Well inclination angle measurement method and system based on inertial navigation

Abstract

The present invention relates to the field of angle measurement technology, and in particular to a method and system for measuring well inclination angle based on inertial navigation. The method comprises the following steps: performing real-time monitoring of equipment downhole operation and inertial state Kalman filtering processing on the operation process of drilling equipment, obtaining inertial state three-axis acceleration filtering data and inertial state three-axis angular velocity filtering data; performing three-axis attitude angle integral operation and three-axis attitude space conversion on the inertial state three-axis acceleration filtering data and the inertial state three-axis angular velocity filtering data to generate an inertial state three-axis attitude angle simulation space; obtaining the downhole inertial navigation initial reference point and performing three-axis attitude geometry mining analysis and well inclination angle inverse solution calculation, and at the same time performing error compensation derivation calculation and well inclination calibration processing on the corresponding sensor output in the downhole inertial navigation system to obtain the inertial navigation well inclination calibration angle. The present invention can realize the real-time and accurate measurement process of well inclination angle.

Description

Technical Field

[0001] The present invention relates to the technical field of angle measurement, and in particular to a method and system for measuring well inclination angle based on inertial navigation. Background Art

[0002] With the continuous development of oil, gas and mineral resource exploration technology, the accuracy and safety of underground operations have received more and more attention. In the drilling process, the well inclination angle (i.e. the inclination angle of the wellbore relative to the vertical line) is a crucial parameter, which directly affects the drilling efficiency, operation safety and subsequent oil and gas production. Therefore, the technology of accurately measuring the well inclination angle is particularly important. In recent years, inertial navigation technology has been gradually applied to well inclination angle measurement due to its high precision, real-time and stability. The inertial navigation system (INS) can monitor the motion state of an object in real time and calculate the position information and attitude angle of the object through the combination of accelerometers and gyroscopes. However, traditional well inclination angle measurement methods mainly include the use of vertical meters, downhole inclinometers and optical measurements. These methods usually require manual operation underground, which is complicated to operate and greatly affected by environmental factors. They are easily affected by external interference factors, resulting in inaccurate well inclination angle measurement results. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for measuring well inclination angle based on inertial navigation to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for measuring well inclination angle based on inertial navigation includes the following steps:

[0005] Step S1: The downhole operation of the drilling equipment is monitored in real time by using the three-axis accelerometer and the three-axis gyroscope built into the downhole inertial navigation system to obtain the three-axis acceleration data of the inertial navigation of the equipment and the three-axis angular velocity data of the inertial navigation of the equipment; the three-axis acceleration data of the inertial navigation of the equipment and the three-axis angular velocity data of the inertial navigation of the equipment are processed by inertial state Kalman filtering to obtain the three-axis acceleration filtering data of the inertial state and the three-axis angular velocity filtering data of the inertial state;

[0006] Step S2: performing a three-axis attitude angle integral operation on the inertial state three-axis acceleration filter data and the inertial state three-axis angular velocity filter data to obtain the inertial state three-axis attitude angle data; performing a three-axis attitude space conversion on the inertial state three-axis attitude angle data to generate an inertial state three-axis attitude angle simulation space;

[0007] Step S3: Acquire the initial reference point of the downhole inertial navigation, and perform three-axis attitude geometry mining analysis on the inertial state three-axis attitude angle simulation space based on the initial reference point of the downhole inertial navigation to obtain the spatial geometric relationship between the inertial reference point and the three-axis attitude angle; perform inverse calculation of the well inclination angle on the corresponding three-axis attitude angle in the inertial state three-axis attitude angle simulation space based on the spatial geometric relationship between the inertial reference point and the three-axis attitude angle to obtain the well inclination angle of the inertial navigation system;

[0008] Step S4: real-time monitoring of the sensor working temperature of the downhole inertial navigation system is performed through a temperature sensor to obtain inertial navigation sensor working temperature change data; error compensation is derived and calculated for the corresponding sensor output in the downhole inertial navigation system based on the inertial navigation sensor working temperature change data to obtain an inertial navigation sensor output error compensation factor; and inertial navigation system well inclination angle is calibrated according to the inertial navigation sensor output error compensation factor to obtain an inertial navigation well inclination calibration angle.

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

[0010] Step S11: using a three-axis accelerometer built into the downhole inertial navigation system to monitor the downhole running acceleration of the drilling equipment in real time during the running process, so as to obtain the three-axis acceleration data of the inertial navigation of the equipment;

[0011] Step S12: using a three-axis gyroscope built into the downhole inertial navigation system to monitor the downhole angular velocity of the drilling equipment in real time during its operation, so as to obtain the three-axis angular velocity data of the inertial navigation of the equipment;

[0012] Step S13: performing sensor noise screening and division on the three-axis acceleration data of the device inertial navigation and the three-axis angular velocity data of the device inertial navigation to obtain a three-axis standard parameter set of the device inertial navigation and a three-axis noise parameter set of the device inertial navigation;

[0013] Step S14: performing inertial state prediction calculation on the device inertial navigation three-axis standard parameter set to obtain device inertial dynamic state prediction variables, wherein the device inertial dynamic state prediction variables include device inertial three-axis acceleration state prediction variables and device inertial three-axis angular velocity state prediction variables;

[0014] Step S15: Based on the device inertial navigation three-axis noise parameter set, the inertial three-axis acceleration state prediction variables and the inertial three-axis angular velocity state prediction variables corresponding to the device inertial dynamic state prediction variables are processed by inertial state Kalman filtering to obtain inertial state three-axis acceleration filtering data and inertial state three-axis angular velocity filtering data.

[0015] Furthermore, the inertia state prediction calculation in step S14 is integrated by an inertia state transfer equation, wherein the inertia state transfer equation is specifically:

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] In the formula, is the equipment inertia dynamic state prediction variable, is the time variable parameter, is the time step, is the integral time variable parameter, The intrinsic time of the three-axis standard parameter set for the inertial navigation of the device The three-axis dynamic state vector at For at the moment Where Axis acceleration components, For at the moment Where Axis acceleration components, For at the moment Where Axis acceleration components, For at the moment Where The axis angular velocity components, For at the moment Where The axis angular velocity components, For at the moment Where The axis angular velocity components, For at the moment The inertial state external control influence component at, is the inertial dynamic state prediction function, for Axis adjustment factor, for Axis adjustment factor, for Axis adjustment factor, For at the moment The external interference noise component at is the external interference noise influence function, is the three-axis noise interference gain matrix of the equipment inertial state, is the three-axis noise vector of the device inertial state, for Axis inertia state dependencies, for Axis inertia state dependencies, for Axis inertia state dependencies, For at the moment Additional interference term of the equipment inertia state at .

[0021] Further, step S15 includes the following steps:

[0022] Step S151: extracting the device inertial navigation three-axis noise parameter set by time sequence point annotation to obtain a device inertial three-axis noise parameter subset corresponding to each time sequence point;

[0023] Step S152: performing noise statistical characteristic characterization analysis on the device inertial three-axis noise parameter subset corresponding to each timing point, and obtaining the device inertial three-axis noise statistical characteristic parameters corresponding to each timing point, wherein the device inertial three-axis noise statistical characteristic parameters include noise mean, noise variance and noise autocorrelation function;

[0024] Step S153: Based on the device inertial three-axis noise statistical characteristic parameters corresponding to each timing point, a noise covariance statistical calculation is performed on the corresponding device inertial three-axis noise parameter subset to obtain the device inertial three-axis noise covariance corresponding to each timing point; a covariance matrix is ​​constructed for the device inertial three-axis noise covariance corresponding to each timing point to generate the device inertial navigation three-axis noise covariance matrix;

[0025] Step S154: performing Kalman gain calculation on the corresponding device inertia three-axis acceleration state prediction variables and device inertia three-axis angular velocity state prediction variables in the device inertia dynamic state prediction variables based on the device inertia navigation three-axis noise covariance matrix to obtain the device inertia three-axis acceleration state Kalman gain and the device inertia three-axis angular velocity state Kalman gain;

[0026] Step S155: Based on the device inertia three-axis acceleration state Kalman gain and the device inertia three-axis angular velocity state Kalman gain, the corresponding device inertia three-axis acceleration state prediction variables and the device inertia three-axis angular velocity state prediction variables are respectively processed by inertia state Kalman filtering to obtain inertia state three-axis acceleration filtering data and inertia state three-axis angular velocity filtering data.

[0027] Further, step S2 includes the following steps:

[0028] Step S21: performing three-axis parameter normalization processing on the inertial state three-axis acceleration filtering data and the inertial state three-axis angular velocity filtering data to obtain the inertial state three-axis acceleration normalized data and the inertial state three-axis angular velocity normalized data;

[0029] Step S22: performing orthogonal decoupling mining analysis on the normalized data of the three-axis acceleration in the inertial state and the normalized data of the three-axis angular velocity in the inertial state to obtain the orthogonal decoupling state mathematical relationship between the three-axis acceleration in the inertial state and the three-axis angular velocity;

[0030] Step S23: acquiring the inertial state sampling time step, and performing a three-axis attitude angle integral operation on the inertial state three-axis acceleration normalized data and the inertial state three-axis angular velocity normalized data based on the inertial state sampling time step and the orthogonal decoupling state mathematical relationship between the inertial state three-axis acceleration and the three-axis angular velocity, so as to obtain the inertial state three-axis attitude angle data;

[0031] Step S24: performing three-axis attitude corner point sampling processing on the inertial state three-axis attitude angle data to obtain an inertial state three-axis attitude angle corner point set;

[0032] Step S25: performing three-axis attitude space conversion on the corresponding inertial state three-axis attitude angle data based on the inertial state three-axis attitude angle corner point set to generate an inertial state three-axis attitude angle simulation space.

[0033] Further, step S23 includes the following steps:

[0034] Step S231: obtaining the inertial state sampling time step, and performing attitude angle definite integral operation on the inertial state three-axis angular velocity normalized data at the corresponding time step based on the inertial state sampling time step, to obtain the inertial state three-axis initial attitude angle estimation data;

[0035] Step S232: performing acceleration orthogonal decoupling influence evaluation and analysis on the normalized data of the inertial state three-axis acceleration based on the orthogonal decoupling state mathematical relationship between the three-axis acceleration in the inertial state and the three-axis angular velocity, so as to obtain the inertial state three-axis acceleration orthogonal decoupling influence factor;

[0036] Step S233: performing attitude angle drift influence evaluation and analysis on the inertial state three-axis initial attitude angle estimation data based on the inertial state three-axis acceleration normalization data to obtain the inertial state three-axis attitude angle drift influence factor;

[0037] Step S234: performing attitude angle influence compensation calculation on the corresponding inertial state three-axis initial attitude angle estimation data based on the inertial state three-axis acceleration orthogonal decoupling influence factor and the inertial state three-axis attitude angle drift influence factor to obtain the inertial state three-axis attitude angle data.

[0038] Further, step S3 includes the following steps:

[0039] Step S31: obtaining an initial reference point for downhole inertial navigation;

[0040] Step S32: extracting the spatial geometric characteristics of the initial reference point of the underground inertial navigation to obtain the spatial geometric characteristic parameters of the inertial navigation reference point, wherein the spatial geometric characteristic parameters of the inertial navigation reference point include the spatial position, spatial orientation and spatial depth of the reference point;

[0041] Step S33: performing spatial geometric constraint analysis on the corresponding downhole inertial navigation initial reference point based on the spatial geometric characteristic parameters of the inertial navigation reference point to obtain the spatial geometric constraint conditions of the inertial navigation reference point;

[0042] Step S34: performing three-axis attitude geometry mining analysis on the corresponding three-axis attitude angles in the inertial state three-axis attitude angle simulation space based on the spatial geometry constraint condition of the inertial navigation reference point, so as to obtain the spatial geometry relationship between the inertial reference point and the three-axis attitude angle;

[0043] Step S35: Based on the spatial geometric relationship between the inertial reference point and the three-axis attitude angle, the well inclination angle calculation formula is used to perform inverse calculation of the well inclination angle of the corresponding three-axis attitude angle in the inertial state three-axis attitude angle simulation space to obtain the well inclination angle of the inertial navigation system.

[0044] Furthermore, the calculation formula of the well deviation angle in step S35 is specifically:

[0045] ;

[0046] In the formula, is the well inclination angle of the inertial navigation system, The inner edge of the three-axis attitude angle simulation space in the inertial state The acceleration component in the axial direction, The inner edge of the three-axis attitude angle simulation space in the inertial state The acceleration component in the axial direction, The inner edge of the three-axis attitude angle simulation space in the inertial state The acceleration component in the axial direction, To calculate the length of the time interval for the inverse solution, To inversely calculate the time variable parameters, The spatial intrinsic time of the three-axis attitude angle simulation for the inertial state Winding The roll angle of the axis, The spatial intrinsic time of the three-axis attitude angle simulation for the inertial state Winding The pitch angle of the axis, The spatial intrinsic time of the three-axis attitude angle simulation for the inertial state Winding The yaw angle of the axis, is the correction factor of the well inclination angle of the inertial navigation system.

[0047] Further, step S4 includes the following steps:

[0048] Step S41: using a temperature sensor to monitor the working temperature of the downhole inertial navigation system in real time to obtain inertial navigation sensor working temperature change data;

[0049] Step S42: performing temperature fluctuation amplitude statistical analysis on the inertial navigation sensor working temperature change data to obtain the inertial navigation sensor working temperature fluctuation amplitude;

[0050] Step S43: performing working temperature regression fitting analysis on the corresponding sensor output in the downhole inertial navigation system based on the working temperature fluctuation amplitude of the inertial navigation sensor, and obtaining a linear regression fitting relationship model between the working temperature change of the inertial navigation sensor and the sensor output;

[0051] Step S44: performing error compensation derivation calculation on the corresponding sensor output in the downhole inertial navigation system based on the linear regression fitting relationship model between the inertial navigation sensor working temperature change and the sensor output, so as to obtain the inertial navigation sensor output error compensation factor;

[0052] Step S45: performing well inclination calibration processing on the well inclination angle of the inertial navigation system according to the error compensation factor output by the inertial navigation sensor to obtain the well inclination calibration angle of the inertial navigation system.

[0053] Furthermore, the present invention also provides a well inclination angle measurement system based on inertial navigation, which is used to perform the well inclination angle measurement method based on inertial navigation as described above. The well inclination angle measurement system based on inertial navigation includes:

[0054] The equipment inertial navigation state filtering module is used to monitor the downhole operation of the drilling equipment in real time through the three-axis accelerometer and the three-axis gyroscope built into the downhole inertial navigation system, so as to obtain the equipment inertial navigation three-axis acceleration data and the equipment inertial navigation three-axis angular velocity data; perform inertial state Kalman filtering on the equipment inertial navigation three-axis acceleration data and the equipment inertial navigation three-axis angular velocity data, so as to obtain the inertial state three-axis acceleration filtering data and the inertial state three-axis angular velocity filtering data;

[0055] The inertial state three-axis attitude space conversion module is used to perform three-axis attitude angle integral operation on the inertial state three-axis acceleration filter data and the inertial state three-axis angular velocity filter data to obtain the inertial state three-axis attitude angle data; perform three-axis attitude space conversion on the inertial state three-axis attitude angle data to generate the inertial state three-axis attitude angle simulation space;

[0056] The inertial navigation well inclination angle inverse solution module is used to obtain the initial reference point of the downhole inertial navigation, and perform three-axis attitude geometry mining analysis on the inertial state three-axis attitude angle simulation space based on the initial reference point of the downhole inertial navigation to obtain the spatial geometric relationship between the inertial reference point and the three-axis attitude angle; based on the spatial geometric relationship between the inertial reference point and the three-axis attitude angle, perform well inclination angle inverse solution calculation on the corresponding three-axis attitude angle in the inertial state three-axis attitude angle simulation space, so as to obtain the well inclination angle of the inertial navigation system;

[0057] The well inclination angle error compensation calibration module is used to monitor the sensor working temperature of the downhole inertial navigation system in real time through the temperature sensor to obtain the inertial navigation sensor working temperature change data; based on the inertial navigation sensor working temperature change data, the corresponding sensor output in the downhole inertial navigation system is derived and calculated to obtain the inertial navigation sensor output error compensation factor; according to the inertial navigation sensor output error compensation factor, the well inclination angle of the inertial navigation system is calibrated to obtain the inertial navigation well inclination calibration angle.

[0058] Beneficial effects of the present invention:

[0059] 1. Compared with the prior art, the method for measuring well inclination angle based on inertial navigation proposed in the present invention has the beneficial effect of real-time monitoring of the acceleration and angular velocity of drilling equipment through a three-axis accelerometer and a three-axis gyroscope, providing dynamic change information of the equipment during operation. The acceleration data helps to monitor the motion state of the equipment, while the angular velocity data reveals the rotation and tilt of the equipment. Through the Kalman filtering algorithm, the accuracy of the acceleration and angular velocity data can be improved, and the influence of noise can be reduced, thereby providing more accurate dynamic information for operators. Furthermore, by performing attitude angle integration and spatial transformation on these data, the attitude changes of the equipment can be extracted and represented to provide support for well inclination angle detection. In addition, the initial reference point is determined by high-precision measurement tools to reduce error accumulation and ensure stable operation of the equipment. On this basis, the operating temperature of the equipment is monitored by temperature sensors, and error compensation is performed to ensure that temperature changes do not affect the accuracy of the sensor output, further improving the reliability and stability of the system. Finally, error compensation and well inclination calibration are used to ensure the accuracy of the well inclination angle measurement results, effectively improving the application effect of the system in complex underground environments. This can reduce the impact of external environmental interference factors on the inertial navigation system, thereby improving the precision and accuracy of the well inclination angle measurement results.

[0060] 2. The inertial navigation-based well inclination angle measurement system proposed in the present invention is generally composed of an equipment inertial navigation state filtering module, an inertial state three-axis attitude space conversion module, an inertial navigation well inclination angle inverse solution module and a well inclination angle error compensation calibration module. It can realize any well inclination angle measurement method based on inertial navigation described in the present invention, and is used to combine the operations between computer programs running on various modules to realize the well inclination angle measurement method based on inertial navigation. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient well inclination angle measurement process based on inertial navigation, thereby simplifying the operation flow of the well inclination angle measurement system based on inertial navigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0062] Figure 1 It is a schematic diagram of the steps of the method for measuring well inclination angle based on inertial navigation of the present invention;

[0063] Figure 2 for Figure 1 Detailed step flow chart of step S1 in FIG. DETAILED DESCRIPTION

[0064] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0065] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0066] 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 only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0067] To achieve this, please refer to Figure 1 to Figure 2 The present invention provides a method for measuring well inclination angle based on inertial navigation. In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of the process flow of the method for measuring the well inclination angle based on inertial navigation of the present invention. In this example, the method for measuring the well inclination angle based on inertial navigation includes the following steps:

[0068] Step S1: The downhole operation of the drilling equipment is monitored in real time by using the three-axis accelerometer and the three-axis gyroscope built into the downhole inertial navigation system to obtain the three-axis acceleration data of the inertial navigation of the equipment and the three-axis angular velocity data of the inertial navigation of the equipment; the three-axis acceleration data of the inertial navigation of the equipment and the three-axis angular velocity data of the inertial navigation of the equipment are processed by inertial state Kalman filtering to obtain the three-axis acceleration filtering data of the inertial state and the three-axis angular velocity filtering data of the inertial state;

[0069] In an embodiment of the present invention, the corresponding drilling equipment operation process is monitored in real time in three-axis directions by using a three-axis accelerometer built into the downhole inertial navigation system. The accelerometer is installed at a key position of the drilling equipment to ensure that the acceleration changes of the equipment in vertical, horizontal and oblique motions can be accurately captured, and the acceleration value of each axis is output, thereby obtaining the equipment inertial navigation three-axis acceleration data. The corresponding drilling equipment operation process is monitored in real time in three-axis directions by using a three-axis gyroscope built into the downhole inertial navigation system. The gyroscope is also fixed at a suitable position of the equipment to accurately measure its rotation speed in different directions, and can capture the azimuth change of the equipment, thereby obtaining the equipment inertial navigation three-axis angular velocity data. The sensor noise is also screened and divided by using the three-axis acceleration data of the inertial navigation of the equipment and the three-axis angular velocity data of the inertial navigation of the equipment obtained through real-time monitoring, so as to suppress the noise of the corresponding three-axis acceleration and three-axis angular velocity by using the digital signal processing technology and applying the bandpass filter, and eliminate the interference noise parameters whose frequency is lower than or higher than the frequency range required for the operation state of the equipment, and by analyzing the statistical characteristics of the data, the screened valid data is divided into a standard parameter set and a noise parameter set, and the inertial state is predicted and calculated by the three-axis standard parameter set obtained through the previous screening, so as to dynamically model the operation state prediction data of the acceleration and angular velocity in combination with the corresponding random interference items, and the prediction calculation takes into account the initial state change variables of the inertial three-axis acceleration and three-axis angular velocity of the equipment, and combines the time step, the three-axis dynamic state vector (including , , The three-axis acceleration component and the angular velocity component) and the related interference terms constitute a suitable inertial state transfer equation to estimate the acceleration and angular velocity state of the device at the future moment, so as to obtain the corresponding three-axis acceleration state prediction variables and the three-axis angular velocity state prediction variables. Then, the three-axis noise parameter set with noise obtained previously is divided into time series points by annotation, so as to record each data point by using a timestamp, and a program is written to automatically extract the three-axis noise parameter subset corresponding to each time series point to ensure that the extracted noise parameter subset reflects the real device state at each time series point, that is, the corresponding three-axis acceleration and three-axis angular velocity, and the covariance of the noise parameter subset of each time series point extracted previously is statistically calculated to realize the quantitative calculation of the covariance within each time series point, for example ,in represents the covariance, and are two random variables at the time points, and is their mean. The Kalman filter theory is used to quantify the gain parameters of the three-axis acceleration state prediction variables and the three-axis angular velocity state prediction variables obtained by the state transfer equation analysis to calculate the Kalman gain of the acceleration state and angular velocity state. In the specific implementation, according to the Kalman gain formula ,in is the Kalman gain, is the state covariance matrix of the three-axis parameter predictor variables, is the observation matrix, is the observation noise covariance matrix, To transpose the symbol, the Kalman gain calculation for each time point is realized by writing the corresponding program. At the same time, the corresponding three-axis acceleration state prediction variables and three-axis angular velocity state prediction variables are filtered by combining the previously calculated Kalman gain. Specifically, it is as follows: ， ,in is the updated state estimate, is the observed value, is the updated covariance matrix. This process is implemented by writing corresponding codes, and finally the inertial state three-axis acceleration filtering data and the inertial state three-axis angular velocity filtering data are obtained.

[0070] Step S2: performing a three-axis attitude angle integral operation on the inertial state three-axis acceleration filter data and the inertial state three-axis angular velocity filter data to obtain the inertial state three-axis attitude angle data; performing a three-axis attitude space conversion on the inertial state three-axis attitude angle data to generate an inertial state three-axis attitude angle simulation space;

[0071] In an embodiment of the present invention, the parameters of the three-axis acceleration filtering data and the three-axis angular velocity filtering data of the inertial state obtained after filtering are normalized, and the acceleration and angular velocity signals output by the sensor are read by a data acquisition device. On this basis, the data are normalized to ensure that data of different magnitudes can be uniformly compared. The normalization method is to subtract the mean of the data sequence from each data point and then divide it by the standard deviation of the sequence. The three-axis acceleration and three-axis angular velocity data obtained after normalization are subjected to orthogonal decoupling analysis, and principal component analysis (PCA) or other decoupling algorithms can be used. The goal of this process is to extract the independent mathematical relationship between acceleration and angular velocity, construct a matrix containing normalized acceleration and angular velocity data, and obtain the mathematical relationship between acceleration and angular velocity. At the same time, by obtaining the corresponding inertial state sampling time step, which is usually determined by the internal clock frequency of the inertial sensor, the data unit is usually degree / second or radian / second, the time interval can be calculated by the timestamp, and the normalized three-axis acceleration and three-axis angular velocity data are integrated by combining the inertial state sampling time step and the orthogonal decoupling state mathematical relationship between the inertial state three-axis acceleration and the three-axis angular velocity to obtain the three Axis attitude angle data, specifically, a numerical integration method, such as trapezoidal integration method or Simpson method, can be used to integrate acceleration and angular velocity according to the time step to obtain the change angle of the three-axis attitude, and after obtaining the inertial state three-axis attitude angle data, it is subjected to corner sampling processing. This process can set a threshold (such as a time period where the angle change is less than a certain amplitude) to screen out key attitude change points, loop through the three-axis attitude angle data, record each significant change in angle data and its corresponding timestamp, form a corner point set, each corner point represents a specific attitude state, and then, by combining the previous sampling The obtained three-axis attitude angle corner point set performs three-axis attitude space transformation on the corresponding three-axis attitude angle data. The rotation matrix or quaternion method can be used to construct a rotation matrix or quaternion to represent the spatial position of each attitude angle. These attitude angle data are then input into the coordinate transformation algorithm for three-dimensional space simulation mapping. By performing coordinate transformation on each angle, the corresponding three-axis attitude angle simulation space can be generated. This process can be implemented using mathematical software or programming to ensure that the converted space has practical application value to support the precise measurement and analysis of well inclination angles, and finally the inertial state three-axis attitude angle simulation space is converted.

[0072] Step S3: Acquire the initial reference point of the downhole inertial navigation, and perform three-axis attitude geometry mining analysis on the inertial state three-axis attitude angle simulation space based on the initial reference point of the downhole inertial navigation to obtain the spatial geometric relationship between the inertial reference point and the three-axis attitude angle; perform inverse calculation of the well inclination angle on the corresponding three-axis attitude angle in the inertial state three-axis attitude angle simulation space based on the spatial geometric relationship between the inertial reference point and the three-axis attitude angle to obtain the well inclination angle of the inertial navigation system;

[0073] In an embodiment of the present invention, data is collected by using a high-precision inertial measurement unit (IMU). At this time, the IMU device should be placed at a preset initial position to ensure the stability of the device and avoid external interference. The acceleration and angular velocity data obtained by the sensor will be used to calculate the spatial position of the initial reference point in real time, and record the spatial azimuth and depth information of the point, so as to obtain the corresponding initial reference point of downhole inertial navigation. At the same time, the spatial geometric characteristics of the corresponding downhole inertial navigation initial reference point are extracted and analyzed by using a geometric data extraction algorithm to extract the spatial position (i.e., three-dimensional coordinates), spatial orientation (determined by heading angle, pitch angle, and roll angle) and spatial depth (vertical distance from the ground to the reference point) of the reference point, and the spatial geometric constraint analysis is performed on the initial reference point of the downhole inertial navigation corresponding to the spatial geometric characteristic parameters obtained by previous statistical calculations, so as to involve the construction of a geometric constraint model to describe the constraint relationship of the reference point in three-dimensional space, and define the relative position relationship of the point and its geometric constraints with the surrounding environment by applying the geometric constraint theory, and also conduct geometric relationship mining and analysis on the corresponding three-axis attitude angles in the simulation space of the inertial state three-axis attitude angle obtained by previous simulation by combining the spatial geometric constraints obtained by previous constraint analysis, so as to establish a spatial geometric relationship model between the attitude angle and the reference point, and use vector analysis and spatial geometry principles to establish a mapping relationship between the attitude angle (pitch angle, yaw angle, roll angle) and the inertial reference point, so as to mine and analyze the spatial geometric relationship between the inertial reference point and the three-axis attitude angle. Then, by using the spatial geometric relationship between the inertial reference point and the three-axis attitude angle obtained by the previous mining and analysis, and combining the acceleration components in the three-axis directions, the corresponding three-axis attitude angles (including pitch angle, yaw angle, roll angle) in the inertial state three-axis attitude angle simulation space, and related parameters, a suitable calculation formula is constructed to perform the inverse calculation of the well inclination angle. In combination with the spatial depth and attitude angle of the reference point, the well inclination angle value is calculated and output, and finally the well inclination angle of the inertial navigation system is obtained.

[0074] Step S4: real-time monitoring of the sensor working temperature of the downhole inertial navigation system is performed through a temperature sensor to obtain inertial navigation sensor working temperature change data; error compensation is derived and calculated for the corresponding sensor output in the downhole inertial navigation system based on the inertial navigation sensor working temperature change data to obtain an inertial navigation sensor output error compensation factor; and inertial navigation system well inclination angle is calibrated according to the inertial navigation sensor output error compensation factor to obtain an inertial navigation well inclination calibration angle.

[0075] In the embodiment of the present invention, a high-precision temperature sensor, such as a thermocouple or RTD (resistance temperature detector), is used and installed in a key part of the downhole inertial navigation system. The temperature sensor is connected to the inertial navigation system through a digital interface to realize real-time data collection. The working temperature change of the corresponding sensor in the downhole inertial navigation system can be read every second, thereby obtaining the working temperature change data of the inertial navigation sensor. The working temperature change data of the inertial navigation sensor obtained by real-time monitoring is statistically analyzed by using a statistical method to quantify the range of temperature fluctuation by calculating the maximum value, minimum value and standard deviation of the temperature data, and by using programming languages ​​such as Python, the NumPy library can be used to achieve efficient data processing, generate the temperature fluctuation amplitude, and select a suitable regression analysis method, such as the least squares method, by combining the working temperature fluctuation amplitude obtained by the previous statistical analysis, to perform regression fitting analysis on the relationship between the corresponding sensor output and the temperature change in the downhole inertial navigation system. The Scikit-learn library of Python is used to calculate the coefficients of the linear regression equation by inputting the temperature data and the corresponding sensor output data. This process determines the specific relationship of the influence of temperature change on the sensor output. At the same time, By combining the linear regression fitting relationship model between the working temperature change and the sensor output obtained by the previous fitting analysis, the regression equation is used to perform error compensation calculation on the corresponding sensor output in the downhole inertial navigation system. In the specific implementation, the real-time monitored temperature data is substituted into the regression equation to calculate the deviation between the ideal output value and the actual output value, and the deviation is used as the error compensation factor. Then, the corresponding inertial navigation system well inclination angle is calibrated and calculated by using the inertial navigation sensor output error compensation factor obtained by the previous quantitative calculation, so as to combine the compensation factor with the well inclination angle measurement value, and use the weighted average method or other suitable calibration algorithms to obtain the corrected well inclination angle. This process can be achieved through a custom calculation program to ensure that the measurement result of the well inclination angle is more accurate and adapts to the complex downhole environment, and finally the inertial navigation well inclination calibration angle is obtained.

[0076] Further, as an embodiment of the present invention, refer to Figure 2 As shown, Figure 1Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0077] Step S11: using a three-axis accelerometer built into the downhole inertial navigation system to monitor the downhole running acceleration of the drilling equipment in real time during the running process, so as to obtain the three-axis acceleration data of the inertial navigation of the equipment;

[0078] In an embodiment of the present invention, the three-axis accelerometer built into the downhole inertial navigation system is used to perform real-time monitoring of the acceleration of the corresponding drilling equipment in the three-axis directions during operation. The accelerometer is installed at a key position of the drilling equipment to ensure that the acceleration changes of the equipment in vertical, horizontal and oblique motion can be accurately captured. In specific implementation, the three-axis accelerometer regularly collects the motion data of the equipment in the well, outputs the acceleration value of each axis, and finally obtains the three-axis acceleration data of the equipment inertial navigation.

[0079] Step S12: using a three-axis gyroscope built into the downhole inertial navigation system to monitor the downhole angular velocity of the drilling equipment in real time during its operation, so as to obtain the three-axis angular velocity data of the inertial navigation of the equipment;

[0080] In an embodiment of the present invention, the three-axis gyroscope built into the downhole inertial navigation system is used to perform real-time monitoring of the angular velocity of the corresponding drilling equipment in the three-axis directions during the operation process. The gyroscope is also fixed at a suitable position on the equipment to accurately measure its rotation speed in different directions. By measuring the rotational motion, the azimuth change of the equipment can be captured to ensure that the drilling operation is carried out at a stable angle. The data collection in this process adopts high-frequency sampling technology to obtain high-precision angular velocity data, and finally the three-axis angular velocity data of the equipment inertial navigation is obtained.

[0081] Step S13: performing sensor noise screening and division on the three-axis acceleration data of the device inertial navigation and the three-axis angular velocity data of the device inertial navigation to obtain a three-axis standard parameter set of the device inertial navigation and a three-axis noise parameter set of the device inertial navigation;

[0082] In an embodiment of the present invention, the sensor noise is screened and divided for the three-axis acceleration data of the device inertial navigation and the three-axis angular velocity data of the device inertial navigation previously obtained through real-time monitoring, and the corresponding three-axis acceleration and three-axis angular velocity are subjected to noise suppression by applying a bandpass filter using digital signal processing technology, and interference noise parameters with frequencies lower than or higher than the frequency range required for the device operation state are eliminated. By analyzing the statistical characteristics of the data, the screened valid data is divided into a standard parameter set and a noise parameter set, the standard parameter set contains verified data to reflect the actual motion state of the device, and the noise parameter set records the parameter data identified as noise, and finally the three-axis standard parameter set of the device inertial navigation and the three-axis noise parameter set of the device inertial navigation are obtained.

[0083] Step S14: performing inertial state prediction calculation on the device inertial navigation three-axis standard parameter set to obtain device inertial dynamic state prediction variables, wherein the device inertial dynamic state prediction variables include device inertial three-axis acceleration state prediction variables and device inertial three-axis angular velocity state prediction variables;

[0084] In the embodiment of the present invention, the inertial state prediction calculation is performed on the previously screened three-axis standard parameter set of the device inertial navigation, so as to dynamically model the running state prediction data of acceleration and angular velocity in combination with the corresponding random interference terms. The prediction calculation takes into account the initial state change variables of the device inertial three-axis acceleration and three-axis angular velocity, and combines the time variable parameters, time step, three-axis dynamic state vector (including , , axis acceleration component and angular velocity component), inertial state external control influence component, inertial dynamic state prediction function, , , Axis adjustment coefficient, external interference noise component, external interference noise influence function, equipment inertial state three-axis noise interference gain matrix, equipment inertial state three-axis noise vector, , , The axis inertia state dependency term and the additional interference term of the device inertia state constitute a suitable inertia state transfer equation to estimate the acceleration and angular velocity state of the device at future moments, and finally obtain the device inertia dynamic state prediction variables, including the device inertia three-axis acceleration state prediction variables and the device inertia three-axis angular velocity state prediction variables.

[0085] Step S15: Based on the device inertial navigation three-axis noise parameter set, the inertial three-axis acceleration state prediction variables and the inertial three-axis angular velocity state prediction variables corresponding to the device inertial dynamic state prediction variables are processed by inertial state Kalman filtering to obtain inertial state three-axis acceleration filtering data and inertial state three-axis angular velocity filtering data.

[0086] In an embodiment of the present invention, the three-axis noise parameter set of the inertial navigation of the device with noise that has been previously obtained is marked and divided into time series points, so that each data point is recorded by using a timestamp, and a program is written to automatically extract the three-axis noise parameter subset corresponding to each time series point to ensure that the extracted noise parameter subset reflects the real device state at each time series point, that is, the corresponding three-axis acceleration and three-axis angular velocity, and statistical analysis is performed on the noise parameter subset of each time series point previously extracted to statistically calculate the statistical characteristic parameters such as the mean, variance and autocorrelation function of each noise parameter subset. At the same time, the covariance of the device inertial three-axis noise parameter subset at the corresponding time series point is statistically calculated by combining the statistical characteristic parameters obtained by the previous statistical calculation, so as to realize the quantitative calculation of the covariance within each time series point, using the following formula ,in represents the covariance, and are two random variables at the time points, and is their mean, so as to obtain the equipment inertial three-axis noise covariance corresponding to each time point to construct a covariance matrix, and by combining the previously constructed covariance matrix, the Kalman filter theory is used to quantify the gain parameters of the three-axis acceleration state prediction variables and the three-axis angular velocity state prediction variables obtained by the state transfer equation analysis, so as to calculate the Kalman gain of the acceleration state and angular velocity state. In the specific implementation, according to the Kalman gain formula ,in is the Kalman gain, is the state covariance matrix of the three-axis parameter predictor variables, is the observation matrix, is the observation noise covariance matrix, To transpose the symbol, the Kalman gain calculation for each time point is realized by writing the corresponding program. Then, the corresponding device inertia three-axis acceleration state prediction variable and the device inertia three-axis angular velocity state prediction variable are filtered by combining the previously calculated Kalman gain. Specifically, the Kalman filter formula is as follows: , ,in is the updated state estimate, is the observed value, The updated covariance matrix is ​​implemented by writing corresponding codes to obtain the filtered inertial state three-axis acceleration data and angular velocity data, and finally the inertial state three-axis acceleration filtered data and the inertial state three-axis angular velocity filtered data are obtained.

[0087] Furthermore, the inertia state prediction calculation in step S14 is integrated by an inertia state transfer equation, wherein the inertia state transfer equation is specifically:

[0088] ;

[0089] ;

[0090] ;

[0091] ;

[0092] In the formula, is the equipment inertia dynamic state prediction variable, is the time variable parameter, is the time step, is the integral time variable parameter, The intrinsic time of the three-axis standard parameter set for the inertial navigation of the device The three-axis dynamic state vector at For at the moment Where Axis acceleration components, For at the moment Where Axis acceleration components, For at the moment Where Axis acceleration components, For at the moment Where The axis angular velocity components, For at the moment Where The axis angular velocity components, For at the moment Where The axis angular velocity components, For at the moment The inertial state external control influence component at, is the inertial dynamic state prediction function, for Axis adjustment factor, for Axis adjustment factor, for Axis adjustment factor, For at the moment The external interference noise component at is the external interference noise influence function, is the three-axis noise interference gain matrix of the equipment inertial state, is the three-axis noise vector of the device inertial state, for Axis inertia state dependencies, for Axis inertia state dependencies, for Axis inertia state dependencies, For at the moment Additional interference term of the equipment inertia state at .

[0093] The present invention obtains an inertial state transfer equation by using a specific mathematical model and verifying it, which is used to perform inertial state prediction calculation on the three-axis standard parameter set of the equipment inertial navigation. The inertial state transfer equation can effectively combine the current state with external control factors and interference noise to predict the future state. This process takes into account the dynamic characteristics of the system and external factors, making the prediction more in line with the actual situation. The various parameters in the state transfer equation (such as and ) allows the model to take into account not only the current state, but also external influences and interference factors. This multi-factor comprehensive modeling improves the system's adaptability and prediction accuracy. At the same time, by using the gain matrix and noise vector, it is possible to accurately quantify the impact of external noise on the device state, providing an important reference for subsequent filtering processing. This processing enables the system to adapt to environmental changes, thereby further improving the stability of dynamic state prediction. In summary, this equation fully considers the device inertia dynamic state prediction variables , time variable parameter , time step , integral time variable parameter , the intrinsic time of the three-axis standard parameter set of the inertial navigation device The three-axis dynamic state vector at , at the time Where Axis acceleration components , at the time Where Axis acceleration components , at the time Where Axis acceleration components , at the time Where Axis angular velocity components , at the time Where Axis angular velocity components , at the time Where Axis angular velocity components , at the time The inertial state external control influence component , inertial dynamic state prediction function , Axis adjustment factor , Axis adjustment factor , Axis adjustment factor , at the time The external interference noise component at , external interference noise influence function , equipment inertial state three-axis noise interference gain matrix , the three-axis noise vector of the device inertial state , Axis Inertia State Dependencies , Axis Inertia State Dependencies , Axis Inertia State Dependencies , at the time Additional disturbance term of the device inertia state at , among which, through For at the moment Where Axis acceleration components, For at the moment Where Axis acceleration components, For at the moment Where Axis acceleration components, For at the moment Where The axis angular velocity components, For at the moment Where The axis angular velocity components, For at the moment Where Axis angular velocity components and time-variant parameters It constitutes a three-axis standard parameter set of inertial navigation equipment. The three-axis dynamic state vector at Functional relationship , by combining the intrinsic time of the device's inertial navigation three-axis standard parameter set The three-axis dynamic state vector at , time variable parameter , at the time The inertial state external control influence component , Axis adjustment factor , Axis adjustment factor , Axis adjustment factor and at the time The external interference noise component at Constitutes an inertial dynamic state prediction function Functional relationship , and also by combining the three-axis noise interference gain matrix of the device inertial state , the three-axis noise vector of the device inertial state , Axis Inertia State Dependencies , Axis Inertia State Dependencies , Axis Inertia State Dependencies , the intrinsic time of the three-axis standard parameter set of the inertial navigation device The three-axis dynamic state vector at , time variable parameter and at the time The inertial state external control influence component Constitutes the external interference noise influence function Functional relationship , predicting variables based on the equipment inertia dynamic state The correlation between the above parameters constitutes a functional relationship This equation can realize the inertial state prediction calculation process of the three-axis standard parameter set of the equipment inertial navigation, thereby improving the accuracy and applicability of the inertial state transfer equation.

[0094] Further, step S15 includes the following steps:

[0095] Step S151: extracting the device inertial navigation three-axis noise parameter set by time sequence point annotation to obtain a device inertial three-axis noise parameter subset corresponding to each time sequence point;

[0096] In an embodiment of the present invention, the three-axis noise parameter set of the inertial navigation of the device with noise that has been previously obtained is marked and divided into time series points, and each data point is recorded by using a timestamp. A program is written to automatically extract the three-axis noise parameters corresponding to each time series point to form a parameter subset, so as to ensure that the extracted noise parameter subset reflects the actual device state at each time series point, that is, the corresponding three-axis acceleration and three-axis angular velocity, and finally the device inertial three-axis noise parameter subset corresponding to each time series point is obtained.

[0097] Step S152: performing noise statistical characteristic characterization analysis on the device inertial three-axis noise parameter subset corresponding to each timing point, and obtaining the device inertial three-axis noise statistical characteristic parameters corresponding to each timing point, wherein the device inertial three-axis noise statistical characteristic parameters include noise mean, noise variance and noise autocorrelation function;

[0098] In an embodiment of the present invention, a statistical analysis is performed on a noise parameter subset of each previously extracted timing point to statistically calculate the mean, variance and autocorrelation function of each noise parameter subset. In specific implementation, a programming environment such as MATLAB or Python can be used to perform data analysis using built-in statistical tools. When calculating the noise mean for each subset, the arithmetic mean method is used, and the noise variance is obtained by calculating the average of the square of the difference between each data point and the mean. The calculation of the autocorrelation function can be achieved through the correlation coefficient function, which can help identify the periodic characteristics of the noise data, and finally obtain the statistical characteristic parameters of the device inertial three-axis noise corresponding to each timing point, including the noise mean, noise variance and noise autocorrelation function.

[0099] Step S153: Based on the device inertial three-axis noise statistical characteristic parameters corresponding to each timing point, a noise covariance statistical calculation is performed on the corresponding device inertial three-axis noise parameter subset to obtain the device inertial three-axis noise covariance corresponding to each timing point; a covariance matrix is ​​constructed for the device inertial three-axis noise covariance corresponding to each timing point to generate the device inertial navigation three-axis noise covariance matrix;

[0100] In the embodiment of the present invention, by combining the statistical characteristic parameters of the inertial three-axis noise of the device corresponding to each timing point obtained by previous statistical calculation, the covariance of the device inertial three-axis noise parameter subset at the corresponding timing point is statistically calculated to achieve quantitative calculation of the covariance within each timing point, using the following formula: ,in represents the covariance, and are two random variables at the time points, and is their mean, thus obtaining the device inertial three-axis noise covariance corresponding to each timing point. At the same time, the device inertial three-axis noise covariance corresponding to each timing point obtained by the previous quantization calculation is used to construct a covariance matrix, and finally the device inertial navigation three-axis noise covariance matrix is ​​constructed.

[0101] Step S154: performing Kalman gain calculation on the corresponding device inertia three-axis acceleration state prediction variables and device inertia three-axis angular velocity state prediction variables in the device inertia dynamic state prediction variables based on the device inertia navigation three-axis noise covariance matrix to obtain the device inertia three-axis acceleration state Kalman gain and the device inertia three-axis angular velocity state Kalman gain;

[0102] In the embodiment of the present invention, by combining the previously constructed three-axis noise covariance matrix of the inertial navigation of the device, the Kalman filter theory is used to quantify the gain parameters of the three-axis acceleration state prediction variables and the three-axis angular velocity state prediction variables corresponding to the device inertial dynamic state prediction variables previously obtained by analyzing the state transfer equation, so as to calculate the Kalman gain of the acceleration state and the angular velocity state. In the specific implementation, according to the Kalman gain formula ,in is the Kalman gain, is the state covariance matrix of the three-axis parameter predictor variables, is the observation matrix, is the observation noise covariance matrix, To transpose the symbol, the Kalman gain calculation for each timing point is realized by writing the corresponding program, and finally the Kalman gain of the device inertia three-axis acceleration state and the Kalman gain of the device inertia three-axis angular velocity state are obtained.

[0103] Step S155: Based on the device inertia three-axis acceleration state Kalman gain and the device inertia three-axis angular velocity state Kalman gain, the corresponding device inertia three-axis acceleration state prediction variables and the device inertia three-axis angular velocity state prediction variables are respectively processed by inertia state Kalman filtering to obtain inertia state three-axis acceleration filtering data and inertia state three-axis angular velocity filtering data.

[0104] In the embodiment of the present invention, the corresponding device inertia three-axis acceleration state prediction variable and the device inertia three-axis angular velocity state prediction variable are filtered by combining the previously calculated device inertia three-axis acceleration state Kalman gain and the device inertia three-axis angular velocity state Kalman gain. Specifically, the Kalman filter formula is as follows: , ,in is the updated state estimate, is the observed value, The updated covariance matrix is ​​implemented by writing corresponding codes to obtain the filtered three-axis acceleration data and angular velocity data of the inertial state. This data will be used for subsequent well inclination angle measurement to ensure the accuracy and stability of the measurement results, and finally the inertial state three-axis acceleration filtering data and the inertial state three-axis angular velocity filtering data are obtained.

[0105] Further, step S2 includes the following steps:

[0106] Step S21: performing three-axis parameter normalization processing on the inertial state three-axis acceleration filtering data and the inertial state three-axis angular velocity filtering data to obtain the inertial state three-axis acceleration normalized data and the inertial state three-axis angular velocity normalized data;

[0107] In an embodiment of the present invention, the parameters of the inertial state three-axis acceleration filtering data and the inertial state three-axis angular velocity filtering data obtained after filtering are normalized, so that the acceleration and angular velocity signals output by the sensor are read by a data acquisition device. The data are usually expressed in original units (such as m / s² and rad / s). On this basis, the data are normalized to ensure that data of different magnitudes can be uniformly compared. The normalization method is to subtract the mean of the data sequence from each data point and then divide it by the standard deviation of the sequence. Through this method, the values ​​of the generated three-axis acceleration normalized data and the three-axis angular velocity normalized data are both within the range of [-1, 1], and finally the inertial state three-axis acceleration normalized data and the inertial state three-axis angular velocity normalized data are obtained.

[0108] Step S22: performing orthogonal decoupling mining analysis on the normalized data of the three-axis acceleration in the inertial state and the normalized data of the three-axis angular velocity in the inertial state to obtain the orthogonal decoupling state mathematical relationship between the three-axis acceleration in the inertial state and the three-axis angular velocity;

[0109] In an embodiment of the present invention, by performing orthogonal decoupling analysis on the three-axis acceleration and three-axis angular velocity data obtained after previous normalization, principal component analysis (PCA) or other decoupling algorithms can be used. The goal of this process is to extract the independent mathematical relationship between acceleration and angular velocity, construct a matrix containing normalized acceleration and angular velocity data, and extract relevant features through eigenvalue decomposition. At this time, ensure that the data is de-meaned to eliminate the influence of the average value on the decoupling result. By analyzing the direction of the eigenvector and the size of the eigenvalue, the mathematical relationship between acceleration and angular velocity is obtained, thereby achieving orthogonal decoupling of the three-axis parameters, and finally obtaining the orthogonal decoupling state mathematical relationship between the three-axis acceleration and the three-axis angular velocity in the inertial state.

[0110] Step S23: acquiring the inertial state sampling time step, and performing a three-axis attitude angle integral operation on the inertial state three-axis acceleration normalized data and the inertial state three-axis angular velocity normalized data based on the inertial state sampling time step and the orthogonal decoupling state mathematical relationship between the inertial state three-axis acceleration and the three-axis angular velocity, so as to obtain the inertial state three-axis attitude angle data;

[0111] In an embodiment of the present invention, by obtaining the corresponding inertial state sampling time step, which is usually determined by the internal clock frequency of the inertial sensor, and the data unit is usually degree / second or radian / second, the time interval can be calculated by the timestamp, and by combining the inertial state sampling time step and the orthogonal decoupling state mathematical relationship between the inertial state three-axis acceleration and the three-axis angular velocity, the normalized three-axis acceleration and three-axis angular velocity data are integrated to obtain the three-axis attitude angle data. Specifically, a numerical integration method, such as a trapezoidal integration method or a Simpson method, can be used to integrate the acceleration and angular velocity according to the time step to obtain the change angle of the attitude. At this time, the initial value of the angle integral is set to zero, and then the acceleration and angular velocity values ​​of each time step are gradually accumulated to finally obtain the inertial state three-axis attitude angle data.

[0112] Step S24: performing three-axis attitude corner point sampling processing on the inertial state three-axis attitude angle data to obtain an inertial state three-axis attitude angle corner point set;

[0113] In an embodiment of the present invention, after obtaining the three-axis posture angle data in the inertial state, corner point sampling processing is performed on it. This process can screen out key posture change points by setting a threshold (such as a time period in which the angle change is less than a certain amplitude), and the three-axis posture angle data is traversed in a loop, and the angle data of each significant change and its corresponding timestamp are recorded to form a corner point set, each corner point represents a specific posture state. This process can be achieved by writing a data processing algorithm to ensure that the extracted corner points can fully reflect the main change characteristics of the posture in subsequent analysis, and finally the inertial state three-axis posture angle corner point set is obtained.

[0114] Step S25: performing three-axis attitude space conversion on the corresponding inertial state three-axis attitude angle data based on the inertial state three-axis attitude angle corner point set to generate an inertial state three-axis attitude angle simulation space.

[0115] In an embodiment of the present invention, the three-axis attitude angle data is converted into a three-axis attitude space by combining the three-axis attitude angle corner point set obtained by previous sampling in the inertial state. The rotation matrix or quaternion method can be used to construct a rotation matrix or quaternion to represent the spatial position of each attitude angle. These attitude angle data are then input into the coordinate conversion algorithm to perform simulation mapping in three-dimensional space. By performing coordinate conversion on each angle, the corresponding three-axis attitude angle simulation space can be generated. This process can be implemented using mathematical software or programming to ensure that the converted space has practical application value to support the precise measurement and analysis of well inclination angles, and finally the inertial state three-axis attitude angle simulation space is converted and generated.

[0116] Further, step S23 includes the following steps:

[0117] Step S231: obtaining the inertial state sampling time step, and performing attitude angle definite integral operation on the inertial state three-axis angular velocity normalized data at the corresponding time step based on the inertial state sampling time step, to obtain the inertial state three-axis initial attitude angle estimation data;

[0118] In an embodiment of the present invention, a corresponding inertial state sampling time step is obtained, which is usually determined by the internal clock frequency of the inertial sensor, and the data unit is usually degree / second or radian / second. The three-axis angular velocity data of the inertial state in each time step is integrated by adopting a numerical integration method, such as the trapezoidal rule or the Simpson's rule, so as to obtain the attitude angle on the corresponding coordinate axis. In the calculation process, the time sequence integrity of the data must be ensured, and the deviation and noise of the sensor must be taken into account to improve the estimation accuracy of the attitude angle, specifically including the estimated values ​​of the pitch angle, the roll angle and the heading angle, and finally the inertial state three-axis initial attitude angle estimation data is obtained.

[0119] Step S232: performing acceleration orthogonal decoupling influence evaluation and analysis on the normalized data of the inertial state three-axis acceleration based on the orthogonal decoupling state mathematical relationship between the three-axis acceleration in the inertial state and the three-axis angular velocity, so as to obtain the inertial state three-axis acceleration orthogonal decoupling influence factor;

[0120] In an embodiment of the present invention, an orthogonal decoupling influence evaluation calculation is performed on the normalized data of the inertial state three-axis acceleration obtained previously after normalization by combining the orthogonal decoupling state mathematical relationship between the inertial state three-axis acceleration and the three-axis angular velocity obtained by the previous orthogonal decoupling, so as to be realized by a linear algebra method. The key to this step is to determine the orthogonality of the three-axis acceleration data, ensure their mutual independence in the three-axis coordinate system, and decompose the acceleration data into components related to and unrelated to the angular velocity. This can be achieved by establishing a linear regression model of acceleration and angular velocity. The regression coefficient obtained by solving the least squares method is the acceleration orthogonal decoupling influence factor, and finally the inertial state three-axis acceleration orthogonal decoupling influence factor is obtained.

[0121] Step S233: performing attitude angle drift influence evaluation and analysis on the inertial state three-axis initial attitude angle estimation data based on the inertial state three-axis acceleration normalization data to obtain the inertial state three-axis attitude angle drift influence factor;

[0122] In an embodiment of the present invention, the influence of attitude angle drift is evaluated and calculated by combining the normalized data of the inertial state three-axis acceleration obtained after normalization, and the drift degree of the attitude angle is judged by analyzing the changes of the acceleration data under different attitude angles. In a specific implementation, the acceleration data can be processed by algorithms such as Kalman filtering to eliminate the influence of noise and extract effective information. On this basis, the relationship between the acceleration change and the estimated attitude angle in different time periods is calculated, and then the attitude angle drift influence factor is obtained. The evaluation of this factor can help identify the attitude angle drift caused by sensor errors, environmental changes and other factors, and finally the inertial state three-axis attitude angle drift influence factor is obtained.

[0123] Step S234: performing attitude angle influence compensation calculation on the corresponding inertial state three-axis initial attitude angle estimation data based on the inertial state three-axis acceleration orthogonal decoupling influence factor and the inertial state three-axis attitude angle drift influence factor to obtain the inertial state three-axis attitude angle data.

[0124] In an embodiment of the present invention, a compensation calculation of the attitude angle is performed on the corresponding inertial state three-axis initial attitude angle estimation data by combining the inertial state three-axis acceleration orthogonal decoupling influence factor and the inertial state three-axis attitude angle drift influence factor obtained by previous evaluation calculation, so as to linearly multiply the above two factors with the initial attitude angle estimation value to form a comprehensive compensation model. When implementing it, the initial attitude angle is first updated, and the orthogonal decoupling influence factor of acceleration and the drift influence factor are superimposed on the initial estimation value to obtain more accurate attitude angle data. The model can be continuously optimized in an iterative manner, and historical data can be used for regression analysis to improve the accuracy of compensation, and finally the inertial state three-axis attitude angle data is obtained.

[0125] Further, step S3 includes the following steps:

[0126] Step S31: obtaining an initial reference point for downhole inertial navigation;

[0127] In an embodiment of the present invention, data is collected by using a high-precision inertial measurement unit (IMU). At this time, the IMU device should be placed at a preset initial position to ensure the stability of the device and avoid external interference. The acceleration and angular velocity data obtained by the sensor will be used to calculate the spatial position of the initial reference point in real time. Specifically, through multiple sampling and data smoothing, the position coordinates (X, Y, Z) are obtained, and the spatial azimuth and depth information of the point are recorded, and finally the corresponding initial reference point of downhole inertial navigation is obtained.

[0128] Step S32: extracting the spatial geometric characteristics of the initial reference point of the underground inertial navigation to obtain the spatial geometric characteristic parameters of the inertial navigation reference point, wherein the spatial geometric characteristic parameters of the inertial navigation reference point include the spatial position, spatial orientation and spatial depth of the reference point;

[0129] In an embodiment of the present invention, after obtaining the initial reference point of the downhole inertial navigation, the spatial geometric characteristics of the corresponding initial reference point of the downhole inertial navigation are extracted and analyzed by using a geometric data extraction algorithm to extract the spatial position (i.e., three-dimensional coordinates), spatial orientation (determined by heading angle, pitch angle, and roll angle) and spatial depth (vertical distance from the ground to the reference point) of the reference point. This process can be achieved by combining point cloud processing technology with a spatial analysis algorithm to ensure the accuracy and reliability of the geometric characteristic extraction, and finally the spatial geometric characteristic parameters of the inertial navigation reference point are obtained, wherein the spatial geometric characteristic parameters of the inertial navigation reference point include the spatial position, spatial orientation, and spatial depth of the reference point.

[0130] Step S33: performing spatial geometric constraint analysis on the corresponding downhole inertial navigation initial reference point based on the spatial geometric characteristic parameters of the inertial navigation reference point to obtain the spatial geometric constraint conditions of the inertial navigation reference point;

[0131] In an embodiment of the present invention, a spatial geometric constraint analysis is performed on the downhole inertial navigation initial reference point corresponding to the spatial geometric characteristic parameters of the inertial navigation reference point obtained by previous statistical calculations, so as to construct a geometric constraint model to describe the constraint relationship of the reference point in three-dimensional space. By applying the geometric constraint theory, the relative position relationship of the point and its geometric constraints with the surrounding environment are defined. For example, constraints are performed through the relationship between planes, straight lines, and points to ensure the accurate positioning of the reference point under specific spatial constraint conditions, and finally the spatial geometric constraint conditions of the inertial navigation reference point are obtained.

[0132] Step S34: performing three-axis attitude geometry mining analysis on the corresponding three-axis attitude angles in the inertial state three-axis attitude angle simulation space based on the spatial geometry constraint condition of the inertial navigation reference point, so as to obtain the spatial geometry relationship between the inertial reference point and the three-axis attitude angle;

[0133] In an embodiment of the present invention, by combining the spatial geometric constraints of the inertial navigation reference point obtained by the previous constraint analysis, the geometric relationship of the corresponding three-axis attitude angle in the simulation space of the inertial state three-axis attitude angle constructed by the previous simulation is mined and analyzed, so as to establish a spatial geometric relationship model between the attitude angle and the reference point, and use vector analysis and spatial geometry principles to establish a mapping relationship between the attitude angle (pitch angle, yaw angle, roll angle) and the inertial reference point. This step will involve combining the spatial geometric characteristics with the actual attitude measurement, and calculating the change of the three-axis attitude angle through the analytical geometry algorithm, so as to ensure the deep understanding and analysis of its spatial geometric characteristics, and finally mining and analyzing to obtain the spatial geometric relationship between the inertial reference point and the three-axis attitude angle.

[0134] Step S35: Based on the spatial geometric relationship between the inertial reference point and the three-axis attitude angle, the well inclination angle calculation formula is used to perform inverse calculation of the well inclination angle of the corresponding three-axis attitude angle in the inertial state three-axis attitude angle simulation space to obtain the well inclination angle of the inertial navigation system.

[0135] In an embodiment of the present invention, a suitable well inclination angle calculation formula is formed by using the spatial geometric relationship between the inertial reference point and the three-axis attitude angle obtained by previous mining and analysis and combining the acceleration components in the three-axis directions, the length of the inverse calculation time interval, the inverse calculation time variable parameters, the corresponding three-axis attitude angles (including pitch angle, yaw angle, roll angle) in the inertial state three-axis attitude angle simulation space, and related parameters to perform inverse calculation of the well inclination angle, so as to combine the spatial depth and attitude angle of the reference point to calculate and output the well inclination angle value, and finally obtain the well inclination angle of the inertial navigation system.

[0136] Furthermore, the calculation formula of the well deviation angle in step S35 is specifically:

[0137] ;

[0138] In the formula, is the well inclination angle of the inertial navigation system, The inner edge of the three-axis attitude angle simulation space in the inertial state The acceleration component in the axial direction, The inner edge of the three-axis attitude angle simulation space in the inertial state The acceleration component in the axial direction, The inner edge of the three-axis attitude angle simulation space in the inertial state The acceleration component in the axial direction, To calculate the length of the time interval for the inverse solution, To inversely calculate the time variable parameters, The spatial intrinsic time of the three-axis attitude angle simulation for the inertial state Winding The roll angle of the axis, The spatial intrinsic time of the three-axis attitude angle simulation for the inertial state Winding The pitch angle of the axis, The spatial intrinsic time of the three-axis attitude angle simulation for the inertial state Winding The yaw angle of the axis, is the correction factor of the well inclination angle of the inertial navigation system.

[0139] The present invention obtains a well inclination angle calculation formula by using a specific mathematical model and after verification, which is used to perform well inclination angle inverse solution calculation of the corresponding three-axis attitude angle in the inertial state three-axis attitude angle simulation space. The well inclination angle calculation formula can improve the accuracy of underground navigation by comprehensively considering the acceleration and attitude angle in multiple directions, and ensure that the equipment can operate stably in a complex environment. The formula considers the influence of time change on attitude by integration, can effectively cope with dynamic changes in the underground environment, and improves the system's ability to respond to emergencies. When performing spatial geometric constraint analysis, the formula can provide a more accurate geometric relationship, thereby ensuring the reliability of navigation, especially under complex geological conditions. In addition, by introducing a correction coefficient, the system is allowed to perform error correction in actual applications, can adapt to different working environments and sensor errors, and enhance the stability and accuracy of the system. Through comprehensive analysis of multiple sensor data, it can resist navigation errors caused by individual sensor failures and increase the robustness of the system. This formula is particularly suitable for complex underground operating environments and can handle various uncertainties caused by geological changes, thereby ensuring the effectiveness of navigation. In general, the calculation formula of the well inclination angle not only provides a high-precision estimate of the position and direction of downhole equipment by combining multiple physical quantities and geometric characteristics, but also improves the stability and robustness of the calculation process through dynamic response, spatial constraint analysis and correction functions. In summary, this formula fully considers the well inclination angle of the inertial navigation system. , in the three-axis attitude angle simulation space in the inertial state Acceleration component in the axial direction , in the inertial state three-axis attitude angle simulation space along Acceleration component in the axial direction , in the inertial state three-axis attitude angle simulation space along Acceleration component in the axial direction , inversely calculate the length of the time interval , inverse calculation time variable parameters , inertial state three-axis attitude angle simulation space intrinsic time Winding Roll angle of the axis , inertial state three-axis attitude angle simulation space intrinsic time Winding Axis pitch angle , inertial state three-axis attitude angle simulation space intrinsic time Winding Yaw angle of axis , correction factor of well inclination angle of inertial navigation system , according to the well inclination angle of the inertial navigation system The correlation between the above parameters forms a functional relationship:

[0140] ;

[0141] This formula can realize the inverse calculation process of the well inclination angle of the corresponding three-axis attitude angle in the inertial state three-axis attitude angle simulation space. At the same time, the correction coefficient of the well inclination angle of the inertial navigation system is The introduction of can be adjusted according to the errors that occur during the calculation process, thereby improving the accuracy and applicability of the well inclination angle calculation formula.

[0142] Further, step S4 includes the following steps:

[0143] Step S41: using a temperature sensor to monitor the working temperature of the downhole inertial navigation system in real time to obtain inertial navigation sensor working temperature change data;

[0144] In an embodiment of the present invention, a high-precision temperature sensor, such as a thermocouple or RTD (resistance temperature detector), is used and installed at a key position of a downhole inertial navigation system. The temperature sensor is connected to the inertial navigation system through a digital interface to achieve real-time data collection. The working temperature change of the corresponding sensor in the downhole inertial navigation system can be read once every second and stored in the memory. This process ensures continuous monitoring of the downhole environmental temperature change, and ultimately obtains the working temperature change data of the inertial navigation sensor.

[0145] Step S42: performing temperature fluctuation amplitude statistical analysis on the inertial navigation sensor working temperature change data to obtain the inertial navigation sensor working temperature fluctuation amplitude;

[0146] In an embodiment of the present invention, a statistical method is used to perform a statistical analysis of the temperature fluctuation amplitude of the inertial navigation sensor working temperature change data previously obtained through real-time monitoring, so as to quantify the range of temperature fluctuation by statistically calculating the maximum value, minimum value and standard deviation of the temperature data, and by using programming languages ​​such as Python, the NumPy library can be used to implement efficient data processing and generate the temperature fluctuation amplitude, so that the operator can intuitively understand the impact of temperature fluctuations, and finally obtain the working temperature fluctuation amplitude of the inertial navigation sensor.

[0147] Step S43: performing working temperature regression fitting analysis on the corresponding sensor output in the downhole inertial navigation system based on the working temperature fluctuation amplitude of the inertial navigation sensor, and obtaining a linear regression fitting relationship model between the working temperature change of the inertial navigation sensor and the sensor output;

[0148] In an embodiment of the present invention, a suitable regression analysis method, such as the least squares method, is selected by combining the fluctuation amplitude of the working temperature of the inertial navigation sensor obtained by previous statistical analysis, and a regression fitting analysis is performed on the relationship between the corresponding sensor output and the temperature change in the downhole inertial navigation system. The Scikit-learn library of Python is used to calculate the coefficients of the linear regression equation by inputting temperature data and corresponding sensor output data. This process determines the specific relationship of the influence of temperature change on sensor output, and finally a linear regression fitting relationship model between the working temperature change of the inertial navigation sensor and the sensor output is obtained by fitting analysis.

[0149] Step S44: performing error compensation derivation calculation on the corresponding sensor output in the downhole inertial navigation system based on the linear regression fitting relationship model between the inertial navigation sensor working temperature change and the sensor output, so as to obtain the inertial navigation sensor output error compensation factor;

[0150] In an embodiment of the present invention, the linear regression fitting relationship model between the working temperature change of the inertial navigation sensor and the sensor output obtained by the previous fitting analysis is combined to use the regression equation to perform error compensation calculation on the corresponding sensor output in the downhole inertial navigation system. In a specific implementation, the deviation between the ideal output value and the actual output value is calculated by substituting the real-time monitored temperature data into the regression equation, and the deviation is used as the error compensation factor to ensure the accuracy of the sensor output error, and finally the inertial navigation sensor output error compensation factor is obtained.

[0151] Step S45: performing well inclination calibration processing on the well inclination angle of the inertial navigation system according to the error compensation factor output by the inertial navigation sensor to obtain the well inclination calibration angle of the inertial navigation system.

[0152] In an embodiment of the present invention, the corresponding inertial navigation system well inclination angle is calibrated and calculated by using the inertial navigation sensor output error compensation factor obtained by previous quantitative calculation, so as to combine the compensation factor with the well inclination angle measurement value, and adopt the weighted average method or other suitable calibration algorithms to obtain the corrected well inclination angle. This process can be implemented by a custom calculation program to ensure that the measurement result of the well inclination angle is more accurate and adapts to the complex downhole environment, and finally obtains the inertial navigation well inclination calibration angle.

[0153] Furthermore, the present invention also provides a well inclination angle measurement system based on inertial navigation, which is used to perform the well inclination angle measurement method based on inertial navigation as described above. The well inclination angle measurement system based on inertial navigation includes:

[0154] The equipment inertial navigation state filtering module is used to monitor the downhole operation of the drilling equipment in real time through the three-axis accelerometer and the three-axis gyroscope built into the downhole inertial navigation system, so as to obtain the equipment inertial navigation three-axis acceleration data and the equipment inertial navigation three-axis angular velocity data; perform inertial state Kalman filtering on the equipment inertial navigation three-axis acceleration data and the equipment inertial navigation three-axis angular velocity data, so as to obtain the inertial state three-axis acceleration filtering data and the inertial state three-axis angular velocity filtering data;

[0155] The inertial state three-axis attitude space conversion module is used to perform three-axis attitude angle integral operation on the inertial state three-axis acceleration filter data and the inertial state three-axis angular velocity filter data to obtain the inertial state three-axis attitude angle data; perform three-axis attitude space conversion on the inertial state three-axis attitude angle data to generate the inertial state three-axis attitude angle simulation space;

[0156] The inertial navigation well inclination angle inverse solution module is used to obtain the initial reference point of the downhole inertial navigation, and perform three-axis attitude geometry mining analysis on the inertial state three-axis attitude angle simulation space based on the initial reference point of the downhole inertial navigation to obtain the spatial geometric relationship between the inertial reference point and the three-axis attitude angle; based on the spatial geometric relationship between the inertial reference point and the three-axis attitude angle, perform well inclination angle inverse solution calculation on the corresponding three-axis attitude angle in the inertial state three-axis attitude angle simulation space, so as to obtain the well inclination angle of the inertial navigation system;

[0157] The well inclination angle error compensation calibration module is used to monitor the sensor working temperature of the downhole inertial navigation system in real time through the temperature sensor to obtain the inertial navigation sensor working temperature change data; based on the inertial navigation sensor working temperature change data, the corresponding sensor output in the downhole inertial navigation system is derived and calculated to obtain the inertial navigation sensor output error compensation factor; according to the inertial navigation sensor output error compensation factor, the well inclination angle of the inertial navigation system is calibrated to obtain the inertial navigation well inclination calibration angle.

[0158] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for measuring well inclination angle based on inertial navigation, characterized in that: The following steps are involved: Step S1: using the three-axis accelerometer and three-axis gyroscope built into the downhole inertial navigation system to monitor the downhole operation of the drilling equipment in real time, so as to obtain the three-axis acceleration data and the three-axis angular velocity data of the inertial navigation of the equipment; Performing inertial state Kalman filtering on the three-axis acceleration data of the inertial navigation of the device and the three-axis angular velocity data of the inertial navigation of the device to obtain inertial state three-axis acceleration filtering data and inertial state three-axis angular velocity filtering data; Step S2: performing a three-axis attitude angle integral operation on the inertial state three-axis acceleration filter data and the inertial state three-axis angular velocity filter data to obtain the inertial state three-axis attitude angle data; performing a three-axis attitude space conversion on the inertial state three-axis attitude angle data to generate an inertial state three-axis attitude angle simulation space; Step S3: Acquire the initial reference point of the downhole inertial navigation, and perform three-axis attitude geometry mining analysis on the inertial state three-axis attitude angle simulation space based on the initial reference point of the downhole inertial navigation to obtain the spatial geometric relationship between the inertial reference point and the three-axis attitude angle; perform inverse calculation of the well inclination angle on the corresponding three-axis attitude angle in the inertial state three-axis attitude angle simulation space based on the spatial geometric relationship between the inertial reference point and the three-axis attitude angle to obtain the well inclination angle of the inertial navigation system; Step S4: real-time monitoring of the working temperature of the sensor of the downhole inertial navigation system is performed through a temperature sensor to obtain the working temperature change data of the inertial navigation sensor; based on the working temperature change data of the inertial navigation sensor, error compensation is derived and calculated for the corresponding sensor output in the downhole inertial navigation system to obtain the inertial navigation sensor output error compensation factor; well inclination calibration processing is performed on the well inclination angle of the inertial navigation system according to the inertial navigation sensor output error compensation factor to obtain the inertial navigation well inclination calibration angle; Step S41: using a temperature sensor to monitor the working temperature of the downhole inertial navigation system in real time to obtain inertial navigation sensor working temperature change data; Step S42: performing temperature fluctuation amplitude statistical analysis on the inertial navigation sensor working temperature change data to obtain the inertial navigation sensor working temperature fluctuation amplitude; Step S43: performing working temperature regression fitting analysis on the corresponding sensor output in the downhole inertial navigation system based on the working temperature fluctuation amplitude of the inertial navigation sensor, and obtaining a linear regression fitting relationship model between the working temperature change of the inertial navigation sensor and the sensor output; Step S44: performing error compensation derivation calculation on the corresponding sensor output in the downhole inertial navigation system based on the linear regression fitting relationship model between the inertial navigation sensor working temperature change and the sensor output, so as to obtain the inertial navigation sensor output error compensation factor; Step S45: performing well inclination calibration processing on the well inclination angle of the inertial navigation system according to the error compensation factor output by the inertial navigation sensor to obtain the well inclination calibration angle of the inertial navigation system.

2. The method for measuring well inclination angle based on inertial navigation according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: using a three-axis accelerometer built into the downhole inertial navigation system to monitor the downhole running acceleration of the drilling equipment in real time during the running process, so as to obtain the three-axis acceleration data of the inertial navigation of the equipment; Step S12: using a three-axis gyroscope built into the downhole inertial navigation system to monitor the downhole angular velocity of the drilling equipment in real time during its operation, so as to obtain the three-axis angular velocity data of the inertial navigation of the equipment; Step S13: performing sensor noise screening and division on the three-axis acceleration data of the device inertial navigation and the three-axis angular velocity data of the device inertial navigation to obtain a three-axis standard parameter set of the device inertial navigation and a three-axis noise parameter set of the device inertial navigation; Step S14: performing inertial state prediction calculation on the device inertial navigation three-axis standard parameter set to obtain device inertial dynamic state prediction variables, wherein the device inertial dynamic state prediction variables include device inertial three-axis acceleration state prediction variables and device inertial three-axis angular velocity state prediction variables; Step S15: Based on the device inertial navigation three-axis noise parameter set, the inertial three-axis acceleration state prediction variables and the inertial three-axis angular velocity state prediction variables corresponding to the device inertial dynamic state prediction variables are processed by inertial state Kalman filtering to obtain inertial state three-axis acceleration filtering data and inertial state three-axis angular velocity filtering data.

3. The method for measuring well inclination angle based on inertial navigation according to claim 2, characterized in that: The inertia state prediction calculation in step S14 is integrated by the inertia state transfer equation, wherein the inertia state transfer equation is specifically: ； ； ； ； In the formula, is the equipment inertia dynamic state prediction variable, is the time variable parameter, is the time step, is the integral time variable parameter, The intrinsic time of the three-axis standard parameter set for the inertial navigation of the device The three-axis dynamic state vector at For at the moment Where Axis acceleration components, For at the moment Where Axis acceleration components, For at the moment Where Axis acceleration components, For at the moment Where The axis angular velocity components, For at the moment Where The axis angular velocity components, For at the moment Where The axis angular velocity components, For at the moment The inertial state external control influence component at, is the inertial dynamic state prediction function, for Axis adjustment factor, for Axis adjustment factor, for Axis adjustment factor, For at the moment The external interference noise component at is the external interference noise influence function, is the three-axis noise interference gain matrix of the equipment inertial state, is the three-axis noise vector of the device inertial state, for Axis inertia state dependencies, for Axis inertia state dependencies, for Axis inertia state dependencies, For at the moment Additional interference term of the equipment inertia state at .

4. The method for measuring well inclination angle based on inertial navigation according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: extracting the device inertial navigation three-axis noise parameter set by time sequence point annotation to obtain a device inertial three-axis noise parameter subset corresponding to each time sequence point; Step S152: performing noise statistical characteristic characterization analysis on the device inertial three-axis noise parameter subset corresponding to each timing point, and obtaining the device inertial three-axis noise statistical characteristic parameters corresponding to each timing point, wherein the device inertial three-axis noise statistical characteristic parameters include noise mean, noise variance and noise autocorrelation function; Step S153: Based on the device inertial three-axis noise statistical characteristic parameters corresponding to each timing point, a noise covariance statistical calculation is performed on the corresponding device inertial three-axis noise parameter subset to obtain the device inertial three-axis noise covariance corresponding to each timing point; a covariance matrix is ​​constructed for the device inertial three-axis noise covariance corresponding to each timing point to generate the device inertial navigation three-axis noise covariance matrix; Step S154: performing Kalman gain calculation on the corresponding device inertia three-axis acceleration state prediction variables and device inertia three-axis angular velocity state prediction variables in the device inertia dynamic state prediction variables based on the device inertia navigation three-axis noise covariance matrix to obtain the device inertia three-axis acceleration state Kalman gain and the device inertia three-axis angular velocity state Kalman gain; Step S155: Based on the device inertia three-axis acceleration state Kalman gain and the device inertia three-axis angular velocity state Kalman gain, the corresponding device inertia three-axis acceleration state prediction variables and the device inertia three-axis angular velocity state prediction variables are respectively processed by inertia state Kalman filtering to obtain inertia state three-axis acceleration filtering data and inertia state three-axis angular velocity filtering data.

5. The method for measuring well inclination angle based on inertial navigation according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing three-axis parameter normalization processing on the inertial state three-axis acceleration filtering data and the inertial state three-axis angular velocity filtering data to obtain the inertial state three-axis acceleration normalized data and the inertial state three-axis angular velocity normalized data; Step S22: performing orthogonal decoupling mining analysis on the normalized data of the three-axis acceleration in the inertial state and the normalized data of the three-axis angular velocity in the inertial state to obtain the orthogonal decoupling state mathematical relationship between the three-axis acceleration in the inertial state and the three-axis angular velocity; Step S23: acquiring the inertial state sampling time step, and performing a three-axis attitude angle integral operation on the inertial state three-axis acceleration normalized data and the inertial state three-axis angular velocity normalized data based on the inertial state sampling time step and the orthogonal decoupling state mathematical relationship between the inertial state three-axis acceleration and the three-axis angular velocity, so as to obtain the inertial state three-axis attitude angle data; Step S24: performing three-axis attitude corner point sampling processing on the inertial state three-axis attitude angle data to obtain an inertial state three-axis attitude angle corner point set; Step S25: performing three-axis attitude space conversion on the corresponding inertial state three-axis attitude angle data based on the inertial state three-axis attitude angle corner point set to generate an inertial state three-axis attitude angle simulation space.

6. The method for measuring well inclination angle based on inertial navigation according to claim 5, characterized in that: Step S23 includes the following steps: Step S231: obtaining the inertial state sampling time step, and performing attitude angle definite integral operation on the inertial state three-axis angular velocity normalized data at the corresponding time step based on the inertial state sampling time step, to obtain the inertial state three-axis initial attitude angle estimation data; Step S232: performing acceleration orthogonal decoupling influence evaluation and analysis on the normalized data of the inertial state three-axis acceleration based on the orthogonal decoupling state mathematical relationship between the three-axis acceleration in the inertial state and the three-axis angular velocity, so as to obtain the inertial state three-axis acceleration orthogonal decoupling influence factor; Step S233: performing attitude angle drift influence evaluation and analysis on the inertial state three-axis initial attitude angle estimation data based on the inertial state three-axis acceleration normalization data to obtain the inertial state three-axis attitude angle drift influence factor; Step S234: performing attitude angle influence compensation calculation on the corresponding inertial state three-axis initial attitude angle estimation data based on the inertial state three-axis acceleration orthogonal decoupling influence factor and the inertial state three-axis attitude angle drift influence factor to obtain the inertial state three-axis attitude angle data.

7. The method for measuring well inclination angle based on inertial navigation according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: obtaining an initial reference point for downhole inertial navigation; Step S32: extracting the spatial geometric characteristics of the initial reference point of the underground inertial navigation to obtain the spatial geometric characteristic parameters of the inertial navigation reference point, wherein the spatial geometric characteristic parameters of the inertial navigation reference point include the spatial position, spatial orientation and spatial depth of the reference point; Step S33: performing spatial geometric constraint analysis on the corresponding downhole inertial navigation initial reference point based on the spatial geometric characteristic parameters of the inertial navigation reference point to obtain the spatial geometric constraint conditions of the inertial navigation reference point; Step S34: performing three-axis attitude geometry mining analysis on the corresponding three-axis attitude angles in the inertial state three-axis attitude angle simulation space based on the spatial geometry constraint condition of the inertial navigation reference point, so as to obtain the spatial geometry relationship between the inertial reference point and the three-axis attitude angle; Step S35: Based on the spatial geometric relationship between the inertial reference point and the three-axis attitude angle, the well inclination angle calculation formula is used to perform inverse calculation of the well inclination angle of the corresponding three-axis attitude angle in the inertial state three-axis attitude angle simulation space to obtain the well inclination angle of the inertial navigation system.

8. The method for measuring well inclination angle based on inertial navigation according to claim 7, characterized in that: The calculation formula of the well deviation angle in step S35 is specifically: ； In the formula, is the well inclination angle of the inertial navigation system, The inner edge of the three-axis attitude angle simulation space in the inertial state The acceleration component in the axial direction, The inner edge of the three-axis attitude angle simulation space in the inertial state The acceleration component in the axial direction, The inner edge of the three-axis attitude angle simulation space in the inertial state The acceleration component in the axial direction, To calculate the length of the time interval for the inverse solution, To inversely calculate the time variable parameters, The spatial intrinsic time of the three-axis attitude angle simulation for the inertial state Winding The roll angle of the axis, The spatial intrinsic time of the three-axis attitude angle simulation for the inertial state Winding The pitch angle of the axis, The spatial intrinsic time of the three-axis attitude angle simulation for the inertial state Winding The yaw angle of the axis, is the correction factor of the well inclination angle of the inertial navigation system.

9. A well inclination angle measurement system based on inertial navigation, characterized in that: For executing the method for measuring the well inclination angle based on inertial navigation as claimed in claim 1, the well inclination angle measurement system based on inertial navigation comprises: The equipment inertial navigation state filtering module is used to monitor the downhole operation of the drilling equipment in real time through the three-axis accelerometer and the three-axis gyroscope built into the downhole inertial navigation system, so as to obtain the equipment inertial navigation three-axis acceleration data and the equipment inertial navigation three-axis angular velocity data; perform inertial state Kalman filtering on the equipment inertial navigation three-axis acceleration data and the equipment inertial navigation three-axis angular velocity data, so as to obtain the inertial state three-axis acceleration filtering data and the inertial state three-axis angular velocity filtering data; The inertial state three-axis attitude space conversion module is used to perform three-axis attitude angle integral operation on the inertial state three-axis acceleration filter data and the inertial state three-axis angular velocity filter data to obtain the inertial state three-axis attitude angle data; perform three-axis attitude space conversion on the inertial state three-axis attitude angle data to generate the inertial state three-axis attitude angle simulation space; The inertial navigation well inclination angle inverse solution module is used to obtain the initial reference point of the downhole inertial navigation, and perform three-axis attitude geometry mining analysis on the inertial state three-axis attitude angle simulation space based on the initial reference point of the downhole inertial navigation to obtain the spatial geometric relationship between the inertial reference point and the three-axis attitude angle; based on the spatial geometric relationship between the inertial reference point and the three-axis attitude angle, perform well inclination angle inverse solution calculation on the corresponding three-axis attitude angle in the inertial state three-axis attitude angle simulation space, so as to obtain the well inclination angle of the inertial navigation system; The well inclination angle error compensation calibration module is used to monitor the sensor working temperature of the downhole inertial navigation system in real time through the temperature sensor to obtain the inertial navigation sensor working temperature change data; based on the inertial navigation sensor working temperature change data, the corresponding sensor output in the downhole inertial navigation system is derived and calculated to obtain the inertial navigation sensor output error compensation factor; according to the inertial navigation sensor output error compensation factor, the well inclination angle of the inertial navigation system is calibrated to obtain the inertial navigation well inclination calibration angle.

Images