-bit adjustment frequency data, and the differential adjustment frequency characteristic value is calculated according to the change degree of the differential adjustment frequency. Specifically, it includes:

[0026] The differential adjustment frequency of the time sequence of the weight-on-bit adjustment frequency data is calculated, specifically the difference between adjacent time points is calculated to obtain the differential adjustment frequency, all the differential adjustment frequencies are integrated into a differential adjustment frequency time sequence, the differential adjustment frequency time sequence is subjected to empirical mode decomposition to obtain a group of intrinsic mode functions, and further Hilbert transform is performed on each mode function to obtain the instantaneous differential adjustment frequency. The calculation expression is:

[0027] In the formula, t represents a time sequence collection point, ω i (t) represents the instantaneous differential adjustment frequency of the i th intrinsic mode function obtained after Hilbert transform, represents Hilbert transform, a represents an imaginary unit, i represents the number of intrinsic mode functions, and arg represents the amplitude angle of a complex number, represents a derivative operation, and IMF represents an intrinsic mode function.

[0028] The standard deviation value of the instantaneous differential adjustment frequency of all intrinsic mode functions is calculated to obtain the differential adjustment frequency characteristic value.

[0029] Preferably, the evaluation of the stability of the weight-on-bit adjustment specifically includes:​​​​

[0030] In the drilling process, it is judged whether the differential adjustment frequency characteristic value is greater than or equal to a preset threshold value, if yes, the drilling pressure adjustment is unstable, and if no, the drilling pressure adjustment is stable.

[0031] Preferably, the use of the machine learning model to analyze the comprehensive feature vector specifically includes:

[0032] The zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value are constructed into a comprehensive feature vector as the input of the machine learning model, to minimize the error between the predicted drill string resonance risk probability value and the actual drill string resonance risk probability, as the training target of the model, and based on the trained model, the drill string resonance risk probability value is output, wherein the machine learning model is a random forest model.

[0033] Preferably, the construction process of the machine learning model is:

[0034] A plurality of sets of historical drilling zero sequence vibration frequency characteristic values, differential adjustment frequency characteristic values and drill string resonance risk probability values are obtained and constructed into a training set, a random forest extracts a plurality of sub-sample sets from the training set through bootstrap sampling, and an independent decision tree is constructed for each sub-sample set. Each decision tree splits and judges based on the zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value in the feature vector. Finally, the probability prediction value of the drill string resonance risk under the current drilling state is obtained by weighted averaging the output results of all decision trees. In the training process, the error between the predicted drill string resonance risk probability value and the actual drill string resonance risk probability is minimized as the objective function, and the structure and splitting rule of each tree in the forest are optimized.

[0035] Preferably, the drill string is determined to be in a high-frequency resonance critical state according to the analysis result, specifically including:

[0036] It is judged whether the drill string resonance risk probability value is greater than or equal to a preset threshold value, if yes, the drill string is in a high-frequency resonance critical state, and if no, the drill string is not in a high-frequency resonance critical state.

[0037] Preferably, the automatic triggering of the torque compensation mechanism dynamically adjusts the drilling pressure, specifically including:

[0038] When the drill string resonance risk probability value output by the machine learning model is greater than or equal to a preset threshold value, the system automatically starts the torque compensation control logic, adjusts the motor output torque curve and the drilling pressure change rate in real time based on the change trend of the current zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value, introduces the frequency offset strategy and the phase lag compensation mechanism, reduces the coupling strength between the excitation frequency and the inherent frequency of the drill string, thereby inhibiting the concentrated release of torsional vibration energy at the weak part of the drill string, preventing drill string fracture accidents caused by high-frequency resonance, and realizing closed-loop feedback control and intelligent early warning of the drilling process.

[0039] The beneficial effects of the present application are:

[0040] (1) The present application constructs a multi-source heterogeneous signal fusion data perception system by collecting drilling string vibration frequency data and drilling pressure adjustment frequency data in real time during drilling, thereby realizing comprehensive monitoring of the drilling dynamic process. On this basis, the sparse Fourier transform (SFT) technology is innovatively introduced to analyze the vibration signal in the frequency domain, accurately extract the zero sequence vibration frequency component reflecting the overall rigid body vibration characteristics of the drilling string, and obtain the zero sequence vibration frequency characteristic value by calculating its time variation rate, which is used to quantify the vibration stability of the drilling string system; at the same time, aiming at the nonlinearity and time-varying characteristics of the drilling pressure adjustment behavior, the method of empirical mode decomposition (EMD) combined with Hilbert transform is used to carry out multi-level time-frequency analysis on the drilling pressure difference adjustment frequency sequence, extract the instantaneous frequency information of each order intrinsic mode function, and construct the difference adjustment frequency characteristic value based on its standard deviation, which effectively represents the stability and fluctuation risk of the drilling pressure control process. Further, the above two types of characteristic values are constructed into a comprehensive feature vector, which is introduced into an intelligent recognition model trained based on the random forest algorithm as an input parameter, and through minimizing the error between the predicted resonance risk probability and the actual state, the high-precision discrimination of whether the drilling string is in a high-frequency resonance critical state is realized. Once the system determines that there is a resonance risk, an adaptive torque compensation mechanism is triggered, the motor output torque curve and the drilling pressure change rate are dynamically optimized, the frequency offset strategy and phase lag compensation mechanism are introduced, the coupling relationship between the excitation frequency and the natural frequency of the drilling string is actively broken, the concentrated release of torsional vibration energy in local structure is prevented, thereby effectively avoiding the occurrence of malignant accidents such as drilling string fracture and downhole tool damage, and significantly improving the safety, stability and intelligent level of high-torque drilling operation under complex working conditions;

[0041] (2) This invention innovatively introduces a drill string resonance risk prediction model based on the random forest algorithm. By collecting multi-dimensional historical data, including zero-sequence vibration frequency feature values, differential adjustment frequency feature values, and corresponding drill string resonance occurrence state labels, a high-quality training sample set is constructed. Multiple sub-sample sets are generated using a bootstrap sampling method, which are then used to train decision tree-based models with diversity and variability. During model training, the objective function is to minimize the mean square error between the predicted drill string resonance risk probability value and the actual risk state. The splitting rules and node judgment logic of each decision tree are optimized, thereby improving the overall model's generalization ability and robustness against interference. The trained random forest model can perform real-time assessment of the resonance risk of the drill string system under the current drilling state, outputting statistically significant probabilistic prediction results, providing a highly reliable basis for the system control logic. Furthermore, this model, together with an automatically triggered torque compensation mechanism, forms a closed-loop response system: when the probability of resonance risk exceeds a preset threshold, the control system rapidly activates a dynamic adjustment strategy. Combining the changing trends of the current zero-sequence vibration frequency and the differential adjustment frequency, it intelligently adjusts the motor output torque curve and the rate of change of drilling pressure, implementing frequency offset and phase lag compensation mechanisms. This effectively suppresses the coupling effect between the excitation frequency and the drill string's natural frequency, breaks the system's positive feedback loop, and prevents the continuous accumulation of torsional vibration energy in weak parts of the drill string. The entire process achieves fully automated closed-loop control from data perception, feature extraction, intelligent identification to adaptive control, significantly improving system response efficiency, control accuracy, and operational safety under high-torque drilling conditions. This provides solid technical support and an engineering practice foundation for the development of intelligent drilling technology under complex geological conditions. Attached Figure Description

[0042] Figure 1 This is a flowchart of the intelligent torque control method for high-torque drilling screws according to the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Please see Figure 1 As shown, this invention provides an intelligent torque control method suitable for high-torque drilling screws, comprising the following steps:

[0045] S1: Real-time acquisition of drill string vibration frequency data and drill pressure adjustment frequency data during the drilling process;

[0046] S2: Extract the zero sequence vibration frequency component of the drill string from the vibration frequency data, calculate the zero sequence vibration frequency characteristic value according to the change rate of the zero sequence vibration frequency component, and use it to evaluate the vibration stability of the drill string;

[0047] S3: Calculate the differential adjustment frequency based on the weight adjustment frequency data, calculate the differential adjustment frequency characteristic value according to the change degree of the differential adjustment frequency, and use it to evaluate the stability of the weight adjustment;

[0048] S4: Construct the zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value into a comprehensive feature vector, analyze the comprehensive feature vector using a machine learning model, and determine whether the drill string is in a high-frequency resonance critical state according to the analysis result;

[0049] S5: If the drill string is determined to be in a high-frequency resonance critical state, automatically trigger a torque compensation mechanism to dynamically adjust the weight to ensure safe and stable operation of the drill string.

[0050] In S1, the vibration frequency data and the weight adjustment frequency data of the drill string are collected in real time during drilling, specifically including:

[0051] During drilling, the vibration frequency data of the drill string is collected in real time, specifically including: installing high-precision three-axis vibration sensors on the drill string body, the vibration sensors are arranged at intervals along the axial direction of the drill string, for continuously monitoring the dynamic vibration response of the drill string during drilling; the vibration sensors collect acceleration time series signals of each part of the drill string, and convert the analog signals into digital signals through the built-in filter and analog-to-digital conversion module, and are uniformly time-stamped and stored by the data acquisition system, forming a complete drill string vibration frequency data sequence.

[0052] Further, the acquisition of the weight adjustment frequency data includes: integrating pressure sensors and speed encoders in the output end of the drilling motor and the top drive system, and collecting the instantaneous weight value and its change frequency applied to the drill string during the weight adjustment process in real time; the data acquisition system performs sliding window differential processing on the weight signal, extracts the weight fluctuation period within a unit time, calculates the time series data of the weight adjustment frequency, and synchronously archives it with the vibration frequency data for subsequent feature extraction and resonance risk assessment.

[0053] In S2, the zero sequence vibration frequency component of the drill string is extracted from the vibration frequency data, and the zero sequence vibration frequency characteristic value is calculated according to the change rate of the zero sequence vibration frequency component, which is used to evaluate the vibration stability of the drill string, specifically including:

[0054] During drilling, the vibration frequency data of the drill string is obtained, the zero sequence vibration frequency component of the drill string is extracted from the vibration frequency data, and the zero sequence vibration frequency characteristic value is calculated according to the change rate of the zero sequence vibration frequency component, specifically including:

[0055] The time series of vibration frequency data of the acquired drill string is subjected to sparse Fourier transform to obtain its sparse frequency domain representation: ;

[0056] In the formula, This represents the sparse spectrum results. x ( n This represents the time series of vibration frequency data. n Indicates the number of collection points. k The sparsity is set to represent the number of principal frequency components. Indicates frequency components;

[0057] Identifying zero-sequence vibrational frequency components from sparse spectra And calculate the rate of change of the corresponding frequency components over time: ;

[0058] In the formula, Represents the zero-sequence vibrational frequency component. t j Indicates the first j Each data collection time point j Indicates the number of data collection points. Indicates the first j Zero-sequence vibration frequency values ​​extracted at each acquisition time point. Indicates the first j The rate of change of the zero-sequence vibration frequency value at each acquisition time point;

[0059] The characteristic value of the zero-sequence vibration frequency is calculated based on the rate of change of the zero-sequence vibration frequency value. The calculation expression is as follows: ;

[0060] In the formula, F zv Represents the characteristic value of zero-sequence vibration frequency. m This indicates the total number of data collection points.

[0061] During the drilling process, it is determined whether the zero-sequence vibration frequency characteristic value is greater than or equal to a preset threshold. If it is, the vibration of the drill string is unstable; otherwise, the vibration of the drill string is stable.

[0062] It should be noted that: the application extracts the main frequency component in the frequency domain by collecting the drilling string vibration frequency data time series, and identifies the zero sequence vibration frequency representing the overall rigid body vibration mode of the drilling string from the main frequency component; further, the rate of change of the frequency with time is calculated, and the zero sequence vibration frequency characteristic value is obtained by accumulating the absolute value of the rate of change, which is used to quantify the stability of the drilling string vibration; when the characteristic value exceeds the preset threshold value, it is determined that the drilling string vibration tends to be unstable, and there may be a resonance risk. The advantages of the technical scheme of the application are: the application has higher calculation efficiency and frequency resolution, and is especially suitable for feature extraction of non-stationary and short-time signals; combined with the dynamic evaluation mechanism of the zero sequence frequency change rate, the vibration abnormal trend of the drilling string system can be identified earlier and more accurately, and reliable basis is provided for subsequent intelligent early warning and control. The innovation lies in that the sparse Fourier transform is first introduced into the drilling vibration monitoring field, and a vibration stability evaluation index system with the zero sequence frequency characteristic value as the core is constructed, and the perception ability and response accuracy of the resonance risk in the high-torque drilling process are improved.

[0063] In S3, the differential adjustment frequency is calculated based on the drilling pressure adjustment frequency data, and the differential adjustment frequency characteristic value is calculated according to the change degree of the differential adjustment frequency, which is used to evaluate the stability of the drilling pressure adjustment, specifically including:

[0064] In the drilling process, the drilling pressure adjustment frequency data is obtained, the differential adjustment frequency is calculated based on the drilling pressure adjustment frequency data, and the differential adjustment frequency characteristic value is calculated according to the change degree of the differential adjustment frequency, specifically including:

[0065] The differential adjustment frequency of the time series of the drilling pressure adjustment frequency data is calculated, specifically the difference between adjacent time points is calculated to obtain the differential adjustment frequency, all differential adjustment frequencies are integrated into a differential adjustment frequency time series, the differential adjustment frequency time series is subjected to empirical mode decomposition to obtain a group of intrinsic mode functions, and further Hilbert transform is performed on each mode function to obtain the instantaneous differential adjustment frequency, and the calculation expression is: ;

[0066] In the formula, t represents the time series acquisition point, ω i (t) represents the instantaneous differential adjustment frequency of the i th intrinsic mode function obtained after Hilbert transform, represents Hilbert transform, a represents an imaginary unit, i represents the number of intrinsic mode functions, arg represents the amplitude angle of a complex number, represents derivative operation, and IMF represents intrinsic mode function.

[0067] The standard deviation value of the instantaneous differential adjustment frequency of all intrinsic mode functions is calculated to obtain the differential adjustment frequency characteristic value.

[0068] In the drilling process, it is judged whether the differential adjustment frequency characteristic value is greater than or equal to a preset threshold value, if yes, the drilling pressure adjustment is unstable, and if no, the drilling pressure adjustment is stable.

[0069] It should be noted that: the present application carries out time series difference processing on the real-time collected drilling pressure adjustment frequency data to obtain a differential adjustment frequency sequence; then the empirical mode decomposition is used to decompose it into a plurality of intrinsic mode functions, and then the Hilbert transform is performed on each order intrinsic mode function to obtain the instantaneous frequency information thereof, and the standard deviation of each order frequency is calculated, and finally the differential adjustment frequency characteristic value is constructed by weighted average for quantifying the dynamic stability in the drilling pressure regulation process. The technical scheme can effectively capture the small but key frequency fluctuation in the drilling pressure adjustment process by combining the nonlinear and non-stationary signal processing means, improve the sensitivity and identification accuracy of the system to the change of the excitation source; the present application constructs a quantitative criterion with the differential adjustment frequency characteristic value as the core, realizes the fine monitoring of the drilling pressure control behavior, thereby providing a more accurate risk judgment basis for intelligent torque control, and significantly improves the safety and reliability of high torque drilling operation.

[0070] In S4, the zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value are constructed into a comprehensive feature vector, the machine learning model is used to analyze the comprehensive feature vector, and according to the analysis result, it is judged whether the drill string is in a high frequency resonance critical state, which specifically includes:

[0071] The zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value are constructed into a comprehensive feature vector as the input of the machine learning model, and the error between the predicted drill string resonance risk probability value and the actual drill string resonance risk probability is minimized as the training target of the model, and based on the trained model, the drill string resonance risk probability value is output, wherein the machine learning model is a random forest model.

[0072] A plurality of sets of historical drilling zero sequence vibration frequency characteristic values, differential adjustment frequency characteristic values and drill string resonance risk probability values are obtained, and are constructed into a training set, the random forest extracts a plurality of sub-sample sets from the training set through bootstrap sampling, and constructs an independent decision tree for each sub-sample set, each decision tree splits and judges based on the zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value in the feature vector, and finally the weighted average of all decision tree output results is obtained to obtain the probability prediction value of the drill string resonance risk under the current drilling state, and the error between the predicted drill string resonance risk probability value and the actual drill string resonance risk probability is minimized as the objective function in the training process, and the structure and splitting rule of each tree in the forest are optimized.

[0073] After the training is completed, the real-time zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value are constructed into a comprehensive feature vector, which is input into the trained random forest model, and the drill string resonance risk probability value of the drill string system under the current drilling state is output. The drill string resonance risk probability value can be used as an important basis for subsequent control strategy adjustment. When the risk probability exceeds the preset threshold, the system automatically triggers the dynamic adjustment mechanism of torque and weight on bit, realizes the closed-loop early warning and active inhibition of drill string resonance risk.

[0074] In S5, if it is determined that the drill string is in a high-frequency resonance critical state, a torque compensation mechanism is automatically triggered to dynamically adjust the weight on bit, ensuring the safe and stable operation of the drill string, specifically including:

[0075] During drilling, when the system determines that the drill string is in a high-frequency resonance critical state, i.e., the drill string resonance risk probability value output by the machine learning model is greater than or equal to the preset threshold, the torque compensation mechanism is automatically triggered. The torque compensation mechanism sends dynamic adjustment instructions to the drilling motor and weight on bit adjustment device through the control module, specifically including the following control strategies:

[0076] Firstly, based on the change trend of the current zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value, the system evaluates the vibration energy accumulation rate and excitation frequency deviation direction of the drill string system in real time; secondly, according to the evaluation result, the motor output torque curve is dynamically adjusted, and a nonlinear gradual torque reduction strategy is adopted to avoid the impact of instantaneous large torque fluctuation on the drill string; at the same time, the control system synchronously adjusts the weight on bit change rate, introduces a frequency deviation strategy to make the excitation frequency actively deviate from the natural vibration frequency of the drill string, and reduces the resonance coupling possibility; in addition, a phase lag compensation mechanism is added to the control logic, which applies a small time delay to the weight on bit and torque adjustment actions to break the positive feedback cycle of the system and further weaken the continuous accumulation of torsional vibration energy; the whole process continuously monitors the drill string vibration state and control effect through a closed-loop feedback mechanism, and dynamically optimizes the control parameters according to the real-time acquisition data to ensure that the drill string can still maintain safe and stable operation under the high-frequency resonance critical state, preventing the occurrence of downhole accidents such as drill string rupture and tool damage caused by resonance.

[0077] The present application also includes an intelligent torque control method experimental data comparison table suitable for high-torque drilling screw rods:

[0078]

[0079] Data analysis and optimization effect summary:

[0080] Torque stability: the intelligent torque control method reduces the average torque fluctuation to 50 Nm and the maximum torque peak to 200 Nm, significantly improving the stability and controllability of the torque.

[0081] Drilling efficiency: With more precise torque control, drilling speed is increased from 15 meters per hour to 20 meters per hour, increasing by 33.3%, while drilling cycle is shortened by 25%.

[0082] Equipment durability: Tool wear rate is reduced from 10% to 4%, significantly extending the service life of drill bits and other key components, reducing maintenance costs.

[0083] Energy saving: Intelligent control system reduces unit energy consumption by 25%, helping to save energy and reduce emissions, and improving energy utilization efficiency.

[0084] System response capability: System response time is shortened from 30 seconds to 10 seconds, significantly enhancing the rapid response capability to underground environment changes, ensuring the safety and efficiency of the drilling process.

[0085] Drilling success rate: Drilling success rate is increased to 95%, and accident risk is reduced from 0.05 times per drilling to 0.01 times, indicating that the intelligent torque control method has significant advantages in improving operation safety.

[0086] The intelligent torque control method for high-torque drilling screw provided by the embodiment of the present application realizes intelligent identification of whether the drill string is in a high-frequency resonance critical state by real-time acquisition of drill string vibration frequency data and drilling pressure adjustment frequency data, extraction of zero sequence vibration frequency eigenvalue combined with sparse Fourier transform, analysis of differential adjustment frequency by empirical mode decomposition and Hilbert transform, and construction of a comprehensive feature vector input into a risk prediction model trained based on a random forest algorithm. When it is determined that there is a resonance risk, the system automatically triggers a torque compensation mechanism to dynamically adjust the drilling pressure and torque output, ensuring the safe and stable operation of the drill string.

[0087] The working principle of the present application: the present application aims to solve the technical problems of high-frequency resonance caused by the mismatch between drill string vibration and weight adjustment, leading to drill tool damage and even downhole accidents in the existing drilling process. The technical solution of the present application includes the following core steps: first, in the drilling process, through the multi-point three-axis vibration sensor installed on the drill string body and the pressure sensor and rotational speed encoder at the drill motor end, real-time acquisition of drill string vibration frequency data and weight adjustment frequency data; second, using sparse Fourier transform (SFT) to extract the zero sequence vibration frequency component from the vibration frequency data, and calculating the characteristic value of the zero sequence vibration frequency corresponding to the change rate, which is used to quantify the overall vibration stability of the drill string; at the same time, the difference processing is carried out on the weight adjustment frequency time series, and the instantaneous frequency information is extracted by combining empirical mode decomposition (EMD) and Hilbert transform, and the difference adjustment frequency characteristic value is further calculated to evaluate the stability of the weight adjustment behavior; then, the above two characteristic values are constructed into a comprehensive feature vector, which is input into the machine learning model trained based on the random forest algorithm to predict whether the drill string system is in a high-frequency resonance critical state; if it is determined that there is a resonance risk, the torque compensation mechanism is automatically triggered, the motor output torque and the weight change rate are dynamically adjusted, the frequency offset and phase lag compensation strategy is introduced, the coupling relationship between the excitation frequency and the natural frequency is broken, and the concentrated release of torsional vibration energy is suppressed. The present application combines signal processing, feature extraction and machine learning modeling technology to build a closed-loop intelligent torque control system, realizes early identification and active intervention of drill string resonance risk, and significantly improves the safety, stability and intelligent level of high-torque drilling operation.

[0088] The above formulas are dimensionless numerical calculations, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the latest real situation, and the preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0089] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0090] It should be understood that the term "and / or" herein merely describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents that the associated objects before and after are an "or" relationship, but can also represent an "and / or" relationship, which can be understood according to the context before and after.

[0091] It should be understood that in various embodiments of the present application, the size of the sequence number of each process described above does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0092] The above describes one embodiment of the present application in detail, but the content described is only the preferred embodiment of the present application, and cannot be considered as limiting the scope of the implementation of the present application. Any equivalent changes and improvements made within the scope of the present application are still within the scope of the patent coverage of the present application.

Claims

1. An intelligent torque control method suitable for high torque drilling screw rods, characterized in that, The method comprises the following steps: S1: collecting vibration frequency data and drilling pressure adjustment frequency data of the drill string in real time during drilling; S2: extracting zero sequence vibration frequency components of the drill string from the vibration frequency data, and calculating a zero sequence vibration frequency characteristic value according to a change rate of the zero sequence vibration frequency components, which is used to evaluate the vibration stability of the drill string; The process of obtaining the zero sequence vibration frequency characteristic value is as follows: During drilling, the vibration frequency data of the drill string is obtained, the zero sequence vibration frequency components of the drill string are extracted from the vibration frequency data, and the zero sequence vibration frequency characteristic value is calculated according to the change rate of the zero sequence vibration frequency components, which specifically comprises: The time series of the acquired vibration frequency data of the drill string is applied with a sparse Fourier transform to obtain a sparse representation in the frequency domain: ; wherein denotes the sparse spectrum result, x denotes the time series of vibration frequency data, n denotes the number of acquisition points, n denotes the set sparsity, k denotes the number of main frequency components, denotes the frequency components; Identifying zero sequence vibration frequency components from sparse spectra and calculating the rate of change of the corresponding frequency components over time: ; In the formula, denotes the zero sequence oscillation frequency component, t j denotes the j acquisition time point, j denotes the number of acquisition time points, denotes the j acquisition time point, denotes the zero sequence oscillation frequency value of the j acquisition time point; According to the change rate of the zero sequence vibration frequency value, a zero sequence vibration frequency characteristic value is calculated, and the calculation expression is: ; In the formula, F zv denotes the zero sequence oscillation frequency eigenvalue, m denotes the total number of acquisition time points; S3: calculating a differential adjustment frequency based on the drilling pressure adjustment frequency data, and calculating a differential adjustment frequency characteristic value according to a change degree of the differential adjustment frequency, which is used to evaluate the stability of the drilling pressure adjustment; The process of obtaining the differential adjustment frequency characteristic value is as follows: During drilling, the drilling pressure adjustment frequency data is obtained, the differential adjustment frequency is calculated based on the drilling pressure adjustment frequency data, and the differential adjustment frequency characteristic value is calculated according to the change degree of the differential adjustment frequency, which specifically comprises: The difference adjustment frequency of the time sequence of the drilling pressure adjustment frequency data is calculated, specifically, the difference value between adjacent time points is calculated to obtain the difference adjustment frequency, all the difference adjustment frequencies are integrated into a difference adjustment frequency time sequence, the difference adjustment frequency time sequence is subjected to empirical mode decomposition to obtain a group of intrinsic mode functions, and further Hilbert transform is performed on each mode function to obtain an instantaneous difference adjustment frequency, and the expression is: ; wherein t represents a time series collection point, ω i (t) represents an instantaneous differential adjusted frequency of the i-th intrinsic mode function obtained after Hilbert transform, represents Hilbert transform, a represents an imaginary unit, i represents the number of intrinsic mode functions, and arg represents the amplitude angle of a complex number, represents a derivative operation, and IMF represents an intrinsic mode function; calculating the standard deviation of the instantaneous differential adjustment frequency of all inherent modal functions to obtain the differential adjustment frequency characteristic value; S4: constructing a comprehensive feature vector by using the zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value, analyzing the comprehensive feature vector by using a machine learning model, and determining whether the drill string is in a high-frequency resonance critical state according to an analysis result; S5: if it is determined that the drill string is in the high-frequency resonance critical state, automatically triggering a torque compensation mechanism to dynamically adjust the drilling pressure, and ensuring safe and stable operation of the drill string; The torque compensation mechanism automatically triggered by the system dynamically adjusts the drilling pressure, and specifically comprises: When the drill string resonance risk probability value output by the machine learning model is greater than or equal to a preset threshold value, the system automatically starts a torque compensation control logic, adjusts the motor output torque curve and the drilling pressure change rate in real time based on the change trend of the current zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value, introduces a frequency offset strategy and a phase lag compensation mechanism, reduces the coupling strength between the excitation frequency and the inherent frequency of the drill string, thereby inhibiting the concentrated release of torsional vibration energy at weak parts of the drill string caused by high-frequency resonance, preventing drill string fracture accidents caused by high-frequency resonance, and realizing closed-loop feedback control and intelligent early warning of the drilling process.

2. The intelligent torque control method suitable for high torque drilling screw rod according to claim 1, characterized in that, The evaluation of the vibration stability of the drill string specifically comprises: During drilling, it is determined whether the zero sequence vibration frequency characteristic value is greater than or equal to a preset threshold value, if yes, the vibration of the drill string is unstable, and if no, the vibration of the drill string is stable.

3. The intelligent torque control method suitable for high torque drilling screw rod according to claim 1, characterized in that, The evaluation of the stability of the drilling pressure adjustment specifically comprises: During drilling, it is determined whether the differential adjustment frequency characteristic value is greater than or equal to a preset threshold value, if yes, the drilling pressure adjustment is unstable, and if no, the drilling pressure adjustment is stable.

4. The intelligent torque control method suitable for high torque drilling screw rod of claim 1, wherein, The analysis of the comprehensive feature vector by using the machine learning model specifically comprises: The zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value are constructed into a comprehensive characteristic vector as an input of a machine learning model to minimize an error between a predicted drill string resonance risk probability value and an actual drill string resonance risk probability as a training target of the model, and the trained model is used to output the drill string resonance risk probability value, wherein the machine learning model is a random forest model.

5. The intelligent torque control method suitable for high torque drilling screw rod according to claim 4, characterized in that, The machine learning model is constructed in the following process: A plurality of sets of historical drilling zero sequence vibration frequency characteristic values, differential adjustment frequency characteristic values and drill string resonance risk probability values are obtained and constructed into a training set, a plurality of sub-sample sets are extracted from the training set by self-sampling of the random forest, and an independent decision tree is constructed for each sub-sample set, each decision tree is split and judged based on the zero sequence vibration frequency characteristic value and the differential adjustment frequency characteristic value in the characteristic vector, and finally a weighted average of all decision tree output results is obtained to obtain a probability prediction value of the drill string resonance risk under the current drilling state, and the error between the predicted drill string resonance risk probability value and the actual drill string resonance risk probability is minimized as an objective function in the training process to optimize the structure and splitting rule of each tree in the forest.

6. The intelligent torque control method suitable for high torque drilling screw rods of claim 5, wherein, The analysis result is used to determine whether the drill string is in a high-frequency resonance critical state, and specifically includes: It is determined whether the drill string resonance risk probability value is greater than or equal to a preset threshold value, if yes, the drill string is in a high-frequency resonance critical state, and if not, the drill string is not in a high-frequency resonance critical state.

Images