Fatigue monitoring method for directional drilling

Abstract

The invention belongs to the technical field of directional drilling monitoring, and particularly relates to a directional drilling fatigue monitoring method, which comprises the following steps of: 1, data acquisition: mounting a plurality of sensors on a drilling rod and a rotating bearing of directional drilling equipment to monitor the working state of the directional drilling equipment in real time; the sensor selection comprises a strain sensor, a vibration sensor, a temperature sensor and a pressure sensor and is used for collecting strain signal data, vibration signal data, temperature data and pressure data. According to the directional drill fatigue monitoring method, the operation state and the fatigue degree of equipment are comprehensively monitored by integrating various sensors and a fatigue analysis algorithm. Real-time monitoring and alarming are achieved, construction parameters are dynamically adjusted, and equipment faults caused by fatigue damage are avoided. The operation safety of directional drilling equipment is improved, and sudden failures caused by fatigue damage are avoided. The service life of equipment is prolonged, and the maintenance cost and the construction risk are reduced.

Description

Technical Field

[0001] This invention relates to the field of directional drilling monitoring technology, and more particularly to a method for fatigue monitoring in directional drilling. Background Technology

[0002] Directional drilling technology is widely used in trenchless construction, such as underground pipeline laying, engineering construction under complex geological conditions such as crossing rivers or buildings. During long-term use, the key components of directional drilling equipment (such as drill rods, rotary bearings, hydraulic systems, etc.) gradually suffer fatigue damage due to being subjected to complex load conditions (such as periodic impacts, tension, torque, etc.).

[0003] In existing technologies, fatigue monitoring techniques mostly rely on post-event detection, such as manual disassembly and inspection or using a single measurement method (such as ultrasonic testing) to assess equipment fatigue. This approach has the following drawbacks:

[0004] Delayed monitoring, failing to obtain real-time fatigue status of key components, may lead to the accumulation of hidden dangers and eventual component failure. The lack of dynamic data support during construction makes it difficult to adjust construction parameters based on equipment fatigue status, reducing construction efficiency. Furthermore, the monitoring system has low integration with the directional drilling equipment, failing to form a unified monitoring, feedback, and control closed loop.

[0005] Therefore, a fatigue monitoring method for directional drilling is needed. Summary of the Invention

[0006] Based on existing technical problems, this invention proposes a method for fatigue monitoring in directional drilling.

[0007] The present invention proposes a fatigue monitoring method for directional drilling, comprising the following steps: Step 1, data acquisition: multiple sensors are installed on the drill rod and rotary bearing of the directional drilling equipment to monitor its working status in real time; the sensor selection includes strain sensors, vibration sensors, temperature sensors and pressure sensors, used to acquire strain signal data, vibration signal data, temperature data and pressure data; the signal from each sensor is transmitted through the data acquisition system;

[0008] Step 2, Data Preprocessing: The data acquisition module preprocesses the raw sensor data, including denoising, normalization, and feature extraction, to ensure that the data is suitable for subsequent fatigue analysis.

[0009] Step 3: Fatigue Analysis: Based on the collected strain, vibration, temperature and pressure data, the fatigue damage degree and remaining service life of key components are calculated using the fatigue cumulative damage theory.

[0010] Step 4: Real-time monitoring and feedback: Display fatigue status in real time, and display relevant parameters such as strain, vibration, temperature and pressure through a graphical interface, and dynamically adjust construction parameters based on monitoring data;

[0011] Step 5: Alarm and Recording: When fatigue damage reaches the set warning value, an audible and visual alarm is triggered, and the relevant data is saved to the storage module for subsequent analysis.

[0012] Preferably, in step one, the strain signal data is calculated from the strain ε and stress using Hooke's law: σ 应力 =E·ε, where σ 应力 Let E be the stress, and E be the elastic modulus of the material.

[0013] Vibration signal data is obtained by calculating the amplitude and frequency of the vibration using Fourier transform, and is used for spectrum analysis. The calculation formula used is as follows: Where X(f) is the spectrum, representing the amplitude and phase of the signal x(t) at frequency f, x(t) is the time-domain signal, f is the frequency, t is the time, and e -j2πft is a complex exponential function representing the phase information of the frequency component, where j is the imaginary unit, 2πft is the time-dependent term of the phase, and dt is the differential element in the integral operation, used to represent the small change in the integral variable t.

[0014] Preferably, in step two, wavelet transform is used for denoising. The wavelet transform formula is: Where W(a,b) is the small filter transform, ψ is the mother wavelet, a is the scale, and b is the translation;

[0015] Normalization: To ensure consistency in units and dimensions across different sensors, normalization is performed using the following formula: Where x(t1) is the original data, x norm (t) represents the normalized data.

[0016] Preferably, feature extraction: Statistical features, such as mean and standard deviation, are used to extract features from the sensor data. The mean formula used is: Where μ is the mean of the data, N is the total number of data points, and xi is the value of the i-th data point. To sum over all data points;

[0017] The standard deviation formula used is: Where, σ 标准差 Let (xi-μ) be the standard deviation of the data. 2 The square of the difference between each data point and the mean. This is the sum of the squares of the differences between all data points and the mean.

[0018] Preferably, the damage calculation based on strain and vibration data in step three involves determining the fatigue life under each load condition by calculating the cyclic life of the material at different stress levels. The calculation formula used is as follows: Among them, D ε Represented as total fatigue damage based on strain data, it is the accumulation of damage from all strain-loaded cycles; n i (ε) represents the number of strain cycles performed in the i-th strain cycle; N(ε) i ) indicates that at a specific strain amplitude ε i The fatigue life of a material refers to the maximum number of cycles a component can withstand; n represents the total number of strain load cycles.

[0019] Preferably, the damage calculation based on temperature and pressure data involves using the influence coefficients of temperature and pressure, and correcting the fatigue life of the material using a correction factor. The calculation formula is: N adjusted =N·f(T,P), where f(T,P) represents the influence factors of temperature and pressure on fatigue life, T is temperature, and P is pressure.

[0020] Preferably, the remaining life is calculated as follows: After obtaining the total fatigue damage D, the remaining life is calculated using the following formula: Where R represents the proportion of remaining lifetime, N total n represents the total fatigue life of the material. a This represents the number of load cycles that have been performed.

[0021] Preferably, the relationship between remaining lifespan and damage is expressed by the following formula: Where, n current This indicates the number of load cycles the component has experienced. When D = 1, it means the component has fatigued and is no longer usable. When D < 1, it means the component still has a certain service life.

[0022] Preferably, in step four, the drilling speed is adjusted according to the degree of fatigue damage, and the calculation formula used is: v adjusted =v current ×(1-αD), where, v adjusted This represents the adjusted drilling speed, v. current This represents the current drilling speed, and α represents the adjustment coefficient.

[0023] Preferably, in step five, the fatigue damage warning value is set to D. critical When D≥D critical When this happens, an alarm is triggered;

[0024] Data logging: Save each piece of data collected and the alarm trigger time for easy subsequent analysis and review: using the following formula: Record = {time, sensor_data, D}, where Record represents the record and includes time, sensor data, and fatigue damage.

[0025] The beneficial effects of this invention are as follows:

[0026] This invention integrates multiple sensors and fatigue analysis algorithms to comprehensively monitor the operating status and fatigue level of equipment. It enables real-time monitoring and alarms, dynamically adjusts construction parameters, and avoids equipment failures caused by fatigue damage. This improves the operational safety of directional drilling equipment, preventing sudden malfunctions due to fatigue damage. It extends the equipment's service life, reduces maintenance costs and construction risks, and achieves real-time monitoring and dynamic feedback, thereby improving construction efficiency and quality. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of a fatigue monitoring method for directional drilling;

[0028] Figure 2 This is a flowchart of a data acquisition process for a fatigue monitoring method in directional drilling.

[0029] Figure 3 This is a flowchart of a data preprocessing process for a fatigue monitoring method in directional drilling.

[0030] Figure 4 This is a flowchart of a fatigue analysis process for a fatigue monitoring method used in directional drilling.

[0031] Figure 5 This is a flowchart of a real-time monitoring and feedback process for a fatigue monitoring method in directional drilling.

[0032] Figure 6 This is a flowchart of an alarm and recording process for a fatigue monitoring method in directional drilling. Detailed Implementation

[0033] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0034] Reference Figures 1-6A fatigue monitoring method for directional drilling includes the following steps: Step 1, data acquisition: Multiple sensors are installed on the drill rod and rotary bearing of the directional drilling equipment to monitor its working status in real time; the sensor selection includes strain sensors, vibration sensors, temperature sensors and pressure sensors, used to collect strain signal data, vibration signal data, temperature data and pressure data; the signal from each sensor is transmitted through the data acquisition system.

[0035] In step one, the strain signal data is calculated from the strain ε and stress using Hooke's law: σ 应力 =E·ε, where σ 应力 Let E be the stress, and E be the elastic modulus of the material.

[0036] Vibration signal data is obtained by calculating the amplitude and frequency of the vibration using Fourier transform, and is used for spectrum analysis. The calculation formula used is as follows: Where X(f) is the spectrum, representing the amplitude and phase of the signal x(t) at frequency f, x(t) is the time-domain signal, f is the frequency, t is the time, and e -j2πft is a complex exponential function representing the phase information of the frequency component, where j is the imaginary unit, 2πft is the time-dependent term of the phase, and dt is the differential element in the integral operation, used to represent the small change in the integral variable t.

[0037] This implementation significantly improves equipment reliability and operational safety, effectively preventing equipment damage and downhole accidents caused by sudden failures such as bearing seizure and drill pipe breakage. The fatigue monitoring system can issue early warnings before fatal component failures occur, ensuring the safety of personnel and equipment, which is crucial, especially in high-cost offshore directional drilling or complex underground engineering projects. It transforms a seemingly chaotic and uninterpretable physical phenomenon (vibration) into a structured, information-rich, and interpretable "fault map" (spectrum), giving the system the ability to "understand" the health language of the equipment. This fundamentally changes the equipment maintenance model, upgrading from passive, time-based, or post-failure repair to proactive, condition-based predictive maintenance, ultimately bringing revolutionary improvements to directional drilling operations in the three core dimensions of safety, cost, and efficiency.

[0038] Step 2, Data Preprocessing: The data acquisition module preprocesses the raw sensor data, including denoising, normalization, and feature extraction, to ensure the data is suitable for subsequent fatigue analysis. In Step 2, wavelet transform is used for denoising. The wavelet transform formula is: Where W(a,b) is the small filter transform, ψ is the mother wavelet, a is the scale, and b is the translation;

[0039] Normalization: To ensure consistency in units and dimensions across different sensors, normalization is performed using the following formula: Where x(t1) is the original data, x norm (t) represents the normalized data.

[0040] Feature extraction: Statistical features, such as mean and standard deviation, are used to extract features from sensor data. The formula for the mean is: Where μ is the mean of the data, N is the total number of data points, and xi is the value of the i-th data point. To sum over all data points;

[0041] The standard deviation formula used is: Where, σ 标准差 Let (xi-μ) be the standard deviation of the data. 2 The square of the difference between each data point and the mean. This is the sum of the squares of the differences between all data points and the mean.

[0042] Specifically, this is implemented through a carefully designed "combination punch" of data preprocessing, transforming raw, coarse sensor data into standardized, refined, and information-rich feature data. Wavelet denoising acts as a "purifier," removing false data and noise to highlight subtle fault signals. Normalization acts as a "standardizer," unifying metrics and enabling data from different sources to work collaboratively, paving the way for data fusion and model analysis. Feature extraction (mean, standard deviation) acts as a "refiner," condensing the essence and reducing the dimensionality of massive amounts of data into interpretable and computable quantitative indicators, directly serving condition monitoring and fault diagnosis.

[0043] Step 3, Fatigue Analysis: Based on the collected strain, vibration, temperature, and pressure data, the fatigue damage degree and remaining service life of key components are calculated using the fatigue cumulative damage theory. The damage calculation based on strain and vibration data in Step 3 involves determining the fatigue life under each load condition by calculating the cycle life of the material at different stress levels. The calculation formula used is as follows: Among them, D ε Represented as total fatigue damage based on strain data, it is the accumulation of damage from all strain-loaded cycles; n i (ε) represents the number of strain cycles performed in the i-th strain cycle; N(ε) i ) indicates that at a specific strain amplitude ε i The fatigue life of a material refers to the maximum number of cycles a component can withstand; n represents the total number of strain load cycles.

[0044] Damage calculation based on temperature and pressure data: The influence coefficients of temperature and pressure are used, and a correction factor is applied to adjust the fatigue life of the material. The calculation formula used is: N adjusted=N·f(T,P), where f(T,P) represents the influence factors of temperature and pressure on fatigue life, T is temperature, and P is pressure.

[0045] Remaining life calculation: After obtaining the total fatigue damage D, the remaining life is calculated using the following formula: Where R represents the proportion of remaining lifetime, N total n represents the total fatigue life of the material. a This represents the number of load cycles that have been performed.

[0046] The relationship between remaining lifespan and damage is expressed by the following formula: Where, n current This indicates the number of load cycles the component has experienced. When D = 1, it means the component has fatigued and is no longer usable. When D < 1, it means the component still has a certain service life.

[0047] Specifically, this approach integrates multi-dimensional data such as strain, vibration, temperature, and pressure, and incorporates fatigue cumulative damage theory and environmental correction factors to construct a scientific and accurate equipment life assessment system. Its benefits and effects are primarily reflected in the following three aspects:

[0048] First, in terms of accuracy and comprehensiveness, this method surpasses the limitations of single-parameter analysis. It not only calculates basic fatigue damage using strain data but also innovatively introduces vibration data to capture the effects of dynamic loads and utilizes temperature and pressure correction factors to quantify the deteriorating effects of harsh working conditions on material properties. This multi-source data fusion and cross-validation makes the damage calculation results closer to real-world conditions, significantly improving the accuracy and reliability of remaining life prediction and providing a "three-dimensional portrait" of the health status of critical components.

[0049] Secondly, in terms of risk management and cost optimization, this method represents a leap from "fuzzy estimation" to "precise quantification." By outputting specific remaining useful life (LR) ratios, managers can clearly understand the "health balance" of components, thereby formulating optimal maintenance and replacement strategies. This effectively avoids asset waste caused by premature replacement, as well as production interruptions or even safety accidents caused by delayed maintenance, achieving the best balance between safety and economy, and significantly reducing the total lifecycle operating costs of equipment.

[0050] Finally, in promoting intelligent operation and maintenance, this method provides core decision-making basis for predictive maintenance. Its quantified damage and lifespan indicators can be directly integrated into the equipment management system, enabling automatic early warning, intelligent scheduling, and maintenance resource optimization. This not only significantly improves operation and maintenance efficiency and reduces unplanned downtime, but the accumulated lifespan data can also feed back into equipment design optimization and operational standard improvement, forming an intelligent closed loop of "monitoring-analysis-decision-optimization." This is a key technological support for promoting the digital and intelligent transformation and upgrading of directional drilling operations.

[0051] Step 4: Real-time Monitoring and Feedback: The fatigue state is displayed in real-time, with relevant parameters such as strain, vibration, temperature, and pressure shown through a graphical interface. Construction parameters are dynamically adjusted based on the monitoring data. In Step 4, the drilling speed is adjusted according to the degree of fatigue damage, using the following calculation formula: v adjusted =v current ×(1-αD), where, v adjusted This represents the adjusted drilling speed, v. current This represents the current drilling speed, and α represents the adjustment coefficient.

[0052] Specifically, this method dynamically links fatigue analysis results with construction parameters, achieving closed-loop management from passive monitoring to proactive intelligent control, with significant benefits and effects. Firstly, the method visually displays key equipment parameters and fatigue status through a graphical interface, allowing operators to monitor equipment health in real time, greatly improving operational transparency and decision-making efficiency. Its core advantage lies in its dynamic adjustment capability, achieved through the drilling speed adjustment formula v. adjusted =v current ×(1-αD), the system can automatically optimize the drilling rate based on the real-time fatigue damage level. When the damage accumulates and intensifies, the adjustment coefficient α automatically reduces the drilling speed, effectively slowing down the deterioration rate of key components and avoiding sudden failures caused by overload operation. This adaptive control not only significantly extends the service life of the equipment, but also achieves a dynamic balance between efficiency and reliability while ensuring construction safety, directly reflecting intelligent and refined construction management.

[0053] Step 5, Alarm and Recording: When fatigue damage reaches the set warning value, an audible and visual alarm is triggered, and relevant data is saved to the storage module for subsequent analysis; in Step 5, the fatigue damage warning value is set to D. critical When D≥D critical When this happens, an alarm is triggered;

[0054] Data logging: Save each piece of data collected and the alarm trigger time for easy subsequent analysis and review: using the following formula: Record = {time, sensor_data, D}, where Record represents the record and includes time, sensor data, and fatigue damage.

[0055] Specifically, this is implemented by setting a clear fatigue damage warning value D≥D critical The system can trigger audible and visual alarms before risks escalate into malfunctions, giving operators ample time to react and take intervention measures such as stopping the machine or reducing its speed. This effectively prevents serious damage to equipment and the resulting production safety accidents, serving as the last critical barrier to ensure operational safety.

[0056] Secondly, the structured data recording formula Record = {time, sensor_data, D} ensures that every alarm event is completely and accurately archived. This not only provides tamper-proof traceability evidence for accident investigations and clarifies responsibilities, but also accumulates valuable operational data throughout the entire equipment lifecycle. This historical data can be used for subsequent in-depth analysis, such as optimizing warning thresholds, calibrating fatigue models, and improving maintenance strategies, forming a continuous improvement loop of "data-analysis-optimization." This transforms the experience of individual events into systematic knowledge assets, driving continuous improvement in equipment management.

[0057] This invention integrates multiple sensors and fatigue analysis algorithms to comprehensively monitor the operating status and fatigue level of equipment. It enables real-time monitoring and alarms, dynamically adjusts construction parameters, and avoids equipment failures caused by fatigue damage. This improves the operational safety of directional drilling equipment, preventing sudden malfunctions due to fatigue damage. It extends the equipment's service life, reduces maintenance costs and construction risks, and achieves real-time monitoring and dynamic feedback, thereby improving construction efficiency and quality.

[0058] Working principle: After prolonged use, directional drilling rods must withstand cyclic tensile and torque forces. Therefore, real-time monitoring of the drill rod's fatigue condition is necessary to ensure construction safety.

[0059] Strain sensors are uniformly arranged on the drill pipe surface to collect stress data generated during the pullback and push-pull processes. The sensor signals are transmitted to a data processing unit to perform spectral analysis on the stress data and extract high-frequency and low-frequency characteristics.

[0060] Based on the theory of cumulative fatigue damage, the degree of fatigue damage to the drill pipe was calculated, and its remaining service life was assessed. A fatigue damage warning threshold was set in the monitoring system; when the damage value reached 80%, an audible and visual alarm was triggered, reminding construction personnel to reduce operational intensity or replace the drill pipe. Through the monitoring of this device, a potential drill pipe fatigue fracture was successfully predicted, preventing a major construction accident.

[0061] The rotary bearing of a directional drilling machine is an important power transmission component. Due to long-term exposure to vibration and impact loads, it is prone to fatigue damage, which affects the stability of the equipment.

[0062] Vibration and temperature sensors are installed on the housing of the rotating bearing to monitor vibration amplitude and operating temperature in real time. A data processing unit performs time-domain and frequency-domain analysis on the vibration signals to identify abnormal vibration modes of the bearing. Based on the fatigue characteristic curves of the bearing material, combined with vibration and temperature data, the fatigue damage value of the bearing is calculated.

[0063] When the fatigue damage value exceeds the warning threshold, an alarm signal is triggered, and it is recommended to reduce the bearing load or perform repair or replacement. The application of fatigue monitoring devices has revealed the accelerated damage pattern of rotating bearings under high-temperature environments, helping engineers optimize construction processes and extend equipment uptime.

[0064] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for fatigue monitoring in directional drilling, characterized in that: The process includes the following steps: Step 1, Data Acquisition: Multiple sensors are installed on the drill rod and rotary bearing of the directional drilling equipment to monitor its working status in real time; the sensors selected include strain sensors, vibration sensors, temperature sensors, and pressure sensors, used to collect strain signal data, vibration signal data, temperature data, and pressure data; the signals from each sensor are transmitted through the data acquisition system. Step 2, Data Preprocessing: The data acquisition module preprocesses the raw sensor data, including denoising, normalization, and feature extraction, to ensure that the data is suitable for subsequent fatigue analysis. Step 3: Fatigue Analysis: Based on the collected strain, vibration, temperature and pressure data, the fatigue damage degree and remaining service life of key components are calculated using the fatigue cumulative damage theory. Step 4: Real-time monitoring and feedback: Display fatigue status in real time, and display relevant parameters such as strain, vibration, temperature and pressure through a graphical interface, and dynamically adjust construction parameters based on monitoring data; Step 5: Alarm and Recording: When fatigue damage reaches the set warning value, an audible and visual alarm is triggered, and the relevant data is saved to the storage module for subsequent analysis.

2. The fatigue monitoring method for directional drilling according to claim 1, characterized in that: In step one, the strain signal data is calculated from the strain ε and stress using Hooke's law: σ 应力 =E·ε, where σ 应力 Let E be the stress, and E be the elastic modulus of the material. Vibration signal data is obtained by calculating the amplitude and frequency of the vibration using Fourier transform, and is used for spectrum analysis. The calculation formula used is as follows: Where X(f) is the spectrum, representing the amplitude and phase of the signal x(t) at frequency f, x(t) is the time-domain signal, f is the frequency, t is the time, and e -j2πft is a complex exponential function representing the phase information of the frequency component, where j is the imaginary unit, 2πft is the time-dependent term of the phase, and dt is the differential element in the integral operation, used to represent the small change in the integral variable t.

3. The fatigue monitoring method for directional drilling according to claim 1, characterized in that: In step two, wavelet transform is used for denoising. The wavelet transform formula is: Where W(a,b) is the small filter transform, ψ is the mother wavelet, a is the scale, and b is the translation; Normalization: To ensure consistency in units and dimensions across different sensors, normalization is performed using the following formula: Where x(t1) is the original data, x norm (t) represents the normalized data.

4. The fatigue monitoring method for directional drilling according to claim 1, characterized in that: Feature extraction: Statistical features, such as mean and standard deviation, are used to extract features from sensor data. The formula for the mean is: Where μ is the mean of the data, N is the total number of data points, and xi is the value of the i-th data point. To sum over all data points; The standard deviation formula used is: Where, σ 标准差 Let (xi-μ) be the standard deviation of the data. 2 The square of the difference between each data point and the mean. This is the sum of the squares of the differences between all data points and the mean.

5. The fatigue monitoring method for directional drilling according to claim 1, characterized in that: The damage calculation based on strain and vibration data in step three involves determining the fatigue life under each load condition by calculating the cyclic life of the material at different stress levels. The calculation formula used is as follows: Among them, D ε Represented as total fatigue damage based on strain data, it is the accumulation of damage from all strain-loaded cycles; n i (ε) represents the number of strain cycles performed in the i-th strain cycle; N(ε) i ) indicates that at a specific strain amplitude ε i The fatigue life of a material refers to the maximum number of cycles a component can withstand; n represents the total number of strain load cycles.

6. The fatigue monitoring method for directional drilling according to claim 1, characterized in that: Damage calculation based on temperature and pressure data: The influence coefficients of temperature and pressure are used, and a correction factor is applied to adjust the fatigue life of the material. The calculation formula used is: N adjusted =N·f(T,P), where f(T,P) represents the influence factors of temperature and pressure on fatigue life, T is temperature, and P is pressure.

7. The fatigue monitoring method for directional drilling according to claim 1, characterized in that: Remaining life calculation: After obtaining the total fatigue damage D, the remaining life is calculated using the following formula: Where R represents the proportion of remaining lifetime, N total n represents the total fatigue life of the material. a This represents the number of load cycles that have been performed.

8. The fatigue monitoring method for directional drilling according to claim 1, characterized in that: The relationship between remaining lifespan and damage is expressed by the following formula: Where, n current This indicates the number of load cycles the component has experienced. When D = 1, it means the component has fatigued and is no longer usable. When D < 1, it means the component still has a certain service life.

9. A fatigue monitoring method for directional drilling according to claim 1, characterized in that: In step four, the drilling speed is adjusted based on the degree of fatigue damage, and the calculation formula used is: v adjusted =v current ×(1-αD), where, v adjusted This represents the adjusted drilling speed, v. current This represents the current drilling speed, and α represents the adjustment coefficient.

10. A fatigue monitoring method for directional drilling according to claim 1, characterized in that: In step five, the fatigue damage warning value is set to D. critical When D≥D critical When this happens, an alarm is triggered; Data logging: Save each piece of data collected and the alarm trigger time for easy subsequent analysis and review: using the following formula: Record = {time, sensor_data, D}, where Record represents the record and includes time, sensor data, and fatigue damage.

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