Dynamic measurement method and system for attitude parameters of rotary steering tool

Abstract

The invention relates to the technical field of drilling tool attitude dynamic measurement, and discloses a dynamic measurement method and system for attitude parameters of a rotary steering tool, and the method comprises the steps: obtaining a triaxial angular rate, a triaxial earth gravitational field component and a triaxial earth magnetic field component of the rotary steering tool in real time; determining an attitude angle priori estimated value according to the triaxial angular rate; determining an attitude angle posterior observation value according to the three-axis earth gravitational field component and the three-axis earth magnetic field component; fusing the attitude angle priori estimation value and the attitude angle posteriori observation value through iterative extended Kalman filtering to obtain a posteriori optimal attitude angle estimation result; and calculating attitude parameters according to a posterior optimal attitude angle estimation result. According to the method, adaptive fusion is carried out on the dynamic observed quantities measured by the three-axis gyroscope, the three-axis accelerometer and the three-axis magnetometer through the iterative extended Kalman filter, so that noise interference of the external environment, such as high rotating speed, strong vibration and magnetic anomaly, in the dynamic measurement process is eliminated, and the control and measurement precision of the rotary steering tool is improved.

Description

Background Art

[0002] The rotary steerable tool is the most advanced wellbore trajectory closed-loop control system in the field of directional drilling at home and abroad. It can adjust the wellbore trajectory in real time to drill in the preset direction during the rotary drilling of the drill string. According to the tool guidance principle and structural form, the rotary steerable tool can be divided into four types: static push-to-pull type, dynamic push-to-pull type, static directional guidance type and full-rotation directional guidance type. Among them, the full-rotation directional guidance tool has the characteristics of less drilling friction, smoother wellbore trajectory and stronger drilling extension capability due to its special guidance principle and structural form. It has obvious technical advantages in directional drilling of complex wells such as large displacement 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 on the market in the 1990s, foreign countries have undergone nearly 30 years of technological upgrades and iterations, realizing the automatic adjustment of the wellbore trajectory according to the real-time engineering and geological measurement parameters during the drilling process, and promoting the development of wellbore trajectory control technology towards intelligence. At present, the three major oil service companies in foreign countries, Schlumberger, Baker Hughes and Halliburton, have launched intelligent rotary steerable tools with automatic control of wellbore trajectory. Schlumberger's Neosteer drill bit guidance system and Baker Hughes' Lucida rotary steerable system can both automatically correct the actual drilling trajectory of the wellbore by continuously measuring the well inclination and azimuth near the drill bit at milliseconds under high rotation speed, so as to realize the autonomous "cruise" drilling of the steerable tool in the wellbore. In 2021, Halliburton launched the iCruise XTM intelligent rotary steerable system, which realizes the precise guidance and positioning of downhole tools by automatically controlling the well inclination and azimuth during drilling operations. It can be seen that accurately measuring the well inclination, azimuth and tool face size under dynamic rotation is an important prerequisite for realizing automatic control of wellbore trajectory. Commonly used attitude measurement sensors are accelerometers, magnetometers and gyroscopes. However, in the actual drilling process, the centrifugal acceleration generated by the high-speed rotation of the drill string and the vibration acceleration generated by the drill bit breaking the rock will interfere with the accelerometer's ability to sense the earth's gravity field; and the abnormal magnetic field generated by ferromagnetic materials such as motors, casings and adjacent wells will also affect the magnetometer's ability to be sensitive to the earth's magnetic field; in addition, high temperatures underground will increase the zero bias error of the gyroscope, and this effect will become more and more obvious as time accumulates.

[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 steering tool, the present invention provides a dynamic measurement method and system for the attitude parameters of the rotary steering tool.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a dynamic measurement method of attitude parameters of a rotary steering tool, comprising:

[0007] Acquire the three-axis angular rate, three-axis earth gravity field component and three-axis geomagnetic field component of the rotary steerable tool in real time;

[0008] Determine a priori estimated value of attitude angle of the rotary steerable tool according to the three-axis angular rate;

[0009] Determine the attitude angle posterior observation value of the rotary steerable tool according to the three-axis earth gravity field component and the three-axis earth magnetic field component;

[0010] The attitude angle prior estimation value and attitude angle posterior observation value are adaptively fused by iterative extended Kalman filtering to obtain the posterior optimal attitude angle estimation result.

[0011] The attitude parameters are calculated based on the posterior optimal attitude angle estimation results.

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

[0013] A data acquisition module, used for acquiring the three-axis angular rate, three-axis earth gravity field component and three-axis geomagnetic field component of the rotary steering tool in real time;

[0014] An attitude angle prior estimation value determination module is used to determine an attitude angle prior estimation value of the rotary steering tool according to the three-axis angular rate;

[0015] The attitude angle posterior observation value determination module is used to determine the attitude angle posterior observation value of the rotary steering tool according to the three-axis earth gravity field component and the three-axis geomagnetic field component;

[0016] The module for determining the result of the a posteriori optimal attitude angle estimation is used to adaptively fuse the attitude angle prior estimation value and the attitude angle a posteriori observation value by iterative extended Kalman filtering to obtain the a posteriori optimal attitude angle estimation result;

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

[0018] The beneficial effects of the present invention are as follows: by acquiring the three-axis angular rate, the three-axis earth gravity field component and the three-axis geomagnetic field component of the rotary steering tool, the attitude angle prior estimation value and the attitude angle posterior observation value of the rotary steering tool can be obtained, and then the attitude angle prior estimation value and the attitude angle posterior observation value are adaptively fused through the iterative extended Kalman filter to obtain the posterior optimal attitude angle estimation result, and the attitude parameters can be calculated through the posterior optimal attitude angle estimation result. The present application adaptively fuses the attitude angle prior estimation value and the attitude angle posterior observation value through the iterative extended Kalman filter to eliminate the noise interference of the external environment such as high speed, strong vibration and magnetic anomaly faced in the dynamic measurement process, thereby improving the control accuracy of the rotary steering tool, helping to meet the various trajectory control requirements such as increasing inclination, stabilizing inclination, decreasing inclination, twisting azimuth, etc. during the "one-trip drilling" process of the rotary steering tool, reducing the number of times of lifting and lowering the drill bit to replace the bottom drilling tool, and improving the drilling efficiency, which has important practical significance for achieving the goal of reducing costs and increasing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0020] Figure 1 A schematic flow chart of a method for dynamically measuring attitude parameters of a rotary steering tool according to an embodiment of the present invention;

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

[0022] Figure 3 A schematic flow chart of a method for dynamically measuring attitude parameters of a rotary steering tool according to another embodiment of the present invention;

[0023] Figure 4 This is a comparison chart of the effects of pre-filtering, EKF filtering and IEKF filtering for dynamic measurement of well inclination;

[0024] Figure 5 This is a comparison chart of the effects of pre-filtering, EKF filtering and IEKF filtering for dynamic measurement of azimuth;

[0025] Figure 6 This is a comparison chart of the effects of pre-filtering, EKF filtering and IEKF filtering for dynamic measurement of tool face angle;

[0026] Figure 7 It is a schematic structural diagram of a dynamic measurement system for attitude parameters of a rotary steering tool according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following examples are provided to further explain and supplement the present invention and do not constitute any limitation to the present invention.

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

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

[0030] S1. Real-time acquisition of the three-axis angular rate, three-axis earth gravity field component and three-axis geomagnetic field component of the rotary steering tool.

[0031] S2. Determine a priori estimated value of the attitude angle of the rotary steering tool based on the three-axis angular velocity.

[0032] S3. Determine the a posteriori observation value of the attitude angle of the rotary steering tool based on the three-axis earth gravity field components and the three-axis geomagnetic field components.

[0033] S4. Adaptively fuse the attitude angle prior estimation value and the attitude angle posterior observation value by iterative extended Kalman filtering to obtain the posterior optimal attitude angle estimation result.

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

[0035] In this embodiment, the three-axis angular rate, three-axis earth gravity field component and three-axis geomagnetic field component of the rotary steering tool are obtained, and the attitude angle prior estimation value and the attitude angle posterior observation value of the rotary steering tool can be obtained. Then, the attitude angle prior estimation value and the attitude angle posterior observation value are adaptively fused through the iterative extended Kalman filter to obtain the posterior optimal attitude angle estimation result, and the attitude parameters can be calculated through the posterior optimal attitude angle estimation result. This application adaptively fuses the attitude angle prior estimation value and the attitude angle posterior observation value through the iterative extended Kalman filter to eliminate the noise interference of the external environment such as high speed, strong vibration and magnetic anomaly faced in the dynamic measurement process, thereby improving the control accuracy of the rotary steering tool, helping to meet the various trajectory control requirements of increasing inclination, stabilizing inclination, reducing inclination, twisting azimuth, etc. during the "one-trip drilling" process of the rotary steering tool, reducing the number of times of lifting and lowering the drill bit to replace the bottom drilling tool, and improving the drilling efficiency, which has important practical significance for achieving the goal of reducing costs and increasing efficiency.

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

[0037] The gyroscope system state model is constructed by using quaternion differentiation; the gyroscope system state model is as follows:

[0038]

[0039] in, represents the quaternion differential, ω x ,ωy ,ω z Table 3-axis angular rate, q0, q1, q2, q3 are the real part of the quaternion, representing the prior estimate of the attitude angle, Indicates the four-element multiplication of the three-axis angular rate and the real part of the quaternion;

[0040] According to the three-axis angular rate and gyroscope system state model, the attitude angle prior estimate is determined.

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

[0042]

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

[0044] like Figure 2 As shown in the figure, the nine-axis strapdown attitude measurement system is mainly composed of a three-axis accelerometer Three-axis magnetometer and three-axis gyroscope (ω x ,ω y ,ω z ), on the one hand, the three-axis gyroscope alone can be used to obtain a set of dynamic measurement results of the attitude parameters of the rotary steering tool (attitude angle prior estimation values) by sensing the angular velocity of rotation around the axis, on the other hand, the combination of the gravity / magnetic sensor of the three-axis accelerometer and the three-axis magnetometer can also obtain a set of dynamic measurement results of the attitude parameters of the rotary steering tool (attitude angle posterior observation values), 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, so as to obtain the optimal attitude angle parameter results.

[0045] Optionally, determining the attitude angle posterior observation value of the rotary steering tool according to the three-axis earth gravity field component and the three-axis earth magnetic field component includes:

[0046] Obtaining an attitude rotation matrix describing an attitude transformation of the rotational steerable tool between a navigation coordinate system and a carrier coordinate system;

[0047] The observation correction model is constructed using the attitude rotation matrix. The formula is as follows:

[0048]

[0049] in, represents the transposed matrix of the attitude rotation matrix, represents the three-axis earth gravity field components in the carrier coordinate system, represents 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 attitude angle posterior observation value;

[0050] The attitude angle posterior observation value is determined according to 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, the system observation correction model is constructed based on the principle of measuring attitude parameters with a combination of gravity / magnetic sensors. Before constructing the system observation correction model, it is necessary to first define the navigation coordinate system as the "north-east" geographic coordinate system, and the carrier coordinate system as the XYZ drilling tool coordinate system composed of the axial, radial and perpendicular directions of the rotary steering tool, which conforms to the right-hand rule. Then, the attitude transformation of the rotary steering tool from the navigation coordinate system to the carrier coordinate system is described according to the Euler rotation theory, and finally the attitude rotation matrix in the form of Euler angles is obtained as:

[0052]

[0053] The three-axis accelerometer and three-axis magnetometer can be used to directly observe the gravity field and the geomagnetic field. And the output value of the three-axis magnetometer As the observation vector of the system observation correction model, combined with the above attitude rotation matrix, the system observation correction model is obtained, the formula is as follows:

[0054]

[0055] The above formula can be used to output the attitude angle posterior observation value

[0056] Optionally, the attitude angle prior estimation value and the attitude angle posterior observation value are adaptively fused by iterative extended Kalman filtering to obtain the posterior optimal attitude angle estimation result, including:

[0057] Partially differentiate the gyroscope system state model to determine the system state transfer matrix, and partially differentiate the observation correction model to determine the high-dimensional observation matrix;

[0058] The prior estimation value of the attitude angle obtained at the previous moment is updated according to the system state transfer matrix and the system process error, and the prior estimation value of the attitude angle obtained at the current moment is determined;

[0059] Update the first error covariance corresponding to the a posteriori optimal attitude angle estimation result obtained at the previous moment according to the covariance matrix corresponding to the system state transfer matrix and the system process error, and determine the second error covariance corresponding to the a priori estimation 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] The attitude angle prior estimation value obtained at the current moment is updated according to the Kalman gain at the current moment, the attitude angle posterior observation value and the high-dimensional observation matrix, and the posterior optimal attitude angle estimation result and the corresponding second error covariance are determined.

[0062] In this embodiment, by designing an iterative extended Kalman filter, the attitude angle posterior observation value obtained by the gravity / magnetic sensor can be repeatedly used over time to reduce the linearization error, improve the processing performance of the filter, effectively eliminate the noise interference in the sensor measurement signal, and realize dynamic and accurate measurement of the attitude parameters.

[0063] Optionally, the attitude angle prior estimation value obtained at the previous moment is updated according to the state transfer matrix and the system process error to determine the attitude angle prior estimation value obtained at the current moment, and the formula is as follows:

[0064]

[0065] in, represents the prior estimate of the attitude angle at the current moment, A k represents the state transfer matrix, x k-1 represents the prior estimate of the attitude angle obtained at the last moment, w k-1 Represents the systematic process error.

[0066] In the iterative extended Kalman filter, the prior estimate is the predicted state before the measurement is updated, that is, the prior estimate is based on the state estimate at the previous moment and the analysis of the dynamic model (such as physical laws or differential equations) of the nine-axis strapdown attitude measurement system before the arrival of new measurement data to predict the state at the current moment. In this embodiment, the prior estimate of the attitude angle at the previous moment is updated to obtain the prior estimate of the attitude angle at the current moment.

[0067] Optionally, the first error covariance corresponding to the posterior optimal attitude angle estimation result obtained at the previous moment is updated according to the covariance matrix corresponding to the state transfer matrix and the system process error, and the second error covariance corresponding to the attitude angle prior estimation value obtained at the current moment is determined, and the formula is as follows:

[0068]

[0069] represents the second error covariance corresponding to the attitude angle prior 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 unit matrix, represents the Kalman gain at the previous moment, represents the high-dimensional observation matrix, represents the second error covariance corresponding to the attitude angle prior estimate obtained at the previous moment, Indicates A k The transposed matrix, 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 Indicates w k-1 The transposed matrix of .

[0070] In this embodiment, the covariance matrix corresponding to the attitude angle prior estimate quantifies the uncertainty of the state estimation before obtaining new measurement data, and the covariance matrix corresponding to the posterior optimal attitude angle estimation result at the previous moment is passed to the current moment. The error covariance matrix corresponding to the attitude angle prior estimate at the current moment is calculated by using the covariance matrix corresponding to the posterior optimal attitude angle estimation result at the previous moment, which can improve the accuracy of the subsequent acquisition of the posterior optimal attitude angle estimation result.

[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] in, represents the Kalman gain at the current moment, R = E[(w k-1 ) T ] represents the observation error, represents the high-dimensional observation matrix, Express The transposed matrix of .

[0074] In this embodiment, gravity / magnetic sensors are added during continuous iterations to obtain a posteriori observations of the attitude angle, which reduces the linearization error of the filter and improves the filtering accuracy, thereby effectively eliminating noise interference in the sensor measurement signal.

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

[0076]

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

[0078] In this embodiment, the a posteriori optimal estimate is the corrected state after the measurement update, that is, the a posteriori optimal estimate is to update the current state estimate by combining the attitude angle prior estimate and the measurement data after the new measurement data arrives. During the iterative process of the Kalman filter, the attitude angle prior estimate and the a posteriori optimal estimate are continuously and alternately updated to provide an accurate estimate of the state of the nine-axis strapdown attitude measurement system.

[0079] Optionally, the attitude parameters are calculated based on the a posteriori optimal attitude angle estimation result, and the formula is as follows:

[0080] θ=arcsin(2q0q2-2q1q3)

[0081]

[0082] Where 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, q0, q1, q2, q3 represent the posterior optimal attitude angle estimation results.

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

[0084] Alternatively, if Figure 3 As shown, another embodiment of the dynamic measurement method of the attitude parameters of the rotary steering tool is described as follows:

[0085] The three-axis angular rate of the rotary steering tool is obtained in real time by the three-axis gyroscope in the nine-axis strapdown attitude measurement system, and a system state model based on four elements is constructed.

[0086] An observation correction model based on gravity / magnetic sensors is constructed by using the three-axis accelerometer in the nine-axis strapdown attitude measurement system to obtain the three-axis earth gravity field components in real time and the three-axis magnetometer to obtain the three-axis geomagnetic field components in real time.

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

[0088] The system state model is partially differentiated to obtain the system state transfer matrix, and the observation correction model is partially differentiated to obtain a high-dimensional observation matrix. The prior estimate of the attitude angle at the previous moment can be updated through the system state transfer matrix to obtain the prior estimate of the attitude angle at the current moment, and the Kalman gain of the iterative extended Kalman filter can be updated using the high-dimensional observation matrix.

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

[0090] According to the optimal attitude angle estimation result, the well inclination angle θ and azimuth angle are calculated. Tool face angle γ.

[0091] Optionally, in order to verify the effectiveness of this method, a data acquisition experiment of 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, and a nine-axis strapdown attitude measurement system was installed on the experimental platform. At the same time, the tool face speed was set to 120 rpm through the computer control system of the turntable for uniform rotation to simulate the downhole drilling process of the directional tool. Then different well inclination angles and azimuth angles were selected for measurement, and the data of each sensor in the three directions of X-axis, Y-axis and Z-axis were measured and recorded. Then, the collected experimental data were analyzed according to the steps in this method.

[0093] The processing results are as follows Figure 4 , Figure 5 and Figure 6 When the rotary steerable tool face axis is in dynamic rotation state, the azimuth angle is fixed at 0°, and the rotation well inclination angles are 0°, 5°, 50°, 70° and 90° respectively. Then the sensor data is collected, and finally the data is analyzed according to the steps of the method. Because the rotary steerable tool is often used to drill horizontal wells, and the well inclination angle of the horizontal well is about 90°, so the reference Figure 4 The measured data of the 90° well inclination angle were compared with the unfiltered results after EKF (Extended Kalman Filter), IEKF (Iterated Extended Kalman Filter), and it can be found that the well inclination result curve obtained by the dynamic measurement method based on IEKF filtering is obviously smoother and the measurement error is smaller. Then, the well inclination angle is fixed at 90°, and the rotation azimuth angles are 10°, 25°, 45°, 90°, and 180° respectively, and the sensor data is collected. After that, the data is analyzed according to this method, among which, reference Figure 5The comparison of the measurement data of 45° azimuth after EKF and IEKF filtering and the unfiltered results shows that the azimuth result curve obtained after IEKF filtering has smaller fluctuations, indicating that the measurement result error is smaller. Finally, the dynamic tool face angle is calculated based on the sensor data, and the measurement result curve is plotted. Figure 6 As shown in the figure, the unfiltered tool face angle curve has the largest fluctuation, indicating that the error in the measured data is the largest, resulting in inaccurate curve solution; the tool face angle curve after EKF filter processing has smaller fluctuations, but there are still many obvious curve fluctuations, indicating that the error still exists after filtering; the tool face angle curve obtained after the IEKF filter processing proposed in this method can be observed to be smoother than the previous two curves, and there is basically no obvious curve fluctuation, indicating that the IEKF filter processing effect is the best, which effectively eliminates the influence of centrifugal acceleration and magnetic interference on the sensor during the measurement process, and can improve the dynamic measurement accuracy of the attitude angle of the guide tool.

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

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

[0096] An attitude angle prior estimation value determination module is used to determine an attitude angle prior estimation value of the rotary steering tool according to the three-axis angular rate;

[0097] The attitude angle posterior observation value determination module is used to determine the attitude angle posterior observation value of the rotary steering tool according to the three-axis earth gravity field component and the three-axis geomagnetic field component;

[0098] The module for determining the result of the a posteriori optimal attitude angle estimation is used to adaptively fuse the attitude angle prior estimation value and the attitude angle a posteriori observation value by iterative extended Kalman filtering to obtain the a posteriori optimal attitude angle estimation result;

[0099] The attitude parameter determination module is used to calculate the attitude parameters according to the a posteriori optimal attitude angle estimation result.

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

[0101] The gyroscope system state model is constructed by using quaternion differentiation; the gyroscope system state model is as follows:

[0102]

[0103] in, represents the quaternion differential, ωx ,ω y ,ω z Table 3-axis angular rate, q0, q1, q2, q3 are the real part of the quaternion, representing the prior estimate of the attitude angle, Indicates the four-element multiplication of the three-axis angular rate and the real part of the quaternion;

[0104] According to the three-axis angular rate and gyroscope system state model, the attitude angle prior estimate is determined.

[0105] Optionally, the attitude angle posterior observation value determination module is specifically used to:

[0106] Obtaining an attitude rotation matrix describing an attitude transformation of the rotational steerable tool between a navigation coordinate system and a carrier coordinate system;

[0107] The observation correction model is constructed using the attitude rotation matrix. The formula is as follows:

[0108]

[0109] in, represents the transposed 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 a posteriori observation values ​​of the attitude angle;

[0110] The attitude angle posterior observation value is determined according to the three-axis earth gravity field components, the three-axis geomagnetic field components and the observation correction model.

[0111] Optionally, the a posteriori optimal attitude angle estimation result determination module is specifically used to:

[0112] Partially differentiate the gyroscope system state model to determine the system state transfer matrix, and partially differentiate the observation correction model to determine the high-dimensional observation matrix;

[0113] The prior estimation value of the attitude angle obtained at the previous moment is updated according to the state transfer matrix and the system process error, and the prior estimation value of the attitude angle obtained at the current moment is determined;

[0114] Update the first error covariance corresponding to the posterior optimal attitude angle estimation result obtained at the previous moment according to the state transfer matrix and the covariance matrix corresponding to the system process error, and determine the second error covariance corresponding to the attitude angle prior estimation value 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] The attitude angle prior estimation value obtained at the current moment is updated according to the Kalman gain at the current moment, the attitude angle posterior observation value and the high-dimensional observation matrix, and the posterior optimal attitude angle estimation result and the corresponding second error covariance are determined.

[0117] Optionally, the a posteriori optimal attitude angle estimation result determination module is specifically used to:

[0118] The attitude angle prior estimation value obtained at the previous moment is updated according to the state transfer matrix and the system process error, and the attitude angle prior estimation value obtained at the current moment is determined. The formula is as follows:

[0119]

[0120] in, represents the prior estimate of the attitude angle at the current moment, A k represents the state transfer matrix, x k-1 represents the prior estimate of the attitude angle obtained at the last moment, w k-1 Represents the systematic process error.

[0121] Optionally, the a posteriori optimal attitude angle estimation result determination module is specifically used to:

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

[0123]

[0124] represents the second error covariance corresponding to the attitude angle prior 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 unit matrix, represents the Kalman gain at the previous moment, represents the high-dimensional observation matrix, represents the second error covariance corresponding to the attitude angle prior estimate obtained at the previous moment, Indicates A k The transposed matrix, 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 Indicates w k-1 The transposed matrix of .

[0125] Optionally, the a posteriori optimal attitude angle estimation result determination module is specifically used to:

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

[0127]

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

[0129] Optionally, the a posteriori optimal attitude angle estimation result determination module is specifically used to:

[0130] According to the Kalman gain, attitude angle posterior observation value and high-dimensional observation matrix at the current moment, the attitude angle prior estimation value obtained at the current moment is updated, and the posterior optimal estimation value and the corresponding second error covariance are determined. The formula is as follows:

[0131]

[0132] in, represents the posterior optimal estimate, represents the posterior observation value of attitude angle, p k represents the second error covariance at the current moment, I represents the unit matrix, represents the Kalman gain at the current moment, Represents a high-dimensional observation matrix.

[0133] Optionally, the posture parameter determination module is specifically used to:

[0134] The attitude parameters are calculated based on the posterior optimal attitude angle estimation results. The formula is as follows:

[0135]

[0136] Where 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, q0, q1, q2, q3 represent the posterior optimal attitude angle estimation results.

[0137] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, 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, and the computer-readable medium contains computer-readable program code. Computer-readable storage media can be, for example, but not limited to - electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, 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 conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0139] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A dynamic measurement method for the attitude parameters of a rotary steering tool, characterized in that: include: Acquire the three-axis angular rate, three-axis earth gravity field component and three-axis geomagnetic field component of the rotary steerable tool in real time; Determining a priori estimated value of the attitude angle of the rotary steerable tool according to the three-axis angular rate; Determining a posteriori observation value of the attitude angle of the rotary steerable tool according to the three-axis earth gravity field component and the three-axis geomagnetic field component; Adaptively fusing the attitude angle prior estimation value and the attitude angle posterior observation value by iterative extended Kalman filtering to obtain a posterior optimal attitude angle estimation result; The attitude parameters are calculated according to the a posteriori optimal attitude angle estimation result.

2. The method according to claim 1, characterized in that Determining a priori estimated value of the attitude angle of the rotary steerable tool according to the three-axis angular rate comprises: The gyroscope system state model is constructed by using quaternion differentiation; wherein the gyroscope system state model is as follows: in, represents the quaternion differential, ω x ,ω y ,ω z Table 3-axis angular rate, q0, q1, q2, q3 are the real part of the quaternion, representing the prior estimate of the attitude angle, Indicates the four-element multiplication of the three-axis angular rate and the real part of the quaternion; According to the three-axis angular rate and the gyroscope system state model, a priori estimated value of the attitude angle is determined.

3. The method according to claim 2, characterized in that The method of determining the attitude angle posterior observation value of the rotary steering tool according to the three-axis earth gravity field component and the three-axis geomagnetic field component comprises: Obtaining an attitude rotation matrix describing the attitude transformation of the rotary steering tool between the navigation coordinate system and the carrier coordinate system; The observation correction model is constructed using the attitude rotation matrix. The formula is as follows: in, represents the transposed matrix of the attitude rotation matrix, represents the three-axis earth gravity field components in the carrier coordinate system, represents 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 attitude angle posterior observation value; The attitude angle posterior observation value is determined according to the three-axis earth gravity field component, the three-axis geomagnetic field component and the observation correction model.

4. The method according to claim 3, characterized in that The method of adaptively fusing the attitude angle prior estimation value and the attitude angle posterior observation value by iterative extended Kalman filtering to obtain the posterior optimal attitude angle estimation result includes: Partially differentiating the gyroscope system state model to determine a system state transfer matrix, and partially differentiating the observation correction model to determine a high-dimensional observation matrix; The prior estimation value of the attitude angle obtained at the previous moment is updated according to the system state transfer matrix and the system process error, and the prior estimation value of the attitude angle obtained at the current moment is determined; Update the first error covariance corresponding to the a posteriori optimal attitude angle estimation result obtained at the previous moment according to the covariance matrix corresponding to the system state transfer matrix and the system process error, and determine the second error covariance corresponding to the a priori estimation value of the attitude angle obtained at the current moment; Determine the Kalman gain at the current moment according to the second error covariance, the high-dimensional observation matrix and the observation error; The attitude angle prior estimation value obtained at the current moment is updated according to the Kalman gain at the current moment, the attitude angle posterior observation value and the high-dimensional observation matrix, and the posterior optimal attitude angle estimation result and the corresponding second error covariance are determined.

5. The method according to claim 4, characterized in that The prior estimation value of the attitude angle obtained at the previous moment is updated according to the state transfer matrix and the system process error to determine the prior estimation value of the attitude angle obtained at the current moment. The formula is as follows: in, represents the prior estimate of the attitude angle at the current moment, A k represents the state transfer matrix, x k-1 represents the prior estimate of the attitude angle obtained at the last moment, w k-1 Represents the systematic process error.

6. The method according to claim 5, characterized in that The first error covariance corresponding to the posterior optimal attitude angle estimation result obtained at the previous moment is updated according to the covariance matrix corresponding to the state transfer matrix and the system process error, and the second error covariance corresponding to the attitude angle prior estimation value obtained at the current moment is determined, and the formula is as follows: represents the second error covariance corresponding to the attitude angle prior 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 unit matrix, represents the Kalman gain at the previous moment, represents the high-dimensional observation matrix, represents the second error covariance corresponding to the attitude angle prior estimate obtained at the previous moment, Indicates A k The transposed matrix, 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 Indicates w k-1 The transposed matrix of .

7. The method according to claim 6, characterized in that 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: in, represents the Kalman gain at the current moment, R = E[(w k-1 ) T ] represents the observation error, represents the high-dimensional observation matrix, Express The transposed matrix of .

8. The method according to claim 7, characterized in that The Kalman gain at the current moment, the attitude angle posterior observation value and the high-dimensional observation matrix are used to update the attitude angle prior estimation value obtained at the current moment, and determine the posterior optimal estimation value and the corresponding second error covariance, and the formula is as follows: in, represents the posterior optimal estimate, represents the posterior observation value of attitude angle, p k represents the second error covariance at the current moment, I represents the unit matrix, represents the Kalman gain at the current moment, Represents a high-dimensional observation matrix.

9. The method according to any one of claims 1 to 8, characterized in that: The attitude parameters are calculated based on the a posteriori optimal attitude angle estimation result, and the formula is as follows: θ=arcsin(2q0q2-2q1q3) Where 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, q0, q1, q2, q3 represent the posterior optimal attitude angle estimation results.

10. A dynamic measurement system for attitude parameters of a rotary steering tool, characterized in that: A method for dynamically measuring the attitude parameters of a rotary steerable tool according to any one of claims 1 to 9, the system comprising: A data acquisition module, used for acquiring the three-axis angular rate, three-axis earth gravity field component and three-axis geomagnetic field component of the rotary steering tool in real time; An attitude angle prior estimation value determination module is used to determine an attitude angle prior estimation value of the rotary steering tool according to the three-axis angular rate; An attitude angle posterior observation value determination module is used to determine the attitude angle posterior observation value of the rotary steering tool according to the three-axis earth gravity field component and the three-axis geomagnetic field component; A posterior optimal attitude angle estimation result determination module is used to adaptively fuse the attitude angle prior estimation value and the attitude angle posterior observation value by iterative extended Kalman filtering to obtain a posterior optimal attitude angle estimation result; The attitude parameter determination module is used to calculate the attitude parameters according to the a posteriori optimal attitude angle estimation result.

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