Acceleration signal processing method, device and equipment for measurement while drilling and medium

Abstract

The invention discloses an acceleration signal processing method, device and equipment for measurement while drilling and a medium. The method comprises the following steps: processing an acceleration signal by using two alternating filters to obtain two alternating filtering results; carrying out weighted summation on the two alternative filtering results by adopting a first weight and a second weight to obtain a weighted summation result; and performing adaptive adjustment on the first weight and the second weight based on the weighted summation result and an optimization target so as to obtain an optimized weighted summation result based on the optimized first weight and the optimized second weight after adaptive adjustment, and taking the optimized weighted summation result as a processing result of the acceleration signal. Accuracy and stability of acceleration signal processing results can be improved, noise components can be reduced, measurement precision of well track parameters such as well deviation can be improved, the drilling direction can be accurately fed back and adjusted, and the drilling rate of high-quality reservoirs can be effectively guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of oil drilling engineering, and in particular to an acceleration signal processing method, device, equipment and medium for while-drilling measurement. Background Art

[0002] With the in-depth development of resources such as oil and natural gas, the underground stratum structure is complex. In order to ensure that the wellbore trajectory operates in the reservoir, it is usually necessary to use a measurement while drilling system to collect wellbore trajectory parameters, including: well inclination, azimuth and tool face angle, etc., and the real-time and accuracy of the measurement of each parameter and the continuous dynamic measurement are required to be high. The changes in the wellbore trajectory are combined with the geological characteristics of the formation to provide feedback to adjust the drilling direction, ensure the dynamic stability of the wellbore trajectory, and effectively improve the oil and gas drilling rate.

[0003] However, in the research and practice of the prior art, the inventors of the present application found that the outputs of various acceleration sensors in reality have various errors, and the existing denoising processing methods for collected / measured data cannot meet the requirements of stability and accuracy, which seriously affects the results of achieving high-precision dynamic measurement of well inclination of drilling tools. Summary of the invention

[0004] The main purpose of the present invention is to provide an acceleration signal processing method, device, equipment and medium for measurement while drilling, so as to at least solve the technical problem that in the prior art, stable and accurate processing results cannot be obtained after processing the acceleration signal used for measurement while drilling.

[0005] According to one aspect of the present invention, a method for processing acceleration signals for measurement while drilling is provided, comprising:

[0006] Processing the acceleration signal using two alternating filters to obtain two alternating filtering results;

[0007] Performing weighted summation on the two alternating filtering results using a first weight and a second weight to obtain a weighted summation result;

[0008] The first weight and the second weight are adaptively adjusted based on the weighted summation result and the optimization target, so as to obtain an optimized weighted summation result based on the optimized first weight and the optimized second weight after the adaptive adjustment as the processing result of the acceleration signal.

[0009] According to one embodiment of the present invention, the two alternating filters are determined based on an opening operation and a closing operation, and the opening operation and the closing operation are determined based on a dilation operation and an erosion operation;

[0010] The two alternating filters include an open-close filter and a close-open filter, and the formula is:

[0011]

[0012] The formulas for the opening operation and the closing operation are:

[0013]

[0014] The formulas for the dilation operation and the erosion operation are:

[0015]

[0016] Among them, G oc Indicates an open / closed filter; G co Indicates a closed filter; G o Indicates open operation; G c represents the closing operation; represents the dilation operation; (gΘh)(i) represents the erosion operation; g(i) represents the acceleration signal; h(j) represents the structural element.

[0017] According to an embodiment of the present invention, the adaptively adjusting the first weight and the second weight based on the weighted summation result and the optimization target includes:

[0018] Determining a first weight for the optimization based on the weighted summation result and the optimization target;

[0019] Based on the first weight of the optimization and a constraint condition, a second weight of the optimization is determined.

[0020] According to one embodiment of the present invention, weighted summation is performed according to the following formula:

[0021] G out =P1G oc +P2G co

[0022] Among them, G out represents the weighted summation result; P1 represents the first weight; P2 represents the second weight; G oc and G co Representing the two alternating filtering results;

[0023] The formula of the optimization objective is:

[0024] minσ[G out -Ψ(G out )]

[0025] Among them, σ(·) represents the unbiased estimate of the standard deviation, Ψ(·) represents the moving average, and min represents the minimum value.

[0026] According to one embodiment of the present invention, the formula of the constraint condition is:

[0027]

[0028] According to another aspect of the present invention, a measurement while drilling method is provided, comprising:

[0029] Collecting measurement data using a measurement sensor, wherein the measurement data includes an acceleration signal;

[0030] Processing the acceleration signal using the method according to any of the above embodiments;

[0031] The processing result of the acceleration signal is outputted for feedback control of subsequent wellbore trajectory.

[0032] According to an embodiment of the present invention, in a working mode where only vibration and shock exist in the working environment, the measuring sensor is configured to include only a three-axis accelerometer.

[0033] According to one embodiment of the present invention, in a working mode where there is vibration, shock and rotation in the working environment, the measurement sensor is configured to include a three-axis accelerometer, a three-axis magnetic measurement sensor and a gyroscope; and the processing result of the acceleration signal is output for feedback control of subsequent wellbore trajectory, including:

[0034] The processing result of the acceleration signal and the measurement data collected by the three-axis magnetic measurement sensor and the gyroscope are subjected to multi-data fusion, and the fused data output is used for feedback control of subsequent wellbore trajectory.

[0035] According to an embodiment of the present invention, before collecting the measurement data, the method further includes: calibrating the three-axis accelerometer.

[0036] According to another aspect of the present invention, an acceleration signal processing device for measurement while drilling is provided, comprising:

[0037] A filtering module, configured to process the acceleration signal using two alternating filters to obtain two alternating filtering results;

[0038] A summing module, configured to perform weighted summation on the two alternating filtering results using a first weight and a second weight to obtain a weighted summation result;

[0039] An adaptive adjustment module adaptively adjusts the first weight and the second weight based on the weighted summation result and the optimization target, so as to obtain an optimized weighted summation result based on the optimized first weight and the optimized second weight after the adaptive adjustment as the processing result of the acceleration signal.

[0040] According to another aspect of the present invention, a computer device is provided, comprising:

[0041] at least one processor; and

[0042] A memory, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, the method described in any of the above embodiments is implemented.

[0043] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described in any one of the above embodiments is implemented.

[0044] Through the technical solution of the present invention, the accuracy and stability of the acceleration signal processing results can be improved, the noise component can be reduced, the measurement accuracy of wellbore trajectory parameters such as well inclination can be improved, the drilling direction can be accurately adjusted by feedback, and the drilling rate of high-quality reservoirs can be effectively guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A flow chart showing an acceleration signal processing method for measurement while drilling according to an embodiment of the present invention;

[0047] Figure 2 A comparison diagram of expansion and corrosion processing according to an embodiment of the present invention and original data is shown;

[0048] Figure 3 A comparison diagram of an opening operation, a closing operation and original data according to an embodiment of the present invention is shown;

[0049] Figure 4 A comparison diagram of an open-close operation, a close-open operation and original data according to an embodiment of the present invention is shown;

[0050] Figure 5 A schematic diagram of an optimal weight selection position according to an embodiment of the present invention is shown;

[0051] Figure 6 A comparison diagram of the adaptive weighted alternating morphology filtering and the traditional alternating mixed morphology filtering and the original data according to an embodiment of the present invention is shown;

[0052] Figure 7 The well inclination angle after the adaptive weight alternating morphological filtering process according to an embodiment of the present invention is shown;

[0053] Figure 8 A circuit block diagram of a borehole trajectory measurement circuit in a working mode according to an embodiment of the present invention is shown;

[0054] Fig. 9 A circuit block diagram of a borehole trajectory measurement circuit in working mode 2 according to an embodiment of the present invention is shown;

[0055] Fig.10 A schematic diagram of the installation of a coordinate system according to an embodiment of the present invention is shown (Northeast Sky);

[0056] Fig.11 A schematic diagram of a six-position calibration path according to an embodiment of the present invention is shown;

[0057] Fig.12 A schematic diagram showing an acceleration signal processing device for measurement while drilling according to an embodiment of the present invention;

[0058] Fig.13 A schematic structural diagram of a device for dynamic measurement of wellbore inclination while drilling according to an embodiment of the present invention is shown;

[0059] Fig.14 A schematic diagram of the hardware structure of a device for dynamic measurement of wellbore inclination while drilling according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0060] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0061] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are for distinguishing two non-identical entities with the same name or non-identical parameters. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. The subsequent embodiments will not explain this one by one.

[0062] refer to Figure 1 The present invention provides an acceleration signal processing method for measurement while drilling, comprising:

[0063] S1, using two alternating filters to process the acceleration signal to obtain two alternating filtering results;

[0064] S2, performing weighted summation on the two alternating filtering results using a first weight and a second weight to obtain a weighted summation result;

[0065] S3, adaptively adjusting the first weight and the second weight based on the weighted summation result and the optimization target, so as to obtain an optimized weighted summation result based on the optimized first weight and the optimized second weight after adaptive adjustment as the processing result of the acceleration signal.

[0066] The inventors of the present application realized that, in the strong vibration background during drilling, due to the large amplitude and high frequency of the vibration signal near the drill bit, the drill tool is affected by factors such as vibration, impact and rotation. When a three-axis acceleration sensor is used to measure the drill tool attitude, the measurement accuracy error while drilling is large, thereby failing to guarantee a high-quality reservoir encounter rate.

[0067] Based on this, the present invention proposes to provide an adaptive weight alternating morphology filtering processing method for dynamic measurement of wellbore inclination while drilling, by installing a corresponding measurement sensor near the drill bit, such as a three-axis accelerometer, for collecting acceleration signals of three mutually orthogonal axes, and filtering the acceleration signals by adaptively assigning corresponding weight values ​​of the open-close filter and the close-open filter (an adaptive weight alternating morphology filtering processing method, including steps S1 to S3 as described above), and configuring the filtered acceleration signals for feedback control of subsequent wellbore trajectories. Thus, it is possible to reduce noise components, improve the measurement accuracy of wellbore trajectory parameters such as wellbore inclination, accurately feedback and adjust the drilling direction, and effectively ensure the drilling rate of high-quality reservoirs.

[0068] In some embodiments, the two alternating filters are determined based on an opening operation and a closing operation, and the opening operation and the closing operation are determined based on a dilation operation and an erosion operation;

[0069] The two alternating filters include an open-close filter and a close-open filter, and the formula is:

[0070]

[0071] The formulas for the opening operation and the closing operation are:

[0072]

[0073] The formulas for the dilation operation and the erosion operation are:

[0074]

[0075] Among them, G oc Indicates an open / closed filter; G co Indicates a closed filter; G o Indicates open operation; G c represents the closing operation; represents the dilation operation; (gΘh)(i) represents the erosion operation; g(i) represents the acceleration signal; h(j) represents the structural element. The one-dimensional acceleration signal g(i), whose domain is set to D i ∈{0, 1, 2, ..., I-1}, I represents the signal length; the structural element h(j), whose domain is D j ∈{0, 1, 2, …, J-1}, J represents the length of the structure element, where I ≥ J.

[0076] For one-dimensional signals, the dilation operation can eliminate the noise with downward amplitude in the signal and retain the noise with upward amplitude, while the erosion operation is the opposite. However, the filtering effect relying solely on the above two operations is not ideal, so the opening operation and the closing operation are further adopted. Furthermore, two alternating filters are defined, namely, an opening and closing filter and a closing and opening filter. In order to improve the filtering performance, the present invention proposes an adaptive weighted alternating hybrid filtering method, which assigns corresponding weight values ​​based on the predefined opening and closing filters and the closing and opening filters. In the process of filtering the acceleration signal, the optimal alternating filtering output result is obtained through an adaptive weight adjustment algorithm.

[0077] In some embodiments, the adaptive adjustment of the first weight and the second weight based on the weighted summation result and the optimization target includes: determining the optimized first weight based on the weighted summation result and the optimization target; determining the optimized second weight based on the optimized first weight and constraints.

[0078] In some embodiments, the weighted summation is performed according to the following formula:

[0079] G out =P1G oc +P2G co

[0080] Among them, G out represents the weighted summation result; P1 represents the first weight; P2 represents the second weight; G oc and G co Representing the two alternating filtering results;

[0081] The formula of the optimization objective is:

[0082] minσ[G out -Ψ(G out )]

[0083] Among them, σ(·) represents the unbiased estimate of the standard deviation, Ψ(·) represents the moving average, and min represents the minimum value.

[0084] In some embodiments, the constraint condition is formulated as follows:

[0085]

[0086] The following uses a 10-second uniaxial acceleration data as an example to illustrate the process and advantages of adaptive weighted alternating morphological filtering:

[0087] The one-dimensional acceleration data is expanded and eroded, and the processing results are compared with the original data. Figure 2 shown.

[0088] In order to further improve the filtering effect, open and close operations are defined, and the processing results are compared with the original data. Figure 3 shown.

[0089] Define an alternating hybrid filter, and compare the processed result with the original data. Figure 4 shown.

[0090] Using the adaptive weighted alternating morphological filtering method, the output results G of the open-close filter and the close-open filter are defined oc With G co The weights are P1 and P2 respectively, and the optimization target minσ[G out -Ψ(G out )] satisfies convex optimization, and the optimization process is as follows Figure 5 As shown, the optimal solution is at P1 = 0.529, and the corresponding constraint condition is P2 = 0.471 is obtained, and finally the optimal alternating filtering output result G is obtained by synthesizing the adaptive weight value. out =P1G oc +P2G co The comparison between it and the traditional alternating mixed morphological filtering result is Figure 6 As shown, the traditional hybrid filter is defined as: refer to Figure 6 The result obtained by the adaptive weighted alternating morphological filtering method of the present invention is more accurate than the result obtained by the traditional alternating mixed morphological filtering method.

[0091] Taking about 2000 seconds of triaxial accelerometer data collected in the actual field as an example, the process and advantages of using adaptive weighted alternating morphological filtering to calculate well deviation data are explained.

[0092] According to the above method, the X, Y, and Z axis accelerometer data G are processed separately. x / y / z , and calculate the well inclination angle based on the output of adaptive weighted alternating morphological filtering The final result is compared with the original noisy data. Figure 7 shown.

[0093] The present invention also provides a measurement while drilling method, comprising:

[0094] Collecting measurement data using a measurement sensor, wherein the measurement data includes an acceleration signal;

[0095] Processing the acceleration signal using the method according to any of the above embodiments;

[0096] The processing result of the acceleration signal is outputted for feedback control of subsequent wellbore trajectory.

[0097] A suitable measuring sensor can be configured according to the current working mode. In some embodiments, in a working mode where only vibration and shock exist in the working environment, the measuring sensor is configured to include only a three-axis accelerometer. In this embodiment, the near-bit well inclination measurement system only uses a three-axis accelerometer as a measuring sensor, and filters the acceleration signal by adaptively assigning corresponding weight values ​​to the open-close filter and the open-close filter (an adaptive weight alternating morphology filtering processing method), and then transmits it to the main control CPU via the analog-to-digital conversion port for feedback control of subsequent wellbore trajectory.

[0098] In other embodiments, in a working mode where there is vibration, impact and rotation in the working environment, the measurement sensor is configured to include a three-axis accelerometer, a three-axis magnetic measurement sensor and a gyroscope; and the processing result of the acceleration signal is output for feedback control of the subsequent wellbore trajectory, including: performing multi-data fusion on the processing result of the acceleration signal and the measurement data collected by the three-axis magnetic measurement sensor and the gyroscope, and outputting the fused data for feedback control of the subsequent wellbore trajectory.

[0099] In this embodiment, a three-axis accelerometer, a three-axis magnetic measurement sensor and a gyroscope are used as measurement sensors, which are amplified and filtered. The acceleration signal collected by the three-axis accelerometer is first filtered by adaptively assigning corresponding weight values ​​to the open-close filter and the close-open filter (an adaptive weight alternating morphology filtering method). The acceleration signal after filtering is fused with the measurement data collected by the gyroscope and the magnetic sensor using a multi-data fusion algorithm, and then transmitted to the main control CPU via a synchronous sampling analog-to-digital conversion port for feedback control of subsequent wellbore trajectory.

[0100] The following is an explanation of the two working modes described above (① the working environment only has vibration and shock, ② the working environment has vibration, shock and rotation):

[0101] Working mode 1: The circuit structure diagram of the borehole trajectory measurement in this environment is as follows Figure 8 The circuit structure is mainly composed of four parts, namely ① input and output module; ② power module; ③ acceleration measurement module; ④ main control CPU module.

[0102] ① Input and output module: used to power the system and measure and control the two-way serial communication between circuit boards;

[0103] ②Power module: provides corresponding power for the triaxial accelerometer and acceleration measurement circuit board;

[0104] ③ Acceleration measurement unit: collects three mutually orthogonal axial acceleration signals, uses adaptive weight alternating morphology filtering method to filter and convert them into digital signals, and sends them to the main control CPU module for solution;

[0105] ④ Main control CPU module: The DSP function realizes the filtering of the acceleration signal of the three-axis accelerometer, so as to obtain stable and accurate well inclination and gravity high-side tool face angle under the drilling conditions of small well inclination and strong vibration.

[0106] Working mode 2: The circuit structure diagram of the borehole trajectory measurement in this environment is as follows Fig. 9 As shown in the figure, a three-axis accelerometer, a three-axis magnetic measurement sensor and a gyroscope are used as measurement sensors. After the amplification and filtering module, the acceleration signal of the three-axis accelerometer is first filtered by the adaptive weight alternating morphology filtering method. The processed acceleration signal is fused with the measurement data of the gyroscope and the magnetic sensor using a multi-data fusion algorithm, and then sent to the main control CPU module through the synchronous sampling analog-to-digital conversion port for feedback control of subsequent wellbore trajectory.

[0107] The measurement of borehole trajectory parameters is divided into static measurement and dynamic measurement. Static measurement refers to measuring static borehole trajectory parameters when the drill bit is not rotating or vibrating, that is, not drilling. Although this method can reduce vibration noise, it cannot obtain real-time and continuous dynamic parameters. Dynamic measurement refers to the strong vibration background during drilling. Due to the large amplitude and high frequency of the vibration signal near the drill bit position, the drill bit is affected by factors such as vibration, impact and rotation. The three-axis acceleration sensor is used to measure the attitude of the drill bit. The measurement accuracy error is large, and thus the high-quality reservoir encounter rate cannot be guaranteed. Based on this, in order to reduce the noise component and improve the measurement accuracy of the accelerometer, in the above embodiment, a corresponding measurement sensor, such as a three-axis accelerometer, is installed near the drill bit position to collect acceleration signals of three mutually orthogonal axes, and the acceleration signal is filtered by adaptively assigning corresponding weight values ​​to the open-close filter and the open-close filter (an adaptive weight alternating morphology filtering processing method), and the filtered acceleration signal is configured for feedback control of subsequent borehole trajectory. Thus, the noise component can be reduced, the measurement accuracy of wellbore trajectory parameters such as well deviation can be improved, the drilling direction can be accurately adjusted by feedback, and the drilling rate of high-quality reservoirs can be effectively guaranteed.

[0108] In some embodiments, before collecting the measurement data, the method further includes: calibrating the three-axis accelerometer.

[0109] The three-axis gravity accelerometer is a key sensor for high-precision measurement of well inclination in vertical drilling tools under small well inclination construction conditions. It provides the most critical well inclination attitude measurement basis for the active deflection correction strategy of vertical drilling tools. It plays an irreplaceable key role in preventing deflection and fast drilling in vertical drilling operations. However, due to the characteristics of the three-axis accelerometer sensor itself, it cannot provide error-free measurement results like the ideal three-axis accelerometer in theory. The actual accelerometer, whether it is a MEMS three-axis accelerometer or a quartz pendulum accelerometer. There are various errors in their outputs, including zero point error, scale factor error, non-orthogonal installation error, error caused by temperature drift, and measurement error caused by the movement of the carrier on which the accelerometer is installed. In order to correct these errors, calibration tests are required in the three-axis turntable and temperature control box to correct the sensor errors, so that each axis of the three-axis accelerometer can output the correct gravity acceleration component, and finally the three-axis acceleration component is used to calculate the precise well inclination value of the tool carrier on which the accelerometer is installed.

[0110] The combined use of a three-axis gyroscope, a three-axis accelerometer, and a three-axis geomagnetic magnetometer is very important for high-precision measurement of well inclination in vertical drilling. The output of the gyroscope and geomagnetic magnetometer can be used to compensate and correct the measurement of three-axis acceleration in the operating environment of vertical drilling. The cross-axis sensitivity, bias offset, deterministic noise / error, random noise, and temperature effect of the three-axis accelerometer are key factors affecting the measurement accuracy of the three-axis acceleration sensor. To correct these errors of the three-axis accelerometer, various error correction and calibration algorithms are required. By mathematically calculating the measurement results of each output axis of the three-axis acceleration, the error amount mixed into the measurement results of each output axis of the three-axis accelerometer is eliminated.

[0111] The reason for the zero bias error is that each time the accelerometer and related measurement circuits are powered on, the measurement data on each axis of the three-axis accelerometer will produce a fixed offset output. And the zero offset error of each axis is different, so it is necessary to calibrate the zero offset of each axis of the three-axis accelerometer.

[0112] Non-orthogonality error: Each measuring axis of an ideal three-axis accelerometer should be orthogonal to each other, so when one of the axes is completely parallel to one axis of the accelerometer, the other output axes of the accelerometer are perpendicular to the direction of gravity acceleration and therefore have no measurement output.

[0113] The scale factor of the accelerometer is the linearity between the acceleration of gravity and the output value of each axis of the three-axis accelerometer when it is close to the midpoint of the measurement range and close to the limit of the measurement range within the measurement range of the accelerometer. It is usually expressed in mv / g. The main reason is the sensitivity of the accelerometer sensor. Due to the influence of production or welding, the scale factor changes accordingly. It can be understood that the theoretical output slope has changed. Like the zero point error, each axis of the accelerometer also has a corresponding scale factor error.

[0114] Causes of orthogonal error: In addition to the zero bias error and scale factor error of the three-axis accelerometer, when it is installed inside the vertical drilling tool body, there is an angular deviation between the three axes of the three-axis accelerometer and the coordinate axis of the vertical drilling tool carrier itself. This error is caused by installation. The error during installation will produce errors of installation angle θ, This orthogonal installation error requires the relevant orthogonal calibration coefficient matrix to correct the orthogonal error so that the triaxial accelerometer coincides with the vertical drilling tool body coordinate system.

[0115] All these errors of the three-axis accelerometer also need to be corrected on the three-axis turntable.

[0116] In some embodiments, the calibrating the three-axis accelerometer includes: determining a conversion matrix, and configuring a three-axis accelerometer calibration model to perform six-position discrete calibration on the three-axis accelerometer.

[0117] In some embodiments, linear modeling is performed for a three-axis accelerometer calibration.

[0118] The mathematical model for static input and output calibration of a single accelerometer is:

[0119] N m =K m f m +A m0 ,m=x,y,z (1)

[0120] Where N m is the pulse output of the accelerometer, f m K is the specific force of the accelerometer (usually we say "using an accelerometer to measure the acceleration of the carrier", but this statement is not accurate, because the accelerometer does not measure the acceleration of the carrier, but the sum of the absolute acceleration of the carrier relative to the inertial space and the gravitational acceleration, which is called "specific force"), m is the accelerometer scale factor, A m0 To calculate the accelerometer zero bias, the three-axis integrated quartz flexible accelerometer model is combined and written in vector form:

[0121]

[0122] Where N a =[N x N y N z ] T ; K a =diag[K x K y K z ] T ;

[0123] f a =[f x f y f z ] T (3)

[0124] In the formula, f a is the specific force vector in the accelerometer coordinate system (o a -x a y a z a , a system), while the inertial navigation solution requires the force vector in the carrier coordinate system (o b -x b y b z b , projection f under system b) b , f b With f a The following relationship exists:

[0125]

[0126] In the formula, is the conversion matrix from system b to system a. Substituting equation (4) into equation (2), the mathematical model of static input and output calibration of the three-axis integrated quartz flexible accelerometer can be obtained as follows:

[0127]

[0128] If K in formula (5) is known a , Parameters such as accelerometer output N a Obtain the specific force f required for navigation solution b .

[0129] In general, it is difficult to directly use geometric methods to accurately determine the direction of the accelerometer input axis in the linear modeling of the three-axis accelerometer calibration, and it is impossible to obtain the transformation matrix. Therefore, the calibration model of the triaxial accelerometer must be obtained through certain testing methods.

[0130] In some embodiments, for the transformation matrix The b system is a spatial Cartesian rectangular coordinate system, and the a system is a spatial Cartesian oblique coordinate system. Assuming that the origins of the a system and the b system coincide, the two coordinate systems are installed as follows: Fig.10 shown.

[0131] i a , j a , k a represents the unit vector along the axis of the a system, i b , j b , k b represents the unit vector along the axis of the b system. The basis transformation formula from the b system to the a system is:

[0132]

[0133] The coordinate transformation matrix from system b to system a is:

[0134]

[0135] In the formula, (m=i,j,k;n=i,j,k) is m a With n v The angle between. It is generally a non-orthogonal matrix, and its characteristic is that each row vector is a unit vector. Theoretically, the higher the order of the error mathematical model and the more terms it has, the more accurate the description of the error and the better the compensation effect, but the more difficult it is to calibrate and calculate the error coefficient. Therefore, in order to reduce the difficulty of the calibration experiment, starting from the actual engineering installation method of the accelerometer, Make the following two forms of simplification.

[0136] Simplification 1: The accelerometer input axis is installed at a small angle to the corresponding axis of the carrier coordinate system b:

[0137]

[0138]

[0139] Simplified to:

[0140]

[0141] The transformation matrix in equation (7), equation (9), and equation (11) is Each element Cij (i, j = 1, 2, 3) in the coordinate system of the quartz flexible accelerometer has a specific physical meaning. When using equations (9) and (11) to design the accelerometer calibration path, Cij (i≠j) ≤ π / 36 (i.e., 5°) must be satisfied.

[0142] In some embodiments, a six-position discrete calibration is performed.

[0143] See also Fig.11 ,In order to verify the correctness of the three-axis accelerometer calibration model and the ,influence of the three-axis accelerometer calibration model error on the ,navigation results, a six-position discrete calibration path is designed to ,calibrate the three-axis accelerometer.

[0144] In order to verify the correctness of the accelerometer calibration model and the influence of the calibration model error on the navigation results, a six-position calibration path is designed to calibrate the three-axis integrated accelerometer.

[0145] In order to unify the calibration variable form with the traditional three-axis accelerometer, the coordinate transformation matrix Written in the following form:

[0146]

[0147] Table 1 Six-position calibration path of three-axis turntable

[0148] Serial number Tool face (°) Well inclination(°) Azimuth (°) AccX(g) AccY(g) AccZ(g) 1 270 90 0 0 -1 0 2 90 90 0 0 +1 0 3 90 0 0 +1 0 0 4 270 180 0 -1 0 0 5 180 90 90 0 0 -1 6 0 90 270 0 0 +1

[0149] Substituting equation (12) into equation (5), the mathematical model of static input and output calibration of the three-axis integrated accelerometer can be obtained as follows:

[0150]

[0151] The three-axis accelerometer pulse outputs at six locations are shown in Table 2.

[0152] Table 2 Acceleration pulse output

[0153]

[0154] According to the three-axis accelerometer pulse output at six locations, the input and output model parameters of the three-axis accelerometer can be calculated (g = 9.80147m / s in Beijing area 2 )

[0155]

[0156]

[0157]

[0158]

[0159] when When the simplified forms shown in equations (9) and (11) are used, the mathematical model for calibration of the three-axis accelerometer shown in equation (13) is simplified to equations (18) and (19) respectively:

[0160]

[0161]

[0162] The pulse output of the triaxial accelerometer using the simplified model is at six locations, as shown in Table 3.

[0163] Table 3 Triaxial accelerometer pulse output under simplified model

[0164]

[0165] According to the three-axis accelerometer pulse output in Table 3, the zero position and scale factor of the simplified model for accelerometer calibration can be calculated.

[0166]

[0167]

[0168] The installation error in the three-axis integrated accelerometer mathematical model using simplified model 1 is:

[0169]

[0170] The installation error in the three-axis integrated accelerometer mathematical model using simplified model 2 is:

[0171]

[0172] Substituting the calibration parameters of the three-axis accelerometer calculated by equations (14) to (17) and (20) to (23) into equations (13), (18) and (19), the input specific force f of the accelerometer can be calculated according to the pulse output of any acceleration. b .

[0173] Calculate the input specific force f of the accelerometer using equation (13), equation (18) or equation (19): b The following inverse matrix must be found in advance:

[0174]

[0175] Then calculate f as follows b The three components f x 、f y and f z .

[0176]

[0177] or

[0178]

[0179] Since the terms in the inverse matrix and A x0 , A y0 and A z0 are all quantities that change with temperature, so the inverse matrices at different temperatures are calculated in advance according to the results of temperature calibration, and then the items in the inverse matrix and A are x0 , A y0 and A z0 Perform polynomial regression with temperature changes. In real-time calculation, first calculate the inverse matrix and A according to the current temperature sampling value. x0 , A y0 and A z0 , and then calculate f according to formula (25) b The three components f x 、f y and f z .

[0180] Through calibration, the vertical drilling system, the zero bias error, orthogonal installation error and other error effects of the well inclination measurement unit can be eliminated, and finally the high-precision well inclination measurement function of the vertical drilling tool can be realized.

[0181] refer to Fig.12 The present invention further proposes an acceleration signal processing device 10 for measurement while drilling, comprising:

[0182] A filtering module 12 is configured to process the acceleration signal using two alternating filters to obtain two alternating filtering results;

[0183] A summing module 14 is configured to perform weighted summation on the two alternating filtering results using a first weight and a second weight to obtain a weighted summation result;

[0184] The adaptive adjustment module 16 adaptively adjusts the first weight and the second weight based on the weighted summation result and the optimization target, so as to obtain an optimized weighted summation result based on the optimized first weight and the optimized second weight after adaptive adjustment as the processing result of the acceleration signal.

[0185] The present invention further proposes a computer device, comprising: at least one processor; and a memory, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, the method described in any of the above embodiments is implemented.

[0186] The present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described in any of the above embodiments is implemented.

[0187] refer to Fig.13 , Fig.13A schematic diagram of a device for dynamic measurement of wellbore inclination while drilling provided in an embodiment of the present application. The device comprises:

[0188] A data acquisition unit 100, for adapting a measurement sensor for acquiring measurement data according to a current working mode, wherein the measurement data includes an acceleration signal;

[0189] The first processing unit 200 is used to filter the acceleration signal by adaptively assigning corresponding weight values ​​to the open-close filter and the close-open filter;

[0190] The second processing unit 300 is used to configure the filtered acceleration signal for feedback control of subsequent wellbore trajectory.

[0191] refer to Fig.14 , Fig.14 A schematic diagram of the hardware structure of a dynamic measurement device for inclination while drilling provided in an embodiment of the present application. The automated testing device 2 includes a processor 21, a memory 22, an input device 23, and an output device 24. The processor 21, the memory 22, the input device 23, and the output device 24 are coupled via a connector, and the connector includes various interfaces, transmission lines, or buses, etc., which are not limited in the embodiments of the present application. It should be understood that in each embodiment of the present application, coupling refers to mutual connection in a specific manner, including direct connection or indirect connection through other devices, for example, through various interfaces, transmission lines, buses, etc.

[0192] The processor 21 may be one or more graphics processing units (GPUs). When the processor 21 is a GPU, the GPU may be a single-core GPU or a multi-core GPU. Optionally, the processor 21 may be a processor group consisting of multiple GPUs, and the multiple processors are coupled to each other via one or more buses. Optionally, the processor may also be other types of processors, etc., which are not limited in the embodiments of the present application.

[0193] The memory 22 can be used to store computer program instructions and various computer program codes including the program code for executing the program code of the present application. Optionally, the memory includes but is not limited to random access memory (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM), or portable read only memory (CD-ROM), which is used for related instructions and data.

[0194] The input device 23 is used to input data and / or signals, and the output device 24 is used to output data and / or signals. The output device 23 and the input device 24 can be independent devices or an integrated device.

[0195] It can be understood that in the embodiment of the present application, the memory 22 can not only be used to store related instructions, and the embodiment of the present application does not limit the specific data stored in the memory.

[0196] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the embodiments of the present invention as described above, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. A method for processing acceleration signals for measurement while drilling, characterized in that: include: Processing the acceleration signal using two alternating filters to obtain two alternating filtering results; Performing weighted summation on the two alternating filtering results using a first weight and a second weight to obtain a weighted summation result; The first weight and the second weight are adaptively adjusted based on the weighted summation result and the optimization target, so as to obtain an optimized weighted summation result based on the optimized first weight and the optimized second weight after the adaptive adjustment as the processing result of the acceleration signal.

2. The method according to claim 1, characterized in that The two alternating filters are determined based on an opening operation and a closing operation, and the opening operation and the closing operation are determined based on a dilation operation and an erosion operation; The two alternating filters include an open-close filter and a close-open filter, and the formula is: The formulas for the opening operation and the closing operation are: The formulas for the dilation operation and the erosion operation are: Among them, G oc Represents an open / closed filter; G oc Indicates a closed filter; G o Indicates open operation; G c represents the closing operation; represents the dilation operation; (gΘh)(i) represents the erosion operation; g(i) represents the acceleration signal; h(j) represents the structural element.

3. The method according to claim 1, characterized in that The adaptively adjusting the first weight and the second weight based on the weighted summation result and the optimization target includes: Determining a first weight of the optimization based on the weighted summation result and the optimization target; Based on the optimized first weight and a constraint condition, a second optimized weight is determined.

4. The method according to claim 3, characterized in that The weighted summation is performed according to the following formula: G out =P1G oc +P2G co Among them, G out represents the weighted summation result; P1 represents the first weight; P2 represents the second weight; G oc and G co Representing the two alternating filtering results; The formula of the optimization objective is: minσ[G out -Ψ(G out )] Among them, σ(·) represents the unbiased estimate of the standard deviation, Ψ(·) represents the moving average, and min represents the minimum value.

5. The method according to claim 4, characterized in that The constraint condition formula is:

6. A measurement while drilling method, characterized in that: include: Collecting measurement data using a measurement sensor, wherein the measurement data includes an acceleration signal; Processing the acceleration signal using the method according to any one of claims 1 to 5; The processing result of the acceleration signal is outputted for feedback control of subsequent wellbore trajectory.

7. The method according to claim 6, characterized in that In a working mode where only vibration and shock exist in the working environment, the measuring sensor is configured to include only a three-axis accelerometer.

8. The method according to claim 6, characterized in that In a working mode where there is vibration, shock and rotation in the working environment, the measuring sensor is configured to include a three-axis accelerometer, a three-axis magnetic measurement sensor and a gyroscope; and the processing result of the acceleration signal is output for feedback control of subsequent wellbore trajectory, including: The processing result of the acceleration signal and the measurement data collected by the three-axis magnetic measurement sensor and the gyroscope are subjected to multi-data fusion, and the fused data output is used for feedback control of subsequent wellbore trajectory.

9. The method according to claim 7 or 8, characterized in that: Before collecting the measurement data, the method further comprises: calibrating the three-axis accelerometer.

10. An acceleration signal processing device for measurement while drilling, characterized in that: include: A filtering module, configured to process the acceleration signal using two alternating filters to obtain two alternating filtering results; A summing module, configured to perform weighted summation on the two alternating filtering results using a first weight and a second weight to obtain a weighted summation result; An adaptive adjustment module adaptively adjusts the first weight and the second weight based on the weighted summation result and the optimization target, so as to obtain an optimized weighted summation result based on the optimized first weight and the optimized second weight after the adaptive adjustment as the processing result of the acceleration signal.

11. A computer device comprising: at least one processor; as well as A memory storing a computer program executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the program.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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