A multi-type sensor calibration detection method and system

Abstract

The present application relates to the technical field of sensor calibration, and particularly relates to a multi-type sensor calibration detection method and system. The method comprises the following steps: integrating a plurality of high-temperature acceleration sensors, high-temperature directional sensors and high-temperature vibration sensors to be calibrated in a logging-while-drilling high-temperature environment cabin corresponding to high-temperature, high-pressure and strong vibration simulation functions, and performing zero output static calibration to obtain a plurality of zero drift static calibration sensors; simulating and designing a temperature, pressure and vibration frequency constraint condition range corresponding to a wellhead to a well bottom through the logging-while-drilling high-temperature environment cabin, and performing dynamic simulation calibration to generate a plurality of dynamic calibration calibration sensors; simulating corresponding instantaneous strong impact interference constraints and temperature sudden change interference constraints in a downhole through the logging-while-drilling high-temperature environment cabin, and performing sensor detection verification optimization to generate a plurality of calibration verification optimization sensors. The present application can realize high-precision logging-while-drilling high-temperature sensor calibration detection.

Description

Technical Field

[0001] This invention relates to the field of sensor calibration technology, and in particular to a calibration and detection method and system for multiple types of sensors. Background Technology

[0002] Logging While Drilling (LWD), as an important real-time drilling monitoring technology, has been widely used for dynamic monitoring of underground geology and the downhole environment. This technology utilizes sensors to collect downhole data in real time during the drilling process, such as acceleration, pressure, temperature, azimuth, and vibration, providing more comprehensive and accurate formation information. However, the corresponding LWD sensors often need to withstand high temperatures, high pressures, and complex mechanical vibration environments, thus requiring high reliability and accuracy. Currently, the sensors in LWD equipment mainly include high-temperature accelerometers, high-temperature orientation sensors, and high-temperature vibration sensors. Because these sensors operate in extremely harsh environments, their performance can change. However, existing calibration methods are mostly for single-type sensors, lacking an effective mechanism for joint calibration of multiple sensors. Especially in high-temperature environments, the mutual influence between sensors and signal output drift factors are not effectively considered, thus reducing the accuracy of multi-type sensor calibration. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a calibration and detection method and system for multiple types of sensors to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a multi-type sensor calibration and detection method includes the following steps:

[0005] Step S1: Integrate and install various high-temperature accelerometers, high-temperature orientation sensors, and high-temperature vibration sensors into a logging-while-drilling high-temperature environment chamber that integrates high-temperature, high-pressure, and strong vibration simulation functions to generate multiple types of high-temperature sensors to be calibrated; design corresponding ambient temperature and pressure vibration-free static conditions in the logging-while-drilling high-temperature environment chamber, and perform zero-point output static calibration on multiple types of high-temperature sensors to be calibrated based on the ambient temperature and pressure vibration-free static conditions to obtain multiple types of zero-point drift static calibration sensors;

[0006] Step S2: Simulate the temperature, pressure, and vibration frequency constraints from wellhead to bottom using the high-temperature environment chamber during logging-while-drilling. Determine the sub-conditions for different downhole environmental conditions during logging-while-drilling based on these constraints. Then, in response to these sub-conditions, use multiple types of zero-drift static calibration sensors to synchronously collect timestamps, outputting sensor output signal data corresponding to different downhole conditions, including downhole acceleration, downhole orientation, and downhole vibration sensor output signals.

[0007] Step S3: Based on the sensor output signal data of each sensor under different downhole working conditions, perform dynamic simulation calibration of multiple types of zero-drift static calibration sensors to generate multiple types of dynamic calibration sensors.

[0008] Step S4: Simulate the instantaneous strong impact interference constraints and temperature change interference constraints in the downhole environment chamber through logging while drilling. Based on the instantaneous strong impact interference constraints and temperature change interference constraints, perform sensing detection verification and optimization on multiple types of dynamic calibration sensors to generate multiple types of calibration verification optimized sensors.

[0009] Furthermore, step S1 includes the following steps:

[0010] Step S11: Integrate and install various high-temperature acceleration sensors, high-temperature orientation sensors and high-temperature vibration sensors to be calibrated in the high-temperature environment chamber of logging while drilling, which has the functions of simulating high temperature, high pressure and strong vibration. Use special heat insulation and pressure bearing materials to ensure that the environment inside the chamber is stable and uniformly distributed, so as to generate multiple types of high-temperature sensors to be calibrated.

[0011] Step S12: Design the corresponding ambient temperature and pressure vibration-free static conditions for the high-temperature environment chamber during drilling logging;

[0012] Step S13: Based on the static conditions of normal temperature and pressure and vibration-free static conditions, perform static condition zero-point synchronous acquisition on multiple types of high temperature sensors to be calibrated, so as to obtain the zero-point output value of each sensor under static conditions.

[0013] Step S14: Obtain the detection sensitivity and inherent zero drift of each sensor through multiple types of high temperature sensors to be calibrated, and perform zero drift compensation correction on the zero output value of the corresponding sensor under static conditions based on the detection sensitivity and inherent zero drift of each sensor, so as to obtain the zero drift compensation error correction value of each sensor under static conditions.

[0014] Step S15: Based on the zero-point drift compensation error correction value of each sensor under static conditions, perform zero-point output static calibration on multiple types of high-temperature sensors to be calibrated, and obtain multiple types of zero-point drift static calibration sensors.

[0015] Furthermore, step S14 includes the following steps:

[0016] Step S141: Obtain the microstructure changes of each sensor under normal temperature and pressure by using multiple types of high temperature sensors to be calibrated, including the nonlinear change of sensor resistance with temperature and the change of charge output of piezoelectric element under pressure. Based on the microstructure changes of each sensor under normal temperature and pressure, perform detection sensitivity fitting analysis on the corresponding multiple types of high temperature sensors to be calibrated to obtain the detection sensitivity of each sensor.

[0017] Step S142: Obtain the weak electromagnetic interference and airflow micro-disturbance interference in the high-temperature environment chamber of the logging-while-drilling system, and perform sensor inherent drift analysis on multiple types of high-temperature sensors to be calibrated based on the weak electromagnetic interference and airflow micro-disturbance interference to obtain the inherent zero-point drift of each sensor.

[0018] Step S143: Calculate the time series statistics of the zero-point output values ​​of each sensor under static conditions to obtain the zero-point output statistics of each sensor, including the mean of the zero-point output time period and the standard deviation of the zero-point output time period.

[0019] Step S144: Based on the detection sensitivity, inherent zero-point drift, and zero-point output statistics of each sensor, the zero-point drift compensation correction formula is used to perform zero-point drift compensation correction on the corresponding zero-point output value of the corresponding sensor under static conditions, so as to obtain the zero-point drift compensation error correction value of each sensor under static conditions.

[0020] Furthermore, the zero-position drift compensation correction calculation formula mentioned in step S144 is as follows:

[0021] ;

[0022] In the formula, For the first The zero-point drift compensation error correction value of each sensor under static conditions. Where 1 represents a high-temperature accelerometer, 2 represents a high-temperature orientation sensor, and 3 represents a high-temperature vibration sensor. The time interval for the sensor's zero-position output. Output the time variable parameter to zero position. For the first The detection sensitivity corresponding to each sensor For the first Each sensor in time The inherent zero-point drift at that location, For the first Sensitivity weighting coefficients for each sensor For the first Each sensor in time The ambient temperature, For the first Each sensor in time The zero-bit output value at that position. For the first Average value of zero-point output for each sensor during the corresponding time period. For the first Standard deviation of the zero-point output time period corresponding to each sensor For the first Temperature compensation coefficients for each sensor This is the correction coefficient for the zero-position drift compensation error correction value.

[0023] Furthermore, step S2 includes the following steps:

[0024] Step S21: Simulate the range of temperature, pressure and vibration frequency constraints from wellhead to bottom using the high-temperature environment chamber of logging while drilling.

[0025] Step S22: Determine the sub-conditions for different downhole environmental conditions during drilling based on the temperature, pressure, and vibration frequency constraints from the wellhead to the bottom of the well.

[0026] Step S23: Based on the different downhole environmental conditions during drilling, perform detection clock synchronization response control on multiple types of zero-point drift static calibration sensors to generate detection response synchronization clock control commands for each sensor.

[0027] Step S24: Apply the detection response synchronization clock control command corresponding to each sensor to the corresponding multi-type zero-drift static calibration sensor for timestamp synchronization acquisition, so as to output the sensor output signal data corresponding to each sensor under different downhole operating conditions, including downhole acceleration, downhole orientation and downhole vibration sensor output signals.

[0028] Furthermore, the temperature, pressure, and vibration frequency constraints from the wellhead to the bottom of the well mentioned in step S21 are specifically a temperature range of 50℃-200℃, a pressure range of 0-50MPa, and a vibration frequency range of 10Hz-100Hz.

[0029] Furthermore, step S3 includes the following steps:

[0030] Step S31: Perform frequency domain conversion on the sensor output signal data of each sensor under different downhole operating conditions to generate frequency domain representation of the output signal of each sensor under different downhole operating conditions.

[0031] Step S32: Based on the frequency domain representation of the downhole acceleration output signal under different downhole operating conditions, perform signal phase difference analysis on the frequency domain representation of the corresponding downhole vibration output signal to obtain the signal frequency domain phase difference between the downhole acceleration output and the downhole vibration output;

[0032] Step S33: Perform directional frequency band energy and modulation depth analysis on the frequency domain representation of the downhole directional output signal under different downhole operating conditions to obtain the frequency band energy distribution and frequency band modulation depth of the downhole directional output signal.

[0033] Step S34: Based on the signal frequency domain phase difference between downhole acceleration output and downhole vibration output, the energy distribution of the frequency band corresponding to the downhole directional output signal, and the frequency band modulation depth, perform dynamic simulation calibration on multiple types of zero-drift static calibration sensors. This is to dynamically calibrate the high-temperature acceleration sensor and the high-temperature vibration sensor according to the signal frequency domain phase difference between downhole acceleration output and downhole vibration output, and to dynamically calibrate the high-temperature directional sensor according to the energy distribution of the frequency band corresponding to the downhole directional output signal and the frequency band modulation depth, thereby generating multiple types of dynamic calibration sensors.

[0034] Furthermore, step S4 includes the following steps:

[0035] Step S41: Simulate the instantaneous strong impact disturbance constraints and temperature change disturbance constraints in the well using the high-temperature environment chamber of logging while drilling.

[0036] Step S42: Perform impact stress propagation attenuation intensity analysis on the instantaneous strong impact interference constraint corresponding to the well, and obtain the instantaneous impact stress propagation attenuation intensity corresponding to the well.

[0037] Step S43: Calculate the rate of temperature thermal expansion for the temperature change disturbance constraint corresponding to the downhole, so as to analyze the dynamic evolution process of the temperature field in the simulation chamber from steady state to sudden change state, and obtain the rate of temperature thermal expansion for the downhole.

[0038] Step S44: Obtain the sensor output signals corresponding to multiple types of dynamic calibration sensors, and use the output interference response evaluation calculation formula based on the instantaneous impact stress propagation attenuation intensity and temperature thermal expansion rate corresponding to the downhole to evaluate and analyze the interference response of the sensor output signals corresponding to multiple types of dynamic calibration sensors, and obtain the degree of influence of instantaneous strong impact attenuation intensity and temperature change rate on the interference response of sensor output.

[0039] Step S45: Based on the influence of instantaneous strong impact attenuation intensity and temperature change rate on the interference response of sensor output, perform sensing detection verification optimization on multiple types of dynamic calibration sensors, and generate multiple types of calibration verification optimized sensors.

[0040] Furthermore, the specific formula for calculating the output interference response evaluation in step S44 is as follows:

[0041] ;

[0042] In the formula, For the first The degree of impact of interference response on each sensor To determine the time range of the impact of the output interference response, For the time variable of interference response assessment, For the first Each sensor during the time period The instantaneous impact stress propagation attenuation intensity at the corresponding location downhole. For the sensor to output a sensitivity constant, For the first Each sensor during the time period The rate of thermal expansion change at the corresponding temperature downhole location. The effect of thermal expansion due to temperature on the attenuation of impact stress is a factor. For the first Each sensor during the time period The corresponding sensor output signal, This is the attenuation factor of the sensor output signal strength. This is a correction factor for the degree of influence of the interference response.

[0043] Furthermore, the present invention also provides a multi-type sensor calibration and detection system for performing the multi-type sensor calibration and detection method described above, the multi-type sensor calibration and detection system comprising:

[0044] The zero-point output static calibration module is used to integrate various high-temperature accelerometers, high-temperature orientation sensors, and high-temperature vibration sensors into a logging-while-drilling high-temperature environment chamber that integrates high-temperature, high-pressure, and strong vibration simulation functions, thereby generating multiple types of high-temperature sensors to be calibrated. By designing corresponding ambient temperature, ambient pressure, and vibration-free static conditions in the logging-while-drilling high-temperature environment chamber, and performing zero-point output static calibration on multiple types of high-temperature sensors to be calibrated based on these ambient temperature, ambient pressure, and vibration-free static conditions, multiple types of zero-point drift static calibration sensors are obtained.

[0045] The downhole operating condition synchronous acquisition module is used to simulate the temperature, pressure, and vibration frequency constraint range from the wellhead to the bottom of the well through the high-temperature environment chamber of the logging-while-drilling system. Based on the temperature, pressure, and vibration frequency constraint range from the wellhead to the bottom of the well, it determines the sub-conditions of different downhole operating conditions during drilling. Based on the different sub-conditions of the downhole operating conditions during drilling, it responds to the timestamp synchronous acquisition of multiple types of zero-point drift static calibration sensors to output the sensor output signal data of each sensor under different downhole operating conditions.

[0046] The downhole dynamic simulation calibration module is used to perform dynamic simulation calibration of multiple types of zero-point drift static calibration sensors based on the sensor output signal data of each sensor under different downhole working conditions, thereby generating multiple types of dynamic calibration sensors.

[0047] The interference constraint detection and verification module is used to simulate the instantaneous strong impact interference constraint and temperature change interference constraint in the well through the high temperature environment chamber of logging while drilling. Based on the instantaneous strong impact interference constraint and temperature change interference constraint, the module performs sensing detection and verification optimization on multiple types of dynamic calibration sensors, thereby generating multiple types of calibration and verification optimized sensors.

[0048] The beneficial effects of this invention are:

[0049] 1. Compared with the prior art, the multi-type sensor calibration and testing method proposed in this invention has the following advantages: by integrating various high-temperature accelerometers, high-temperature orientation sensors, and high-temperature vibration sensors into a high-temperature environment chamber for logging while drilling, which simulates high temperature, high pressure, and strong vibration, extreme environmental conditions are simulated. This effectively generates multiple types of high-temperature sensors to be calibrated. The core effect of this step is to provide a simulation platform that closely approximates the actual downhole working environment for sensor calibration and testing, avoiding the high cost and potential risks of direct downhole calibration. By designing static conditions with normal temperature, normal pressure, and no vibration, zero-point output static calibration is completed, which can initially eliminate the zero-drift effect of the sensor in the static state. This static calibration process lays the foundation for subsequent dynamic calibration, ensuring the initial accuracy and stability of the sensor. At the same time, it provides a reliable benchmark reference for sensors in practical applications, effectively improving measurement accuracy and data reliability. Especially when facing extreme conditions such as high temperature, high pressure, and strong vibration, this method can obtain basic performance data of the sensor and solve the impact of changes in the external environment, thus ensuring the long-term stability and reliability of the sensor. Secondly, by simulating the temperature, pressure, and vibration frequency constraints from the wellhead to the bottom of the well through the high-temperature environment chamber of logging while drilling, the actual impact of the downhole environment on sensor performance can be accurately reflected. Different operating conditions are divided according to the specific conditions of the downhole environment, and the output signal data of each sensor is collected synchronously under these conditions. This provides sensor response data under real environmental conditions, thereby ensuring efficient operation and accurate output of the sensors under various working conditions, reducing the error rate in actual downhole operations. By synchronously collecting data with precise timestamps, possible interference factors such as signal delay can also be effectively eliminated, thereby improving the accuracy and timeliness of the data. Then, by performing dynamic simulation calibration on the sensor based on previously synchronously acquired data, the dynamic changes of the actual downhole working environment can be simulated to reveal the sensor's response characteristics under various dynamic environments, especially the impact of rapid changes in temperature and pressure, and frequent changes in vibration on sensor performance. This dynamic calibration process can effectively eliminate signal output drift errors caused by environmental changes and between sensors, ensuring that the sensor can continuously provide accurate measurement data in practical applications. By comparing and correcting the output signals of various sensors under different downhole working conditions with the actual calibration data, the sensor performance can be optimized, thereby improving the wide applicability and reliability of the calibration results. It can greatly enhance the sensor's response capability in complex downhole environments, enabling it to maintain high accuracy and stability under harsh conditions such as high temperature, high pressure, and vibration, ensuring the accuracy and reliability of logging sensor calibration and testing.Finally, by simulating sudden strong impact interference and temperature change interference in the well, it can effectively take into account extreme working conditions such as sudden impact and temperature change during downhole operations. These factors usually lead to unstable sensor performance or drastic fluctuations in output signal. By simulating these interference conditions, the sensor can be comprehensively tested during the experimental stage, potential performance problems can be identified in time, and necessary adjustments and optimizations can be made to the sensor. This process can greatly improve the reliability of the sensor in the actual downhole working environment, ensuring that the sensor can still work normally and output accurate data under sudden conditions. This enables the calibrated sensor to cope with various common interference factors in the downhole environment, thereby improving the sensor's adaptability and stability in complex environments, thus providing a reliable guarantee for subsequent logging data.

[0050] 2. The multi-type sensor calibration and testing system proposed in this invention consists of a zero-point output static calibration module, a downhole operating condition synchronous acquisition module, a downhole dynamic simulation calibration module, and an interference constraint detection and verification module. It can realize the calibration and testing method for any type of sensor described in this invention. It is used to combine the operations between the computer programs running on each module to realize the calibration and testing method for multiple types of sensors. The internal structure of the system cooperates with each other, which can greatly reduce repetitive work and manpower input, and can quickly and effectively provide a more accurate and efficient calibration and testing process for multiple types of sensors, thereby simplifying the operation process of the multi-type sensor calibration and testing system. Attached Figure Description

[0051] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0052] Figure 1 This is a schematic diagram of the steps of the multi-type sensor calibration and detection method of the present invention;

[0053] Figure 2 for Figure 1 A detailed flowchart of step S1;

[0054] Figure 3 for Figure 2 A detailed flowchart of step S14. Detailed Implementation

[0055] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0056] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0057] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0058] To achieve the above objectives, please refer to Figures 1 to 2 This invention provides a calibration and detection method for multiple types of sensors, the method comprising the following steps:

[0059] Step S1: Integrate and install various high-temperature accelerometers, high-temperature orientation sensors, and high-temperature vibration sensors into a logging-while-drilling high-temperature environment chamber that integrates high-temperature, high-pressure, and strong vibration simulation functions to generate multiple types of high-temperature sensors to be calibrated; design corresponding ambient temperature and pressure vibration-free static conditions in the logging-while-drilling high-temperature environment chamber, and perform zero-point output static calibration on multiple types of high-temperature sensors to be calibrated based on the ambient temperature and pressure vibration-free static conditions to obtain multiple types of zero-point drift static calibration sensors;

[0060] Step S2: Simulate the temperature, pressure, and vibration frequency constraints from wellhead to bottom using the high-temperature environment chamber during logging-while-drilling. Determine the sub-conditions for different downhole environmental conditions during logging-while-drilling based on these constraints. Then, in response to these sub-conditions, use multiple types of zero-drift static calibration sensors to synchronously collect timestamps, outputting sensor output signal data corresponding to different downhole conditions, including downhole acceleration, downhole orientation, and downhole vibration sensor output signals.

[0061] Step S3: Based on the sensor output signal data of each sensor under different downhole working conditions, perform dynamic simulation calibration of multiple types of zero-drift static calibration sensors to generate multiple types of dynamic calibration sensors.

[0062] Step S4: Simulate the instantaneous strong impact interference constraints and temperature change interference constraints in the downhole environment chamber through logging while drilling. Based on the instantaneous strong impact interference constraints and temperature change interference constraints, perform sensing detection verification and optimization on multiple types of dynamic calibration sensors to generate multiple types of calibration verification optimized sensors.

[0063] In the embodiments of this invention, please refer to Figure 1 The diagram shown is a flowchart illustrating the steps of the multi-type sensor calibration and detection method of the present invention. In this example, the multi-type sensor calibration and detection method includes the following steps:

[0064] Step S1: Integrate and install various high-temperature accelerometers, high-temperature orientation sensors, and high-temperature vibration sensors into a logging-while-drilling high-temperature environment chamber that integrates high-temperature, high-pressure, and strong vibration simulation functions to generate multiple types of high-temperature sensors to be calibrated; design corresponding ambient temperature and pressure vibration-free static conditions in the logging-while-drilling high-temperature environment chamber, and perform zero-point output static calibration on multiple types of high-temperature sensors to be calibrated based on the ambient temperature and pressure vibration-free static conditions to obtain multiple types of zero-point drift static calibration sensors;

[0065] In this embodiment of the invention, the high-temperature accelerometer, high-temperature orientation sensor, and high-temperature vibration sensor to be calibrated are integrated and installed in the high-temperature environment chamber of the logging-while-drilling system. This environment chamber is designed to simulate high-temperature, high-pressure, and strong vibration environments to ensure that the sensors can exhibit corresponding working states in a real downhole environment. Each type of sensor is installed in a corresponding position to facilitate the subsequent acquisition and calibration of the output signals of different types of sensors. In this step, it is important to simulate a static environment with normal temperature, normal pressure, and no vibration. By precisely controlling the temperature, pressure, and vibration state inside the chamber, a stable calibration environment is provided without external interference. All sensors to be calibrated are zero-point output calibrated according to the static conditions of normal temperature, normal pressure, and no vibration. The calibration operation is performed using a dedicated zero-point calibration device. The device can accurately measure the static zero-point output of each sensor and record the initial voltage or current output of each sensor. At this time, the zero-point drift of each sensor is recorded by the system and initially compensated to ensure that accurate sensor response signals can be obtained in the subsequent calibration process, ultimately resulting in multi-type zero-point drift static calibration sensors.

[0066] Step S2: Simulate the temperature, pressure, and vibration frequency constraints from wellhead to bottom using the high-temperature environment chamber during logging-while-drilling. Determine the sub-conditions for different downhole environmental conditions during logging-while-drilling based on these constraints. Then, in response to these sub-conditions, use multiple types of zero-drift static calibration sensors to synchronously collect timestamps, outputting sensor output signal data corresponding to different downhole conditions, including downhole acceleration, downhole orientation, and downhole vibration sensor output signals.

[0067] In this embodiment of the invention, a high-temperature environment chamber for logging while drilling simulates the environmental constraints of temperature, pressure, and vibration frequency from the wellhead to the bottom of the well. This chamber contains a simulation system capable of precisely controlling different temperatures, pressures, and vibration frequencies, covering the range of operating conditions from the wellhead to the bottom of the well. Specifically, the temperature range is between 50°C and 200°C, the pressure range is between 0 MPa and 50 MPa, and the vibration frequency range, depending on the operating characteristics of the downhole equipment, ranges from 10 Hz to 100 Hz. By precisely controlling these parameters, multiple different downhole operating conditions are created within the chamber. After determining the different downhole operating condition ranges, these conditions are further subdivided into multiple sub-conditions based on the required response characteristics of the sensors. For example, temperature, pressure, and vibration frequency each correspond to several specific values ​​or ranges of variation. By setting these sub-conditions, it is ensured that the response data under each sub-condition can reflect the true performance of the sensor under different operating conditions. Under each condition, timestamps are synchronized to collect the output signals of various sensors, including acceleration data from the accelerometer, orientation change data from the orientation sensor, and vibration data from the vibration sensor. During data collection, high-frequency data acquisition is performed through a sampling card and signal processing unit to ensure accurate recording of the output data of each sensor. Finally, the sensor output signal data corresponding to each sensor under different downhole operating conditions are output, including downhole acceleration, downhole orientation, and downhole vibration sensor output signals.

[0068] Step S3: Based on the sensor output signal data of each sensor under different downhole working conditions, perform dynamic simulation calibration of multiple types of zero-drift static calibration sensors to generate multiple types of dynamic calibration sensors.

[0069] In this embodiment of the invention, dynamic simulation calibration of multiple types of zero-drift static calibration sensors is performed based on previously collected sensor output signal data. The key to this process is to use the sensor response data under different environmental conditions, combined with existing static calibration data, to perform dynamic response compensation and calibration. First, by comparing the collected sensor output signals with changes in actual downhole conditions (such as temperature, pressure, vibration frequency, etc.), a dynamic response model of the sensor is established. To this end, a dynamic calibration method based on mathematical modeling is also adopted, which uses the relationship between the sensor output signal and known environmental condition data to perform error analysis and compensation. By comparing the sensor data after static calibration with the dynamic response data, the dynamic drift of the sensor caused by factors such as temperature, pressure, and vibration can be identified. Then, these dynamic drifts are corrected by calibration algorithms to make the sensor output signal closer to the true value. During this calibration process, the system uses dynamic calibration technology to continuously adjust the sensor response curve until the output of various sensors under different environmental conditions stabilizes within an ideal range, ultimately generating multiple types of dynamically calibrated sensors.

[0070] Step S4: Simulate the instantaneous strong impact interference constraints and temperature change interference constraints in the downhole environment chamber through logging while drilling. Based on the instantaneous strong impact interference constraints and temperature change interference constraints, perform sensing detection verification and optimization on multiple types of dynamic calibration sensors to generate multiple types of calibration verification optimized sensors.

[0071] In this embodiment of the invention, the constraints of instantaneous strong impact interference and sudden temperature change interference in the wellbore are simulated through a high-temperature environment chamber during logging-while-drilling. This operation evaluates the performance of sensors in the face of these extreme environments by simulating instantaneous impacts and sudden temperature changes (such as severe impacts caused by sudden equipment start-up or shutdown, and rapid fluctuations in downhole temperature). Instantaneous strong impact interference is usually caused by rapidly changing mechanical loads, while sudden temperature change interference is mainly caused by rapid changes in the downhole working environment. Under these interference conditions, sensors will experience relatively severe response fluctuations, especially accelerometers and vibration sensors, whose output signals will change significantly. This process is used to verify and optimize the sensors. The sensor output signal needs to be collected under each interference condition and compared with the standard interference response model. Through data analysis, abnormal fluctuations in the sensor output signal can be identified and further optimization can be performed. In this process, the optimization strategy includes the introduction of filtering algorithms, signal smoothing algorithms, and fault tolerance mechanisms to ensure that the sensor can maintain stable and accurate output under extreme environments. After the above optimization, the sensor performance is verified and improved, ensuring that it can provide high-precision sensing data in complex and extreme downhole environments. At this point, the sensor can be regarded as a fully calibrated and verified sensor, and finally, multiple types of calibrated and verified optimized sensors are generated.

[0072] Furthermore, step S1 includes the following steps:

[0073] Step S11: Integrate and install various high-temperature acceleration sensors, high-temperature orientation sensors and high-temperature vibration sensors to be calibrated in the high-temperature environment chamber of logging while drilling, which has the functions of simulating high temperature, high pressure and strong vibration. Use special heat insulation and pressure bearing materials to ensure that the environment inside the chamber is stable and uniformly distributed, so as to generate multiple types of high-temperature sensors to be calibrated.

[0074] Step S12: Design the corresponding ambient temperature and pressure vibration-free static conditions for the high-temperature environment chamber during drilling logging;

[0075] Step S13: Based on the static conditions of normal temperature and pressure and vibration-free static conditions, perform static condition zero-point synchronous acquisition on multiple types of high temperature sensors to be calibrated, so as to obtain the zero-point output value of each sensor under static conditions.

[0076] Step S14: Obtain the detection sensitivity and inherent zero drift of each sensor through multiple types of high temperature sensors to be calibrated, and perform zero drift compensation correction on the zero output value of the corresponding sensor under static conditions based on the detection sensitivity and inherent zero drift of each sensor, so as to obtain the zero drift compensation error correction value of each sensor under static conditions.

[0077] Step S15: Based on the zero-point drift compensation error correction value of each sensor under static conditions, perform zero-point output static calibration on multiple types of high-temperature sensors to be calibrated, and obtain multiple types of zero-point drift static calibration sensors.

[0078] As an embodiment of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart of step S1 is shown below. In this embodiment, step S1 includes the following steps:

[0079] Step S11: Integrate and install various high-temperature acceleration sensors, high-temperature orientation sensors and high-temperature vibration sensors to be calibrated in the high-temperature environment chamber of logging while drilling, which has the functions of simulating high temperature, high pressure and strong vibration. Use special heat insulation and pressure bearing materials to ensure that the environment inside the chamber is stable and uniformly distributed, so as to generate multiple types of high-temperature sensors to be calibrated.

[0080] In this embodiment of the invention, various high-temperature accelerometers, high-temperature orientation sensors, and high-temperature vibration sensors are integrated and installed in a dedicated logging-while-drilling high-temperature environment chamber. This environment chamber is designed to meet the conditions for simulating high temperature, high pressure, and strong vibration, in order to simulate the real working environment in practical applications. To ensure the stability and uniform distribution of the environment, highly efficient heat-insulating and pressure-bearing materials, such as ceramic fibers, high-temperature alloys, and composite materials, are selected. These materials possess excellent heat insulation properties and can withstand extreme high-temperature and high-pressure conditions that may occur during operation. During installation, each sensor is first precisely positioned using precision mechanical tools to ensure uniform spatial distribution within the environment chamber, avoiding data errors caused by uneven spatial distribution. Simultaneously, the electrical connections of all sensors undergo rigorous testing to ensure stable and interference-free signal transmission and long-term stable operation in a high-temperature environment. The temperature, pressure, and vibration parameters within the environment chamber are monitored in real time using precise testing instruments (such as thermocouples, pressure sensors, and vibration monitoring instruments), ultimately generating multiple types of high-temperature sensors to be calibrated.

[0081] Step S12: Design the corresponding ambient temperature and pressure vibration-free static conditions for the high-temperature environment chamber during drilling logging;

[0082] In this embodiment of the invention, a static environment with ambient temperature and pressure and no vibration is required to facilitate zero-point synchronous data acquisition of the high-temperature sensor to be calibrated. To achieve this static environmental condition, a constant temperature and pressure static chamber is designed. This chamber employs a precise temperature control system, which maintains the temperature within the chamber within the ambient temperature range by adjusting the working status of the heater and cooling system. The temperature control accuracy is required to reach ±0.1°C to ensure that the sensor test at the standard temperature is not affected by environmental changes. The pressure is maintained at ambient pressure, and a high-precision pressure regulation system is used to ensure that the pressure value fluctuation does not exceed ±1 kPa. At the same time, in order to eliminate vibration interference, a vibration isolation system is installed in the chamber. By installing vibration isolation pads and using suspension devices, it is ensured that external vibration does not affect the test environment. The chamber is also equipped with multiple monitoring devices to display temperature, pressure, and vibration data in real time. Finally, the corresponding ambient temperature and pressure vibration-free static conditions are designed and generated.

[0083] Step S13: Based on the static conditions of normal temperature and pressure and vibration-free static conditions, perform static condition zero-point synchronous acquisition on multiple types of high temperature sensors to be calibrated, so as to obtain the zero-point output value of each sensor under static conditions.

[0084] In this embodiment of the invention, a dedicated data acquisition system (such as a high-precision digital signal processing unit or data acquisition card) is used to synchronously acquire data from each sensor. This system supports multi-channel synchronous operation and can accurately record the zero-point output value of each sensor under static conditions. To avoid interference from changes in the external environment, all acquisition operations are performed after the temperature, pressure, and vibration conditions have stabilized. During acquisition, it is ensured that the output signal of each sensor is clearly and accurately recorded, and the sampling frequency is not less than 100 times per second to ensure the accuracy of the zero point. During the acquisition process, zero-point detection technology must be used to avoid the influence of system noise on the data and to ensure that the zero-point output value of each sensor reflects its own characteristics and is not affected by external factors. Finally, the zero-point output value of each sensor under static conditions is obtained.

[0085] Step S14: Obtain the detection sensitivity and inherent zero drift of each sensor through multiple types of high temperature sensors to be calibrated, and perform zero drift compensation correction on the zero output value of the corresponding sensor under static conditions based on the detection sensitivity and inherent zero drift of each sensor, so as to obtain the zero drift compensation error correction value of each sensor under static conditions.

[0086] In this embodiment of the invention, the zero-point drift of each sensor is compensated and corrected. Based on the detection sensitivity and inherent zero-point drift of each sensor, error analysis is performed using the aforementioned collected data. First, the sensitivity of each sensor is experimentally tested to obtain its corresponding sensitivity curve, and the drift under different operating conditions is calculated. Sensitivity can be obtained by applying a standard signal excitation to the sensor (such as using a force or temperature source with known accuracy) and experimentally measuring the relationship between the output signal and the input signal. Next, the zero-point output value and sensitivity of each sensor under static conditions are combined to calculate the zero-point drift compensation factor. These factors are used to correct the zero-point output of the sensor and compensate for the drift error caused by environmental changes or unstable sensor characteristics. During the correction process, mathematical models (such as linear regression, least squares method, etc.) are used to analyze the original data to ensure that the output data of each sensor reaches the expected accuracy. Finally, the zero-point drift compensation error correction value corresponding to each sensor under static conditions is obtained.

[0087] Step S15: Based on the zero-point drift compensation error correction value of each sensor under static conditions, perform zero-point output static calibration on multiple types of high-temperature sensors to be calibrated, and obtain multiple types of zero-point drift static calibration sensors.

[0088] In this embodiment of the invention, zero-point output static calibration is performed on the high-temperature sensors to be calibrated based on the aforementioned zero-point drift compensation correction value. First, a known standard input signal is provided using a precision calibration instrument (such as a standard force source or standard temperature source), and each sensor to be calibrated is tested. The accuracy of the compensation correction is verified by comparing the difference between the sensor's output value and the standard value. Multiple calibration tests are performed for each sensor to ensure that the correction effect of the zero-point drift compensation error is verified and calibrated. During the calibration process, the zero-point output value of each sensor is recorded and compared with the preset standard value to ensure that the output of each sensor meets the specified zero-point requirements. If the output value of a certain sensor deviates significantly from the standard value, the compensation correction value needs to be further adjusted or the sensor needs to be recalibrated until the requirements of zero-point static calibration are met. After calibration, the zero-point drift error correction values ​​of all sensors are within a tolerance range, thus obtaining calibrated zero-point drift static calibration sensors of various types. This ensures that they can provide high-precision and high-reliability measurement data in actual work, ultimately resulting in multiple types of zero-point drift static calibration sensors.

[0089] Furthermore, step S14 includes the following steps:

[0090] Step S141: Obtain the microstructure changes of each sensor under normal temperature and pressure by using multiple types of high temperature sensors to be calibrated, including the nonlinear change of sensor resistance with temperature and the change of charge output of piezoelectric element under pressure. Based on the microstructure changes of each sensor under normal temperature and pressure, perform detection sensitivity fitting analysis on the corresponding multiple types of high temperature sensors to be calibrated to obtain the detection sensitivity of each sensor.

[0091] Step S142: Obtain the weak electromagnetic interference and airflow micro-disturbance interference in the high-temperature environment chamber of the logging-while-drilling system, and perform sensor inherent drift analysis on multiple types of high-temperature sensors to be calibrated based on the weak electromagnetic interference and airflow micro-disturbance interference to obtain the inherent zero-point drift of each sensor.

[0092] Step S143: Calculate the time series statistics of the zero-point output values ​​of each sensor under static conditions to obtain the zero-point output statistics of each sensor, including the mean of the zero-point output time period and the standard deviation of the zero-point output time period.

[0093] Step S144: Based on the detection sensitivity, inherent zero-point drift, and zero-point output statistics of each sensor, the zero-point drift compensation correction formula is used to perform zero-point drift compensation correction on the corresponding zero-point output value of the corresponding sensor under static conditions, so as to obtain the zero-point drift compensation error correction value of each sensor under static conditions.

[0094] As an embodiment of the present invention, reference is made to... Figure 3 As shown, Figure 2 A detailed flowchart of step S14 is shown in this embodiment. Step S14 includes the following steps:

[0095] Step S141: Obtain the microstructure changes of each sensor under normal temperature and pressure by using multiple types of high temperature sensors to be calibrated, including the nonlinear change of sensor resistance with temperature and the change of charge output of piezoelectric element under pressure. Based on the microstructure changes of each sensor under normal temperature and pressure, perform detection sensitivity fitting analysis on the corresponding multiple types of high temperature sensors to be calibrated to obtain the detection sensitivity of each sensor.

[0096] In this embodiment of the invention, various high-temperature sensors to be calibrated are tested to obtain their microstructural changes under normal temperature and pressure conditions. During the test, the sensors are stably placed in a normal temperature environment using a temperature control system, and the sensor resistance changes with temperature are recorded using a precision temperature sensor. The temperature range used in this process should be normal temperature (20-25°C), and the sensor output signal is collected. Specifically, for each increase in temperature (e.g., an increase of 1°C), the sensor resistance change is recorded, and a curve of resistance change with temperature is plotted. For piezoelectric sensors, a known pressure is applied using a standard loading device, and its charge output signal is obtained using a high-sensitivity charge amplifier. During this process, the applied pressure is gradually adjusted, and the amount of charge output by the sensor is recorded at each pressure level. Combining the microstructural characteristics of the sensor material and the output change law, the data analysis model is fitted to obtain the detection sensitivity of each sensor, that is, the amount of sensor output change caused by a unit temperature or pressure change, and finally the detection sensitivity of each sensor is obtained.

[0097] Step S142: Obtain the weak electromagnetic interference and airflow micro-disturbance interference in the high-temperature environment chamber of the logging-while-drilling system, and perform sensor inherent drift analysis on multiple types of high-temperature sensors to be calibrated based on the weak electromagnetic interference and airflow micro-disturbance interference to obtain the inherent zero-point drift of each sensor.

[0098] In this embodiment of the invention, by analyzing the external interference, particularly electromagnetic interference and airflow disturbance, experienced by the sensor under high-temperature conditions, a standard high-temperature environment chamber is first set up to simulate the temperature and airflow conditions during drilling. A dedicated electromagnetic shielding device is used to limit external electromagnetic wave interference, while electromagnetic noise data within the chamber is recorded. Furthermore, a high-frequency electromagnetic wave detector is used to capture any weak electromagnetic interference signals that may exist within the chamber. These signals reflect electromagnetic interference in high-temperature environments, especially during drilling. Next, a high-precision airflow detection instrument is used to record airflow fluctuations within the chamber. The micro-disturbances in the airflow experienced by the sensor cause its zero-point drift, especially when fluid dynamic conditions are unstable. By combining the relationship between airflow velocity, temperature changes, and sensor output, the inherent drift of the sensor can be modeled and analyzed. Specific analysis methods include performing spectral analysis on multiple sets of test data to identify the characteristic frequencies of electromagnetic interference and airflow disturbance, and calculating the impact on the sensor's zero-point drift. Finally, the inherent zero-point drift of each sensor is obtained.

[0099] Step S143: Calculate the time series statistics of the zero-point output values ​​of each sensor under static conditions to obtain the zero-point output statistics of each sensor, including the mean of the zero-point output time period and the standard deviation of the zero-point output time period.

[0100] In this embodiment of the invention, the zero-point signal output by the sensor under static conditions is analyzed, and its time-series statistics are calculated to further understand the pattern of zero-point drift of the sensor. During the experiment, each sensor is placed in a static environment free from external interference, and its output value is recorded over a period of time. The recording time of the output signal can be selected as several hours or several days to ensure that it can cover the zero-point drift situation over a long period of time. By continuously collecting the sensor output signal and calculating its time-series statistics, the mean and standard deviation of the zero-point output period of each sensor are obtained. Specifically, the mean of the collected zero-point output data is first calculated to determine the expected value of the zero point; then the standard deviation of the data is calculated to evaluate the volatility of the zero-point output signal. The core of this process is to extract the stability characteristics of the sensor during long-term static operation through data processing technology, and finally obtain the zero-point output statistics corresponding to each sensor, including the mean and standard deviation of the zero-point output period.

[0101] Step S144: Based on the detection sensitivity, inherent zero-point drift, and zero-point output statistics of each sensor, the zero-point drift compensation correction formula is used to perform zero-point drift compensation correction on the corresponding zero-point output value of the corresponding sensor under static conditions, so as to obtain the zero-point drift compensation error correction value of each sensor under static conditions.

[0102] In this embodiment of the invention, by using the detection sensitivity, inherent zero-point drift, and zero-point output statistics obtained in the preceding steps, a specialized compensation algorithm is used to correct the zero-point output value of each sensor. First, based on the obtained sensitivity data, the zero-point output value is converted into an output signal related to changes in external temperature or pressure, and correction is performed based on this. Then, using the inherent zero-point drift as an adjustment benchmark, a compensation correction formula is designed. This formula considers the sensor's sensitivity, inherent drift, and real-time zero-point output fluctuations. In the formula, the zero-point output drift is related to the sensor's operating environment (such as temperature, airflow, electromagnetic interference, etc.) to correct the zero-point drift of each sensor. The zero-point output value after compensation correction should significantly reduce the sensor's system error, and finally, the zero-point drift compensation error correction value corresponding to each sensor under static conditions is obtained.

[0103] Furthermore, the zero-position drift compensation correction calculation formula mentioned in step S144 is as follows:

[0104] ;

[0105] In the formula, For the first The zero-point drift compensation error correction value of each sensor under static conditions. Where 1 represents a high-temperature accelerometer, 2 represents a high-temperature orientation sensor, and 3 represents a high-temperature vibration sensor. The time interval for the sensor's zero-position output. Output the time variable parameter to zero position. For the first The detection sensitivity corresponding to each sensor For the first Each sensor in time The inherent zero-point drift at that location, For the first Sensitivity weighting coefficients for each sensor For the first Each sensor in time The ambient temperature, For the first Each sensor in time The zero-bit output value at that position. For the first Average value of zero-point output for each sensor during the corresponding time period. For the first Standard deviation of the zero-point output time period corresponding to each sensor For the first Temperature compensation coefficients for each sensor This is the correction coefficient for the zero-position drift compensation error correction value.

[0106] This invention, through the use of a specific mathematical model and verification, derives a zero-point drift compensation and correction formula. This formula is used to compensate for and correct the zero-point drift of the corresponding sensor's output value under static conditions. By comprehensively considering factors such as time, temperature, and sensitivity weighting, this formula can effectively compensate for drifts caused by high temperatures and environmental changes. For example, This approach considers the impact of temperature on the sensor's zero-point output, enabling dynamic correction of zero-point drift based on changes in ambient temperature and reducing errors caused by temperature fluctuations. By introducing an inherent zero-point drift, the formula models and compensates for the inherent drift of each sensor at different time points, resulting in a more accurate zero-point output correction value. This correction can be calculated or predicted using experimental and environmental data, ensuring that the drift correction value for each sensor better reflects actual conditions. The formula considers multiple factors, particularly sensitivity, ambient temperature, and zero-point output statistics (such as mean and standard deviation). It not only corrects drift caused by high temperatures but also considers errors caused by environmental noise (such as electromagnetic interference and airflow disturbances). In this way, the formula achieves precise correction of the zero-point output under static conditions, ensuring the stability and reliability of various sensor types in complex environments. This formula treats time-series data as a variable through integration, considering the sensor's performance over the entire time interval. The mean and standard deviation of the zero-point output reflect the stability and volatility of the sensor output. Through these time-series statistics, dynamic corrections can be made to address the impact of time variations on zero-point drift, thereby improving the accuracy of zero-point drift compensation. In this formula, sensitivity and temperature compensation coefficients are combined with other parameters... and Two coefficients are used to adjust the compensation effects for sensitivity and temperature, respectively. This allows for dynamic adjustment of the zero-point drift correction value based on the characteristics of different sensors. The sensitivity and temperature compensation coefficients of a sensor will vary due to differences in material properties and operating environment. The formula can provide targeted compensation schemes for different types of sensors, enabling the compensation calculation formula to be adjusted according to the needs of different sensors and experimental environments. By adjusting these coefficients, the drift compensation effect can be further optimized according to the actual application scenario. For example, the correction coefficient is used to fine-tune the error correction value, ensuring that the sensor output value is as close as possible to the true value under specific conditions. In summary, this formula fully considers the first... The zero-point drift compensation error correction value of each sensor under static conditions , Where 1 represents a high-temperature accelerometer, 2 represents a high-temperature orientation sensor, and 3 represents a high-temperature vibration sensor. The sensor's zero-point output time interval is... Zero-position output time variable parameter , No. The detection sensitivity of each sensor , No. Each sensor in time Inherent zero-point drift at the location , No. Sensitivity weighting coefficients for each sensor , No. Each sensor in time ambient temperature , No. Each sensor in time Zero output value at the position , No. Average value of zero-point output time period corresponding to each sensor , No. Standard deviation of zero-point output time period corresponding to each sensor , No. Temperature compensation coefficient for each sensor Correction coefficient for zero-point drift compensation error correction value According to the The zero-point drift compensation error correction value of each sensor under static conditions The interrelationships between the above parameters constitute a functional relationship:

[0107] ;

[0108] This formula enables the zero-point drift compensation and correction process for the corresponding sensor's zero-point output value under static conditions. Simultaneously, it uses the correction coefficient of the zero-point drift compensation error correction value. The introduction of this feature allows for adjustments based on errors that occur during the calculation process, thereby improving the accuracy and applicability of the zero-point drift compensation correction formula.

[0109] Furthermore, step S2 includes the following steps:

[0110] Step S21: Simulate the range of temperature, pressure and vibration frequency constraints from wellhead to bottom using the high-temperature environment chamber of logging while drilling.

[0111] In this embodiment of the invention, a simulation system for a high-temperature environment chamber for logging while drilling is constructed to accurately reflect the changes in temperature, pressure, and vibration frequency at different depths from the wellhead to the bottom of the well. By analyzing the actual working conditions downhole, a corresponding simulation environment chamber is designed to ensure that it can cover the temperature (50°C to 200°C), pressure (0MPa to 50MPa), and vibration frequency (10Hz to 100Hz) range in the real downhole environment. The design of the environment chamber must be able to simulate the temperature rise, pressure increase, and mechanical vibration changes caused by drilling operations at different depths downhole. The simulation chamber typically includes a temperature control device, a pressure vessel, and a vibration generator. All these devices need to be adjusted and monitored in real time through a computer control system to ensure that the simulated conditions are consistent with the changes in the actual downhole environment and meet the sensor detection requirements. Finally, the design generates the temperature, pressure, and vibration frequency constraint conditions range corresponding to the wellhead to the bottom of the well.

[0112] Step S22: Determine the sub-conditions for different downhole environmental conditions during drilling based on the temperature, pressure, and vibration frequency constraints from the wellhead to the bottom of the well.

[0113] In this embodiment of the invention, different sub-conditions for downhole environmental conditions are designed based on previously determined constraints of temperature, pressure, and vibration frequency. For example, the temperature range can be divided into multiple sub-intervals, such as 50℃-100℃, 100℃-150℃, and 150℃-200℃; the pressure range can be divided into 0-20MPa, 20MPa-35MPa, and 35MPa-50MPa; and the vibration frequency can be divided into 10Hz-30Hz, 30Hz-60Hz, and 60Hz-100Hz. Through a combination of experiments and calculations, the environmental characteristics under each sub-condition are further analyzed to ensure comprehensive coverage of various extreme situations that occur during actual downhole operations. In addition, the conditions need to be refined according to different depths, considering the gradual changes in downhole pressure and temperature with depth, and combining various faults or interferences that occur during actual downhole logging operations to form a comprehensive downhole condition model, ultimately determining different sub-conditions for downhole environmental conditions during drilling.

[0114] Step S23: Based on the different downhole environmental conditions during drilling, perform detection clock synchronization response control on multiple types of zero-point drift static calibration sensors to generate detection response synchronization clock control commands for each sensor.

[0115] In this embodiment of the invention, zero-drift calibration is performed on various types of sensors based on previously defined sub-conditions for different downhole environmental conditions. These sensors may include accelerometers, orientation sensors, vibration sensors, etc. Each type of sensor has its unique zero-drift characteristics and needs to be calibrated under different temperature, pressure, and vibration frequency environments. To ensure high accuracy of measurement data, clock synchronization control of multiple sensors must be implemented. Specifically, a high-precision clock synchronization module generates accurate timestamp instructions and applies these instructions to each sensor. These instructions ensure that all sensors can synchronously detect data under different environmental conditions, avoiding measurement deviations caused by time errors. The synchronization control system needs to have real-time feedback and adjustment functions so that it can automatically adjust the clock synchronization when downhole environmental conditions change, ensuring that the responses of different sensors remain consistent, and finally generating the corresponding detection response synchronization clock control instructions for each sensor.

[0116] Step S24: Apply the detection response synchronization clock control command corresponding to each sensor to the corresponding multi-type zero-drift static calibration sensor for timestamp synchronization acquisition, so as to output the sensor output signal data corresponding to each sensor under different downhole operating conditions, including downhole acceleration, downhole orientation and downhole vibration sensor output signals.

[0117] In this embodiment of the invention, by applying the previously generated synchronization clock control command to each sensor, and through a timestamp synchronization mechanism, it is ensured that the timestamp of the data is consistent when each sensor acquires signals. In this way, the system can accurately record the signal data output by each sensor and ensure the timing and authenticity of these signal data. In specific operation, the synchronization clock signal is first transmitted to each sensor through the control system command. These sensors will perform synchronous acquisition based on the received clock signal. After receiving the clock synchronization signal, each sensor immediately begins to acquire and output the corresponding sensing data. This data includes acceleration, downhole orientation, vibration signals, etc. This signal data is transmitted to the main control system through a high-speed data acquisition system for real-time monitoring and storage. As the downhole working conditions change, the acquired data will be sorted according to the timestamp, and finally, the sensing output signal data of each sensor under different downhole working conditions will be output.

[0118] Furthermore, step S3 includes the following steps:

[0119] Step S31: Perform frequency domain conversion on the sensor output signal data of each sensor under different downhole operating conditions to generate frequency domain representation of the output signal of each sensor under different downhole operating conditions.

[0120] In this embodiment of the invention, output signal data of various types of sensors under different downhole operating conditions are collected. These data include raw signals output by multiple sensors such as downhole acceleration, vibration, and orientation sensors. The raw signals are converted from the time domain to the frequency domain using Fast Fourier Transform (FFT) or other frequency domain conversion methods. Specifically, the sensor output signals are processed by a piecewise window function and converted into frequency domain data to obtain the frequency characteristic representation of each sensor signal under different downhole operating conditions. Assuming the output signal of the acceleration sensor under operating condition A is acceleration data a(t), after performing FFT on this signal, its frequency domain representation A(f) is obtained. This frequency domain representation of the signal contains the amplitude information of each frequency band and its corresponding frequency position. After completing the frequency domain conversion of all sensors, the frequency domain signals of all sensors under different downhole operating conditions are obtained, and finally, the frequency domain representation of the output signal of each sensor under different downhole operating conditions is generated.

[0121] Step S32: Based on the frequency domain representation of the downhole acceleration output signal under different downhole operating conditions, perform signal phase difference analysis on the frequency domain representation of the corresponding downhole vibration output signal to obtain the signal frequency domain phase difference between the downhole acceleration output and the downhole vibration output;

[0122] In this embodiment of the invention, the phase difference between the signals of the downhole acceleration and vibration sensors is calculated based on the previously obtained frequency domain representations. Assuming the frequency domain representation of the acceleration sensor is A(f) and the frequency domain representation of the vibration sensor is V(f), for each frequency point... The phase difference between the acceleration signal and the vibration signal at that frequency point can be calculated. This process yields the phase difference at each frequency point. Common phase difference analysis methods are employed, using complex amplitude and phase calculations to extract phase information for each frequency point. This method allows for the acquisition of the frequency domain phase difference between downhole acceleration output and downhole vibration output. Furthermore, the changing trend of this phase difference under different operating conditions is analyzed to determine the synchronization or existing errors of the signals, ultimately yielding the frequency domain phase difference between the downhole acceleration output and downhole vibration output.

[0123] Step S33: Perform directional frequency band energy and modulation depth analysis on the frequency domain representation of the downhole directional output signal under different downhole operating conditions to obtain the frequency band energy distribution and frequency band modulation depth of the downhole directional output signal.

[0124] In this embodiment of the invention, the analysis of downhole orientation sensor signals involves first obtaining the frequency domain representation of the orientation sensor under different downhole operating conditions, and then analyzing its frequency band energy distribution and modulation depth. First, the frequency domain representation D(f) of the orientation sensor output signal is obtained using the FFT method. Then, energy spectrum analysis is performed on this frequency domain signal to divide it into multiple frequency bands, and the energy E(f) of each frequency band is calculated. Specifically, for each frequency band... Calculate its corresponding energy The energy is then normalized to the total energy to obtain the relative energy distribution of each frequency band. In addition, the modulation depth of the frequency domain signal needs to be analyzed, which is usually done by extracting the amplitude modulation characteristics of the frequency. For example, the modulation depth can be calculated by Hilbert transform to obtain the modulation amplitude of each frequency band, thus obtaining the frequency band energy distribution and modulation depth of the downhole directional sensor under different operating conditions. Finally, the frequency band energy distribution and frequency band modulation depth corresponding to the downhole directional output signal are obtained.

[0125] Step S34: Based on the signal frequency domain phase difference between downhole acceleration output and downhole vibration output, the energy distribution of the frequency band corresponding to the downhole directional output signal, and the frequency band modulation depth, perform dynamic simulation calibration on multiple types of zero-drift static calibration sensors. This is to dynamically calibrate the high-temperature acceleration sensor and the high-temperature vibration sensor according to the signal frequency domain phase difference between downhole acceleration output and downhole vibration output, and to dynamically calibrate the high-temperature directional sensor according to the energy distribution of the frequency band corresponding to the downhole directional output signal and the frequency band modulation depth, thereby generating multiple types of dynamic calibration sensors.

[0126] In this embodiment of the invention, dynamic calibration of multiple types of sensors is performed based on the previously obtained signal frequency domain phase difference and frequency band energy distribution. First, based on the signal frequency domain phase difference between the downhole acceleration output and the downhole vibration output, the high-temperature acceleration sensor and the high-temperature vibration sensor are dynamically calibrated. Specifically, the previously obtained phase difference data is compared with a known standard signal, and the calibration coefficients of the sensors are adjusted using the least squares method or other optimization algorithms to make their output consistent with the standard signal. For example, if the phase difference of the output signals of the acceleration sensor and the vibration sensor in a certain frequency band exceeds a predetermined threshold, the sensor gain or phase shift in that frequency band is calibrated until it reaches a state consistent with the standard signal. Similarly, for downhole directional sensors, based on the obtained frequency band energy distribution and modulation depth, frequency band selective adjustment is performed, and the gain and modulation depth of each frequency band are calibrated to ensure that their output meets the standard values ​​under downhole working conditions. This process can be iteratively optimized through an adaptive algorithm, repeatedly adjusting the calibration parameters of the sensors until a dynamic calibration result that meets the requirements is obtained, ultimately generating multiple types of dynamically calibrated sensors.

[0127] Furthermore, step S4 includes the following steps:

[0128] Step S41: Simulate the instantaneous strong impact disturbance constraints and temperature change disturbance constraints in the well using the high-temperature environment chamber of logging while drilling.

[0129] In this embodiment of the invention, a high-temperature environment chamber for logging while drilling is constructed to simulate the high-temperature environment downhole and its corresponding instantaneous strong impact interference and temperature change interference. This environment chamber is made of high-strength alloy material and has the ability to withstand high temperatures and strong impacts. The temperature simulation system controls the temperature inside the environment chamber through a heating device to simulate the high-temperature environment from room temperature to deep downhole. By installing multiple high-precision sensors, the temperature change inside the chamber is monitored in real time to ensure that the temperature change can accurately simulate downhole working conditions. In order to simulate instantaneous strong impact interference, an impact testing device is used to simulate violent vibration inside the chamber for a short period of time. The impact testing device uses electromagnetic or mechanical means to generate instantaneous strong impact force, which acts on the environment chamber. The impact sensor records the intensity, propagation speed and duration of the impact force in detail, and finally obtains the instantaneous strong impact interference constraint and temperature change interference constraint.

[0130] Step S42: Perform impact stress propagation attenuation intensity analysis on the instantaneous strong impact interference constraint corresponding to the well, and obtain the instantaneous impact stress propagation attenuation intensity corresponding to the well.

[0131] In this embodiment of the invention, based on previously obtained test data of instantaneous strong impact interference, an analysis of the attenuation intensity of impact stress propagation is conducted. First, the impact stress propagation process is modeled as a three-dimensional finite element model. By simulating the propagation path and attenuation law of stress in the downhole environment, the spatial distribution and attenuation intensity of impact stress are obtained. In this analysis, the complex geological structure, rock strata physical properties, and rigidity and structural characteristics of logging-while-drilling tools in the well need to be considered. By comparing the attenuation intensity under different rock strata and temperature conditions, the propagation attenuation law of instantaneous impact stress in different downhole environments is determined. Then, by combining the mechanical property data of the material, such as elastic modulus, density, and shear modulus, a specialized finite element analysis software is used for precise calculation. Based on the propagation attenuation model, the maximum intensity and transmission path of instantaneous impact stress in the well are obtained, and finally, the corresponding instantaneous impact stress propagation attenuation intensity in the well is obtained.

[0132] Step S43: Calculate the rate of temperature thermal expansion for the temperature change disturbance constraint corresponding to the downhole, so as to analyze the dynamic evolution process of the temperature field in the simulation chamber from steady state to sudden change state, and obtain the rate of temperature thermal expansion for the downhole.

[0133] In this embodiment of the invention, the rate of thermal expansion change is calculated by constraining the sudden temperature change in the well. This step first establishes temperature change curves in a simulated experiment based on environmental conditions at different depths and temperatures in the well. The experiment uses a temperature control device to simulate a sudden temperature change. During the rapid transition from a steady-state temperature to a sudden temperature change state, temperature change data within the environmental chamber is measured and recorded. Through precise calculation of the rate of temperature change, the rate of thermal expansion change during the sudden temperature change in the well is obtained. In this process, the thermal expansion coefficients, temperature gradients, and internal thermal conductivity characteristics of different materials must be considered. To ensure accuracy, high-precision thermocouples and infrared sensors are used for real-time temperature monitoring. Simultaneously, a mathematical model is used to analyze the dynamic evolution of temperature changes. The ultimate goal of this analysis is to quantify the rate of temperature change in order to assess the interference impact of sudden temperature changes on the well environment and its corresponding sensors. ,in For the thermal conductivity of the sensor material, For material density, For the specific heat capacity of the material, The second-order disturbance constraint derivative of the temperature change in space is used to obtain the corresponding rate of thermal expansion change of temperature downhole.

[0134] Step S44: Obtain the sensor output signals corresponding to multiple types of dynamic calibration sensors, and use the output interference response evaluation calculation formula based on the instantaneous impact stress propagation attenuation intensity and temperature thermal expansion rate corresponding to the downhole to evaluate and analyze the interference response of the sensor output signals corresponding to multiple types of dynamic calibration sensors, and obtain the degree of influence of instantaneous strong impact attenuation intensity and temperature change rate on the interference response of sensor output.

[0135] In this embodiment of the invention, dynamic calibration is performed using multiple types of sensors, including high-temperature accelerometers, high-temperature orientation sensors, and high-temperature vibration sensors. These sensors are installed at different locations within the high-temperature environment chamber of the logging-while-drilling system to collect various physical quantities in the downhole environment. For each sensor, a baseline calibration is first performed to ensure that it can accurately measure its corresponding physical quantity under normal environmental conditions. Subsequently, based on previously simulated instantaneous impact interference and sudden temperature change interference, the output signals of the sensors under these interference conditions are acquired. These signals are recorded by a data acquisition system and digitally processed. To ensure measurement accuracy under interference conditions, real-time signal compensation and filtering are also required. Wave processing is used to remove noise signals and extract the interference response to obtain the corresponding sensor output signal. Simultaneously, by combining the time range of the output interference response, the intensity of instantaneous impact stress propagation attenuation, the sensor output sensitivity constant, the rate of change of thermal expansion, the influence factor of thermal expansion on impact stress attenuation, the sensor output signal, the sensor output signal intensity attenuation factor, and related parameters, a suitable output interference response evaluation calculation formula is constructed to quantify the interference response evaluation. This quantifies the influence of impact attenuation intensity and temperature change on the sensor output, ultimately obtaining the degree of influence of instantaneous strong impact attenuation intensity and temperature change rate on the interference response of the sensor output.

[0136] Step S45: Based on the influence of instantaneous strong impact attenuation intensity and temperature change rate on the interference response of sensor output, perform sensing detection verification optimization on multiple types of dynamic calibration sensors, and generate multiple types of calibration verification optimized sensors.

[0137] In this embodiment of the invention, the corresponding sensors are verified and optimized by considering the impact of the instantaneous strong impact attenuation intensity and temperature change rate on the interference response of the sensor output, which are obtained by quantification. First, the interference response is evaluated using the acquired sensor output signals and interference models of instantaneous strong impact and sudden temperature change. The evaluation considers factors such as the sensitivity, anti-interference ability, and response time of each sensor, and quantitatively analyzes the output changes of each sensor under different interferences. Through these analyses, interference sources affecting the accuracy and stability of the sensors are identified. Then, based on the interference response analysis results, the sensors are optimized. Optimization measures include adjusting the material properties of the sensors, designing the structure, or improving their signal processing algorithms. The optimized sensors are then re-tested to ensure higher accuracy and robustness under instantaneous strong impact and sudden temperature change interferences. This generates multiple types of calibrated and optimized sensors.

[0138] Furthermore, the specific formula for calculating the output interference response evaluation in step S44 is as follows:

[0139] ;

[0140] In the formula, For the first The degree of impact of interference response on each sensor To determine the time range of the impact of the output interference response, For the time variable of interference response assessment, For the first Each sensor during the time period The instantaneous impact stress propagation attenuation intensity at the corresponding location downhole. For the sensor to output a sensitivity constant, For the first Each sensor during the time period The rate of thermal expansion change at the corresponding temperature downhole location. The effect of thermal expansion due to temperature on the attenuation of impact stress is a factor. For the first Each sensor during the time period The corresponding sensor output signal, This is the attenuation factor of the sensor output signal strength. This is a correction factor for the degree of influence of the interference response.

[0141] This invention, through the use of a specific mathematical model and verification, derives an output interference response evaluation formula for analyzing the interference response of sensor output signals corresponding to various types of dynamically calibrated sensors. This formula comprehensively considers the influence of instantaneous impact stress propagation attenuation intensity and the rate of thermal expansion on the sensor output, which is crucial because downhole environments typically exhibit strong dynamic changes, such as impact and temperature variations. Traditional static calibration cannot cope with such rapid changes and complex environments; using a dynamic model to evaluate the interference response more accurately reflects the actual situation. The formula combines thermal expansion and impact stress attenuation through an influence factor, revealing the coupling effect between temperature changes and impact stress. This helps to more accurately analyze how the sensor operates under combined temperature and mechanical impact conditions. The simultaneous effect of sudden temperature changes and impact forces often exacerbates the interference response; therefore, the influence of both needs to be considered simultaneously during sensor optimization design. By evaluating the attenuation factor corresponding to the sensor output signal strength, the formula can predict the trend of sensor output signal changes over time and provide a basis for adjusting signal gain or calibration, reducing the impact of external interference on the final measurement results. This formula not only considers the impact of instantaneous impact stress but also incorporates various possible temperature change rates and attenuation factors, making the entire evaluation process adaptable to various dynamic environmental conditions. By using an integral form, it enables continuous evaluation of the disturbance response over the entire time period, further improving the accuracy and applicability of the calculation formula. Furthermore, the correction coefficient in the formula provides a flexible parameter for adjusting the degree of response influence. This coefficient can be adjusted according to actual application scenarios or experimental data, thereby enabling personalized disturbance response correction under different environmental conditions, making the formula more closely aligned with practical application needs. In summary, this formula fully considers the impact of instantaneous impact stress and incorporates various possible temperature change rates and attenuation factors, making the formula more closely aligned with practical application needs. The degree of influence of interference response corresponding to each sensor Output interference response affects time range Interference response assessment time variable , No. Each sensor during the time period The instantaneous impact stress propagation attenuation intensity at the downhole location The sensor outputs a sensitivity constant. , No. Each sensor during the time period Rate of thermal expansion at the corresponding temperature downhole Influence factors of thermal expansion on impact stress attenuation , No. Each sensor during the time period The corresponding sensor output signal Sensor output signal strength attenuation factor Correction factor for the degree of influence of interference response According to the The degree of influence of interference response corresponding to each sensor The interrelationships between the above parameters constitute a functional relationship. This formula enables the evaluation and analysis of interference response of sensor output signals for various types of dynamic calibration sensors. Furthermore, it uses a correction coefficient to adjust the degree of interference response. The introduction of this feature allows for adjustments based on errors that occur during the calculation process, thereby improving the accuracy and applicability of the output interference response evaluation formula.

[0142] Furthermore, the present invention also provides a multi-type sensor calibration and detection system for performing the multi-type sensor calibration and detection method described above, the multi-type sensor calibration and detection system comprising:

[0143] The zero-point output static calibration module is used to integrate various high-temperature accelerometers, high-temperature orientation sensors, and high-temperature vibration sensors into a logging-while-drilling high-temperature environment chamber that integrates high-temperature, high-pressure, and strong vibration simulation functions, thereby generating multiple types of high-temperature sensors to be calibrated. By designing corresponding ambient temperature, ambient pressure, and vibration-free static conditions in the logging-while-drilling high-temperature environment chamber, and performing zero-point output static calibration on multiple types of high-temperature sensors to be calibrated based on these ambient temperature, ambient pressure, and vibration-free static conditions, multiple types of zero-point drift static calibration sensors are obtained.

[0144] The downhole operating condition synchronous acquisition module is used to simulate the temperature, pressure, and vibration frequency constraint range from the wellhead to the bottom of the well through the high-temperature environment chamber of the logging-while-drilling system. Based on the temperature, pressure, and vibration frequency constraint range from the wellhead to the bottom of the well, it determines the sub-conditions of different downhole operating conditions during drilling. Based on the different sub-conditions of the downhole operating conditions during drilling, it responds to the timestamp synchronous acquisition of multiple types of zero-point drift static calibration sensors to output the sensor output signal data of each sensor under different downhole operating conditions.

[0145] The downhole dynamic simulation calibration module is used to perform dynamic simulation calibration of multiple types of zero-point drift static calibration sensors based on the sensor output signal data of each sensor under different downhole working conditions, thereby generating multiple types of dynamic calibration sensors.

[0146] The interference constraint detection and verification module is used to simulate the instantaneous strong impact interference constraint and temperature change interference constraint in the well through the high temperature environment chamber of logging while drilling. Based on the instantaneous strong impact interference constraint and temperature change interference constraint, the module performs sensing detection and verification optimization on multiple types of dynamic calibration sensors, thereby generating multiple types of calibration and verification optimized sensors.

[0147] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0148] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A multi-type sensor calibration detection method, characterized in that, The method comprises the following steps: Step S1: integrating a plurality of high-temperature acceleration sensors, high-temperature orientation sensors and high-temperature vibration sensors to be calibrated in a logging-while-drilling high-temperature environment cabin corresponding to high-temperature, high-pressure and strong vibration simulation functions to generate a plurality of types of high-temperature sensors to be calibrated; designing a corresponding normal-temperature, normal-pressure and non-vibration static condition by the logging-while-drilling high-temperature environment cabin, and performing zero-output static calibration on the plurality of types of high-temperature sensors to be calibrated based on the normal-temperature, normal-pressure and non-vibration static condition to obtain a plurality of types of zero-drift static calibration sensors; Step S2: simulating and designing a temperature, pressure and vibration frequency constraint condition range from a wellhead to a well bottom by the logging-while-drilling high-temperature environment cabin, and determining different logging-while-drilling downhole environment working condition range sub-conditions according to the temperature, pressure and vibration frequency constraint condition range from the wellhead to the well bottom; performing time stamp synchronous acquisition on the plurality of types of zero-drift static calibration sensors according to the different logging-while-drilling downhole environment working condition range sub-conditions to output corresponding sensor output signal data of each sensor under different downhole working condition conditions, which include downhole acceleration, downhole orientation and downhole vibration sensor output signals; Step S3: performing dynamic simulation calibration on the plurality of types of zero-drift static calibration sensors based on the corresponding sensor output signal data of each sensor under different downhole working condition conditions to generate a plurality of types of dynamic calibration sensors; Step S4: simulating corresponding instantaneous strong impact interference constraints and temperature sudden change interference constraints downhole by the logging-while-drilling high-temperature environment cabin, and performing sensor detection verification optimization on the plurality of types of dynamic calibration sensors based on the instantaneous strong impact interference constraints and the temperature sudden change interference constraints to generate a plurality of types of calibration verification optimized sensors.

2. The multi-type sensor calibration detection method of claim 1, wherein, Step S1 comprises the following steps: Step S11: integrating a plurality of high-temperature acceleration sensors, high-temperature orientation sensors and high-temperature vibration sensors to be calibrated in a logging-while-drilling high-temperature environment cabin corresponding to high-temperature, high-pressure and strong vibration simulation functions, and using special heat insulation and pressure bearing materials to ensure that the cabin environment is stable and uniformly distributed to generate a plurality of types of high-temperature sensors to be calibrated; Step S12: designing a corresponding normal-temperature, normal-pressure and non-vibration static condition by the logging-while-drilling high-temperature environment cabin; Step S13: performing static condition zero synchronous acquisition on the plurality of types of high-temperature sensors to be calibrated based on the normal-temperature, normal-pressure and non-vibration static condition to obtain corresponding zero output values of each sensor under the static condition; Step S14: acquiring corresponding detection sensitivities and inherent zero drift amounts of each sensor by the plurality of types of high-temperature sensors to be calibrated, and performing zero drift compensation correction on the corresponding zero output values of the corresponding sensors under the static condition based on the corresponding detection sensitivities and inherent zero drift amounts of each sensor to obtain zero drift compensation error correction values of each sensor under the static condition; Step S15: performing zero-output static calibration on the plurality of types of high-temperature sensors to be calibrated based on the corresponding zero drift compensation error correction values of each sensor under the static condition to obtain a plurality of types of zero-drift static calibration sensors.

3. The multi-type sensor calibration detection method of claim 2, wherein, Step S14 comprises the following steps: Step S141: Obtain the microstructure changes of each sensor under normal temperature and pressure conditions by the multiple types of high-temperature sensors to be calibrated, including the nonlinear change of the resistance value with temperature and the change of the charge output of the piezoelectric element affected by pressure, and perform detection sensitivity fitting analysis on the corresponding multiple types of high-temperature sensors to be calibrated based on the microstructure changes of each sensor under normal temperature and pressure conditions, to obtain the detection sensitivity of each sensor; Step S142: Obtain the weak electromagnetic interference and airflow micro-disturbance interference in the logging-while-drilling high-temperature environment cabin, and perform sensor inherent drift analysis on the multiple types of high-temperature sensors to be calibrated based on the weak electromagnetic interference and airflow micro-disturbance interference, to obtain the inherent zero drift of each sensor; Step S143: Perform time series statistical calculation on the zero output value of each sensor under static conditions, to obtain the zero output statistics of each sensor, including the zero output time period mean and the zero output time period standard deviation; Step S144: Based on the detection sensitivity, inherent zero drift and zero output statistics of each sensor, use the zero drift compensation correction formula to correct the zero output value of the corresponding sensor under static conditions, to obtain the zero drift compensation error correction value of each sensor under static conditions.

4. The multi-type sensor calibration detection method according to claim 3, wherein The zero drift compensation correction formula in step S144 is specifically: ； In the formula, is the zero drift compensation error correction value of the first sensor under static conditions, wherein 1 represents a high-temperature acceleration sensor, 2 represents a high-temperature orientation sensor, and 3 represents a high-temperature vibration sensor, is the zero output time interval of the sensor, is the zero output time variable parameter, is the detection sensitivity of the first sensor, is the inherent zero drift of the first sensor at time , is the sensitivity weight coefficient of the first sensor, is the ambient temperature of the first sensor at time , is the zero output value of the first sensor at time , is the zero output period mean of the first sensor, is the zero output period standard deviation of the first sensor, is the temperature compensation coefficient of the first sensor, is the correction coefficient of the zero drift compensation error correction value.

5. The multi-type sensor calibration detection method of claim 1, wherein, Step S2 includes the following steps: Step S21: Simulate and design the temperature, pressure and vibration frequency constraint condition range from the wellhead to the bottomhole by the logging-while-drilling high-temperature environment cabin; Step S22: Determine different logging-while-drilling downhole environment condition range sub-conditions according to the temperature, pressure and vibration frequency constraint condition range from the wellhead to the bottomhole; Step S23: Perform detection clock synchronization response control on the multiple types of zero drift static calibration sensors in response to the different logging-while-drilling downhole environment condition range sub-conditions, to generate the detection response synchronization clock control instruction of each sensor; Step S24: Apply the detection response synchronization clock control instruction of each sensor to the corresponding multiple types of zero drift static calibration sensors for time stamp synchronous collection, to output the sensor output signal data of each sensor under different downhole condition conditions, including downhole acceleration, downhole orientation and downhole vibration sensor output signals.

6. The multi-type sensor calibration detection method of claim 5, wherein, The temperature, pressure and vibration frequency constraint condition range from the wellhead to the bottomhole in step S21 is specifically a temperature range of 50-200℃, a pressure range of 0-50MPa and a vibration frequency range of 10-100Hz.

7. The multi-type sensor calibration detection method of claim 1, wherein, Step S3 includes the following steps: Step S31: Perform signal frequency domain conversion on the sensor output signal data of each sensor under different downhole condition conditions, to generate the output signal frequency domain representation of each sensor under different downhole condition conditions; Step S32: based on the corresponding downhole acceleration output signal frequency domain representation under different downhole working conditions, signal phase difference analysis is performed on the corresponding downhole vibration output signal frequency domain representation, and signal frequency domain phase difference between the downhole acceleration output and the downhole vibration output is obtained; Step S33: directional frequency band energy and modulation depth analysis is performed on the corresponding downhole directional output signal frequency domain representation under different downhole working conditions, and frequency band energy distribution and frequency band modulation depth corresponding to the downhole directional output signal are obtained; Step S34: based on the signal frequency domain phase difference between the downhole acceleration output and the downhole vibration output and the frequency band energy distribution and the frequency band modulation depth corresponding to the downhole directional output signal, dynamic simulation calibration is performed on the multi-type zero-position drift static calibration sensor, the high-temperature acceleration sensor and the high-temperature vibration sensor are dynamically calibrated according to the signal frequency domain phase difference between the downhole acceleration output and the downhole vibration output, and the high-temperature directional sensor is dynamically calibrated according to the frequency band energy distribution and the frequency band modulation depth corresponding to the downhole directional output signal, and a multi-type dynamic calibration sensor is generated.

8. The multi-type sensor calibration detection method of claim 7, wherein, Step S4 includes the following steps: Step S41: simulating the corresponding instantaneous strong impact interference constraint and the temperature sudden change interference constraint under the downhole through the logging while drilling high-temperature environment cabin; Step S42: impact stress propagation attenuation intensity analysis is performed on the corresponding instantaneous strong impact interference constraint under the downhole, and the corresponding instantaneous impact stress propagation attenuation intensity under the downhole is obtained; Step S43: temperature thermal expansion change rate calculation is performed on the corresponding temperature sudden change interference constraint under the downhole, so as to analyze the dynamic evolution process of the temperature field in the simulation cabin from the steady state to the sudden state, and the temperature thermal expansion change rate under the downhole is obtained; Step S44: the sensor output signal corresponding to the multi-type dynamic calibration sensor is obtained, and based on the corresponding instantaneous impact stress propagation attenuation intensity and the temperature thermal expansion change rate under the downhole, interference response evaluation analysis is performed on the sensor output signal corresponding to the multi-type dynamic calibration sensor by using the output interference response evaluation calculation formula, and the interference response influence degree of the instantaneous strong impact attenuation intensity and the temperature change rate on the sensor output is obtained; Step S45: based on the interference response influence degree of the instantaneous strong impact attenuation intensity and the temperature change rate on the sensor output, sensing detection verification optimization is performed on the multi-type dynamic calibration sensor, and a multi-type calibration verification optimized sensor is generated.

9. The multi-type sensor calibration detection method of claim 8, wherein, The output interference response evaluation calculation formula in step S44 is specifically: ； In the formula, is the interference response influence degree corresponding to the first sensor, is the output interference response influence time range, is the interference response evaluation time variable, is the instantaneous impact stress propagation decay intensity corresponding to the first sensor downhole at time period , is the sensor output sensitivity constant, is the temperature thermal expansion change rate corresponding to the first sensor downhole at time period , is the impact stress decay influence factor of the temperature thermal expansion, is the sensor output signal corresponding to the first sensor at time period , is the sensor output signal intensity decay factor, is the interference response influence degree correction coefficient.

10. A multi-type sensor calibration detection system, comprising: A multi-type sensor calibration detection method is executed as claimed in claim 1, and the multi-type sensor calibration detection system comprises: The zero output static calibration module is used for integrating and installing a plurality of high-temperature acceleration sensors, high-temperature directional sensors and high-temperature vibration sensors to be calibrated in a logging-while-drilling high-temperature environment cabin corresponding to high-temperature, high-pressure and strong vibration simulation functions, to generate a plurality of types of high-temperature sensors to be calibrated; a corresponding normal-temperature, normal-pressure and non-vibration static condition is designed for the logging-while-drilling high-temperature environment cabin, and the plurality of types of high-temperature sensors to be calibrated are calibrated based on the normal-temperature, normal-pressure and non-vibration static condition, so as to obtain a plurality of types of zero-drift static calibration sensors; The downhole working condition synchronous acquisition module is used for simulating and designing, by the logging-while-drilling high-temperature environment cabin, a temperature, pressure and vibration frequency constraint condition range corresponding to a range from a wellhead to a well bottom, and determining different logging-while-drilling downhole environment working condition range sub-conditions according to the temperature, pressure and vibration frequency constraint condition range corresponding to the range from the wellhead to the well bottom; the time stamp synchronous acquisition is performed on the plurality of types of zero-drift static calibration sensors according to the different logging-while-drilling downhole environment working condition range sub-conditions, to output corresponding sensor output signal data of each sensor under different downhole working condition conditions; The downhole dynamic simulation calibration module is used for performing dynamic simulation calibration on the plurality of types of zero-drift static calibration sensors based on the corresponding sensor output signal data of each sensor under different downhole working condition conditions, to generate a plurality of types of dynamic calibration sensors; The interference constraint detection verification module is used for simulating, by the logging-while-drilling high-temperature environment cabin, a corresponding instantaneous strong impact interference constraint and a temperature sudden change interference constraint downhole, and performing sensor detection verification optimization on the plurality of types of dynamic calibration sensors based on the instantaneous strong impact interference constraint and the temperature sudden change interference constraint, to generate a plurality of types of calibration verification optimization sensors.

Images