Well deviation measuring method under near-bit strong vibration condition and electronic equipment

Abstract

The invention relates to the technical field of well drilling measurement, in particular to a well deviation measurement method and electronic equipment under the condition of near-bit strong vibration, and the method comprises the following steps: respectively carrying out five-point three-time smooth iteration on x and y transverse orthogonal axes and z normal axes of data after temperature excursion processing of an inclinometry sensor in a space, and then substituting the data into a well deviation formula to obtain a well deviation data model; and the average value is taken to correct the inclination measurement signals under the near-bit strong vibration condition, and corrected well inclination data under the near-bit strong vibration condition are obtained. According to the method, the well deviation can be measured under the near-bit strong vibration condition in the drilling process, the accuracy of well deviation calculation under the near-bit strong vibration condition is improved, misoperation of a deviation correction system is effectively avoided, and the deviation correction effect is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of drilling measurement, and is an inclination measurement method and an electronic device under strong vibration conditions near the bit. Background Art

[0002] After investigating the current situation of world oil and gas resources and the future development trend, the oil drilling industry will face more complex geological conditions and harsher natural environments. These have put forward higher requirements for downhole process monitoring and precision control of oil and gas drilling. In addition, with the in-depth exploration and development of domestic oilfields, the development of difficult-to-produce reserves such as thin oil reservoirs, fault block oil reservoirs, marginal oil reservoirs, and remaining oil reservoirs has been carried out on a large scale, and the requirements for trajectory control precision have also been increasing day by day. In order to increase the exposed area of the oil reservoir in the wellbore and improve the oil well production, it is required that the wellbore trajectory accurately penetrate the reservoir. The inclination measurement parameters near the bit are very important information to ensure the drilling quality and achieve the above purposes.

[0003] However, in the prior art, the inclination measurement under strong vibration conditions near the bit has not been well solved. At present, the measurement tools for wireless measurement while drilling in China are usually located behind the whipstock tool, and the measurement point is far from the bit. The trajectory parameters at the bit position cannot be actually measured and can only be obtained by prediction. For the measurement of the wellbore trajectories of conventional vertical wells, directional wells, and horizontal wells, the inclination, azimuth, and other parameters are measured within a few meters from the bit position at the bottom of the well and are used for trajectory calculation and determination of the wellbore spatial position. However, with the improvement of the whipstock tool's inclination ability, the downhole motion state is complex, which brings great difficulties to accurately determining the inclination angle at the bit position.

[0004] Some integrated near-bit measurement instruments are extremely expensive and have very poor versatility, and some foreign technologies are blocked to China. Therefore, the acquisition and accuracy of inclination measurement information under strong vibration conditions near the bit have become a hot issue concerned in the domestic and foreign drilling engineering fields. Summary of the Invention

[0005] The present invention provides an inclination measurement method and an electronic device under strong vibration conditions near the bit, which overcome the above deficiencies of the prior art and can effectively solve the problem that the prior art has not well solved the inclination measurement under strong vibration conditions near the bit.

[0006] One of the technical solutions of the present invention is achieved by the following measures: an inclination measurement method under strong vibration conditions near the bit, including the following steps: performing five-point cubic smoothing iteration on the data after temperature drift processing of the inclination sensor for the x and y transverse orthogonal axes and the z normal axis in space respectively, substituting the result into the inclination formula to obtain an inclination data model, and taking its average value to correct the inclination signal under strong vibration conditions near the bit to obtain the corrected inclination data under strong vibration conditions near the bit.

[0007] The following is a further optimization and / or improvement of one of the above-mentioned inventive technical solutions:

[0008] The data collected by the well inclination sensor can be corrected for temperature drift using the least squares fitting method to obtain first-level sampled data;

[0009] Perform five-point cubic smoothing iteration on the first-level sampled data for the x, y horizontal orthogonal axes, and z normal axis in space respectively, and substitute them into the well inclination calculation formula to obtain second-level sampled data;

[0010] Perform mean processing on every five of all the second-level sampled data within the set sampling time period to obtain third-level sampled data;

[0011] Judge whether |INC 测量 -INC 真实 | < 0.1° holds, where INC 测量 is the measured third-level sampled data, and INC 真实 is the true third-level sampled data;

[0012] If it holds, use the third-level sampled data to obtain the full-rotation well inclination data for the set time period;

[0013] If it does not hold, discard the third-level sampled data for this time period and continue the five-point cubic smoothing iteration.

[0014] The above may specifically include the following steps:

[0015] Perform temperature drift correction on the inclinometer sensor at different temperatures using the least squares fitting method. Assume that the input-output polynomial of the inclinometer sensor at a fixed temperature is:

[0016]

[0017] Calculate the normalization matrix according to the least squares fitting method:

[0018] A = (a 0 a 1 … a N )

[0019]

[0020] Assume that a i at different temperatures is a polynomial function of temperature t:

[0021]

[0022] Summarize as:

[0023]

[0024] All bji Arrange into a column vector:

[0025]

[0026] Define an auxiliary query matrix:

[0027]

[0028] According to matrix b and F, the temperature-corrected measurement value of the sensor is:

[0029]

[0030] According to the formula β = F·b, the true value of the sensor measurement is

[0031] Obtain the first-level sampled data after temperature drift correction of the data collected by the inclinometer sensor using the least squares fitting method;

[0032] For the first-level discrete data sequence x(nT s ) collected after the temperature drift correction algorithm of the well inclination sensor, perform smoothing processing. Let the three-axis acceleration sampled values at 2N + 1 equally spaced points be:

[0033]

[0034] Suppose to use an m-degree polynomial: A = a 0 + a 1 t + … + a m t m

[0035] Find a set of appropriate coefficients a j (j = 0, 1, …, m), substitute all points (t i , A i ) into the polynomial, and there are 2N + 1 equations

[0036] Calculate to obtain:

[0037] Since the smoothed curve does not necessarily pass through all points (t i , A i ), these equations are not all zero. According to the least squares principle, for (2N + 1) groups of data (t i , A i ), find its best coefficients a j , which are the a j values that can minimize the sum of the squares of the error R j ; Suppose:

[0038] Normalize to:

[0039] According to the formula When N = 2 and m = 3, solve for a 0 , a 1 , a 2 , a 3 , let t = 0, ±1, ±2 and calculate to obtain the five - point cubic smoothing formula:

[0040] x and y represent the horizontal orthogonal axes in space, and z represents the normal longitudinal axis in space;

[0041] According to the well deviation calculation formula Perform five - point cubic smoothing iteration on the first - level sampled data of the x, y, and z axes in space respectively to obtain the second - level sampled data:

[0042]

[0043]

[0044]

[0045]

[0046]

[0047] Perform mean processing on every five of all the second - level sampled data in the set sampling time period to obtain the third - level sampled data:

[0048]

[0049] According to the formula Judge that |INC 测量 -INC 真实 | < 0.1° within the sampling period. If this requirement is not met, discard the third - level sampled data within this time period and continue the five - point cubic smoothing iteration; if this requirement is met, use the third - level sampled data to output the full - rotation well deviation data for the set time period.

[0050] The five - point cubic smoothing algorithm can be used to remove periodic noise or reduce high - frequency interference and smooth the signal curve.

[0051] The second technical solution of the present invention is achieved by the following measures: An electronic device includes a memory and a processor. A program that can run on the processor is stored on the memory. When the processor executes the program, the well deviation measurement method under the condition of strong vibration near the bit described above is implemented.

[0052] The third technical solution of the present invention is achieved through the following measures: A storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned well inclination measurement method under strong vibration conditions near the bit.

[0053] The present invention can measure the well inclination under strong vibration conditions near the bit during the drilling process, can effectively correct the data processing after the temperature drift of the inclinometer sensor, and can effectively eliminate the interference of vibration and noise near the bit on the acceleration data of the transverse orthogonal axis and the normal axis in the space of the well inclination data measurement signal, avoiding the distortion of the measurement signal, further improving the accuracy of well inclination calculation under strong vibration conditions near the bit, effectively avoiding misoperation of the deviation correction system, and ensuring the deviation correction effect. The present invention is mainly applied to process the triaxial accelerometer data collected by the inclinometer sensor under strong vibration conditions near the bit, and makes the measured well inclination more accurate through this optimization algorithm. The present invention solves the problem of well inclination measurement under strong vibration conditions near the bit underground. On the basis of not abandoning the existing instruments, the well inclination sensor is placed as close to the bit as possible for measurement, and the measured parameters are optimized and refined by using an algorithm, and finally the well inclination measurement under strong vibration conditions near the bit is realized. The present invention can effectively optimize and refine the measured parameters and realize the well inclination measurement under strong vibration near the bit. It breaks through the key of traditional static well inclination measurement, will improve the measurement technologies of vertical wells, directional wells, and horizontal wells, improve the trajectory control accuracy, increase the drilling speed, reduce the drilling cost, meet the needs of oilfield reserve increase and production and economic benefit improvement, and has a very broad application prospect. Brief Description of the Drawings

[0054] Appendix Figure 1 It is a schematic flowchart of an embodiment of the present invention. Detailed Embodiments

[0055] The present invention is not limited by the following embodiments, and the specific implementation manners can be determined according to the technical solution of the present invention and the actual situation.

[0056] The following further describes the present invention in conjunction with embodiments:

[0057] Embodiment 1: As shown in the appendix Figure 1 The well inclination measurement method under strong vibration conditions near the bit includes the following steps: The data after temperature drift processing of the inclinometer sensor is respectively subjected to five-point cubic smoothing iteration on the x and y transverse orthogonal axes and the z normal axis in space, and then substituted into the well inclination formula to obtain a well inclination data model, and the mean value is taken to correct the inclinometer signal under strong vibration conditions near the bit, and the corrected well inclination data under strong vibration conditions near the bit is obtained.

[0058] As shown in the appendix Figure 1As shown in the figure, first, the data collected by the well inclination sensor is corrected for temperature drift using the least squares fitting method to obtain the first-level sampled data. The five-point cubic smoothing iteration of the first-level sampled data is performed on the x and y horizontal orthogonal axes and the z normal axis in the space respectively, and the second-level sampled data is obtained by substituting into the well inclination calculation formula. The mean value of every five of all the second-level sampled data in the set sampling time period is processed to obtain the third-level sampled data. Determine whether |INC 测量 -INC 真实 | < 0.1° holds, where INC 测量 is the measured third-level sampled data, and INC 真实 is the true third-level sampled data; if it holds, the full rotation well inclination data for the set time period is obtained using the third-level sampled data; if it does not hold, the third-level sampled data for that time period is discarded and the five-point cubic smoothing iteration is continued.

[0059] The embodiment of the present invention can realize the measurement of well inclination under the condition of strong vibration near the bit during the drilling process, can effectively correct the data processing after the temperature drift of the inclinometer sensor, and can effectively eliminate the interference of vibration and noise near the bit on the acceleration data of the horizontal orthogonal axis and the normal axis in the space of the well inclination data measurement signal, avoid the distortion of the measurement signal, further improve the accuracy of well inclination calculation under the condition of strong vibration near the bit, effectively avoid misoperation of the hole deviation correction system, and ensure the hole deviation correction effect. The embodiment of the present invention is mainly applied to the processing of the triaxial accelerometer data collected by the inclinometer sensor under the condition of strong vibration near the bit, and makes the measured well inclination more accurate through this optimized algorithm. The embodiment of the present invention solves the problem of well inclination measurement under the condition of strong vibration near the bit underground. On the basis of not discarding the existing instruments, the inclinometer sensor is placed as close to the bit as possible for measurement, and the measured parameters are optimized and refined using the algorithm, and finally the well inclination measurement under the condition of strong vibration near the bit is realized.

[0060] Embodiment 2: As shown in the appendix Figure 1 The embodiment of the present invention discloses a specific well inclination measurement method under the condition of strong vibration near the bit, including the following steps:

[0061] Sensor temperature drift refers to the stability change of the resistance change rate of the sensor during operation with the change of temperature. The inclinometer sensor is no exception. First, without considering the influence of temperature, at a certain fixed temperature, let the input and output values of the inclinometer sensor be represented by a polynomial function:

[0062]

[0063] In the formula, X is the output of the sensor, and α is the true value of the physical quantity measured by the sensor; the response of the sensor is represented as a monotonic continuous function, and is approximated to any precision using a polynomial function; a iIt can be calculated by using the least squares fitting method based on experimental data. For an Nth-degree polynomial, at least N + 1 data points are required for calculation.

[0064] The above formula can be written in matrix form:

[0065]

[0066] A = (a 0 a 1 … a N )

[0067]

[0068] Then consider the influence of temperature. At different temperatures, a i in the above formula will change. That is to say, a i is a function of temperature t. Similarly, it can also be approximated by a polynomial function:

[0069]

[0070] The final output can then be expressed as:

[0071]

[0072] The most intuitive way is to calculate a series of polynomial coefficients a i (t) by using the least squares fitting method at a series of different temperatures; a i (t) is an Mth-degree polynomial function. Therefore, we need to calculate at least M + 1 coefficients at different temperatures, that is, we need to perform at least M + 1 polynomial fittings first; then fit b i (t) according to a ji , which requires N + 1 polynomial fittings; a total of at least M + N + 2 polynomial fittings need to be performed before and after to find all the coefficients; once b ji is determined, the temperature correction work can be completed.

[0073] Another method is to directly find all b ji . Arrange all b ji into a column vector:

[0074]

[0075] Define an auxiliary query matrix:

[0076]

[0077] The subscript in the above formula indicates the number of the measurement value, x pDenotes the p-th measured value.

[0078] Then the measured value of the sensor after temperature correction is:

[0079]

[0080] And the true value measured by the sensor is:

[0081]

[0082] Then our goal is to find b such that the vector α - β is minimized. If this minimum is in the sense of the 2-norm, it is the least squares fitting. This is equivalent to finding the optimal solution in the sense of the least squares for the following linear algebraic equation.

[0083] β = F·b

[0084] In the embodiment of the present invention, the five-point cubic smoothing algorithm is a classical signal smoothing algorithm, mainly used to remove periodic noise or reduce high-frequency interference and smooth the signal curve. Its functions include the following aspects: (1) Smoothing the signal curve: The five-point cubic smoothing algorithm smooths the signal, eliminates the noise and other unnecessary fluctuations in the signal curve, makes the signal curve smoother, which helps to generate a more accurate signal prediction model. (2) Filtering periodic noise: The five-point cubic smoothing algorithm has a good filtering effect on periodic noise. This is because this algorithm considers the weighted average of adjacent five points, reduces the influence of periodic noise, and makes the signal more reliable and accurate. (3) Weakening high-frequency noise: Due to different sampling periods and noise amplitudes, high-frequency noise often causes great interference to the signal; the five-point cubic smoothing algorithm flattens the contribution of the original signal and its neighboring points through polynomial fitting, thereby reducing the noise of the signal in the high-frequency domain and improving the quality of the signal. (4) Data smoothing processing: The five-point cubic smoothing algorithm can smooth the data within a small window, reduce the error caused by random fluctuations; the average sensitivity of this algorithm is low, so it can prevent the data from changing too suddenly and is more easy to identify and predict.

[0085] Perform smoothing processing on the discrete data sequence x(nT s ) that has been corrected by the well deviation sensor temperature drift correction algorithm. Let the acceleration three-axis sampling values at 2N + 1 equally spaced points sampled be:

[0086]

[0087] Also assume that an m-th degree polynomial:

[0088] A = a 0 + a 1 t + … + a m tm

[0089] To smooth the obtained sampled values, in order for the polynomial to smooth the sampled discrete values well, a set of appropriate coefficients a must be found j (j = 0, 1, …, m), substituting all points (t i , A i ) into the polynomial, there are 2N + 1 equations

[0090]

[0091] Since the smoothed curve does not necessarily pass through all points (t i , A i ), these equations are not all zero. According to the least squares principle, for the (2N + 1) sets of data (t i , A i ), finding the best coefficients a j means finding those a j values that can minimize the sum of the squares of the error R j .

[0092] Let:

[0093]

[0094] That is:

[0095]

[0096] When N = 2, m = 3, solve for a 0 , a 1 , a 2 , a 3 , and let t = 0, ±1, ±2 to obtain the five - point cubic smoothing formula:

[0097]

[0098] Well deviation calculation formula

[0099] Here, x and y represent the transverse orthogonal axes in space, and z represents the normal longitudinal axis in space.

[0100] During the drilling process, the inclinometer sensor is subjected to the forces and noise interference generated by the strong vibration near the bit. Among them, the vibration is divided into transverse vibration and normal vibration, resulting in very large errors in dynamic measurement under drilling conditions.

[0101] Performing five - point cubic smoothing processing on the vibrations of the x, y, and z axes respectively, the following five well deviation data smoothing points can be obtained.

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] We perform mean processing on these 5 adjacent well inclination data

[0109] If |INC 测量 -INC 真实 | < 0.1°, we consider that the result can be output as the true value, then this algorithm has a good calibration function for well inclination measurement under the condition of strong vibration near the bit.

[0110] The embodiment of the present invention can effectively optimize and accurately measure the measured parameters, and realize the well inclination measurement under strong vibration near the bit. It breaks through the key of traditional static well inclination measurement, will improve the measurement technologies of vertical wells, directional wells and horizontal wells, improve the trajectory control accuracy, increase the drilling speed, reduce the drilling cost, meet the needs of increasing oil reserves and production and improving economic benefits in oil fields, and has a very broad application prospect. It promotes a qualitative leap in the downhole near-bit measurement technology for drilling, and plays a great role in promoting the development of China's drilling technology towards low cost, high efficiency and high precision.

[0111] Embodiment 3: As shown in the appendix Figure 1 The embodiment of the present invention discloses a well inclination measurement method under the condition of strong vibration near the bit, which specifically includes the following steps:

[0112] Perform temperature drift correction on the inclinometer sensor at different temperatures by using the least square fitting method. Let the input-output polynomial of the inclinometer sensor at a fixed temperature be:

[0113]

[0114] Calculate the normalization matrix according to the least square fitting method:

[0115] A = (a 0 a 1 …a N )

[0116]

[0117] Let a i at different temperatures be a polynomial function of temperature t:

[0118]

[0119] Summarize as:

[0120]

[0121] Arrange all b ji into a column vector:

[0122]

[0123] Define an auxiliary query matrix:

[0124]

[0125] According to matrix b and F, the measured value of the sensor after temperature correction is:

[0126]

[0127] According to the formula β = F·b, the true value of the sensor measurement is

[0128] Obtain the first-level sampled data after temperature drift correction of the data collected by the inclinometer sensor using the least squares fitting method;

[0129] Smooth the first-level discrete data sequence x(nT s ) collected after the temperature drift correction algorithm of the well inclination sensor. Assume that the three-axis acceleration sampled values at 2N + 1 equally spaced points are:

[0130]

[0131] Assume using an m-degree polynomial: A = a 0 + a 1 t + … + a m t m

[0132] Find a set of appropriate coefficients a j (j = 0, 1, …, m), substitute all points (t i , A i ) into the polynomial, and there are 2N + 1 equations

[0133] Calculate to get:

[0134] Since the smoothed curve does not necessarily pass through all points (t i , A i ), so these equations are not all zero. According to the least squares principle, for (2N + 1) groups of data (t i , A i), find its best coefficient a j , that is, the a values that can minimize the sum of the squares of the error R j ; Let: j Normalize to:

[0135] According to the formula

[0136] When N = 2 and m = 3, solve for a , a 0 , a 1 , a 2 , a 3 , let t = 0, ±1, ±2 and calculate to obtain the five-point cubic smoothing formula:

[0137] x and y represent the horizontal orthogonal axes in space, and z represents the normal longitudinal axis in space;

[0138] During the drilling process, the inclinometer sensor is subjected to the forces and noise interference generated by the strong vibration near the bit. Among them, the vibration is divided into lateral vibration and normal vibration, resulting in very large errors in dynamic measurement under drilling conditions.

[0139] According to the well inclination calculation formula Perform five-point cubic smoothing iteration on the first-level sampling data of the x, y, and z axes in space respectively to obtain the second-level sampling data:

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] Perform mean processing on every five of all the second-level sampling data in the set sampling time period to obtain the third-level sampling data:

[0146]

[0147] According to the formula Judge whether |INC 测量 -INC 真实 | < 0.1° within the sampling period. If this requirement is not met, discard the third-level sampling data within this time period and continue the five-point cubic smoothing iteration; if this requirement is met, use the third-level sampling data to output the full-rotation well inclination data for the set time period.

[0148] The results of the embodiments of the present invention can be output as true values, having a good calibration function for well inclination measurement under strong vibration conditions near the bit, and can effectively optimize and precise the measured parameters to achieve well inclination measurement under strong vibration near the bit. The embodiments of the present invention break through the key of traditional static well inclination measurement, will improve the measurement technologies of vertical wells, directional wells and horizontal wells, improve the trajectory control accuracy, increase the drilling speed, reduce the drilling cost, and meet the needs of increasing oil reserves and production and improving economic benefits in oil fields.

[0149] Embodiment 4: The embodiments of the present invention provide an electronic device, which includes a memory, a processor, a communication interface and a communication bus. A program that can run on the processor is stored on the memory. When the processor executes the program, it implements the well inclination measurement method under strong vibration conditions near the bit described in the above embodiments.

[0150] The processor can be a central processing unit, and the processor can also be other general-purpose processors, digital signal processors, application-specific integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or combinations of the above types of chips.

[0151] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer-executable programs and units, such as the corresponding program units in the method embodiments of the present invention above. The processor executes various functional applications and data processing of the workpieces by running the non-transitory software programs, instructions and modules stored in the memory, that is, implements the well inclination measurement method under strong vibration conditions near the bit described in the above embodiments.

[0152] The memory can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created by the processor and the like. In addition, the memory can include high-speed random access memory, and can also include non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. The memory can optionally include a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. The one or more programs are stored in the memory and, when executed by the processor, implement the well inclination measurement method under strong vibration conditions near the bit described in the above embodiments.

[0153] Embodiment 5: The embodiments of the present invention provide a storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the well inclination measurement method under strong vibration conditions near the bit provided in the above method embodiments.

[0154] Among them, the storage medium can be an internal storage unit of the electronic device, such as the hard disk or memory of the electronic device. The storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash card, etc. equipped on the electronic device.

[0155] The above technical features constitute an embodiment of the present invention, which has strong adaptability and implementation effects. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

Claims

1. A well deviation measurement method under the condition of strong vibration near the bit, characterized in that it includes the following steps: For the data after temperature drift processing of the inclinometer sensor, perform five-point cubic smoothing iteration on the x and y horizontal orthogonal axes and the z normal axis in space respectively, and then substitute them into the well deviation formula to obtain the well deviation data model. Take its average value to correct the inclinometer signal under the condition of strong vibration near the bit, and obtain the corrected well deviation data under the condition of strong vibration near the bit.

2. The well deviation measurement method under the condition of strong vibration near the bit according to claim 1, characterized in that Use the least squares fitting method to correct the temperature drift of the data collected by the inclinometer sensor to obtain the first-level sampling data; Perform five-point cubic smoothing iteration on the first-level sampling data of the x and y horizontal orthogonal axes and the z normal axis in space respectively, and substitute them into the well deviation calculation formula to obtain the second-level sampling data; Perform average processing on every five of all the second-level sampling data in the set sampling time period to obtain the third-level sampling data; Judge the three - level sampling data | INC 测量 -INC 真实 | < 0.1° holds, where INC 测量 is the measured three - level sampling data, and INC 真实 is the true three - level sampling data; If it holds, use the third-level sampling data to obtain the full-rotation well deviation data in the set time period; If it does not hold, discard the third-level sampling data of this time period and continue the five-point cubic smoothing iteration.

3. The well deviation measurement method under the condition of strong vibration near the bit according to claim 1 or 2, characterized in that specifically includes the following steps: Use the least squares fitting method to correct the temperature drift of the inclinometer sensor at different temperatures. Let the input-output polynomial of the inclinometer sensor at a fixed temperature be: Calculate the normalization matrix according to the least squares fitting method: A = (a 0 a 1 … a N ) Let a at different temperatures i be a polynomial function of temperature t: Summarized as: Arrange all the b ji into a column vector: Define an auxiliary query matrix: According to matrix b and F, the measured value of the sensor after temperature correction is: According to the formula β = F·b, the true value measured by the sensor is Obtain the first-level sampling data after correcting the temperature drift of the data collected by the inclinometer sensor using the least squares fitting method; The first-level discrete data sequence x(nT) collected after the well deviation sensor temperature drift correction algorithm is smoothed. Let the acceleration three-axis sampling values at 2N + 1 equally spaced points sampled be: s ) Let the m - degree polynomial be: A = a 0 + a 1 t + … + a m t m Find a set of appropriate coefficients a j (j = 0, 1, …, m), substitute all points (t i , A i ) into the polynomial, and there are 2N + 1 equations Calculated as: Since the smooth curve does not necessarily pass through all the points (t i , A i ), these equations are not all zero. According to the least squares principle, for the (2N + 1) sets of data (t i , A i ), finding its best coefficient a j means finding those a j values that can minimize the sum of the squares of the error R j ; Let: Normalize to: According to the formula When N = 2 and m = 3, solve for a 0 , a 1 , a 2 , a 3 , let t = 0, ±1, ±2, and calculate to obtain the five-point cubic smoothing formula: x and y represent the horizontal orthogonal axes in space, and z represents the normal longitudinal axis in space; According to the well deviation calculation formula Perform five-point cubic smoothing iteration on the first-level sampling data for the x, y, and z axes in space respectively to obtain the second-level sampling data: Perform average processing on every five of all the second-level sampling data in the set sampling time period to obtain the third-level sampling data: According to the formula judge that within the sampling period, |INC 测量 - INC 真实 | < 0.1°. If this requirement is not met, discard the three - level sampling data within this time period and continue the five - point three - time smoothing iteration; if this requirement is met, use the three - level sampling data to output the full - rotation well deviation data for the set time period.

4. The well deviation measurement method under the condition of strong vibration near the bit according to claim 1 or 2, characterized in that Use the five-point cubic smoothing algorithm to remove periodic noise or reduce high-frequency interference and smooth the signal curve.

5. The well deviation measurement method under the condition of strong vibration near the bit according to claim 3, characterized in that Use the five-point cubic smoothing algorithm to remove periodic noise or reduce high-frequency interference and smooth the signal curve.

6. An electronic device, including a memory and a processor, and a program is stored on the memory and can run on the processor, characterized in that when the processor executes the program, it implements the well deviation measurement method under the condition of strong vibration near the bit according to any one of claims 1 to 5.

7. A storage medium, characterized in that the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the well deviation measurement method under the condition of strong vibration near the bit according to any one of claims 1 to 5.

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