Directional drilling clustering trajectory correction method based on acceleration sensing

Abstract

The invention discloses a directional drilling clustering trajectory correction method based on acceleration sensing. The method comprises the steps that acceleration signal data of each measurement point in the drilling process are obtained through an acceleration sensor; carrying out denoising processing on the signal data by adopting a variational mode decomposition mode to obtain denoised acceleration signal data; calculating the acceleration signal data to obtain the azimuth angle, the inclination angle and the drilling depth of the drill hole when the drill hole passes through each measuring point in the drilling process; and generating an actual centroid vector position in real time by using the obtained data, and comparing and analyzing the actual centroid vector position and an initial centroid vector position of a preset track by using a K-means optimization algorithm, so that the direction of a drill bit in the drilling process is adjusted, and correction of the track in the drilling process is realized. By means of the mode, the drilling track can be monitored in real time, the drilling direction can be adjusted in time, and therefore it is guaranteed that the drilling process conforms to the preset track, and the requirement for arranging other devices in a drilled hole subsequently is met.

Description

Technical Field

[0001] The invention relates to the field of drilling efficiency and safety of a drilling rig in the drilling field, and in particular to a directional drilling clustering trajectory correction method based on acceleration sensing. Background Art

[0002] The drilling process mainly includes three stages: acceleration stage, uniform motion stage, and deceleration stage. The rotary drilling of the drill rod and the forward and backward movement after adding the drill rod will cause vibration and the occurrence of problems such as drilling trajectory deviation. In addition, the drill bit and the drill rod are prone to tilt toward the softer rock during the drilling process. Once the drilling trajectory deviates greatly, it will increase the construction time, increase the drilling risk, deviate from the established target, and lead to poor conditions for drilling operations in the later drilling transient electromagnetic equipment.

[0003] The existing technology mainly relies on stabilizers or horizontal directional drilling, but the construction cost of the guide drill bit of horizontal directional drilling is relatively high. The principle of the stabilizer is to connect 3 to 5 stabilizers (also called straighteners) close to the diameter of the drill bit on the drill rod 10 to 20 meters close to the drill bit. By adjusting the position and number of stabilizers and matching the corresponding drilling parameters, the drill rod can exert control force on the drill bit to drill upward, downward or straighten. However, this technology can only maintain short-distance drilling operations, and fails to maintain effective drill rod sinking for long-distance drilling. Once the stabilizer deviates, it is difficult to correct the subsequent drilling direction to the preset trajectory.

[0004] In view of the problem that drilling trajectory deviation is prone to occur during the drilling process, how to provide a new method to monitor the drilling trajectory in real time and adjust the drilling direction to ensure that the drilling process conforms to the preset trajectory is the research direction that this industry needs to study. Summary of the invention

[0005] In view of the problems existing in the above-mentioned prior art, the present invention provides a directional drilling cluster trajectory correction method based on acceleration sensing, which can monitor the drilling trajectory in real time and adjust the drilling direction, thereby ensuring that the drilling process conforms to the preset trajectory.

[0006] In order to achieve the above purpose, the technical solution adopted by the present invention is: a directional drilling cluster trajectory correction method based on acceleration sensing, the specific steps are:

[0007] Step 1: Install the acceleration sensor in the drill rod near the drill bit, pre-set the required drilling trajectory, and set multiple measuring points at different drilling depths. After completion, use the drill bit to start drilling according to the set trajectory. Each time a measuring point of the drilling depth is reached, the acceleration signal data is obtained through the acceleration sensor.

[0008] Step 2: The acceleration signal data obtained in step 1 is first analyzed by an autocorrelation function to determine whether the collected signal data contains noise. This judgment process is a well-known technology and noise generally exists in the drilling process. If it is determined that noise exists, the signal data is denoised by using variational mode decomposition to obtain denoised acceleration signal data; if not, directly proceed to step 3;

[0009] Step 3: Using the acceleration signal data obtained in step 2, the azimuth angle, inclination angle and drilling depth of the borehole when passing through each measuring point during the drilling process are calculated;

[0010] Step 4: Generate the actual centroid vector position in real time based on the data obtained in step 3, and use the K-means Optimizer (KO) algorithm to compare and analyze the actual centroid vector position with the initial centroid vector position of the preset trajectory, so as to adjust the direction of the drill bit during drilling and realize the correction of the trajectory during drilling.

[0011] Furthermore, the step 2 uses variational mode decomposition to denoise the signal data, and the specific process is as follows:

[0012] Perform variational mode decomposition on the acceleration signal data:

[0013]

[0014] Where: {u k}={u 1 ,…,u K} represents the kth IMF component, {ω k}={ω 1 ,…,ω K} represents the center frequency of the kth IMF component, and f(t) represents the original acceleration signal data;

[0015] In order to obtain the optimal solution of the constrained variational problem, the Lagrange multiplication operator λ(t) and the quadratic penalty factor α are used to transform the constrained variational problem into an unconstrained variational problem. The Lagrange multiplication operator λ(t) can ensure the strictness of the constraints, and the quadratic penalty factor α can ensure the accuracy of signal reconstruction in a Gaussian noise environment. The expanded Lagrange expression is:

[0016]

[0017] The alternating direction method of multipliers (ADMM) is used to continuously update the IMF components and their center frequencies to solve the saddle points in the Lagrange expression, and u is updated through a cyclic process. k n+1 ,ω k n+1 , k n+1 The update formula is:

[0018]

[0019] Where: ^ represents Fourier transform; τ represents the time step;

[0020] Initialize {w k 1}、{u k 1}、λ 1 , n, iteratively update u k , ε, λ, until the allowable error ε is met, the decomposition stops, and finally K IMF components are output. The allowable error discriminant is as follows:

[0021]

[0022] The K IMF components finally outputted from the stripped data include 1 denoised acceleration signal data and K-1 noise components. After deleting the noise components, the acceleration signal data under noise-free conditions is obtained to obtain more accurate inclination data, providing more accurate original data for the next step of intelligent clustering correction of drilling trajectory.

[0023] Furthermore, the inclination angle of the borehole at each measuring point is obtained in step 3. The specific process is as follows: the inclination angle is determined by measuring the projection of the acceleration signal data on the X-axis of the acceleration sensor according to the denoised acceleration signal data. If the acceleration value measured on the X-axis is ax, the inclination angle a is calculated by the following formula:

[0024] a=arcsin(ax / g)

[0025] where g is the acceleration due to gravity.

[0026] Furthermore, the specific process of the K-means optimization algorithm in step 4 for analysis and processing is as follows:

[0027] The formula for setting the Euclidean distance weight is as follows:

[0028]

[0029] The K-means optimization algorithm is used to establish the centroid vector of the clustering area, specifically:

[0030] 1. The random initialization formula is described as follows:

[0031] X i =lb+(ub-lb)·rand(0,1,[1,D]),i=1,2,…,N

[0032] Where: Xi represents the ith candidate solution, lb and ub represent the lower and upper bounds of the search space respectively; rand is a random number uniformly distributed in the range [0,1]; N represents the number of solutions;

[0033] 2. Update process of centroid vector position

[0034] The K-means optimization algorithm uses a linear population size reduction method. As the number of iterations increases, the population size in the data will decrease in a linear function;

[0035]

[0036] where t is the current iteration, T max is the maximum number of iterations; therefore, the population size is N at the first iteration t = 1 and T at the last iteration max The overall size is 4;

[0037] 3. Optimization strategy

[0038] In order to diversify the search space of the K-means optimization algorithm, in addition to utilizing three centroid vector positions in each iteration, the K-means optimization algorithm also adds a new search area called the average centroid vector position As shown below:

[0039]

[0040] The Euclidean distance between the actual center of mass vector position and the initial center of mass vector position in the preset trajectory can be obtained more accurately by averaging the center of mass vector position during drilling. The initial center of mass position is determined as α and the average center of mass vector position is determined as β according to the following formula. If the drill bit drills along the preset trajectory during drilling, d(α, β) approaches 0; if d(α, β) continues to increase with the drilling time, it means that the drilling direction has a trajectory deviation. At this time, the drilling direction of the drill bit is adjusted until d(α, β) approaches 0, thereby keeping the subsequent drilling trajectory consistent with the preset trajectory. The specific formula is:

[0041]

[0042] In the above formula, d(α,β) is the Euclidean distance between any drilling time α and any drilling time β, x α ,y α 、z α are the x, y, and z coordinates of the α event, respectively. β ,y β 、z β are the x, y, and z coordinates of the β event, respectively.

[0043] Compared with the prior art, the present invention first obtains the acceleration signal data of each measuring point of the drilling depth through an acceleration sensor; then uses the variational mode decomposition method to denoise the signal data to obtain the denoised acceleration signal data; then the denoised acceleration signal data is calculated to obtain the azimuth angle, inclination angle and drilling depth of the borehole when passing through each measuring point during the drilling process; the actual centroid vector position is generated in real time using the above-obtained data, and the actual centroid vector position is compared and analyzed with the initial centroid vector position of the preset trajectory using the K-means optimization algorithm, thereby adjusting the direction of the drill bit during the drilling process and realizing the correction of the trajectory during the drilling process. In the above manner, the present invention can monitor the drilling trajectory in real time and adjust the drilling direction in a timely manner, thereby ensuring that the drilling process conforms to the preset trajectory and meeting the needs of subsequently deploying other equipment in the borehole. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0045] Figure 2 It is a schematic diagram of the installation of the acceleration sensor in the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described below.

[0047] like Figure 1 As shown, the specific steps of the present invention are:

[0048] Step 1: Install the acceleration sensor in the drill rod near the drill bit. Figure 2 As shown, the required drilling trajectory is pre-set, and multiple measuring points are set at different drilling depths. After completion, the drill bit is used to start drilling according to the set trajectory, and each time a measuring point of the drilling depth is reached, the acceleration signal data is obtained through the acceleration sensor;

[0049] Step 2: The acceleration signal data obtained in step 1 is first analyzed by an autocorrelation function to determine whether the collected signal data contains noise. This judgment process is a well-known technology. If it is determined that there is noise, the signal data is denoised by variational mode decomposition to obtain denoised acceleration signal data; if not, directly proceed to step 3; the specific process of denoising is as follows:

[0050] Perform variational mode decomposition on the acceleration signal data:

[0051]

[0052] Where: {u k}={u 1 ,…,u K} represents the kth IMF component, {ω k}={ω 1 ,…,ω K} represents the center frequency of the kth IMF component, and f(t) represents the original acceleration signal data;

[0053] In order to obtain the optimal solution of the constrained variational problem, the Lagrange multiplication operator λ(t) and the quadratic penalty factor α are used to transform the constrained variational problem into an unconstrained variational problem. The Lagrange multiplication operator λ(t) can ensure the strictness of the constraints, and the quadratic penalty factor α can ensure the accuracy of signal reconstruction in a Gaussian noise environment. The expanded Lagrange expression is:

[0054]

[0055] The alternating direction method of multipliers (ADMM) is used to continuously update the IMF components and their center frequencies to solve the saddle points in the Lagrange expression, and u is updated through a cyclic process. k n+1 ,ω k n+1 , k n+1 The update formula is:

[0056]

[0057] Where: ^ represents Fourier transform; τ represents the time step;

[0058] Initialize {w k 1}、{u k 1}、λ 1 , n, iteratively update uk , ε, λ, until the allowable error ε is met, the decomposition stops, and finally K IMF components are output. The allowable error discriminant is as follows:

[0059]

[0060] The K IMF components finally outputted from the stripped data include 1 denoised acceleration signal data and K-1 noise components. After deleting the noise components, the acceleration signal data under noise-free conditions is obtained to obtain more accurate inclination data, providing more accurate original data for the next step of intelligent clustering correction of drilling trajectory.

[0061] Step 3: Using the acceleration signal data obtained in step 2, the azimuth, inclination and drilling depth of the borehole when passing through each measuring point during the drilling process are calculated; wherein the inclination of the borehole at each measuring point is obtained by measuring the projection of the acceleration signal data on the X-axis of the acceleration sensor to determine the inclination angle. If the acceleration value measured on the X-axis is ax, the inclination angle a is calculated by the following formula:

[0062] a=arcsin(ax / g)

[0063] where g is the acceleration due to gravity.

[0064] Step 4: Generate the actual centroid vector position in real time based on the data obtained in step 3, and use the K-means Optimizer (KO) algorithm to compare and analyze the actual centroid vector position with the initial centroid vector position of the preset trajectory, so as to adjust the direction of the drill bit during drilling and realize the correction of the trajectory during drilling. The specific process is as follows:

[0065] The formula for setting the Euclidean distance weight is as follows:

[0066]

[0067] The K-means optimization algorithm is used to establish the centroid vector of the clustering area, specifically:

[0068] 1. The random initialization formula is described as follows:

[0069] X i =lb+(ub-lb)·rand(0,1,[1,D]),i=1,2,…,N

[0070] Where: Xi represents the ith candidate solution, lb and ub represent the lower and upper bounds of the search space respectively; rand is a random number uniformly distributed in the range [0,1]; N represents the number of solutions;

[0071] 2. Update process of centroid vector position

[0072] The K-means optimization algorithm uses a linear population size reduction method. As the number of iterations increases, the population size in the data will decrease in a linear function;

[0073]

[0074] where t is the current iteration, T max is the maximum number of iterations; therefore, the population size is N at the first iteration t = 1 and T at the last iteration max The overall size is 4;

[0075] 3. Optimization strategy

[0076] In order to diversify the search space of the K-means optimization algorithm, in addition to utilizing three centroid vector positions in each iteration, the K-means optimization algorithm also adds a new search area called the average centroid vector position As shown below:

[0077]

[0078] The Euclidean distance between the actual center of mass vector position and the initial center of mass vector position in the preset trajectory can be obtained more accurately by averaging the center of mass vector position during drilling. The initial center of mass position is determined as α and the average center of mass vector position is determined as β according to the following formula. If the drill bit drills along the preset trajectory during drilling, d(α, β) approaches 0; if d(α, β) continues to increase with the drilling time, it means that the drilling direction has a trajectory deviation. At this time, the drilling direction of the drill bit is adjusted until d(α, β) approaches 0, thereby keeping the subsequent drilling trajectory consistent with the preset trajectory. The specific formula is:

[0079]

[0080] In the above formula, d(α,β) is the Euclidean distance between any drilling time α and any drilling time β, x α ,y α 、z α are the x, y, and z coordinates of the α event, respectively. β ,y β 、z β are the x, y, and z coordinates of the β event, respectively.

[0081] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A clustering trajectory correction method for directional drilling based on acceleration sensing, characterized in that: The specific steps are: Step 1: Install the acceleration sensor in the drill rod near the drill bit, pre-set the required drilling trajectory, and set multiple measuring points at different drilling depths. After completion, use the drill bit to start drilling according to the set trajectory. Each time a measuring point of the drilling depth is reached, the acceleration signal data is obtained through the acceleration sensor. Step 2: The acceleration signal data obtained in step 1 is first analyzed by an autocorrelation function to determine whether the collected signal data contains noise. If it is determined that there is noise, the signal data is denoised by using a variational mode decomposition method to obtain denoised acceleration signal data; if not, directly proceed to step 3; Step 3: Using the acceleration signal data obtained in step 2, the azimuth angle, inclination angle and drilling depth of the borehole when passing through each measuring point during the drilling process are calculated; Step 4: Generate the actual centroid vector position in real time based on the data obtained in step 3, and use the K-means optimization algorithm to compare and analyze the actual centroid vector position with the initial centroid vector position of the preset trajectory, so as to adjust the direction of the drill bit during drilling and realize the correction of the trajectory during drilling.

2. The directional drilling clustering trajectory correction method based on acceleration sensing according to claim 1 is characterized in that: The second step uses variational mode decomposition to denoise the signal data. The specific process is as follows: Perform variational mode decomposition on the acceleration signal data: Where: {u k }={u1,…,u K } represents the kth IMF component, {ω k }={ω1,…,ω K } represents the center frequency of the kth IMF component, and f(t) represents the original acceleration signal data; In order to obtain the optimal solution of the constrained variational problem, the Lagrange multiplication operator λ(t) and the quadratic penalty factor α are used to transform the constrained variational problem into an unconstrained variational problem. The expanded Lagrange expression is: The alternating direction multiplier method is used to continuously update each IMF component and its central frequency to solve the saddle point in the Lagrange expression, and u is updated through a cyclic process. k n+1 ,ω k n+1 , k n+1 The update formula is: Where: ^ represents Fourier transform; τ represents the time step; Initialize {w k 1 }、{u k 1 }、λ 1 , n, iteratively update u k , ε, λ, until the allowable error ε is met, the decomposition stops, and finally K IMF components are output. The allowable error discriminant is as follows: The K IMF components finally outputted from the stripped data include 1 denoised acceleration signal data and K-1 noise components. After deleting the noise components, the acceleration signal data under noise-free conditions is obtained.

3. The directional drilling clustering trajectory correction method based on acceleration sensing according to claim 1 is characterized in that: In step 3, the inclination angle of the borehole at each measuring point is obtained. The specific process is: the inclination angle is determined by measuring the projection of the acceleration signal data on the X-axis of the acceleration sensor according to the denoised acceleration signal data. If the acceleration value measured on the X-axis is ax, the inclination angle a is calculated by the following formula: a=arcsin(ax / g) where g is the acceleration due to gravity.

4. The directional drilling clustering trajectory correction method based on acceleration sensing according to claim 1 is characterized in that: The specific process of the K-means optimization algorithm in step 4 for analysis and processing is as follows: The formula for setting the Euclidean distance weight is as follows: The K-means optimization algorithm is used to establish the centroid vector of the clustering area, specifically:

1. The random initialization formula is described as follows: X i =lb+(ub-lb)·rand(0,1,[1,D]),i=1,2,...,N Where: Xi represents the ith candidate solution, lb and ub represent the lower and upper bounds of the search space respectively; rand is a random number uniformly distributed in the range [0,1]; N represents the number of solutions; 2. Update process of centroid vector position: The K-means optimization algorithm uses a linear population size reduction method. As the number of iterations increases, the population size in the data will decrease in a linear function; where t is the current iteration, T max is the maximum number of iterations; therefore, the population size is N at the first iteration t = 1 and T at the last iteration max The overall size is 4; 3. Optimization strategy: In order to diversify the search space of the K-means optimization algorithm, in addition to utilizing three centroid vector positions in each iteration, the K-means optimization algorithm also adds a new search area called the average centroid vector position As shown below: The Euclidean distance between the actual center of mass vector position and the initial center of mass vector position in the preset trajectory can be obtained more accurately by averaging the center of mass vector position during drilling. The initial center of mass position is determined as α and the average center of mass vector position is determined as β according to the following formula. If the drill bit drills along the preset trajectory during drilling, d(α, β) approaches 0; if d(α, β) continues to increase with the drilling time, it means that the drilling direction has a trajectory deviation. At this time, the drilling direction of the drill bit is adjusted until d(α, β) approaches 0, thereby keeping the subsequent drilling trajectory consistent with the preset trajectory. The specific formula is: In the above formula, d(α,β) is the Euclidean distance between any drilling time α and any drilling time β, x α ,y α 、z α are the x, y, and z coordinates of the α event, respectively. β ,y β 、z β are the x, y, and z coordinates of the β event, respectively.

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