Low power consumption and high precision well deviation measurement system and implementation method thereof

Abstract

The present application discloses a low-power and high-precision well inclination measurement system and its implementation method, which relates to the field of oil well drilling detection. The system includes a downhole sensor module, a single-chip microprocessor and a host computer module. The host computer module is used to perform the following operations: according to the downhole ambient temperature and downhole ambient pressure, the wellbore acceleration, the wellbore geomagnetic field direction and the wellbore angular velocity are compensated and calibrated, and the real-time measurement of the wellbore inclination angle and the real-time measurement of the azimuth angle are calculated to update the wellbore measurement trajectory, and the trajectory deviation between the wellbore measurement trajectory and the drilling operation design trajectory is calculated, and the target sensor sampling frequency matching the trajectory deviation is determined; the single-chip microprocessor is used to adjust the operating frequency of the MEMS sensor unit according to the target sensor sampling frequency. As a result, without sacrificing the measurement accuracy, the energy consumption of the wellbore inclination measurement system can be greatly reduced, and the data reliability and long-term stability can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of oil well drilling detection, and in particular to a low-power consumption and high-precision well deviation measurement system and an implementation method thereof. Background Art

[0002] The well inclination measurement system plays a vital role in modern drilling operations. It can provide key information such as the trajectory, direction and position of the wellbore, and provide important data support for the safety, accuracy and efficiency of drilling operations.

[0003] Existing well inclination measurement technologies mainly rely on sensors such as magnetometers, accelerometers, and gyroscopes. By measuring physical quantities such as the acceleration, magnetic field, and angular velocity of the wellbore, the well inclination angle and azimuth are calculated using a three-dimensional coordinate system. However, these well inclination measurement systems usually rely on high-power electronic components and sensors, such as high-power inertial sensors and frequent data transmission, resulting in high energy consumption of the entire system. Especially in deep and ultra-deep well drilling, due to the limited battery capacity of downhole batteries, batteries often need to be replaced frequently, which increases the cost and operational difficulty of well inclination measurement in ultra-deep well drilling.

[0004] In recent years, some well inclination measurement systems based on low-power sensors and integrated circuits (ICs) have emerged, which aim to reduce power consumption while maintaining high measurement accuracy. However, under extreme working conditions underground, the existing low-power designs still face considerable challenges in terms of long-term stability, measurement accuracy, and anti-interference capabilities, making it an urgent problem to balance power consumption, accuracy, and cost.

[0005] In response to the above problems, the industry has not yet proposed a better technical solution. Summary of the invention

[0006] The present application provides a low-power and high-precision well inclination measurement system and an implementation method thereof, which are used to at least solve the problem of excessive power consumption and low precision of well inclination measurement in ultra-deep well drilling in traditional technologies.

[0007] On the one hand, the embodiment of the present application provides a low-power and high-precision well inclination measurement system, including a downhole sensor module, a single-chip microprocessor and a host computer module, wherein the downhole sensor module includes a MEMS sensing unit and an environmental sensing unit; the MEMS sensing unit includes a MEMS accelerometer, a MEMS magnetometer and a MEMS gyroscope, and the environmental sensing unit includes a temperature sensor and a pressure sensor; the MEMS accelerometer is used to measure wellbore acceleration, the MEMS magnetometer is used to measure the wellbore geomagnetic field direction, and the MEMS gyroscope is used to measure the wellbore angular velocity; the temperature sensor and the pressure sensor are used to measure the downhole environmental temperature and the downhole environmental pressure respectively;

[0008] The host computer module is used to perform the following operations:

[0009] Compensating and calibrating the wellbore acceleration, the wellbore geomagnetic field direction and the wellbore angular velocity according to the downhole ambient temperature and the downhole ambient pressure;

[0010] The compensated and calibrated borehole acceleration, borehole geomagnetic field direction and borehole angular velocity are integrated to calculate the real-time measurement of well inclination angle and real-time measurement of azimuth angle.

[0011] updating the borehole measurement trajectory according to the real-time measured well inclination angle, the real-time measured azimuth angle, the historically measured well inclination angle and the historically measured azimuth angle;

[0012] Calculating a trajectory deviation between the wellbore measurement trajectory and the drilling operation design trajectory, and determining a target sensor sampling frequency that matches the trajectory deviation;

[0013] Wherein, the single chip microprocessor is used to adjust the operating frequency of the MEMS sensing unit according to the sampling frequency of the target sensor.

[0014] Another aspect of the present application provides a method for implementing a low-power and high-precision well deviation measurement system, wherein the low-power and high-precision well deviation measurement system comprises a downhole sensor module, a single-chip microprocessor and a host computer module, wherein the downhole sensor module comprises a MEMS sensing unit and an environmental sensing unit; the MEMS sensing unit comprises a MEMS accelerometer, a MEMS magnetometer and a MEMS gyroscope, and the environmental sensing unit comprises a temperature sensor and a pressure sensor; wherein the method comprises:

[0015] Measuring borehole acceleration based on the MEMS accelerometer, measuring the direction of the borehole geomagnetic field based on the MEMS magnetometer, measuring the borehole angular velocity based on the MEMS gyroscope, measuring the downhole ambient temperature based on the temperature sensor, and measuring the downhole ambient pressure based on the pressure sensor;

[0016] Based on the host computer module, perform the following operations:

[0017] Compensating and calibrating the wellbore acceleration, the wellbore geomagnetic field direction and the wellbore angular velocity according to the downhole ambient temperature and the downhole ambient pressure;

[0018] The compensated and calibrated borehole acceleration, borehole geomagnetic field direction and borehole angular velocity are integrated to calculate the real-time measurement of well inclination angle and real-time measurement of azimuth angle.

[0019] updating the borehole measurement trajectory according to the real-time measured well inclination angle, the real-time measured azimuth angle, the historically measured well inclination angle and the historically measured azimuth angle;

[0020] Calculating a trajectory deviation between the wellbore measurement trajectory and the drilling operation design trajectory, and determining a target sensor sampling frequency that matches the trajectory deviation;

[0021] Based on the single chip microprocessor, the operating frequency of the MEMS sensing unit is set to the target sensor sampling frequency.

[0022] The low-power and high-precision well inclination measurement system and its implementation method provided by the present application can produce at least the following technical effects:

[0023] (1) MEMS sensor units (including MEMS accelerometers, magnetometers and gyroscopes) are used to replace traditional high-power inertial sensors. At the same time, a single-chip microprocessor is used to calculate the trajectory deviation between the wellbore measurement trajectory and the drilling operation design trajectory, and the sensor sampling frequency of the MEMS sensor unit is dynamically adjusted. When the drilling trajectory deviates greatly, the sampling rate is increased to accurately capture the trajectory changes. When the trajectory is stable, the sampling rate is reduced to reduce unnecessary measurement calculations. This improves the energy efficiency while ensuring measurement accuracy, greatly extends the continuous working time of the downhole measurement system, and reduces the cost and operation difficulty of well inclination measurement in ultra-deep well drilling.

[0024] (2) Due to the complex downhole environment, changes in temperature and pressure will affect the stability and accuracy of sensor measurements. By introducing an environmental sensor unit to measure the downhole ambient temperature and pressure, and using the host computer module to compensate and calibrate the borehole acceleration, geomagnetic field direction and angular velocity data, the measurement errors caused by temperature drift and pressure changes are corrected to ensure that high-precision measurements can be maintained in extreme downhole environments. In addition, through multi-sensor data fusion analysis and trend analysis combined with historical measurement data, the stability of the measurement data is further improved, which can effectively reduce the error of the well inclination measurement results caused by a single sensor error, ensuring that the system can maintain a high measurement stability after long-term operation.

[0025] Through this technical solution, by integrating low-power MEMS sensors, intelligent sampling frequency adjustment, environmental parameter compensation and multimodal sensor fusion, the energy consumption of the well inclination measurement system can be greatly reduced without sacrificing measurement accuracy, and data reliability and long-term stability can be improved. At the same time, equipment costs and maintenance costs are reduced, which has obvious advantages in extreme environments such as deep wells and ultra-deep wells. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 A schematic structural diagram of an example of a low-power and high-precision well deviation measurement system according to an embodiment of the present application is shown;

[0028] Figure 2 An operation flow chart of an example of processing multi-modal sensing parameters by a host computer module is shown;

[0029] Figure 3 An operation flow chart of an example of compensating and calibrating MEMS sensing parameters according to downhole ambient temperature and downhole ambient pressure according to an embodiment of the present application is shown;

[0030] Figure 4 An operation flow chart showing an example of calculating the sampling frequency of a target sensor according to an embodiment of the present application is shown;

[0031] Figure 5 An operational flow chart showing an example of a method for implementing a low-power and high-precision well inclination measurement system according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0033] It should be noted that in the current related technologies, the well inclination measurement system usually relies on high-power electronic components and sensors, such as high-power inertial sensors and frequent data transmission, resulting in high system energy consumption. Although there are some low-power sensors available, in actual use, their accuracy is usually affected by the following factors: sensor stability, environmental noise, system errors, etc. In addition, complex formations and drilling conditions at extreme angles further reduce the measurement accuracy, especially traditional magnetometers and accelerometers are often affected by electromagnetic interference inside and outside the wellbore, formation changes and other factors, resulting in reduced reliability of measurement data. In addition, in the underground working environment, in addition to factors such as high temperature, humidity and vibration, magnetic field interference, pressure changes, etc. will also affect the accuracy of the measurement data, making it difficult to provide accurate real-time measurement data.

[0034] It should be understood that the purpose of the above description of the current related art is only to facilitate the public to better understand the inventive spirit and motivation of the present application, and is not to be regarded as a limitation of the present application. In addition, the technical solutions described in the above-mentioned current related art are not prior art, and may also be undisclosed technical solutions, such as solutions under research or in the laboratory stage.

[0035] Figure 1 A schematic structural diagram of an example of a low-power and high-precision well inclination measurement system according to an embodiment of the present application is shown.

[0036] like Figure 1 As shown, the low-power high-precision well inclination measurement system 100 includes a downhole sensor module 110, a single-chip microprocessor 120 and a host computer module 130. Specifically, the downhole sensor module 110 includes a MEMS (Micro-Electro-Mechanical Systems) sensor unit 111 and an environmental sensor unit 112. The MEMS sensor unit 111 includes a MEMS accelerometer 1111, a MEMS magnetometer 1112 and a MEMS gyroscope 1113, and the environmental sensor unit 112 includes a temperature sensor 1121 and a pressure sensor 1122.

[0037] Here, the downhole sensor module 110 adopts a low-power MEMS solution. The characteristic size of the MEMS sensor is in the micron level (such as 1-100μm), and its mechanical structure mass is only one millionth of that of the traditional sensor. According to Newton's law, the energy required to drive a tiny mass is proportional to the square of the displacement, and miniaturization directly reduces the energy consumption of mechanical motion. Compared with traditional fiber optic gyroscopes (power consumption > 5W) and electromagnetic accelerometers (power consumption > 100 mW), the MEMS solution can achieve more than 1,000 times power consumption reduction in well inclination measurement, while maintaining 0.1° well inclination angle accuracy and 1° azimuth angle accuracy (typical value). The improvement in energy efficiency in this regard provides feasibility for full life cycle monitoring of ultra-deep wells (> 6,000 meters).

[0038] Specifically, the MEMS accelerometer 1111 is used to measure the borehole acceleration, the MEMS magnetometer 1112 is used to measure the borehole geomagnetic field direction, and the MEMS gyroscope 1113 is used to measure the borehole angular velocity. The temperature sensor 1121 and the pressure sensor 1122 are used to measure the downhole ambient temperature and the downhole ambient pressure respectively.

[0039] In some examples, the single-chip microprocessor 120 may be a MC9S08SG32 single-chip microcomputer, which uses an HCS08 core and has a streamlined instruction set, which means that the instruction format and addressing method are relatively simple, and the clock cycles required to execute each instruction are relatively small, so that the CPU can complete operations more efficiently when processing tasks and reduce unnecessary energy consumption. In addition, the MC9S08SG32 single-chip microcomputer adopts a highly integrated design, which reduces the connection lines and interfaces between chips and reduces the energy loss during signal transmission.

[0040] It should be noted that in this embodiment, the single-chip microprocessor 120 does not need to have powerful general computing capabilities like general processors. It only needs to handle tasks related to sensor data acquisition, simple data processing and communication settings, etc., avoiding power waste due to over-configuration, and effectively reducing system power consumption, providing endurance guarantee for ultra-deep well drilling measurement.

[0041] Figure 2 An operational flow chart showing an example of processing multi-modal sensing parameters by a host computer module.

[0042] like Figure 2 As shown, in step S210, the borehole acceleration, the borehole geomagnetic field direction and the borehole angular velocity are compensated and calibrated according to the downhole ambient temperature and the downhole ambient pressure.

[0043] It should be noted that the sensitive structures of MEMS sensors are mostly silicon-based micromechanical structures, and temperature changes will cause changes in the elastic modulus and thermal expansion coefficient of the material. Specifically, when the temperature rises, the elastic modulus of silicon decreases, resulting in a decrease in sensor sensitivity; the temperature gradient will induce structural stress and produce zero-point drift. In addition, the high-pressure environment downhole will cause the sensor package to deform, which will then be transmitted to the sensitive structure, changing its natural frequency or static offset.

[0044] In some embodiments, the temperature and pressure data are used to correct the measurement errors of MEMS sensing units (accelerometers, magnetometers, gyroscopes), specifically including temperature drift compensation and pressure effect compensation. In temperature drift compensation, temperature compensation is performed for the bias error (Bias) and scale factor error (Scale Factor) of the accelerometer, magnetometer, and gyroscope to ensure that the sensor can still maintain high accuracy in a high temperature environment. In addition, in pressure effect compensation, since the high-pressure environment of ultra-deep wells may affect the sensitivity of the sensor, the experimentally calibrated pressure compensation model can be used to correct the measurement data. In this way, the impact of temperature and pressure changes on the measurement accuracy of the sensor is reduced, and the stability of the measurement data is improved.

[0045] In step S220, the compensated and calibrated borehole acceleration, borehole geomagnetic field direction and borehole angular velocity are integrated to calculate the real-time measured well inclination angle and the real-time measured azimuth angle.

[0046] In some embodiments, the borehole acceleration, geomagnetic field direction and angular velocity data calibrated with temperature and pressure compensation are fused through a multi-sensor data fusion algorithm such as Kalman filtering, complementary filtering or particle filtering to further improve the accuracy and robustness of the measurement results by considering the correlation between different sensor data.

[0047] Regarding the calculation of real-time measurement of well inclination and real-time measurement of azimuth, it can be comprehensively determined by a multi-modal fusion sensor parameter group. For example, based on the gravity data collected by the acceleration sensor and the direction information provided by the geomagnetic field sensor, the real-time inclination of the wellbore is calculated through a three-dimensional vector calculation method. In addition, combining the geomagnetic field direction data with the acceleration data to calculate the azimuth can effectively eliminate the magnetic field disturbance downhole and obtain an accurate real-time azimuth. Therefore, through the comprehensive analysis of multi-modal sensing parameters, the influence of single sensor noise is reduced, and the real-time measurement accuracy of well inclination and azimuth is improved.

[0048] In step S230, the borehole measurement trajectory is updated according to the real-time measured well inclination angle, the real-time measured azimuth angle, the historically measured well inclination angle and the historically measured azimuth angle.

[0049] In some embodiments, the borehole measurement data at each moment will be synchronously stored in a memory or database to form a trajectory database. By accessing the trajectory database, the borehole measurement trajectory is calculated in real time based on the well inclination and azimuth measured in real time, combined with historical data, through the trajectory update algorithm. In addition, the type of trajectory update algorithm can be diverse, such as interpolation algorithm, fitting algorithm, INS (Inertial Navigation System, inertial navigation system algorithm), incremental measurement method, etc. Then, the trajectory is smoothed by the weighted sliding average method to reduce the impact of measurement noise on trajectory update.

[0050] In step S240, the trajectory deviation between the wellbore measurement trajectory and the drilling operation design trajectory is calculated, and the target sensor sampling frequency matching the trajectory deviation is determined.

[0051] In some embodiments, the trajectory deviation is determined by calculating the difference between the actually measured wellbore trajectory and the predetermined drilling operation design trajectory, and the deviation can be measured by using metrics such as Euclidean distance and Manhattan distance. Then, according to the magnitude of the trajectory deviation, the sensor sampling frequency that needs to be adjusted is determined based on a preset rule or model. For example, a lower frequency sampling can be used when the deviation is small, while a higher sampling frequency needs to be increased to obtain higher precision data.

[0052] Finally, the single chip microprocessor 120 adjusts the operating frequency of the MEMS sensing unit 111 according to the target sensor sampling frequency.

[0053] In some embodiments, the single-chip microprocessor 120 adjusts the operating frequency of the MEMS sensor unit in real time based on a PID control algorithm or an adaptive control algorithm according to changes in the sampling frequency of the target sensor, thereby better regulating the power consumption of the MEMS sensor unit in different scenarios. By dynamically adjusting the sampling frequency, the sampling rate is reduced to reduce power consumption when the trajectory deviation is small, thereby extending the system working time, and the sampling rate is increased to ensure measurement accuracy when the trajectory deviation is large, thereby reducing trajectory deviation and ensuring real-time monitoring and feedback. This can reduce power consumption while ensuring measurement accuracy, thereby improving the energy efficiency of the measurement system.

[0054] Through the embodiments of the present application, environmental parameter compensation, magnetic field interference correction, multi-sensor fusion and other technical means are used to improve the measurement accuracy of well inclination and azimuth. The trajectory is updated using historical data combined with current data to reduce short-term error feedback and improve trajectory calculation stability. As a result, the measurement frequency can be intelligently adjusted according to the deviation of the wellbore trajectory, power consumption can be reduced when the drilling trajectory is stable, and measurement accuracy can be improved when the trajectory deviates. By dynamically adjusting the sensor sampling frequency, the best balance between accuracy and power consumption can be found to reduce battery consumption.

[0055] In some examples of the embodiments of the present application, a data transmission module (not shown) is also provided in the low-power and high-precision well inclination measurement system, which is used to relay data between the host computer module 130 and the downhole sensor module 110 or the single-chip microprocessor 120. Through the signal relay of the data transmission module, the stability and real-time performance of the data interaction between the host computer module and the downhole module can be further guaranteed.

[0056] In some embodiments, the data transmission module can use low-power wireless communication, such as LoRa signal, which has the characteristics of low power consumption and strong signal penetration, and can run for a long time. LoRa is a long-distance wireless transmission solution based on spread spectrum technology, and has excellent long-distance transmission capability. In addition, the data transmission module can also use various other non-restrictive transmission signal modules, such as mud signals, etc., thereby realizing efficient, low-power, and anti-interference transmission of downhole measurement data, so that the entire system can still maintain stable and accurate operation in ultra-deep wells and complex working conditions.

[0057] Figure 3 An operational flow chart of an example of compensating and calibrating MEMS sensing parameters according to downhole ambient temperature and downhole ambient pressure according to an embodiment of the present application is shown.

[0058] like Figure 3 As shown, in step S310, the real-time change degree of the downhole environment is calculated according to the downhole environment temperature and the downhole environment pressure and in combination with the downhole environment historical measurement values.

[0059] In some embodiments, the downhole ambient temperature ( ) and pressure ( ), and compare and analyze with historical data. Specifically, in order to calculate the real-time change degree, the historical environmental data of the past period of time (for example, the last 5 minutes or 1 hour) can be used as a reference. Assume that the historical ambient temperature is and historical pressure , the real-time change degree of the underground environment can be calculated by the following formula , which reflects the rate of change of temperature and pressure:

[0060] , Formula (1)

[0061] Therefore, by real-time monitoring of changes in temperature and pressure, the system can adapt to environmental fluctuations, identify trends in environmental changes in advance, effectively prevent measurement errors caused by environmental changes, and provide a data basis for subsequent segmented filtering strategies.

[0062] In step S320, a target filtering strategy that matches the real-time change of the downhole environment is determined, and the borehole acceleration, the borehole geomagnetic field direction and the borehole angular velocity are filtered according to the target filtering strategy.

[0063] Here, according to actual application requirements, different change thresholds are preset in the system, wherein the first change threshold is used to judge the situation where the underground environment changes slightly, and the second change threshold is used to judge the situation where the underground environment changes significantly.

[0064] In some embodiments, when the real-time change degree of the downhole environment is lower than a first change degree threshold, the borehole acceleration, the borehole geomagnetic field direction and the borehole angular velocity are filtered using a low-pass filter.

[0065] In this way, when the underground environment is relatively stable, high-frequency noise can be effectively removed through low-pass filtering to maintain the stability of the data.

[0066] On the other hand, when the real-time change degree of the downhole environment is greater than the first change degree threshold and less than the second change degree threshold, an adaptive Kalman filter is used to filter the wellbore acceleration, wellbore geomagnetic field direction and wellbore angular velocity.

[0067] Here, when the environment changes, the state estimation and covariance matrix of the system can be dynamically adjusted through the adaptive Kalman filter to adapt to the uncertainty brought by the environmental changes.

[0068] On the other hand, when the real-time change degree of the downhole environment is greater than the second change degree threshold, enhanced filtering is used to filter the wellbore acceleration, wellbore geomagnetic field direction and wellbore angular velocity.

[0069] Here, when the environment changes dramatically, enhanced filtering (such as weighted averaging, maximum likelihood estimation, etc.) can be used to enhance the processing of environmental fluctuations and improve the accuracy and anti-interference ability of the data.

[0070] Through the embodiment of the present application, a segmented filtering strategy is adopted, and the filtering strategy is dynamically adjusted by using the environmental change degree, which can effectively cope with different working conditions underground. For example, when the environmental change degree is small (when the temperature and pressure change little), a relatively simple low-pass filter is used to reduce the amount of calculation; when the environmental change degree is large, an adaptive Kalman filter or an enhanced filter is used to improve the measurement accuracy.

[0071] In step S330, the downhole ambient temperature, downhole ambient pressure, and filtered borehole acceleration, borehole geomagnetic field direction, and borehole angular velocity are substituted into the polynomial regression function to obtain corresponding compensated and calibrated borehole acceleration, borehole geomagnetic field direction, and borehole angular velocity.

[0072] It should be noted that since the influence of temperature and pressure on the sensor measurement results is very complex, polynomial regression is used here to fit the relationship between input and output to provide accurate compensation results.

[0073] Specifically, the polynomial regression function is:

[0074] , Formula (2)

[0075] , Formula (3)

[0076] , Formula (4)

[0077] In the formula, represents the compensated and calibrated borehole acceleration, represents the compensated and calibrated borehole geomagnetic field direction, represents the compensated and calibrated borehole angular velocity, Indicates time t The underground ambient temperature, Indicates time t downhole environmental pressure; Represents the filtered moment t Wellbore acceleration data, Represents the filtered moment t The borehole geomagnetic field direction data, Represents the filtered moment t Wellbore angular velocity data; represents the polynomial regression coefficients used to fit the wellbore acceleration, represents the polynomial regression coefficient used to fit the borehole geomagnetic field direction, Represents the polynomial regression coefficients used to fit the borehole angular velocity.

[0078] It should be noted that in the polynomial regression functions of the above equations (2) to (4), cross terms are used. For example, the regression function of borehole acceleration (2) involves the direction of the geomagnetic field. and angular velocity , which can effectively model the relationship between different variables, rather than just considering the relationship between each variable and the target output separately.

[0079] In particular, in actual downhole measurement systems, borehole acceleration, geomagnetic field direction, and angular velocity are not completely independent and may affect each other. For example, high temperature may cause signal offsets of accelerometers and magnetometers, and this effect may be interdependent, or changes in acceleration may affect the reading of magnetic field direction. By introducing cross terms, the interaction between these variables can be effectively captured, so that the regression model can accurately reflect the complex dependency between sensor data and environmental factors, thereby optimizing the compensation calibration effect.

[0080] Through the embodiments of the present application, by adopting a segmented filtering strategy, different filtering methods are selected according to the real-time downhole environmental change degree, ensuring that reliable measurement data can be provided even when the downhole environmental changes are unstable. In addition, real-time dynamic adjustment and compensation based on changes in temperature and pressure can effectively compensate for the measurement errors of borehole acceleration, geomagnetic field direction and angular velocity, maintain the accuracy of sensor data, and reduce errors caused by environmental factors. The use of a cross-term-based polynomial regression function to compensate for borehole acceleration, geomagnetic field direction and angular velocity can capture the impact of nonlinear environmental changes on sensor output, ensure the comprehensiveness and accuracy of compensation calibration, and significantly improve the compensation effect.

[0081] In some examples of the embodiments of the present application, the polynomial regression coefficients are determined by applying the least square method based on adaptive weighting to process the historical measurement samples, including:

[0082] The error function based on weighted least squares is:

[0083] , Formula (5)

[0084] , Formula (6)

[0085] In the formula, represents the weighted error function, n represents the total number of samples, Indicates The actual observed value of borehole acceleration of samples, The regression model is used for The predicted value of borehole acceleration output by samples, It is The weighting coefficient of samples, Indicates The degree of environmental variability indicated by the samples, Represents the environmental change adjustment weight.

[0086] Minimize the weighted error function according to the gradient descent method , to solve the optimal coefficients as the polynomial regression coefficients for fitting the wellbore acceleration:

[0087] , Formula (7)

[0088] In the formula, is the learning rate, which is used to control the step size of each update; Indicates j The term is used to fit the polynomial regression coefficient of the wellbore acceleration; is the weighted error function with respect to the regression coefficients The partial derivative of .

[0089] In formula (7), the error function By introducing a weighting coefficient for each data point The goal is to minimize the weighted regression error. When the environment changes drastically, higher weights are given to the corresponding samples, so that these samples have a greater impact on the optimization of the regression coefficient, ensuring the accuracy of the regression model in a complex environment. Furthermore, the regression coefficient is optimized by the gradient descent method to ensure that the regression coefficient Gradually converge to the optimal value and achieve the goal of minimizing the weighted error.

[0090] Through the weighted least squares method provided in the embodiment of the present application, as the degree of environmental variability increases (such as when the temperature and pressure change greatly), the impact on the calculation of the regression coefficient also increases accordingly. The system can calibrate the sensor data more accurately when the environment changes greatly. By enhancing the emphasis on data samples when the environment changes drastically, it ensures that the system can still provide high-precision compensation results in complex and dynamic environments.

[0091] It should be noted that in well inclination measurement, a single sensor has limitations. Accelerometers are susceptible to vibration and impact, magnetometers are susceptible to magnetic field interference, and gyroscopes have drift errors. The data fusion of MEMS accelerometers, MEMS magnetometers, and MEMS gyroscopes can complement and correct each other to improve measurement accuracy.

[0092] In some examples of the embodiments of the present application, the wellbore acceleration, wellbore geomagnetic field direction and wellbore angular velocity after compensation and calibration are integrated to calculate the real-time measured well inclination angle and the real-time measured azimuth angle, including:

[0093] , Formula (8)

[0094] , Formula (9)

[0095] In the formula, Indicates real-time measurement of well inclination angle. , , The MEMS accelerometer after compensation and calibration is x , y , z The acceleration component on the axis. The MEMS gyroscope is compensated and calibrated. y Angular velocity component on the axis, select y The axis is considered to be relevant to the calculation of well inclination. is the maximum angular velocity threshold measured by the gyroscope, used for normalization. The MEMS magnetometer after compensation and calibration is x Component of the magnetic field on the axis. is the maximum magnetic field strength threshold measured by the magnetometer, used for normalization. It is the weighted adjustment coefficient of the well inclination angle, which is used to balance the influence of each sensor data on the well inclination angle calculation. By dynamically adjusting the weighted coefficient, the fusion calculation can be more adapted to different downhole working conditions. To measure the azimuth in real time, , , They are the MEMS magnetometer after compensation and calibration. x , y , z The magnetic field component on the axis, The MEMS gyroscope is compensated and calibrated. z Angular velocity component about the axis. The MEMS accelerometer is compensated and calibrated. y The acceleration component on the axis, is the maximum acceleration threshold measured by the accelerometer. is the azimuth weighting adjustment factor, The dynamic adjustment can enhance the adaptability of azimuth angle calculation to different working conditions.

[0096] Explanation of the calculation formula of well inclination angle in formula (8): It is based on the principle of calculating the well inclination angle by measuring the gravity acceleration component with an accelerometer. In addition, since the angular velocity information of the gyroscope can reflect the dynamic changes of the wellbore, and the magnetic field information of the magnetometer can assist in correcting the error caused by the inclination of the accelerometer, by introducing , and use the weighting coefficient Adjusting the weight of its influence on the calculation of well inclination angle, especially when the wellbore changes rapidly, can further improve the stability and accuracy of the well inclination angle measurement results through multi-modal parameter fusion.

[0097] Regarding the explanation of the azimuth angle calculation formula in formula (9), It is based on the principle of using a magnetometer to measure the direction of the Earth's magnetic field to determine the azimuth, and combining it with the calculated well inclination angle Because the angular velocity of the gyroscope and the acceleration component of the accelerometer can provide additional orientation information, adding , and through the weighting coefficient Adjust the weight of its influence on the calculation of well inclination to make the azimuth calculation more accurate. Using the direction measurement of the magnetometer and the dynamic correction of the angular velocity sensor can reduce the error caused by magnetic field disturbance or sensor drift and maintain the high accuracy of the azimuth calculated in real time.

[0098] Through the embodiment of the present application, the multi-sensor fusion and dynamic weighting coefficient can effectively reduce the influence of single sensor error, improve the measurement accuracy of well inclination and azimuth, and meet the needs of modern drilling operations for high-precision well inclination measurement. and , can be adjusted according to experiments and actual conditions to make the fusion calculation more adaptable to different downhole working conditions, such as different geological conditions, magnetic field environment and dynamic changes of wellbore, and integrate the maximum measurement value compensation method to enhance the robustness of the system.

[0099] In some examples of the embodiments of the present application, the wellbore measurement trajectory may be calculated and updated in the following manner.

[0100] , Formula (10)

[0101] , Formula (11)

[0102] , Formula (12)

[0103] , Formula (13)

[0104] , Formula (14)

[0105] In the formula, represents the weighted measured well inclination angle, represents the weighted measurement azimuth, Indicates at time t Real-time measurement of well inclination, Indicates at time t Real-time measurement of azimuth, Indicates at time t -1 historical measured well inclination angle, Indicates at time t -1 historical measurement azimuth, represents the real-time dependence coefficient of well inclination angle, represents the real-time dependency coefficient of azimuth; Indicates the borehole measurement trajectory at time tUpdated real-time wellbore position, Indicates the borehole measurement trajectory at time t -1 Determined historical wellbore locations; Represents the displacement, which is determined according to the downhole equipment speed and sampling time interval.

[0106] In the above equations (10)-(14), by combining the well inclination and azimuth measured in real time ( ) and historical measurements ( ), which can effectively balance dynamic changes and long-term stability, ensure high-precision updates of wellbore trajectories, respond to wellbore movement in real time, and avoid errors due to noise or sensor drift, ensuring the stability of updated trajectories. and Indicates the dependence weight on the real-time measurement results and historical measurement results. On the one hand, it can be preset, and on the other hand, it can be adaptively adjusted according to the degree of environmental change. For example, in the case of large environmental changes, the and , in order to increase the trajectory calculation weight of historical data, it can rely on historical data for smoothing and improve the trajectory calculation accuracy. In addition, when the downhole environment is relatively stable, the impact of real-time measurement data can be further enhanced.

[0107] Through the embodiment of the present application, the measurement of real-time well inclination and azimuth and the displacement of sampling interval are integrated. , can quickly respond to the movement of the wellbore and update its position in real time, so that the update of the wellbore trajectory can be synchronized with the downhole operation, without waiting for batch processing calculation, ensuring real-time performance. In addition, by adopting multi-sensor data fusion and smooth correction of historical data, the precise position of the wellbore in the wellbore can be calculated in real time, and the fusion of real-time data and historical data can effectively smooth the wellbore trajectory. In particular, when the wellbore measurement position changes abnormally quickly or is subject to external interference, the trajectory can transition smoothly to avoid sharp trajectory jumps, improving the controllability and safety of the operation process.

[0108] Figure 4 An operation flow chart of an example of calculating the sampling frequency of a target sensor according to an embodiment of the present application is shown.

[0109] In step S410 , the cumulative trajectory deviation is calculated.

[0110] , Formula (15)

[0111] , Formula (16)

[0112] In the formula, is the cumulative trajectory deviation, which represents the sum of the deviations between the actual trajectory of the wellbore and the designed trajectory within the deviation analysis window; is the number of sampling measurements within the deviation analysis window, It is at the moment The trajectory deviation, The drilling operation design trajectory at time target wellbore location.

[0113] Regarding the explanation of equations (15) and (16), they are obtained by calculating the actual trajectory of the wellbore ( ) and drilling operation design trajectory ( ) to measure the distance between the designed trajectory and the actual trajectory to quantify the real-time deviation of the trajectory. By accumulating the deviation, the overall deviation of the trajectory is evaluated to better represent the total deviation of the trajectory during the entire measurement process.

[0114] In step S420, if the cumulative trajectory deviation exceeds a preset trajectory deviation threshold, the sampling frequency is increased according to the cumulative trajectory deviation, and if the cumulative trajectory deviation is less than the trajectory deviation threshold, the sampling frequency is reduced according to the cumulative trajectory deviation to determine the target sensor sampling frequency.

[0115] , Formula (17)

[0116] , Formula (18)

[0117] In the formula, It is calculated based on the cumulative trajectory deviation at time t The sampling frequency, is the trajectory deviation threshold, is the adjustment factor used to control the sampling frequency, is the basic sampling frequency, represents the target sensor sampling frequency, represents the upper limit of the sampling frequency, and Indicates the lower limit of the sampling frequency.

[0118] Regarding the explanation of formula (17), when the trajectory deviation Exceeding the set threshold The sampling frequency will increase accordingly. The ratio of deviation to threshold is used to adjust the sampling frequency increase, and the adjustment coefficient is introduced. To control the size of the increase, it helps to dynamically respond to changes in the trajectory and ensure that the sampling accuracy can be adaptively improved when the trajectory deviates greatly. Regarding the description of formula (18), it ensures the upper and lower limits of the sampling frequency by setting the maximum frequency and minimum frequency , which can avoid the sampling frequency being too high or too low, ensuring that the system does not consume too many resources due to overly frequent sampling, and also avoid affecting the measurement accuracy due to too little sampling.

[0119] Through the embodiment of the present application, the cumulative deviation between the real-time trajectory and the designed trajectory is calculated. When the trajectory is relatively stable and the deviation is small, the system automatically reduces the sampling frequency, which can effectively save energy, avoid battery consumption caused by unnecessary high-frequency sampling, and significantly extend the long-term operation of downhole equipment. When the trajectory deviates significantly, the system can quickly adjust the sampling frequency, improve the measurement accuracy, ensure that there is no error accumulation during the trajectory update process, and can accurately track the wellbore trajectory in real time.

[0120] Figure 5 An operational flow chart showing an example of a method for implementing a low-power and high-precision well inclination measurement system according to an embodiment of the present application is shown.

[0121] The low-power and high-precision well inclination measurement system includes a downhole sensor module, a single-chip microprocessor and a host computer module. The downhole sensor module includes a MEMS sensing unit and an environmental sensing unit; the MEMS sensing unit includes a MEMS accelerometer, a MEMS magnetometer and a MEMS gyroscope, and the environmental sensing unit includes a temperature sensor and a pressure sensor.

[0122] like Figure 5 As shown, in step S510, the borehole acceleration is measured based on the MEMS accelerometer, the borehole geomagnetic field direction is measured based on the MEMS magnetometer, the borehole angular velocity is measured based on the MEMS gyroscope, the downhole ambient temperature is measured based on the temperature sensor, and the downhole ambient pressure is measured based on the pressure sensor.

[0123] In step S520, based on the host computer module, the borehole acceleration, the borehole geomagnetic field direction and the borehole angular velocity are compensated and calibrated according to the downhole ambient temperature and the downhole ambient pressure.

[0124] In step S530, based on the host computer module, the compensated and calibrated borehole acceleration, borehole geomagnetic field direction and borehole angular velocity are integrated to calculate the real-time measured well inclination angle and the real-time measured azimuth angle.

[0125] In step S540, based on the host computer module, the borehole measurement trajectory is updated according to the real-time measured well inclination angle, the real-time measured azimuth angle, the historical measured well inclination angle and the historical measured azimuth angle.

[0126] In step S550, based on the host computer module, the trajectory deviation between the wellbore measurement trajectory and the drilling operation design trajectory is calculated, and the target sensor sampling frequency matching the trajectory deviation is determined.

[0127] In step S560, based on the single chip microprocessor, the operating frequency of the MEMS sensing unit is set to the target sensor sampling frequency.

[0128] It should be noted that, for the aforementioned system embodiments, for the sake of simplicity of description, they are all expressed as a series of combined actions, but those skilled in the art should be aware that the present application is not limited by the order of actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application. In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0129] The system embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0130] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the system described in each embodiment or some parts of the embodiment.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A low-power and high-precision well deviation measurement system, characterized in that: It includes a downhole sensor module, a single-chip microprocessor and a host computer module, wherein the downhole sensor module includes a MEMS sensing unit and an environmental sensing unit; the MEMS sensing unit includes a MEMS accelerometer, a MEMS magnetometer and a MEMS gyroscope, and the environmental sensing unit includes a temperature sensor and a pressure sensor; the MEMS accelerometer is used to measure the wellbore acceleration, the MEMS magnetometer is used to measure the wellbore geomagnetic field direction, and the MEMS gyroscope is used to measure the wellbore angular velocity; the temperature sensor and the pressure sensor are used to measure the downhole environmental temperature and the downhole environmental pressure respectively; The host computer module is used to perform the following operations: Compensating and calibrating the wellbore acceleration, the wellbore geomagnetic field direction and the wellbore angular velocity according to the downhole ambient temperature and the downhole ambient pressure; The compensated and calibrated borehole acceleration, borehole geomagnetic field direction and borehole angular velocity are integrated to calculate the real-time measurement of well inclination angle and real-time measurement of azimuth angle. updating the borehole measurement trajectory according to the real-time measured well inclination angle, the real-time measured azimuth angle, the historically measured well inclination angle and the historically measured azimuth angle; Calculating a trajectory deviation between the wellbore measurement trajectory and the drilling operation design trajectory, and determining a target sensor sampling frequency that matches the trajectory deviation; Wherein, the single chip microprocessor is used to adjust the operating frequency of the MEMS sensing unit according to the sampling frequency of the target sensor; Wherein, the compensation calibration of the borehole acceleration, the borehole geomagnetic field direction and the borehole angular velocity according to the downhole ambient temperature and the downhole ambient pressure includes: Calculating the real-time change degree of the downhole environment according to the downhole environment temperature and the downhole environment pressure and in combination with the downhole environment historical measurement values; Determining a target filtering strategy that matches the real-time change degree of the downhole environment, and filtering the wellbore acceleration, the wellbore geomagnetic field direction, and the wellbore angular velocity according to the target filtering strategy, including: When the real-time change degree of the downhole environment is lower than a first change degree threshold, filtering the wellbore acceleration, the wellbore geomagnetic field direction and the wellbore angular velocity according to a low-pass filter; When the real-time change degree of the downhole environment is greater than the first change degree threshold and less than the second change degree threshold, an adaptive Kalman filter is used to filter the wellbore acceleration, the wellbore geomagnetic field direction and the wellbore angular velocity; When the real-time change degree of the downhole environment is greater than the second change degree threshold, using enhanced filtering to filter the wellbore acceleration, the wellbore geomagnetic field direction and the wellbore angular velocity; Substituting the downhole ambient temperature, the downhole ambient pressure, and the filtered borehole acceleration, borehole geomagnetic field direction, and borehole angular velocity into a polynomial regression function to obtain corresponding compensated and calibrated borehole acceleration, borehole geomagnetic field direction, and borehole angular velocity; Wherein, the polynomial regression function is: ， ， ， In the formula, represents the compensated and calibrated borehole acceleration, represents the compensated and calibrated borehole geomagnetic field direction, represents the compensated and calibrated borehole angular velocity, Indicates time t The underground ambient temperature, Indicates time t downhole environmental pressure; Represents the filtered moment t Wellbore acceleration data, Represents the filtered moment t The borehole geomagnetic field direction data, Represents the filtered moment t Wellbore angular velocity data; represents the polynomial regression coefficients used to fit the wellbore acceleration, represents the polynomial regression coefficient used to fit the borehole geomagnetic field direction, Represents the polynomial regression coefficients used to fit the borehole angular velocity.

2. The system according to claim 1, characterized in that The polynomial regression coefficients are determined by applying an adaptively weighted least squares method to the historical measurement samples, including: The error function based on weighted least squares is: ， ， In the formula, represents the weighted error function, n represents the total number of samples, Indicates The actual observed value of borehole acceleration of samples, The regression model is used for The predicted value of borehole acceleration output by samples, It is The weighting coefficient of samples, Indicates The degree of environmental variability indicated by the samples, represents the weight of environmental change regulation; Minimize the weighted error function according to the gradient descent method , to solve the optimal coefficients as the polynomial regression coefficients for fitting the wellbore acceleration: ， In the formula, is the learning rate, which is used to control the step size of each update; Indicates j The term is used to fit the polynomial regression coefficient of the wellbore acceleration; is the weighted error function with respect to the regression coefficients The partial derivative of .

3. The system according to claim 1, characterized in that The method of fusing the compensated and calibrated borehole acceleration, borehole geomagnetic field direction and borehole angular velocity to calculate the real-time measured well inclination angle and the real-time measured azimuth angle includes: ， ， In the formula, Indicates real-time measurement of well inclination angle. , , The MEMS accelerometer after compensation and calibration is x , y , z The acceleration component on the axis, The MEMS gyroscope is compensated and calibrated. y The angular velocity component about the axis, is the maximum angular velocity threshold measured by the gyroscope, The MEMS magnetometer after compensation and calibration is x The magnetic field component on the axis, is the maximum magnetic field strength threshold measured by the magnetometer, is the weighted adjustment coefficient of well inclination; To measure the azimuth in real time, , , They are the MEMS magnetometer after compensation and calibration. x , y , z The magnetic field component on the axis, The MEMS gyroscope is compensated and calibrated. z The angular velocity component about the axis, The MEMS accelerometer is compensated and calibrated. y The acceleration component on the axis, is the maximum acceleration threshold measured by the accelerometer, is the azimuth weighting adjustment factor.

4. The system according to claim 1, characterized in that The updating of the borehole measurement trajectory according to the real-time measured well inclination angle, the real-time measured azimuth angle, the historically measured well inclination angle and the historically measured azimuth angle comprises: ， ， ， ， ， In the formula, represents the weighted measured well inclination angle, represents the weighted measurement azimuth, Indicates at time t Real-time measurement of well inclination, Indicates at time t Real-time measurement of azimuth, Indicates at time t -1 historical measured well inclination angle, Indicates at time t -1 historical measurement azimuth, represents the real-time dependence coefficient of well inclination angle, represents the real-time dependency coefficient of azimuth; Indicates the borehole measurement trajectory at time t Updated real-time wellbore position, Indicates the borehole measurement trajectory at time t -1 Determined historical wellbore locations; Represents the displacement, which is determined according to the downhole equipment speed and sampling time interval.

5. The system according to claim 4, characterized in that The calculating of the trajectory deviation between the borehole measurement trajectory and the drilling operation design trajectory, and determining the target sensor sampling frequency matching the trajectory deviation, comprises: Calculate the cumulative trajectory deviation: ， ， In the formula, is the cumulative trajectory deviation, which represents the sum of the deviations between the actual wellbore trajectory and the designed trajectory within the deviation analysis window; is the number of sampling measurements within the deviation analysis window, It is at the moment The trajectory deviation, The drilling operation design trajectory at time The target wellbore location; If the cumulative trajectory deviation exceeds a preset trajectory deviation threshold, the sampling frequency is increased according to the cumulative trajectory deviation, and if the cumulative trajectory deviation is less than the trajectory deviation threshold, the sampling frequency is reduced according to the cumulative trajectory deviation to determine the target sensor sampling frequency: ， ， In the formula, It is calculated based on the cumulative trajectory deviation at time t The sampling frequency, is the trajectory deviation threshold, is the adjustment factor used to control the sampling frequency, is the basic sampling frequency, represents the target sensor sampling frequency, represents the upper limit of the sampling frequency, and Indicates the lower limit of the sampling frequency.

6. The system according to any one of claims 1 to 5, characterized in that: The system also includes a data transmission module for performing data relay transmission between the host computer module and the downhole sensor module or the single-chip microprocessor.

7. A method for realizing a low-power and high-precision well deviation measurement system, characterized in that: The low-power and high-precision well deviation measurement system comprises a downhole sensor module, a single-chip microprocessor and a host computer module, wherein the downhole sensor module comprises a MEMS sensing unit and an environmental sensing unit; the MEMS sensing unit comprises a MEMS accelerometer, a MEMS magnetometer and a MEMS gyroscope, and the environmental sensing unit comprises a temperature sensor and a pressure sensor; wherein the method comprises: Measuring borehole acceleration based on the MEMS accelerometer, measuring the direction of the borehole geomagnetic field based on the MEMS magnetometer, measuring the borehole angular velocity based on the MEMS gyroscope, measuring the downhole ambient temperature based on the temperature sensor, and measuring the downhole ambient pressure based on the pressure sensor; Based on the host computer module, perform the following operations: Compensating and calibrating the wellbore acceleration, the wellbore geomagnetic field direction and the wellbore angular velocity according to the downhole ambient temperature and the downhole ambient pressure; The compensated and calibrated borehole acceleration, borehole geomagnetic field direction and borehole angular velocity are integrated to calculate the real-time measurement of well inclination angle and real-time measurement of azimuth angle. updating the borehole measurement trajectory according to the real-time measured well inclination angle, the real-time measured azimuth angle, the historically measured well inclination angle and the historically measured azimuth angle; Calculating a trajectory deviation between the wellbore measurement trajectory and the drilling operation design trajectory, and determining a target sensor sampling frequency that matches the trajectory deviation; Based on the single chip microprocessor, the operating frequency of the MEMS sensing unit is set to the target sensor sampling frequency; Wherein, the compensation calibration of the borehole acceleration, the borehole geomagnetic field direction and the borehole angular velocity according to the downhole ambient temperature and the downhole ambient pressure includes: Calculating the real-time change degree of the downhole environment according to the downhole environment temperature and the downhole environment pressure and in combination with the downhole environment historical measurement values; Determining a target filtering strategy that matches the real-time change degree of the downhole environment, and filtering the wellbore acceleration, the wellbore geomagnetic field direction, and the wellbore angular velocity according to the target filtering strategy, including: When the real-time change degree of the downhole environment is lower than a first change degree threshold, filtering the wellbore acceleration, the wellbore geomagnetic field direction and the wellbore angular velocity according to a low-pass filter; When the real-time change degree of the downhole environment is greater than the first change degree threshold and less than the second change degree threshold, an adaptive Kalman filter is used to filter the wellbore acceleration, the wellbore geomagnetic field direction and the wellbore angular velocity; When the real-time change degree of the downhole environment is greater than the second change degree threshold, using enhanced filtering to filter the wellbore acceleration, the wellbore geomagnetic field direction and the wellbore angular velocity; Substituting the downhole ambient temperature, the downhole ambient pressure, and the filtered borehole acceleration, borehole geomagnetic field direction, and borehole angular velocity into a polynomial regression function to obtain corresponding compensated and calibrated borehole acceleration, borehole geomagnetic field direction, and borehole angular velocity; Wherein, the polynomial regression function is: ， ， ， In the formula, represents the compensated and calibrated borehole acceleration, represents the compensated and calibrated borehole geomagnetic field direction, represents the compensated and calibrated borehole angular velocity, Indicates time t The downhole ambient temperature, Indicates time t downhole environmental pressure; Represents the filtered moment t Wellbore acceleration data, Represents the filtered moment t The borehole geomagnetic field direction data, Represents the filtered moment t Wellbore angular velocity data; represents the polynomial regression coefficients used to fit the wellbore acceleration, represents the polynomial regression coefficients used to fit the borehole geomagnetic field direction, Represents the polynomial regression coefficients used to fit the borehole angular velocity.

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