High-precision borehole trajectory underground self-closed-loop control system and method

Abstract

The invention provides a high-precision borehole trajectory underground self-closed-loop control system and method. Measurement data acquisition and trajectory comparison are rapidly completed underground, and a high-precision control instruction is rapidly output by means of artificial intelligence prediction; the control system comprises an attitude measurement unit; the central control unit is in communication connection with the attitude measurement unit; a real drilling track calculation program module in the central control unit obtains measurement data of the attitude measurement unit and calculates and generates a real drilling track; the design track module obtains a design track downloaded from a ground system; a real drilling track and a design track are obtained through the self-closed-loop control program, and the deviation value delta from the moment t0 to the moment t1 is compared; the control instruction artificial intelligence prediction module is configured as follows: when the deviation value delta is greater than epsilon, the control instruction artificial intelligence prediction module redesigns a to-be-drilled track m meters in front of the drill bit, and the joint of the to-be-drilled track and the designed track is completely jointed; and a to-be-drilled track and drilling operation parameters are obtained, and a new rotary steering tool control instruction is output and obtained.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas exploration and development drilling, and in particular to a downhole self-closed loop control system and method for a high-precision wellbore trajectory. Background Art

[0002] Patent application number 202311781609.2 is a method for implementing downhole closed-loop control of wellbore trajectory, which can complete the calculation of actual drilling trajectory underground, and its operation method is as follows: S1, sending well depth information and control instructions to the downhole central control; S2, recording the current time t0 corresponding to the current well depth information d0; S3, reading the measurement data of each attitude sensor; S4, data matching analysis, when the matching degree is the highest, marking the time as t1, and obtaining the well depth information d1 at the position of attitude sensor 1 at time t1 through trajectory calculation to complete the calculation of the wellbore trajectory; S5, comparing the trajectory requirements in the control instruction with the calculated wellbore trajectory, adjusting the rotary steering execution force parameters, so that the actual drilling trajectory is consistent with the control instruction.

[0003] Although it can complete the actual drilling trajectory calculation underground, reducing the time of uploading the measured data to the ground to complete the actual drilling trajectory calculation, and using PID self-feedback control to complete data measurement, comparison and execution underground, it can complete the underground self-feedback process, but in actual use, there are at least the following problems: 1. The distance interval of downhole trajectory calculation is the distance between two attitude sensors, so the adjustment interval of control instructions is also constrained by this distance, thus affecting the accuracy of control instructions; 2. PID self-feedback control is adopted. During the drilling process, the control command changes continuously with the progress of drilling. It is a dynamic change process, but the dynamic quality margin of PID is not large. The closed-loop dynamic quality is sensitive to the change of PID gain. Therefore, it is necessary to adjust the PID gain frequently according to environmental changes, resulting in low accuracy of command output; 3. It is not entirely reasonable for the PID controller to directly take the error between the target and the actual behavior, which causes overshoot in the output process and slow command output; 4. The integral link in PID control has a significant effect on suppressing constant disturbances. However, when there is no disturbance, the integral link makes the dynamic characteristics of the system worse, resulting in a sluggish response of the downhole closed-loop control, which is prone to side effects such as oscillation and control quantity saturation. As for disturbances that change over time, the integral link's suppression ability is not significant. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0005] Figure 1 A data processing flow block diagram of a downhole self-closed-loop control system for a high-precision wellbore trajectory according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the main functional units of a downhole self-closed-loop control system for a high-precision wellbore trajectory according to an embodiment of the present invention; Figure 3 A schematic diagram of the arrangement of multiple dynamic posture measurement sensors according to an embodiment of the present invention; Figure 4 is an execution flow chart of a real drilling trajectory calculation program module according to an embodiment of the present invention; Figure 5 This is a flowchart of the use of a downhole self-closed loop control system for a high-precision wellbore trajectory according to an embodiment of the present invention; Figure 6 This is a flow chart of the use of the control instruction artificial intelligence prediction module of an embodiment of the present invention; Figure 7 This is a flow chart of a method for downhole self-closed-loop control of a high-precision wellbore trajectory according to an embodiment of the present invention.

[0006] Icons: attitude measurement unit 110; first attitude measurement sensor 111; second attitude measurement sensor 112; central control unit 120; actual drilling trajectory calculation program module 121; designed trajectory acquisition module 122; self-closed loop control program module 123; control instruction artificial intelligence prediction module 124; communication module 125; execution unit 130; wing rib 131. DETAILED DESCRIPTION

[0007] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0008] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0009] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0010] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0011] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the present application.

[0012] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0013] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0014] First embodiment See also Figure 1 and Figure 2 This embodiment provides a high-precision wellbore trajectory downhole self-closed loop control system, the control system comprising: The posture measurement unit 110 includes a first posture measurement sensor 111 disposed on the rotary steering tool and a plurality of second posture measurement sensors 112 disposed at intervals on the drill rod; The central control unit 120 is connected to the attitude measurement unit 110 in communication; the central control unit 120 is provided with an actual drilling trajectory calculation program module 121, a design trajectory acquisition module 122, a self-closed loop control program module 123, a control instruction artificial intelligence prediction module 124 and a communication module 125; the actual drilling trajectory calculation program module 121 obtains the measurement data of the first attitude measurement sensor 111 and the second attitude measurement sensor 112 to calculate and generate the actual drilling trajectory; the design trajectory module obtains the design trajectory transmitted from the ground system; the self-closed loop control program obtains the actual drilling trajectory and the design trajectory, and compares the deviation Δ from time t0 to time t1; when the deviation Δ is less than or equal to ε, the design trajectory m meters ahead of the drill bit is used as the trajectory to be drilled; the control instruction artificial intelligence prediction module 124 is configured as follows: when the deviation Δ is greater than ε, the control instruction artificial intelligence prediction module 124 redesigns the trajectory to be drilled m meters ahead of the drill bit, and the connection between the trajectory to be drilled and the design trajectory is completely connected; then the trajectory to be drilled and the drilling operation parameters are obtained, and a new rotary steering tool control instruction is output; The execution unit 130 is connected to the central control unit 120 for communication; the execution unit 130 obtains the control instruction of the rotary steering tool to control the rib 131 to guide drilling; the attitude measurement unit 110 continuously obtains the measurement data of the execution unit 130 to form an underground self-closed loop control; it is used to receive the control instruction of the central control unit 120 and realize the control of the resultant force and direction of the steering force output by the rib 131 according to the instruction requirements.

[0015] See also Figure 3 In this embodiment, the attitude measurement unit 110 includes a first attitude measurement sensor 111 provided on the rotary steering tool, and a plurality of second attitude measurement sensors 112 provided on the drill rod, which are used to measure the attitude of the drill tool at the sensor position. The first attitude measurement sensor 111 is as close to the drill bit as possible, and the second attitude measurement sensor 112 only needs to be placed at a certain distance, and there is no restriction on whether it is integrated with other measurement or control tools, and there is no restriction on the distance between them, which can generally be within a range of several meters to more than ten meters.

[0016] The central control unit 120 is connected to the posture measurement unit 110 by communication, which may be a wireless connection or an electrical connection, and is used to transmit the measurement data of the posture measurement unit 110 to the actual drilling trajectory calculation program module 121 of the central control unit 120 to fit and generate the actual drilling trajectory.

[0017] The central control unit 120 is used to receive command information uploaded from the wellbore and upload downhole information; collect data from each posture measurement unit 110 to realize downhole calculation of the well trajectory, and use the proposed steering tool control instruction prediction model to predict the control instruction; send the control instruction prediction result to the execution unit 130 of the rotary steering tool; calculate the deviation between the actual drilling trajectory and the designed well trajectory in real time according to the data of each posture measurement unit 110 during drilling, and adjust the control instruction by self-feedback to make the actual drilling trajectory as consistent as possible with the designed trajectory. That is, all downhole processing methods disclosed in this article are completed in the central control unit 120, which is a key point in this embodiment.

[0018] The designed trajectory acquisition module 122 is used to store or actively acquire the designed trajectory before going down into the well.

[0019] The self-closed loop control program module 123 is used to obtain the actual drilling trajectory and the designed trajectory from t0 to t1, and to obtain the deviation by difference. When the deviation Δ is greater than the threshold ε, it is determined that the artificial intelligence prediction module needs to be enabled. When Δ is less than or equal to ε, the self-closed loop control program module 123 directly uses the designed trajectory m meters ahead of the drill bit as the trajectory to be drilled.

[0020] When the deviation Δ is greater than the threshold ε, the control instruction artificial intelligence prediction module 124 generates a trajectory to be drilled m meters ahead of the drill bit, so that the joint of the trajectory to be drilled and the designed trajectory is completely connected, that is, the well inclination and azimuth at the connection point or the connection section are exactly the same. Then, the drilling operation parameters of the new trajectory to be drilled and the end point of the actual drilling trajectory are input into the artificial intelligence prediction model to obtain a new rotary steering tool control instruction.

[0021] In this embodiment, the actual drilling trajectory calculation program module 121, the designed trajectory acquisition module 122, the self-closed-loop control program module 123, the control instruction artificial intelligence prediction module 124 and the communication module 125 are all integrated in the central control unit 120, so that it can complete data measurement, calculation and control instruction output underground, thereby improving the efficiency of instruction making.

[0022] For further information, see Figure 4 , the actual drilling trajectory calculation program module 121 is configured as follows: Receive the well depth information d0 underground, and record the current time as t0; the well depth information d0 includes: depth measurement, vertical depth and horizontal displacement, drilling operation parameters (bit pressure, rotation speed, torque, displacement, drilling speed, etc.), initial control instructions of the rotary steering tool (force magnitude, force direction) and designed trajectory (segmented trajectory design mark points and spatial positions and postures of intermediate discrete points); Read the measurement data of the first posture measurement sensor 111 within the Δt time period before t0 from the storage data of the central control unit 120 , and the measurement data of the plurality of second posture measurement sensors 112 , …, ; Data length n=Fs*Δt, Fs is the sampling frequency; The measurement data of the first posture measurement sensor 111 As the reference value R; During drilling, real-time matching and reference value R, when When the correlation with the reference value R is the largest, the time at this time is marked as t1, and the distance ΔL between the two attitude measurement sensors is obtained; real-time matching The method of the reference value R is further configured as follows: Slide to get data, each time you slide to a position, and Perform data matching analysis, and when the matching degree is the highest, record the moment as t1; The first attitude sensor measurement data at time t0 and t1 and the drilling distance ΔL are imported into the actual drilling trajectory calculation model to calculate the actual drilling trajectory; The time t1 is taken as the initial time t0, and the actual drilling trajectory is calculated in a loop; the well depth information d1 of the first attitude measurement sensor 111 at the next time t1 can be obtained, and then the actual drilling wellbore trajectory is calculated, and according to the deviation between the actual drilling trajectory and the designed trajectory, the artificial intelligence control command prediction model is used to automatically update the control command of the rotary steering tool, so as to realize high-precision downhole self-feedback closed-loop control of the wellbore trajectory.

[0023] Will As a reference, the drilling is performed according to the initial control instruction of the rotary steering tool. As time goes by, the well depth increases, and the data obtained from the second attitude measurement sensor 112 It is similar to sliding the sensor to obtain data with Δt as the time window. When sliding to a certain position, the current value is recorded. and To perform data matching analysis, a cross-correlation method may be used, but is not limited to this method. When the second posture measurement sensor moves forward to the position of the first posture measurement sensor, and The two sets of numbers should be basically the same, and the matching degree of the two sets of numbers is the highest at this time. Based on this feature, this moment can be marked as time t1. Obviously, from time t0 to time t1, the length of the drill bit drilling is equal to the distance ΔL between sensors 1 and 2. Based on this distance and the drilling tool posture measured by sensor 1 at time t0 and t1, and , combined with the well depth information d0 at time t0 received by the central control, according to the common trajectory calculation method in the industry, the well depth information d1 at the position of sensor 1 at time t1 can be easily obtained, thereby realizing the calculation of the wellbore trajectory.

[0024] The difference is that in the new downhole closed-loop control, artificial intelligence prediction methods are used to improve the accuracy of downhole self-feedback control, thereby improving the trajectory control accuracy, quality and efficiency. Figure 5 Based on the design trajectory information and the current actual drilling trajectory, the artificial intelligence control command prediction model is used to automatically update the control instructions of the rotary steerable tool under the drilling parameter conditions in the downlink instructions, so that the actual drilling trajectory is as consistent as possible with the design trajectory.

[0025] The control command artificial intelligence prediction module 124 is integrated into the central control unit 120, see Figure 6 , and is further configured as: The drilling operation data and rotary steering operation instruction data of the drilled wells are obtained, and abnormal data cleaning and data enhancement are performed; the drilling operation data are the well deviation a at the drill bit, the drill bit position b, the drill tool shape data c within n meters behind the drill bit, the average drilling pressure d within m meters in front of the drill bit, the average speed e, the average torque f, the drilling time g, the average displacement h, the well deviation change rate i and the change direction j within m meters in front of the drill bit; the rotary steering operation instruction data are the average value k and the average direction l of the resultant force of the steering tool within m meters in front of the drill bit; Constructing a sample set, using the cleaning and enhancement completed data to construct a sample set, using the drilling operation data as input, and using the rotary steering operation instruction data as output; Select a machine learning model with a regression algorithm, divide the constructed sample set into a training set, a validation set, and a test set, and perform training, validation, and testing on the machine learning model to obtain a mature control command artificial intelligence prediction model; The data related to the sample set is extracted from the actual drilling data and input into a mature artificial intelligence prediction model for control instructions to obtain new rotary steerable tool control instructions in real time.

[0026] The selection of drilling operation data types and the data cleaning method have a great influence on the speed and accuracy of the control command output of the control command artificial intelligence prediction module 124. Therefore, the drilling operation data in this embodiment only needs to select the well deviation a at the drill bit, the drill bit position b, the drilling tool shape data c within n meters behind the drill bit, the average drilling pressure d within m meters in front of the drill bit, the average speed e, the average torque f, the drilling time g, the average displacement h, the well deviation change rate i and the change direction j within m meters in front of the drill bit, so as to improve the output speed of the control command.

[0027] In order to improve the accuracy of the control instructions, this embodiment also uses different methods to clean and enhance different drilling operation data.

[0028] Among them, the abnormal data cleaning method of the first type of drilling operation data, namely, the well deviation a at the drill bit, the drill bit position b, and the drilling tool shape data c within n meters behind the drill bit, is further configured as follows: Sorting the dynamic well inclination and dynamic azimuth data during the drilling operation of a single well in chronological order; A first sliding window with a width of W is formed with a drill rod length W. For example, in this embodiment, a conventional drill rod length of 10 meters is selected to form a 10*10 square sliding window; Assume that the deflection capability of the steering tool is X° / Ym, for example, the maximum deflection rate of the current downhole tool is 18° / 30m, calculate the median M1 of all the measured values ​​in the first sliding window, and obtain the determination intervals [M1-X*W / Y, M1+X*W / Y] of the well inclination a at the drill bit, the drill bit position b, and the drilling tool shape data c within n meters behind the drill bit; The first sliding window is used to slide and judge and clean the well deviation a at the drill bit, the drill bit position b, and the drilling tool shape data c within n meters behind the drill bit in the entire well section, and the data outside the judgment interval is deleted.

[0029] The first sliding window is used to slide and judge and clean all the dynamic well inclination and dynamic azimuth data of the entire well section, and the dynamic well inclination and dynamic azimuth data outside the judgment interval of the well section corresponding to the sliding window are deleted, and the normal dynamic well inclination and dynamic azimuth data are retained, thereby improving the prediction accuracy; for the dynamic well inclination and dynamic azimuth data, since the drilling process is continuous, the changes of these two types of data are also continuous. According to this feature, the technical method uses the sliding window method to clean abnormal values, which can quickly and effectively complete the cleaning of abnormal data.

[0030] Then, the dynamic well deviation and dynamic azimuth data after cleaning are enhanced, and the enhanced dynamic well deviation and dynamic azimuth data are smoothed. Data enhancement is to fill in the missing values ​​and encrypt and interpolate the data after cleaning along the well depth, wherein the interpolation algorithm must meet the curve smoothness and have the characteristics of interpolation while providing data points, such as the Akima interpolation method. In this embodiment, all data enhancements use the cubic spline interpolation method.

[0031] Among them, the selection of interpolation spacing is the key. If the spacing is too small, the amount of data will increase and redundant data will be easily generated. If the spacing is too large, the data continuity will be poor, affecting the stability of the subsequent prediction model. In this embodiment, the change of the trajectory itself is a slow process. Taking the current maximum inclination rate of the downhole tool of 18° / 30m as an example, the average change rate per meter is 0.6°. Calculated at a well inclination change rate of 0.1°, the spacing is about 0.17m. In order to accurately describe the trajectory change, the data interpolation spacing can be taken as 0.1m, which is in line with engineering practice.

[0032] Among them, the abnormal data cleaning method of the second type of drilling operation data, that is, the well inclination change rate i and change direction j within m meters before the drill bit, is further configured as follows: According to different instructions, the magnitude and direction data of the deflection force corresponding to the instructions are filtered out; Arrange the deflection force magnitude and direction data according to the commanded start and end depths; Calculate the median M2 of all deflection force magnitude and direction data under the instruction condition; According to the fluctuation amplitude of the deflecting force and direction data under the command conditions, the threshold adjustment proportional coefficient r1 is determined, and the determination interval of the well inclination change rate h and the change direction j is [M2- M2*r1, M2+ M2*r1], and the data outside the determination interval is deleted.

[0033] For the guide force size and direction data, due to the large differences between different control instructions, these two data show discontinuous characteristics during the instruction switching process, and it is difficult to clean multiple instruction data at the same time, but within an instruction interval, these two types of numbers basically fluctuate within a smaller range.

[0034] Based on this, this solution processes the data of each instruction interval separately, that is, the starting and ending depths of each instruction are taken as a processing unit. In this unit, the median M2 of all measured guiding force magnitudes or orientations is used as the benchmark, and a suitable threshold is selected according to the actual fluctuation range of the guiding force magnitude and orientation. For example, the threshold can be set as the ratio of the benchmark value, and the proportional coefficient r1 can be adjusted according to the actual effect, generally set to r=30%. Then, when the measured value q exceeds the interval [M2- M2*r1, M2+ M2*r1], it is judged as abnormal and deleted, and the rest is judged as normal value.

[0035] After the same processing is performed on the units corresponding to each instruction interval, the cleaning of this type of data is completed. The thresholds of the size and direction of the guiding force in each processing unit can be flexibly adjusted according to the actual effect to achieve the optimal cleaning effect.

[0036] After cleaning, the data is enhanced in the same way as the first type of drilling operation design to complete missing values ​​and perform data encryption interpolation.

[0037] Among them, the abnormal data cleaning method of the third type of drilling operation data, namely the average drilling pressure d, average speed e, average torque f, drilling time g, and average displacement h within m meters before the drill bit, is further configured as follows: Arrange the drilling pressure, speed, torque, drilling time and displacement data in chronological order; A second sliding window with a width of W is formed with a drill rod length W; Calculate the median M3 of the corresponding data type in the second sliding window; According to the fluctuation range of different types of data, the threshold adjustment proportional coefficient r2 is determined. The judgment interval of drilling pressure, torque, speed, displacement and drilling time data is [M3- M3*r2, M3+ M3*r2], and the data outside the judgment interval is deleted.

[0038] For the data of drilling pressure, torque, rotation speed, displacement, drilling time, etc., they are all obtained by ground measurement, with few data anomalies, and the actual changes also have certain continuity characteristics. This embodiment adopts the sliding window method to clean abnormal values. The window length and benchmark are the same as the cleaning method of dynamic well deviation, that is, the window length W=10m, and the median M3 of the data in the window is used as the benchmark value. The selection of the threshold is the same as the cleaning method of the guide force size, and is set as the proportion of the benchmark value. The proportional coefficient r can be adjusted according to the actual effect, and can generally be set to r2=30%. When the measured value s exceeds the interval [M3-M3*r2, M3+M3*r2], it is judged as abnormal and deleted, and the rest is judged as normal value. According to this principle, the data abnormality cleaning of the entire well section is completed by sliding the window.

[0039] After data preprocessing is completed, a sample set is further established. The drilling operation data is used as input, and the rotary steering operation instruction data is used as output, and is organized into the format shown in Table 1 to facilitate the rapid output of control instructions later:

[0040] Table 1 The sample set construction in this step is highly related to the command output speed of the rotary steerable drilling operation process, and is one of the key technical points of the present invention.

[0041] The input of each sample is: well inclination a at the drill bit, drill bit position b, drill tool shape data c within n meters behind the drill bit, average values ​​c, d, e, f, g of drilling pressure, rotation speed, torque, drilling time and displacement within m meters in front of the drill bit, and well inclination change rate i and change direction j within m meters in front of the drill bit; the output of each sample is: the average value of the resultant force j and direction k of the guide tool within m meters in front of the drill bit.

[0042] Among them, in terms of the drill tool shape within n meters behind the drill bit, n meters are subdivided at intervals of 1m to form n drill tool shape data, so as to describe the drill tool shape more carefully. It should be noted that all parameters of each sample only correspond to one rotary steering control instruction and cannot span two instructions.

[0043] The method of dividing the constructed sample set into a training set, a validation set and a test set for training, validation and testing on the machine learning model is further configured as follows: the test set is the samples of one or several wells; the training set and validation set are samples obtained from other wells different from the test set; 80% of the data are extracted from samples of different wells in a random order as the training set, and the remaining 20% ​​of the data are used as the validation set.

[0044] In this embodiment, the sample set is divided into a training set, a validation set and a test set. To test the generalization of the prediction model, the test set can be selected as samples from one or several wells, while the training set and validation set are obtained from samples from other wells.

[0045] In terms of the division of training sets and validation sets, due to the particularity of drilling projects, the input and output parameters in samples of different operating sections are quite different. For example, the well inclination change rate in samples of the build-up section is usually larger, while the well inclination change rate in samples of the horizontal section is usually smaller. In order to make the training set cover samples with a wider parameter range, samples can be randomly selected from samples of multiple wells in a certain proportion (such as 80%) in a disordered manner as the training set, and the remaining 20% ​​of the samples are used as the validation set.

[0046] The construction principle of the training set, the validation set and the test set is closely related to the drilling operation process of the rotary steerable tool and is one of the key technical points of the present invention.

[0047] The machine learning regression model includes any one or more regression model combinations of SVM, BP, LSTM, and deep learning. For the training set and validation set constructed by the above method, a machine learning regression prediction method, such as SVM, BP, LSTM, deep learning, etc., is selected to train the training set composed of multiple samples. The parameter adjustment process in the specific training process needs to be carried out according to the characteristics of the specific algorithm. Through training, the control instruction prediction model of the rotary steering tool, that is, the combined force size and direction instruction prediction model of the steering tool, is obtained. The more wells that have been drilled for constructing the training set, the more samples there are, and the more accurate the prediction model is. In addition, the prediction model can be dynamically updated, that is, the drilled depth data of the well being drilled can be added to the training set to predict the control instructions in the subsequent drilling process of the well.

[0048] Therefore, in the actual drilling process, the actual drilling data is organized into input parameters as shown in Table 1 and input into the prediction model, so that the best control instructions can be obtained in real time, avoiding the problem of inaccurate control instructions caused by insufficient human experience, thereby reducing the number of downlink instructions and improving drilling efficiency and wellbore trajectory quality.

[0049] Specifically, the control instruction prediction model is in implicit form and can be represented by f, that is, (k, l) = f (a, b, c, d, e, f, g, h, I, j). Taking the LSTM machine learning prediction method as an example, the training set constructed by the drilling data is trained, and the training model is optimized using the validation set to obtain the optimal model f1 under the current sample. The prediction example is shown in Table 2 below:

[0050] Table 2 The present embodiment integrates an actual drilling trajectory calculation program module 121, a designed trajectory acquisition module 122, a self-closed-loop control program module 123, and a control instruction artificial intelligence prediction module 124 in the central control unit 120, which can complete the actual drilling trajectory calculation and the comparison between the actual drilling trajectory and the designed trajectory underground, and use artificial intelligence to predict accurate control instructions based on the comparison results, so that the new trajectory to be drilled and the designed trajectory can be connected with high quality, thereby improving the trajectory control quality, drilling quality and efficiency.

[0051] The new control instruction artificial intelligence prediction module 124 uses a unique method to clean and enhance various types of data and then trains the control instruction artificial intelligence prediction model, so that the control instructions output by the control instruction artificial intelligence prediction model are more accurate, effectively avoiding the technical problems of the original control instructions being inaccurate and the control instructions being given slowly, and can greatly improve the intelligence level of the rotary guide tool; The new control command artificial intelligence prediction module 124 constructs a unique sample set, and takes the drilling operation data of the well inclination a at the drill bit, the drill bit position b, the drill tool shape data c within n meters behind the drill bit, the average drilling pressure d within m meters in front of the drill bit, the average speed e, the average torque f, the drilling time g, the average displacement h, the well inclination change rate i and the change direction j within m meters in front of the drill bit as input; and takes the rotary steering operation command data of the guide tool within m meters in front of the drill bit as output. The average force size k and the average direction are used to make control commands accurately and quickly, thereby improving the trajectory control accuracy, quality and efficiency. It avoids the problems of command errors and slow command output caused by the original downhole self-feedback control method using the PID control method.

[0052] Second embodiment The present invention also provides a downhole self-closed loop control method for a high-precision wellbore trajectory, which uses the downhole self-closed loop control system in the first embodiment. The downhole self-closed loop control method includes: S1: Continuously acquire real-time measurement data using the posture measurement unit 110; S2: The central control unit 120 obtains the measurement data and generates an actual drilling trajectory; the deviation Δ is calculated between the actual drilling trajectory and the preset design trajectory. If the deviation Δ is greater than ε, the self-closed-loop control program redesigns the trajectory to be drilled m meters ahead of the drill bit, and the connection between the trajectory to be drilled and the design trajectory is completely connected; when the deviation Δ is less than or equal to ε, the design trajectory m meters ahead of the drill bit is used as the trajectory to be drilled; the control instruction artificial intelligence prediction module 124 obtains the trajectory to be drilled and the drilling operation parameters, and outputs a new rotary steering tool control instruction; S3: The execution unit 130 executes the drilling operation according to the rotary steerable tool control instruction.

[0053] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0054] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0055] The units described as separate components may or may not be physically separated. As a unit, it can be appreciated by those skilled in the art that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0056] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0057] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a grid device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0058] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A high-precision downhole self-closed loop control system for wellbore trajectory, characterized in that: The control system comprises: An attitude measurement unit, comprising a first attitude measurement sensor disposed on the rotary steering tool and a plurality of second attitude measurement sensors disposed at intervals on the drill rod; The central control unit is communicatively connected with the attitude measurement unit; the central control unit is provided with an actual drilling trajectory calculation program module, a design trajectory acquisition module, a self-closed loop control program module, a control instruction artificial intelligence prediction module and a communication module; the actual drilling trajectory calculation program module acquires the measurement data of the first attitude measurement sensor and the second attitude measurement sensor to calculate and generate the actual drilling trajectory; the design trajectory module acquires the design trajectory transmitted from the ground system; the self-closed loop control program acquires the actual drilling trajectory and the design trajectory, and compares the deviation Δ from time t0 to time t1; when the deviation Δ is less than or equal to ε, the design trajectory m meters ahead of the drill bit is used as the trajectory to be drilled; the control instruction artificial intelligence prediction module is configured as follows: when the deviation Δ is greater than ε, the control instruction artificial intelligence prediction module redesigns the trajectory to be drilled m meters ahead of the drill bit, and the connection between the trajectory to be drilled and the design trajectory is completely engaged; then the trajectory to be drilled and the drilling operation parameters are acquired, and a new rotary steering tool control instruction is output; The execution unit is communicatively connected with the central control unit; the execution unit obtains the control instruction of the rotary steering tool to control the rib-guided drilling; the attitude measurement unit continuously obtains the measurement data of the execution unit to form an underground self-closed loop control.

2. The high-precision wellbore trajectory downhole self-closed loop control system according to claim 1 is characterized in that: The actual drilling trajectory calculation program module is integrated into the central control unit and is further configured as follows: Receive the well depth information d0 underground, and record the current time as t0; Read the measurement data of the first attitude measurement sensor within the Δt period before t0 , and measurement data of multiple second posture measurement sensors , …, ; The measurement data of the first posture measurement sensor As the reference value R; During the drilling process, real-time matching and reference value R, when When the correlation with the reference value R is the largest, the time at this time is marked as t1, and the distance ΔL between the two attitude measurement sensors is obtained; The first attitude sensor measurement data at time t0 and t1 and the drilling distance ΔL are imported into the actual drilling trajectory calculation model to calculate the actual drilling trajectory; The time t1 is taken as the initial time t0, and the actual drilling trajectory is calculated in this cycle.

3. The high-precision wellbore trajectory downhole self-closed loop control system according to claim 1 is characterized in that: The control instruction artificial intelligence prediction module is integrated into the central control unit and is further configured as follows: Acquire the drilling operation data and rotary steering operation instruction data of the drilled well, and perform abnormal data cleaning and data enhancement; the drilling operation data are the well deviation a at the drill bit, the drill bit position b, the drill tool shape data c within n meters behind the drill bit, the average drilling pressure d within m meters before the drill bit, the average speed e, the average torque f, the drilling time g, the average displacement h, the well deviation change rate i and the change direction j within m meters before the drill bit; the rotary steering operation instruction data are the average value k and the average direction l of the resultant force of the guide tool within m meters before the drill bit; Constructing a sample set, constructing a sample set with the cleaning enhancement completed data, taking the drilling operation data as input, and taking the rotary steering operation instruction data as output; Select a machine learning model with a regression algorithm, divide the constructed sample set into a training set, a validation set, and a test set, and perform training, validation, and testing on the machine learning model to obtain a mature control command artificial intelligence prediction model; The data related to the sample set is extracted from the actual drilling data and input into the mature control instruction artificial intelligence prediction model to obtain new rotary steering tool control instructions in real time.

4. The high-precision wellbore trajectory downhole self-closed loop control system according to claim 3 is characterized in that: The abnormal data cleaning method of the well deviation a at the drill bit, the drill bit position b, and the drilling tool shape data c within n meters behind the drill bit is further configured as follows: Sorting the dynamic well inclination and dynamic azimuth data during the drilling operation of a single well in chronological order; A first sliding window with a width of W is formed with a drill rod length W; Assuming that the deflection capability of the steering tool is X° / Ym, calculate the median M1 of all the measured values ​​in the first sliding window, and obtain the determination intervals [M1-X*W / Y, M1+X*W / Y] of the well inclination a at the drill bit, the drill bit position b, and the drilling tool shape data c within n meters behind the drill bit; The first sliding window is used to slide and judge and clean the well deviation a at the drill bit, the drill bit position b, and the drilling tool shape data c within n meters behind the drill bit in the entire well section, and the data outside the judgment interval is deleted.

5. The high-precision wellbore trajectory downhole self-closed loop control system according to claim 3 is characterized in that: The abnormal data cleaning method of the well deviation change rate i and change direction j within m meters before the drill bit is further configured as follows: According to different instructions, the magnitude and direction data of the deflecting force corresponding to the instructions are filtered out; Arranging the deflecting force magnitude and direction data according to the starting and ending depths of the instructions; Calculate the median M2 of all deflection force magnitude and direction data under the instruction condition; According to the fluctuation amplitude of the deflecting force size and direction data under the command conditions, the threshold adjustment proportional coefficient r1 is determined, and the determination interval of the well inclination change rate h and the change direction j is [M2- M2*r1, M2+ M2*r1], and the data outside the determination interval is deleted.

6. The high-precision wellbore trajectory downhole self-closed loop control system according to claim 3 is characterized in that: The abnormal data cleaning method of the average drilling pressure d, the average rotation speed e, the average torque f, the drilling time g, and the average displacement h within the first m meters of the drill bit is further configured as follows: Arrange the drilling pressure, rotation speed, torque, drilling time and displacement data in chronological order; A second sliding window with a width of W is formed with a drill rod length W; Calculate the median M3 of the corresponding data type in the second sliding window; According to the fluctuation range of different types of data, the threshold adjustment ratio coefficient r2 is determined. The determination interval of the drilling pressure, torque, speed, displacement and drilling time data is [M3- M3*r2, M3+ M3*r2], and the data outside the determination interval is deleted.

7. The high-precision wellbore trajectory downhole self-closed loop control system according to claim 3 is characterized in that: The data enhancement method is further configured to: perform missing value completion and data encryption interpolation on the data cleaned along the well depth to make the data complete and smooth.

8. The high-precision wellbore trajectory downhole self-closed loop control system according to claim 3 is characterized in that: The method of dividing the constructed sample set into a training set, a validation set and a test set for training, validation and testing on a machine learning model is further configured as follows: the test set is samples from one or several wells; the training set and validation set are samples obtained from other wells different from the test set; 80% of the data are extracted from samples from different wells in a random order as the training set, and the remaining 20% ​​of the data are used as the validation set.

9. The high-precision wellbore trajectory downhole self-closed loop control system according to claim 2, characterized in that: The real-time matching The method of the reference value R is further configured as follows: Slide to get data, each time you slide to a position, and Perform data matching analysis, and when the matching degree is the highest, record the moment as t1.

10. A high-precision downhole self-closed-loop control method for wellbore trajectory, characterized in that: The downhole self-closed loop control system according to any one of claims 1 to 9 is used, and the downhole self-closed loop control method comprises: Continuously acquiring real-time measurement data using the posture measurement unit; The central control unit obtains the measurement data and generates an actual drilling trajectory; the deviation Δ is calculated between the actual drilling trajectory and the preset design trajectory. If the deviation Δ is greater than ε, the self-closed-loop control program redesigns the trajectory to be drilled m meters ahead of the drill bit, and the connection between the trajectory to be drilled and the design trajectory is completely connected; when the deviation Δ is less than or equal to ε, the design trajectory m meters ahead of the drill bit is used as the trajectory to be drilled; the control instruction artificial intelligence prediction module obtains the trajectory to be drilled and the drilling operation parameters, and outputs a new rotary steering tool control instruction; The execution unit executes the drilling operation according to the control instruction of the rotary steering tool.

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