Dynamic measurement method and system for attitude parameters of rotary steerable tools

Abstract

The present invention relates to the technical field of dynamic measurement of drill tool attitude, and discloses a dynamic measurement method and system for attitude parameters of a rotary steering tool. The method includes: obtaining in real time the three-axis angular rate, the three-axis earth gravity field components, and the three-axis geomagnetic field components of the rotary steering tool; determining a prior estimate value of the attitude angle according to the three-axis angular rate; determining a posteriori observation values of the attitude angle according to the three-axis earth gravity field components and the three-axis geomagnetic field components; obtaining a posteriori optimal attitude angle estimation result by fusing the prior estimate value of the attitude angle and the posteriori observation values of the attitude angle through iterative extended Kalman filtering; and calculating the attitude parameters according to the posteriori optimal attitude angle estimation result. In this application, an iterative extended Kalman filter is used to adaptively fuse the dynamic observables measured by a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer, so as to eliminate the noise interference of external environments such as high rotational speed, strong vibration, and magnetic anomaly faced during the dynamic measurement process, thereby improving the control and measurement accuracy of the rotary steering tool.

Description

Background Art

[0002] Rotary steerable tools are the most advanced closed-loop wellbore trajectory control systems in the field of directional drilling at home and abroad. They can adjust the wellbore trajectory in real time along the preset direction during the rotary drilling process of the drill string. According to the tool steering principle and structural form, rotary steerable tools can be divided into four types: static push-type, dynamic push-type, static pointing-type steering, and full-rotation pointing-type. Among them, the full-rotation pointing-type steering tool, with its special steering principle and structural form, has the characteristics of smaller drilling friction, smoother wellbore trajectory, and stronger drilling extension ability. It has obvious technical advantages in the directional drilling of complex structure wells such as extended reach wells and long horizontal wells, and has important practical significance for improving drilling efficiency, reducing drilling costs, and reducing drilling risks.

[0003] Since the rotary steerable tool was officially launched into the market for application in the 1990s, after nearly 30 years of technological upgrading and iteration abroad, it has realized the automatic adjustment of the wellbore trajectory according to real-time measurement parameters of engineering and geology during the drilling process, promoting the development of wellbore trajectory control technology towards the intelligent direction. Currently, the three major oilfield service companies abroad, Schlumberger, Baker Hughes, and Halliburton, have all launched intelligent rotary steerable tools with the ability to automatically control the wellbore trajectory. Schlumberger's Neosteer bit steering system and Baker Hughes' Lucida rotary steerable system can, at high rotational speeds, continuously measure the well inclination and azimuth near the bit at the millisecond level, automatically correct the actual drilled trajectory of the wellbore, and achieve the "cruising" drilling of the steering tool independently underground. In 2021, Halliburton launched the iCruise XTM intelligent rotary steerable system, which realizes precise guidance and positioning of downhole tools by automatically controlling the well inclination angle and azimuth angle during the drilling operation. Thus, accurately measuring the well inclination, azimuth, and the size of the tool face in the dynamic rotating state is an important prerequisite for realizing the automatic control of the wellbore trajectory. The commonly used attitude measurement sensors are accelerometers, magnetometers, and gyroscopes. However, during the actual drilling process, the centrifugal acceleration generated when the drill string rotates at high speed, as well as the vibration acceleration generated when the bit breaks rock, will interfere with the ability of the accelerometer to sense the Earth's gravity field; and the abnormal magnetic fields generated by ferromagnetic materials such as motors, casings, and adjacent wells will also affect the ability of the magnetometer to sense the geomagnetic field sensitively; in addition, the high temperature underground will increase the zero-offset error of the gyroscope, and over time, this effect will become more and more obvious.

[0004] In summary, the harsh downhole operating environment will seriously affect the dynamic measurement accuracy of the attitude angle of the rotary steerable tool. Summary of the Invention

[0005] In order to overcome the problem that the harsh downhole operating environment will seriously affect the dynamic measurement accuracy of the attitude angle of the rotary steerable tool, the present invention provides a dynamic measurement method and system for the attitude parameters of the rotary steerable tool.

[0006] In a first aspect, to solve the above technical problems, the present invention provides a method for dynamically measuring the attitude parameters of a rotary steering tool, including:

[0007] Obtaining in real time the triaxial angular rate, the triaxial components of the earth's gravity field, and the triaxial components of the geomagnetic field of the rotary steering tool;

[0008] Determining a prior estimate value of the attitude angle of the rotary steering tool according to the triaxial angular rate;

[0009] Determining a posteriori observation values of the attitude angle of the rotary steering tool according to the triaxial components of the earth's gravity field and the triaxial components of the geomagnetic field;

[0010] Adaptive fusion of the prior estimate value of the attitude angle and the posteriori observation values of the attitude angle is performed by means of iterative extended Kalman filtering to obtain a posteriori optimal attitude angle estimation result;

[0011] Calculating attitude parameters according to the posteriori optimal attitude angle estimation result.

[0012] In a second aspect, the present invention provides a dynamic measurement system for the attitude parameters of a rotary steering tool, including:

[0013] A data acquisition module for obtaining in real time the triaxial angular rate, the triaxial components of the earth's gravity field, and the triaxial components of the geomagnetic field of the rotary steering tool;

[0014] A prior estimate value determination module for the attitude angle, which is used to determine a prior estimate value of the attitude angle of the rotary steering tool according to the triaxial angular rate;

[0015] A posteriori observation value determination module for the attitude angle, which is used to determine a posteriori observation values of the attitude angle of the rotary steering tool according to the triaxial components of the earth's gravity field and the triaxial components of the geomagnetic field;

[0016] A posteriori optimal attitude angle estimation result determination module, which is used to perform adaptive fusion of the prior estimate value of the attitude angle and the posteriori observation values of the attitude angle by means of iterative extended Kalman filtering to obtain a posteriori optimal attitude angle estimation result;

[0017] An attitude parameter determination module, which is used to calculate attitude parameters according to the posteriori optimal attitude angle estimation result.

[0018] The beneficial effects of the present invention are as follows: By obtaining the three-axis angular rate, three-axis Earth gravity field components, and three-axis geomagnetic field components of the rotary steerable tool, prior estimated values and posterior observed values of the attitude angle of the rotary steerable tool can be obtained. Then, through an iterative extended Kalman filter, the prior estimated values and posterior observed values of the attitude angle are adaptively fused to obtain a posterior optimal attitude angle estimation result. The attitude parameters can be calculated based on the posterior optimal attitude angle estimation result. In this application, the iterative extended Kalman filter is used to adaptively fuse the prior estimated values and posterior observed values of the attitude angle to eliminate noise interference from external environments such as high rotational speed, strong vibration, and magnetic anomalies during the dynamic measurement process, thereby improving the control accuracy of the rotary steerable tool, helping to meet various trajectory control requirements such as build-up, hold, drop-off, and azimuth change during the "one-trip drilling" process of the rotary steerable tool, reducing the number of trips to replace the bottom hole assembly, improving the drilling efficiency, and having important practical significance for achieving the goal of cost reduction and efficiency increase. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be further described below with reference to the drawings and embodiments.

[0020] Figure 1 Schematic flow chart of the dynamic measurement method for the attitude parameters of the rotary steerable tool in the embodiment of the present invention;

[0021] Figure 2 Schematic structural diagram of a nine-axis strapdown attitude measurement system;

[0022] Figure 3 Schematic flow chart of the dynamic measurement method for the attitude parameters of the rotary steerable tool in another embodiment of the present invention;

[0023] Figure 4 Comparison diagram of the filtering effect before, EKF filtering, and IEKF filtering for dynamically measuring the well inclination angle;

[0024] Figure 5 Comparison diagram of the filtering effect before, EKF filtering, and IEKF filtering for dynamically measuring the azimuth angle;

[0025] Figure 6 Comparison diagram of the filtering effect before, EKF filtering, and IEKF filtering for dynamically measuring the tool face angle;

[0026] Figure 7 Schematic structural diagram of the dynamic measurement system for the attitude parameters of the rotary steerable tool in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The following embodiments are further explanations and supplements to the present invention and do not constitute any limitation to the present invention.

[0028] The following describes a dynamic measurement method and system for the attitude parameters of a rotary steerable tool according to an embodiment of the present invention in conjunction with the accompanying drawings.

[0029] As Figure 1 shown, the present invention provides a dynamic measurement method for the attitude parameters of a rotary steerable tool, including:

[0030] S1. Real-time obtain the three-axis angular rate, three-axis earth gravity field components, and three-axis geomagnetic field components of the rotary steerable tool.

[0031] S2. Determine a prior estimate value of the attitude angle of the rotary steerable tool according to the three-axis angular rate.

[0032] S3. Determine a posteriori observation values of the attitude angle of the rotary steerable tool according to the three-axis earth gravity field components and the three-axis geomagnetic field components.

[0033] S4. Adaptively fuse the prior estimate value of the attitude angle and the posteriori observation values of the attitude angle by means of iterative extended Kalman filtering to obtain a posteriori optimal attitude angle estimation result.

[0034] S5. Calculate the attitude parameters according to the posteriori optimal attitude angle estimation result.

[0035] In this embodiment, by obtaining the three-axis angular rate, three-axis earth gravity field components, and three-axis geomagnetic field components of the rotary steerable tool, a prior estimate value of the attitude angle and a posteriori observation values of the attitude angle of the rotary steerable tool can be obtained. Then, the prior estimate value of the attitude angle and the posteriori observation values of the attitude angle are adaptively fused through an iterative extended Kalman filter to obtain a posteriori optimal attitude angle estimation result. The attitude parameters can be calculated through the posteriori optimal attitude angle estimation result. In this application, the prior estimate value of the attitude angle and the posteriori observation values of the attitude angle are adaptively fused through an iterative extended Kalman filter to eliminate the noise interference of external environments such as high rotational speed, strong vibration, and magnetic anomaly faced in the dynamic measurement process, thereby improving the control accuracy of the rotary steerable tool, helping to meet various trajectory control requirements such as build-up, hold, drop-off, and azimuth change during the "one-trip drilling" process of the rotary steerable tool, reducing the number of trips to replace the bottom hole assembly, improving the drilling efficiency, and having important practical significance for achieving the goal of cost reduction and efficiency increase.

[0036] Optionally, determining a prior estimate value of the attitude angle of the rotary steerable tool according to the three-axis angular rate includes:

[0037] Construct a gyroscope system state model by means of quaternion differentiation; wherein, the gyroscope system state model is as follows:

[0038]

[0039] Wherein, represents quaternion differentiation, ω x 、ωy , ω z The triaxial angular rate, where q0, q1, q2, and q3 are the real parts of the quaternion, representing the prior estimated values of the attitude angles. Represents the quaternion multiplication of the triaxial angular rate and the real part of the quaternion.

[0040] Determine the prior estimated values of the attitude angles based on the triaxial angular rate and the gyroscope system state model.

[0041] In this embodiment, by analyzing the kinematic characteristics of the nine-axis strapdown attitude measurement system and using the attitude kinematic equation of the carrier rotation, a gyroscope system state model based on quaternions is constructed, and the formula is as follows:

[0042]

[0043] The prior estimated values of the attitude angles represented by q0, q1, q2, and q3 can be obtained through the above formula.

[0044] As Figure 2 shown, the nine-axis strapdown attitude measurement system mainly consists of a triaxial accelerometer a triaxial magnetometer and a triaxial gyroscope (ω x , ω y , ω z ). On the one hand, the triaxial gyroscope can be used alone to obtain the dynamic measurement results (prior estimated values of the attitude angles) of a set of attitude parameters of the rotary steering tool by sensing the angular velocity of rotation around the axis. On the other hand, the combination of the triaxial accelerometer and the triaxial magnetometer's gravity / magnetic sensors can also obtain a set of dynamic measurement results (posterior estimated values of the attitude angles) of the rotary steering tool's attitude parameters. Finally, the data fusion algorithm of the iterative extended Kalman filter is used to fuse the dynamic observation results obtained by the above two measurement methods to eliminate external interference noise, thereby obtaining the optimal results of the attitude angle parameters.

[0045] Optionally, determining the posterior estimated values of the attitude angles of the rotary steering tool based on the triaxial earth gravity field components and the triaxial geomagnetic field components includes:

[0046] Obtain the attitude rotation matrix that describes the attitude transformation between the navigation coordinate system and the carrier coordinate system of the rotary steering tool;

[0047] Use the attitude rotation matrix to construct an observation correction model, and the formula is as follows:

[0048]

[0049] Among them, represents the transpose matrix of the attitude rotation matrix, represents the triaxial earth gravity field components in the carrier coordinate system, represent the three-axis geomagnetic field components in the carrier coordinate system, are the three-axis Earth gravity field components and the three-axis geomagnetic field components in the navigation coordinate system, representing the posteriori observation values of the attitude angles;

[0050] Determine the posteriori observation values of the attitude angles based on the three-axis Earth gravity field components, the three-axis geomagnetic field components, and the observation correction model.

[0051] In this embodiment, by analyzing the kinematic characteristics of the nine-axis strapdown attitude measurement system and the principle of measuring attitude parameters with a gravity / magnetic sensor combination, a system observation correction model is constructed. Before constructing the system observation correction model, it is necessary to first define the navigation coordinate system as the "north-east-down" geographic coordinate system, and the carrier coordinate system as the XYZ drill string coordinate system composed of the axial, radial, and perpendicular directions of the rotary steerable tool, which conforms to the right-hand rule. Then, according to the Euler rotation theory, describe the attitude transformation of the rotary steerable tool from the navigation coordinate system to the carrier coordinate system. Finally, the attitude rotation matrix in the form of Euler angles is obtained as:

[0052]

[0053] The triaxial accelerometer and the triaxial magnetometer can directly observe the magnitudes of the gravity field and the geomagnetic field. Therefore, the output value of the triaxial accelerometer and the output value

[0054]

[0055] of the triaxial magnetometer

[0056] are used as the observation vectors of the system observation correction model. Combining with the above attitude rotation matrix, the system observation correction model is obtained, and the formula is as follows:

[0057] Take the partial derivative of the gyroscope system state model to determine the system state transition matrix, and take the partial derivative of the observation correction model to determine the high-dimensional observation matrix;

[0058] Update the a priori estimated value of the attitude angle obtained at the previous moment according to the system state transition matrix and the system process error to determine the a priori estimated value of the attitude angle obtained at the current moment;

[0059] Update the first error covariance corresponding to the posteriori optimal estimated result of the attitude angle obtained at the previous moment according to the system state transition matrix and the covariance matrix corresponding to the system process error to determine the second error covariance corresponding to the a priori estimated value of the attitude angle obtained at the current moment;

[0060] Determine the Kalman gain at the current moment according to the second error covariance, the high-dimensional observation matrix, and the observation error;

[0061] Update the prior attitude angle estimate obtained at the current moment according to the Kalman gain at the current moment, the posterior attitude angle observation value, and the high-dimensional observation matrix, and determine the posterior optimal attitude angle estimation result and the corresponding second error covariance.

[0062] In this embodiment, by designing an iterated extended Kalman filter, the posterior attitude angle observation values obtained by the gravity / magnetic sensor can be repeatedly utilized over time, reducing the linearization error, improving the processing performance of the filter, effectively eliminating the noise interference in the sensor measurement signal, and realizing the dynamic and accurate measurement of the attitude parameters.

[0063] Optionally, update the prior attitude angle estimate obtained at the previous moment according to the state transition matrix and the system process error to determine the prior attitude angle estimate obtained at the current moment. The formula is as follows:

[0064]

[0065] Where, represents the prior attitude angle estimate at the current moment, A k represents the state transition matrix, x k-1 represents the prior attitude angle estimate obtained at the previous moment, w k-1 represents the system process error.

[0066] In the iterated extended Kalman filter, the prior estimate is the predicted state before measurement update. That is, the prior estimate is to predict the state at the current moment according to the state estimate at the previous moment and the dynamic model (such as physical laws or difference equations) of the nine-axis strapdown attitude measurement system before the new measurement data arrives. In this embodiment, the prior attitude angle estimate at the current moment is obtained by updating the prior attitude angle estimate at the previous moment.

[0067] Optionally, update the first error covariance corresponding to the posterior optimal attitude angle estimation result obtained at the previous moment according to the covariance matrix corresponding to the state transition matrix and the system process error to determine the second error covariance corresponding to the prior attitude angle estimate obtained at the current moment. The formula is as follows:

[0068]

[0069] represents the second error covariance corresponding to the prior attitude angle estimate obtained at the current moment, P k-1 represents the first error covariance corresponding to the posterior optimal attitude angle estimation result obtained at the previous moment, I represents the identity matrix, represents the Kalman gain at the previous moment, represents the high-dimensional observation matrix, represents the second error covariance corresponding to the prior estimate of the attitude angle obtained at the previous moment, represents the transpose matrix of A k , Q = E[w k-1 (w k-1 ) T represents the covariance matrix corresponding to the system process error, E[·] represents the covariance function, (w k-1 ) T represents the transpose matrix of w k-1 .

[0070] In this embodiment, the covariance matrix corresponding to the prior estimate of the attitude angle quantifies the uncertainty of the state estimate before obtaining new measurement data, and the covariance matrix corresponding to the posterior optimal attitude angle estimate result at the previous moment will be transmitted to the current moment. By calculating the error covariance matrix corresponding to the prior estimate of the attitude angle at the current moment through the covariance matrix corresponding to the posterior optimal attitude angle estimate result at the previous moment, the accuracy of obtaining the posterior optimal attitude angle estimate result subsequently can be improved.

[0071] Optionally, the Kalman gain at the current moment is determined according to the second error covariance, the high-dimensional observation matrix, and the observation error, and the formula is as follows:

[0072]

[0073] where, represents the Kalman gain at the current moment, R = E[(w k-1 ) T represents the observation error, represents the high-dimensional observation matrix, represents the transpose matrix of .

[0074] In this embodiment, when iterating continuously, adding the gravity / magnetic sensor to obtain the posterior attitude angle observation reduces the linearization error of the filter and improves the filtering accuracy, thereby effectively eliminating the noise interference in the sensor measurement signal.

[0075] Optionally, the prior estimate of the attitude angle obtained at the current moment is updated according to the Kalman gain at the current moment, the posterior attitude angle observation value, and the high-dimensional observation matrix, and the posterior optimal estimate value and the corresponding second error covariance are determined, and the formula is as follows:

[0076]

[0077] where, represents the posterior optimal estimate value, Represents the posterior observation value of the attitude angle, p k Represents the second error covariance at the current moment, and I represents the identity matrix. Represents the Kalman gain at the current moment. Represents the high-dimensional observation matrix.

[0078] In this embodiment, the posterior optimal estimate value is the corrected state after measurement update. That is, the posterior optimal estimate value is to update the state estimate at the current moment by combining the prior estimate value of the attitude angle and the measurement data after the new measurement data arrives. In the iterative process of the Kalman filter, the prior estimate value of the attitude angle and the posterior optimal estimate value will be continuously updated alternately to provide an accurate estimate of the state of the nine-axis strapdown attitude measurement system.

[0079] Optionally, the attitude parameters are calculated according to the posterior optimal attitude angle estimation result, and the formula is as follows:

[0080] θ = arcsin(2q0q2 - 2q1q3)

[0081]

[0082] where θ is the well deviation angle, is the azimuth angle, γ is the tool face angle, and the target attitude parameters include θ, and γ, and q0, q1, q2, q3 represent the posterior optimal attitude angle estimation result.

[0083] In this embodiment, the processing result of the iterative extended Kalman filter fusion algorithm, that is, the posterior optimal attitude angle estimation result, is used to measure and calculate the well deviation angle, azimuth angle and tool face angle, and finally obtain the dynamic measurement result of the optimal attitude angle.

[0084] Optionally, as Figure 3 shown, another embodiment is used to illustrate the dynamic measurement method of the attitude parameters of the rotary steerable tool, as follows:

[0085] The three-axis angular rate of the rotary steerable tool obtained in real time by the three-axis gyroscope in the nine-axis strapdown attitude measurement system is used to construct a system state model based on quaternions.

[0086] The three-axis earth gravity field components obtained in real time by the three-axis accelerometer in the nine-axis strapdown attitude measurement system and the three-axis geomagnetic field components obtained in real time by the three-axis magnetometer are used to construct an observation correction model based on gravity / magnetic sensors.

[0087] Based on the system state model, the prior estimate value of the attitude angle can be obtained, and based on the observation correction model, the posterior observation value of the attitude angle can be obtained.

[0088] The partial derivative of the system state model is taken to obtain the system state transition matrix, and the partial derivative of the observation correction model is taken to obtain the high-dimensional observation matrix. Then, the prior estimate value of the attitude angle at the previous moment can be updated through the system state transition matrix to obtain the prior estimate value of the attitude angle at the current moment, and the Kalman gain of the iterated extended Kalman filter is updated using the high-dimensional observation matrix.

[0089] The actual measurement of the gravity / magnetic sensor, the updated Kalman gain, and the high-dimensional observation matrix are used to update the prior estimate value of the attitude angle at the current moment to obtain the optimal attitude angle estimation result.

[0090] The well inclination angle θ and the azimuth angle tool face angle γ are calculated according to the optimal attitude angle estimation result.

[0091] Optionally, to verify the effectiveness of this method, a data acquisition experiment on the dynamic measurement of attitude parameters under multi-physical field coupling was carried out.

[0092] A three-axis non-magnetic simulation turntable was used as the experimental platform. The nine-axis strapdown attitude measurement system was installed on the experimental platform. At the same time, through the computer control system of the turntable, the tool face rotation speed was set to 120 rpm for uniform rotation to simulate the downhole drilling process of the steering tool. Then, different well inclination angles and azimuth angles were selected for measurement, and the data of each sensor in the X-axis, Y-axis, and Z-axis directions were measured and recorded respectively. Then, the collected experimental data were analyzed according to the step process in this method.

[0093] The processing results are as Figure 4 、 Figure 5 and Figure 6 shown. Under the dynamic rotation state of the rotary steering tool face axis, the azimuth angle is fixed at 0°, and the rotating well inclination angles are 0°, 5°, 50°, 70°, and 90° respectively. Then, the sensor data are collected, and finally the data are analyzed according to the step process of this method. Since the rotary steering tool is often used in the drilling of horizontal wells, and the well inclination angle of horizontal wells is about 90° or so, so, referring to Figure 4 the comparison diagrams of the measurement data of the 90° well inclination angle passing through the EKF (Extended Kalman Filter), IEKF (Iterated Extended Kalman Filter), and unfiltered results respectively, it can be found that the curve of the well inclination angle obtained by the dynamic measurement method based on IEKF filtering is significantly smoother and the measurement error is smaller. Then, the well inclination angle is fixed at 90°, and the rotating azimuth angles are 10°, 25°, 45°, 90°, 180° respectively. The sensor data are collected, and then the data are analyzed according to this method. Among them, referring to Figure 5The comparison chart of the measurement data at an azimuth angle of 45° after being processed by EKF and IEKF filtering respectively and the unfiltered results shows that the curve of the azimuth angle result obtained after IEKF filtering fluctuates less, indicating that the error of the measurement result is smaller. Finally, the dynamic tool face angle is calculated based on the sensor data, and the measurement result curve is plotted. See Figure 6 As shown, the curve of the unfiltered tool face angle fluctuates the most, indicating that it is most affected by the error in the measurement data, resulting in inaccurate calculation of the result curve; the curve of the tool face angle after being processed by the EKF filter fluctuates less, but there are still many obvious fluctuations, indicating that the error still exists after filtering; for the curve of the tool face angle obtained after being processed by the IEKF filter proposed in this method, it can be observed that compared with the previous two curves, this result curve is smoother and there are basically no obvious fluctuations, indicating that the IEKF filter has the best processing effect, effectively eliminating the influence of the centrifugal acceleration and magnetic interference suffered by the sensor during the measurement process, and can improve the dynamic measurement accuracy of the attitude angle of the steering tool.

[0094] As Figure 7 shown, the present invention provides a dynamic measurement system for the attitude parameters of a rotary steering tool, including:

[0095] A data acquisition module for real-time acquisition of the three-axis angular rate, three-axis earth gravity field components, and three-axis geomagnetic field components of the rotary steering tool;

[0096] An attitude angle prior estimate value determination module for determining the prior estimate value of the attitude angle of the rotary steering tool according to the three-axis angular rate;

[0097] An attitude angle posterior observation value determination module for determining the posterior observation value of the attitude angle of the rotary steering tool according to the three-axis earth gravity field components and three-axis geomagnetic field components;

[0098] A posterior optimal attitude angle estimation result determination module for adaptively fusing the prior estimate value of the attitude angle and the posterior observation value of the attitude angle by means of iterative extended Kalman filtering to obtain the posterior optimal attitude angle estimation result;

[0099] An attitude parameter determination module for calculating attitude parameters according to the posterior optimal attitude angle estimation result.

[0100] Optionally, the attitude angle prior estimate value determination module is specifically used for:

[0101] Constructing a gyroscope system state model by means of quaternion differentiation; wherein, the gyroscope system state model is as follows:

[0102]

[0103] wherein, represents quaternion differentiation, ωx , ω y , ω z represents the three-axis angular rate, and q0, q1, q2, q3 are the real parts of the quaternion, representing the prior estimated values of the attitude angles. represents the quaternion multiplication of the three-axis angular rate and the real part of the quaternion.

[0104] Determine the prior estimated values of the attitude angles according to the three-axis angular rate and the gyroscope system state model.

[0105] Optionally, the posterior observation value determination module is specifically used for:

[0106] Obtain the attitude rotation matrix that describes the attitude transformation between the navigation coordinate system and the vehicle coordinate system of the rotary steerable tool.

[0107] Use the attitude rotation matrix to construct an observation correction model, and the formula is as follows:

[0108]

[0109] where represents the transpose matrix of the attitude rotation matrix. represents the three-axis earth gravity field components. represents the three-axis geomagnetic field components. are the three-axis earth gravity field components and the three-axis geomagnetic field components in the navigation coordinate system respectively, representing the posterior observation values of the attitude angles.

[0110] Determine the posterior observation values of the attitude angles according to the three-axis earth gravity field components, the three-axis geomagnetic field components and the observation correction model.

[0111] Optionally, the posterior optimal attitude angle estimation result determination module is specifically used for:

[0112] Take the partial derivative of the gyroscope system state model to determine the system state transition matrix, and take the partial derivative of the observation correction model to determine the high-dimensional observation matrix.

[0113] Update the prior estimated values of the attitude angles obtained at the previous moment according to the state transition matrix and the system process error, and determine the prior estimated values of the attitude angles obtained at the current moment.

[0114] Update the first error covariance corresponding to the posterior optimal attitude angle estimation result obtained at the previous moment according to the state transition matrix and the covariance matrix corresponding to the system process error, and determine the second error covariance corresponding to the prior estimated values of the attitude angles obtained at the current moment.

[0115] Determine the Kalman gain at the current moment according to the second error covariance, the high-dimensional observation matrix and the observation error.

[0116] Update the prior attitude angle estimate value obtained at the current moment according to the Kalman gain, the posterior attitude angle observation value, and the high-dimensional observation matrix at the current moment, and determine the posterior optimal attitude angle estimation result and the corresponding second error covariance.

[0117] Optionally, the posterior optimal attitude angle estimation result determination module is specifically configured to:

[0118] Update the prior attitude angle estimate value obtained at the previous moment according to the state transition matrix and the system process error, and determine the prior attitude angle estimate value obtained at the current moment. The formula is as follows:

[0119]

[0120] Where, represents the prior attitude angle estimate value at the current moment, A k represents the state transition matrix, x k-1 represents the prior attitude angle estimate value obtained at the previous moment, w k-1 represents the system process error.

[0121] Optionally, the posterior optimal attitude angle estimation result determination module is specifically configured to:

[0122] Update the first error covariance corresponding to the posterior optimal attitude angle estimation result obtained at the previous moment according to the covariance matrix corresponding to the state transition matrix and the system process error, and determine the second error covariance corresponding to the prior attitude angle estimate value obtained at the current moment. The formula is as follows:

[0123]

[0124] represents the second error covariance corresponding to the prior attitude angle estimate value obtained at the current moment, P k-1 represents the first error covariance corresponding to the posterior optimal attitude angle estimation result obtained at the previous moment, I represents the identity matrix, represents the Kalman gain at the previous moment, represents the high-dimensional observation matrix, represents the second error covariance corresponding to the prior attitude angle estimate value obtained at the previous moment, represents the transpose matrix of A k , Q = E[w k-1 (w k-1 ) T represents the covariance matrix corresponding to the system process error, E[·] represents the covariance function, (w k-1 ) T represents the transpose matrix of w k-1 .

[0125] Optionally, the posterior optimal attitude angle estimation result determination module is specifically configured to:

[0126] Determine the Kalman gain at the current moment according to the second error covariance, the high-dimensional observation matrix, and the observation error. The formula is as follows:

[0127]

[0128] Wherein, represents the Kalman gain at the current moment, R = E[(w k-1 ) T represents the observation error, represents the high-dimensional observation matrix, represents the transpose matrix of.

[0129] Optionally, the posterior optimal attitude angle estimation result determination module is specifically configured to:

[0130] Update the prior estimated value of the attitude angle obtained at the current moment according to the Kalman gain, the posterior observation value of the attitude angle, and the high-dimensional observation matrix at the current moment, and determine the posterior optimal estimated value and the corresponding second error covariance. The formula is as follows:

[0131]

[0132] Wherein, represents the posterior optimal estimated value, represents the posterior observation value of the attitude angle, p k represents the second error covariance at the current moment, I represents the identity matrix, represents the Kalman gain at the current moment, represents the high-dimensional observation matrix.

[0133] Optionally, the attitude parameter determination module is specifically configured to:

[0134] Calculate the attitude parameters according to the posterior optimal attitude angle estimation result. The formula is as follows:

[0135]

[0136] Wherein, θ is the well inclination angle, is the azimuth angle, γ is the tool face angle, and the target attitude parameters include θ, and γ, and q0, q1, q2, q3 represent the posterior optimal attitude angle estimation result.

[0137] Those skilled in the art of the present technology know that the present invention can be implemented as a system, a method, or a computer program product. Therefore, the present disclosure can be specifically implemented in the following forms: it can be completely hardware, can be completely software (including firmware, resident software, microcode, etc.), or can be in the form of a combination of hardware and software, which is generally referred to as "circuit", "module", or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, which contain computer-readable program codes. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above.

[0138] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0139] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A dynamic measurement method for attitude parameters of a rotary steering tool, characterized in that, Including: Obtaining the three-axis angular rate, three-axis Earth gravity field components, and three-axis geomagnetic field components of the rotary steerable tool in real time; Determining a prior estimate value of the attitude angle of the rotary steerable tool according to the three-axis angular rate; Determining a posteriori observation values of the attitude angle of the rotary steerable tool according to the three-axis Earth gravity field components and the three-axis geomagnetic field components; Adaptive fusion of the prior estimate value of the attitude angle and the posteriori observation values of the attitude angle is performed by means of iterative extended Kalman filtering to obtain a posteriori optimal attitude angle estimation result; Calculating attitude parameters according to the posteriori optimal attitude angle estimation result; The adaptive fusion of the prior estimate value of the attitude angle and the posteriori observation values of the attitude angle by means of iterative extended Kalman filtering to obtain a posteriori optimal attitude angle estimation result includes: Taking partial derivatives of the gyroscope system state model to determine the system state transition matrix, and taking partial derivatives of the observation correction model to determine the high-dimensional observation matrix; Updating the prior estimate value of the attitude angle obtained at the previous moment according to the system state transition matrix and the system process error to determine the prior estimate value of the attitude angle obtained at the current moment; Updating the first error covariance corresponding to the posteriori optimal attitude angle estimation result obtained at the previous moment according to the system state transition matrix and the covariance matrix corresponding to the system process error to determine the second error covariance corresponding to the prior estimate value of the attitude angle obtained at the current moment; Determining the Kalman gain at the current moment according to the second error covariance, the high-dimensional observation matrix, and the observation error; Updating the prior estimate value of the attitude angle obtained at the current moment according to the Kalman gain at the current moment, the posteriori observation values of the attitude angle, and the high-dimensional observation matrix to determine the posteriori optimal attitude angle estimation result and the corresponding second error covariance; Among them, the gyroscope system state model is constructed by means of quaternion differentiation, an attitude rotation matrix describing the attitude transformation between the navigation coordinate system and the carrier coordinate system of the rotary steerable tool is obtained, and the observation correction model is constructed by using the attitude rotation matrix.

2. The method according to claim 1, wherein The determining the prior estimate value of the attitude angle of the rotary steerable tool according to the three-axis angular rate includes: Constructing a gyroscope system state model by means of quaternion differentiation; wherein, the gyroscope system state model is as follows: Among them, represents the quaternion differential, and ω x , ω y , ω z represent the three-axis angular rates, and q0, q1, q2, q3 are the real parts of the quaternion, representing the prior estimated values of the attitude angles. represents performing a four-element multiplication on the three-axis angular rates and the real parts of the quaternion; Determining a prior estimate value of the attitude angle according to the three-axis angular rate and the gyroscope system state model.

3. The method according to claim 2, characterized in that, The determining the posteriori observation values of the attitude angle of the rotary steerable tool according to the three-axis Earth gravity field components and the three-axis geomagnetic field components includes: Obtaining an attitude rotation matrix describing the attitude transformation between the navigation coordinate system and the carrier coordinate system of the rotary steerable tool; Constructing an observation correction model by using the attitude rotation matrix, and the formula is as follows: Among them, represents the transpose matrix of the attitude rotation matrix, represents the three-axis earth gravity field components in the vehicle coordinate system, represents the three-axis geomagnetic field components in the vehicle coordinate system, is the three-axis earth gravity field components and three-axis geomagnetic field components in the navigation coordinate system, representing the posteriori observation values of the attitude angles; Determining the posteriori observation values of the attitude angle according to the three-axis Earth gravity field components, the three-axis geomagnetic field components, and the observation correction model.

4. The method according to claim 1, wherein The updating the prior estimate value of the attitude angle obtained at the previous moment according to the state transition matrix and the system process error to determine the prior estimate value of the attitude angle obtained at the current moment, and the formula is as follows: Among them, represents the prior estimated value of the attitude angle at the current moment, A k represents the state transition matrix, represents the prior estimated value of the attitude angle obtained at the previous moment, w k-1 represents the system process error.

5. The method according to claim 4, wherein Update the first error covariance corresponding to the posterior optimal attitude angle estimation result obtained at the previous moment according to the state transition matrix and the covariance matrix corresponding to the system process error, and determine the second error covariance corresponding to the prior estimated value of the attitude angle obtained at the current moment. The formula is as follows: Denote the second error covariance corresponding to the prior estimated value of the attitude angle obtained at the current moment, P k-1 Denote the first error covariance corresponding to the posterior optimal attitude angle estimation result obtained at the previous moment, I denotes the identity matrix, Denote the Kalman gain at the previous moment, Denote the high-dimensional observation matrix, Denote the second error covariance corresponding to the prior estimated value of the attitude angle obtained at the previous moment, Denote the transpose matrix of A k , Q = E[w k-1 (w k-1 ) T denotes the covariance matrix corresponding to the system process error, E[·] denotes the covariance function, (w k-1 ) T Denote the transpose matrix of w k-1 .

6. The method according to claim 5, characterized in that, Determine the Kalman gain at the current moment according to the second error covariance, the high-dimensional observation matrix, and the observation error. The formula is as follows: Among them, represents the Kalman gain at the current moment, R = E[(w k-1 ) T represents the observation error, represents the high-dimensional observation matrix, represents the transpose matrix of.

7. The method according to claim 6, wherein Update the prior estimated value of the attitude angle obtained at the current moment according to the Kalman gain at the current moment, the posterior observation value of the attitude angle, and the high-dimensional observation matrix, and determine the posterior optimal estimated value and the corresponding second error covariance. The formula is as follows: Among them, represents the posterior optimal estimate value, represents the posterior observation value of the attitude angle, p k represents the second error covariance at the current moment, and I represents the identity matrix. represents the Kalman gain at the current moment, represents the high-dimensional observation matrix.

8. The method according to any one of claims 1-7, characterized in that, Calculate the attitude parameters according to the posterior optimal attitude angle estimation result. The formula is as follows: θ = arcsin(2q0q2 - 2q1q3) where θ is the well deviation angle, is the azimuth angle, γ is the tool face angle, and the target attitude parameters include θ, and γ, and q0, q1, q2, q3 represent the estimated results of the posterior optimal attitude angles.

9. A dynamic measurement system for the attitude parameters of a rotary steering tool, characterized in that, A system for implementing the dynamic measurement method of the attitude parameters of the rotary steering tool according to any one of claims 1 to 8, comprising: A data acquisition module for real-time acquiring the three-axis angular rate, the three-axis earth gravity field components, and the three-axis geomagnetic field components of the rotary steering tool; A prior estimated value determination module of the attitude angle for determining the prior estimated value of the attitude angle of the rotary steering tool according to the three-axis angular rate; A posterior observation value determination module of the attitude angle for determining the posterior observation value of the attitude angle of the rotary steering tool according to the three-axis earth gravity field components and the three-axis geomagnetic field components; A posterior optimal attitude angle estimation result determination module for adaptively fusing the prior estimated value of the attitude angle and the posterior observation value of the attitude angle by means of iterative extended Kalman filtering to obtain the posterior optimal attitude angle estimation result; An attitude parameter determination module for calculating the attitude parameters according to the posterior optimal attitude angle estimation result.

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