Well-ground geosteering closed-loop control method and system

Abstract

According to the well-ground geosteering closed-loop control method and system, the design track can be adaptively adjusted according to geological information of real-time drilling, and drilling safety is guaranteed; an artificial intelligence prediction technology is applied to well track and instruction prediction, the accuracy and response speed of a control instruction are improved, and the control method comprises the steps that underground continuous attitude measurement data and LWD measurement parameter data are obtained; continuously comparing geological deviation values between the LWD measurement parameter data and geological requirements along the actual drilling track; when the geology deviation value is larger than a threshold value epsilon 0, a second design track is obtained through automatic design; when the geology deviation value is smaller than or equal to a threshold value epsilon 0, the continuous actual drilling track is compared with the first / second design track, and geometric deviation is calculated; when the geometric deviation is larger than a threshold value epsilon 1, a second to-be-drilled track is recalculated according to the first / second design track; and inputting the to-be-drilled track and the drilling operation parameters corresponding to the to-be-drilled track into the control instruction prediction model to obtain a new control instruction.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas exploration and development drilling, and in particular to a method and system for geosteering closed-loop control between a well and a ground. Background Art

[0002] In rotary steerable drilling, the actual borehole trajectory is usually calculated based on the downhole measurement while drilling data combined with the footage depth of the surface drill string using mature methods in the industry. Then, the deviation between the actual borehole trajectory on the ground and the expected borehole trajectory is used as the basis for trajectory adjustment decisions.

[0003] Since the uncertainty of geological factors has a great impact on the safety of drilling projects, a detailed geological survey must be conducted before drilling construction to ensure the scientificity and rationality of drilling safety construction, provide important reference value for exploration and development, and form a design trajectory. However, during the drilling process, when the geological conditions of the actual drilling trajectory deviate from the geological conditions at the point corresponding to the design trajectory, drilling based on the control instructions made by the preset design trajectory may cause stuck drill, well leakage, overflow and other phenomena, which will directly affect drilling safety.

[0004] At the same time, when performing self-feedback control of control instructions, how to timely feedback accurate control instructions to the well is crucial to improving the control accuracy and trajectory quality of the trajectory. Some self-feedback control methods commonly used in industry are difficult to meet the requirements.

[0005] A real-time prediction method for steering instructions of a push-to-pull rotary steering tool with Chinese patent application number CN202310223456.3 can predict the next steering instruction by real-time feedback of inclination data, thereby optimizing the instruction feedback process. It uses the inclination data fed back in real time by the push-to-pull rotary steering tool and the steering instructions implemented during the steering tool operation to calculate the well inclination change rate and azimuth change rate in the well depth interval for each steering instruction, and then gives the steering force percentage and steering tool face angle required by the rotary steering tool based on the steering instruction model and the target point for the next drilling.

[0006] In the calculation of wellbore trajectory inclination, the well inclination change rate and azimuth change rate are very important reference data. In the current calculation method, the well inclination change rate and azimuth change rate are calculated using the well inclination angle change and azimuth change of the well section. However, in a well section, if the well inclination angle or azimuth angle does not change linearly with the well depth, then the well inclination change rate or azimuth change rate is not a constant. And when the well section is relatively long, the actual well inclination change rate and azimuth change rate have non-negligible deviations from the well inclination change rate and azimuth change rate of the constant value.

[0007] Therefore, the real-time prediction method of the push-type rotary steering tool guidance command is adopted to directly obtain the force and angle adjustment of the drill bit to the target point through the well inclination change rate and the azimuth change rate, which will directly lead to the defect of low accuracy of the control command obtained intelligently, resulting in low control accuracy and trajectory quality of the new trajectory during the adjustment process. Summary of the invention

[0008] The purpose of the present invention is to provide a closed-loop control method and system for geological guidance between wells and ground, which can adaptively adjust and update the designed trajectory according to the real-time drilling situation to ensure the safety of the drilling process; at the same time, new artificial intelligence prediction technology is applied to wellbore trajectory and control command prediction to improve the accuracy and response speed of control commands, thereby improving the accuracy, quality and efficiency of trajectory control.

[0009] The embodiment of the present invention is achieved as follows: A closed-loop control method for geosteering between wells and ground, the control method comprising: Obtain downhole continuous attitude measurement data and LWD measurement parameter data; The continuous actual drilling trajectory is obtained by fitting the continuous posture measurement data; The geological deviation Δ0 between the LWD measurement parameter data and the geological requirements is continuously compared along the actual drilling trajectory; when the geological deviation Δ0 is greater than the threshold ε0, the first trajectory to be drilled m meters ahead of the drill bit is automatically designed using the artificial intelligence prediction model according to the geological guidance trajectory adjustment principle, and the first design trajectory is updated according to the first trajectory to be drilled to obtain the second design trajectory; when the geological deviation Δ0 is less than or equal to the threshold ε0, the continuous actual drilling trajectory is compared with the first / second design trajectory to calculate the geometric deviation Δ1; When the geometric deviation Δ1 is greater than the threshold ε1, the second trajectory to be drilled m meters ahead of the drill bit is recalculated according to the first / second designed trajectory, and the connection between the second trajectory to be drilled and the first / second designed trajectory is completely connected; When the geometric deviation Δ1 is less than or equal to the threshold ε1, the drilling parameters and control instructions are kept unchanged, and the control instructions are issued to the rotary steerable tool; and continuous attitude measurement data and LWD measurement parameter data are continuously obtained; The first trajectory to be drilled or the second trajectory to be drilled, and the drilling operation parameters corresponding to the trajectory to be drilled are input into the rotary steering tool control instruction prediction model to obtain new control instructions; the control instructions are issued to the rotary steering tool, and continuous attitude measurement data and LWD measurement parameter data are continuously obtained.

[0010] In a preferred embodiment of the present invention, the above-mentioned downhole continuous attitude measurement data and LWD measurement parameter data are respectively obtained by a continuous attitude measurement sensor arranged on a rotary steering tool and multiple types of LWD short sections arranged on a cable drill pipe, and each type of LWD short section is arranged at intervals.

[0011] In a preferred embodiment of the present invention, the LWD sub measures at least azimuthal gamma or azimuthal resistivity.

[0012] In a preferred embodiment of the present invention, the method for constructing the above-mentioned rotary steerable tool control instruction prediction model includes: 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; A machine learning model with a regression algorithm is selected, and the constructed sample set is divided into a training set, a validation set, and a test set for training, validation, and testing on the machine learning model to obtain a mature artificial intelligence prediction model for control instructions.

[0013] In a preferred embodiment of the present invention, the abnormal data cleaning method of the above-mentioned 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.

[0014] In a preferred embodiment of the present invention, 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 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 judgment 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 judgment interval is deleted.

[0015] In a preferred embodiment of the present invention, the abnormal data cleaning method of the average drilling pressure d, the average 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, 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.

[0016] In a preferred embodiment of the present invention, the above data enhancement method is further configured to: fill in missing values ​​and perform data encryption interpolation on the data cleaned along the well depth to make the data complete and smooth.

[0017] In a preferred embodiment of the present invention, the above-mentioned 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 the 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.

[0018] A well-ground geosteering closed-loop control system, the control system comprising: A measurement data acquisition module is used to acquire downhole continuous attitude measurement data and LWD measurement parameter data; A real drilling trajectory calculation module is used to obtain a continuous real drilling trajectory according to the continuous posture measurement data; The geological deviation calculation module continuously compares the geological deviation Δ0 between the LWD measurement parameter data and the geological requirements along the actual drilling trajectory; when the geological deviation Δ0 is greater than the threshold ε0, the first trajectory to be drilled m meters ahead of the drill bit is automatically designed using the artificial intelligence prediction model according to the geological guidance trajectory adjustment principle, and the first designed trajectory is updated according to the first trajectory to be drilled to obtain the second designed trajectory; The geometric deviation calculation module is used to compare the continuous actual drilling trajectory with the first / second design trajectory to calculate the geometric deviation Δ1 when the geological deviation Δ0 is less than or equal to the threshold ε0; when the geometric deviation Δ1 is greater than the threshold ε1, the second trajectory to be drilled m meters in front of the drill bit is recalculated according to the first / second design trajectory, and the connection between the second trajectory to be drilled and the first / second design trajectory is completely connected; when the geometric deviation Δ1 is less than or equal to the threshold ε1, the drilling parameters and control instructions are kept unchanged, and the control instructions are issued to the rotary steering tool; and continuous attitude measurement data and LWD measurement parameter data are continuously obtained; The rotary steering tool control instruction prediction module is used to input the first trajectory to be drilled or the second trajectory to be drilled, and the drilling operation parameters corresponding to the trajectory to be drilled into the rotary steering tool control instruction prediction model to obtain new control instructions; issue the control instructions to the rotary steering tool, and continuously obtain continuous attitude measurement data and LWD measurement parameter data.

[0019] The beneficial effects of the embodiments of the present invention are: 1. Before trajectory matching, the present invention also compares the geological deviation Δ0 on the actual drilling trajectory path; when the geological deviation is too large, the first trajectory to be drilled m meters before the drill bit of the actual drilling trajectory is redesigned according to the geological guidance trajectory adjustment principle. The first trajectory to be drilled m meters before is generated by an artificial intelligence prediction model, and the first trajectory to be drilled is spliced ​​on the first design trajectory to form a second design trajectory; until the geological deviation meets the requirements, the actual drilling trajectory and the design trajectory are compared again, so that the later control instructions can be adjusted in real time according to geological factors. The control accuracy of downhole drilling is improved, and the quality and efficiency of the drilling trajectory are ensured; 2. The second designed trajectory and the first m meters of the trajectory to be drilled are determined by the artificial intelligence prediction model. Intelligent design shortens the trajectory design time and the decision-making output time of the control instructions, thereby improving work efficiency; 3. In the control instruction giving link, an artificial intelligence prediction model is used to output the control instructions of the rotary steering tool, which can improve the intelligence level of the rotary steering tool, improve the trajectory control accuracy, quality and efficiency, and reduce the occurrence of errors in control instructions. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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.

[0021] Figure 1 This is a flow chart of a closed-loop control method for geosteering between wells and ground according to an embodiment of the present invention; Figure 2 A schematic diagram of the connection of a downhole drilling tool assembly according to an embodiment of the present invention; Figure 3 This is a flow chart of a method for constructing a control instruction artificial intelligence prediction module according to an embodiment of the present invention; Figure 4 Schematic diagram of the main functional units of the well-ground geosteering closed-loop control system according to an embodiment of the present invention.

[0022] Icon: continuous attitude measurement sensor 001; LWD short section 002; cable drill rod 003; measurement data acquisition module M1; actual drilling trajectory calculation module M2; geological deviation calculation module M3; geometric deviation calculation module M4; rotary steering tool control instruction prediction module M5. DETAILED DESCRIPTION

[0023] 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.

[0024] 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.

[0025] 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.

[0026] The described features, structures or characteristics may 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, it will be appreciated by those skilled in the art that the technical scheme of the present application may be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present application.

[0027] 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.

[0028] 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.

[0029] First embodiment See also Figure 1 This embodiment provides a closed-loop control method for geosteering between wells and ground, the control method comprising: S1: Acquire downhole continuous attitude measurement data and LWD measurement parameter data; S2: fitting the continuous actual drilling trajectory according to the continuous attitude measurement data. This step uses the trajectory calculation method commonly used in the industry, such as the minimum curvature method, to calculate the continuous attitude measurement data to obtain the continuous actual drilling wellbore trajectory; S3: Continuously compare the geological deviation Δ0 between the LWD measurement parameter data and the geological requirements along the actual drilling trajectory; when the geological deviation Δ0 is greater than the threshold ε0, according to the geological guidance trajectory adjustment principle, use the artificial intelligence prediction model to automatically design the first trajectory to be drilled m meters ahead of the drill bit, and update the first design trajectory according to the first trajectory to be drilled to obtain the second design trajectory; when the geological deviation Δ0 is less than or equal to the threshold ε0, compare the continuous actual drilling trajectory with the first / second design trajectory to calculate the geometric deviation Δ1; S4: When the geometric deviation Δ1 is greater than the threshold ε1, the second trajectory to be drilled m meters ahead of the drill bit is recalculated according to the first / second design trajectory, and the connection between the second trajectory to be drilled and the first / second design trajectory is completely connected; when the geometric deviation Δ1 is less than or equal to the threshold ε1, the drilling parameters and control instructions are kept unchanged, and the control instructions are issued to the rotary steering tool; and continuous attitude measurement data and LWD measurement parameter data are continuously obtained to form a closed-loop control; S5: Input the first trajectory to be drilled or the second trajectory to be drilled, and the drilling operation parameters corresponding to the trajectory to be drilled into the rotary steering tool control instruction prediction model to obtain new control instructions; issue the control instructions to the rotary steering tool, and continuously obtain continuous attitude measurement data and LWD measurement parameter data.

[0030] In this example, see Figure 2, downhole continuous attitude measurement data and LWD measurement parameter data are obtained by a continuous attitude measurement sensor 001 set on the rotary steering tool and multiple types of LWD short sections 002 set on the cable drill pipe. Each type of LWD short section 002 is set at intervals, and the more types of LWD short sections 002, the better. LWD short section 002 measures at least azimuthal gamma or azimuthal resistivity. The real-time upload of downhole measurement information and the download of instructions are realized between the measurement sensor and the ground system / geological steering closed-loop control system between the well and the ground through the cable drill pipe 003.

[0031] Since geological factors have a great impact on the safety of drilling projects during drilling operations, inaccurate geological predictions will significantly increase the probability of drilling project accidents, which may cause significant losses to workers and oilfield companies.

[0032] The existing trajectory comparison method is: when designing a drilling project, the geological analysis of the exploration project should be carried out in depth according to the actual situation, and then the design trajectory preset before going down the well is compared with the actual drilling trajectory. According to the comparison, it is determined whether the trajectory needs to be adjusted. When there is an error between the geological conditions during the actual drilling process and the pre-collected geological data, the subsequent drilling trajectory will have a large error, thus affecting the entire drilling operation.

[0033] In S3 of this embodiment, a geological deviation calculation is newly added before trajectory comparison, so that the designed trajectory can determine whether to use the pre-designed first designed trajectory or the second designed trajectory redesigned according to geological data when matching the later trajectory based on the geological deviation result, thereby ensuring that the drilling operation is smoother, safer and more efficient.

[0034] For how to operate, see Figure 1 : S31: Continuously subtract the LWD measurement parameter data from the geological requirement data along the actual drilling trajectory to obtain a geological deviation Δ0; S32: When the geological deviation Δ0 is greater than the threshold ε0, according to the geological guidance trajectory adjustment principle, the artificial intelligence prediction model is used to design the first trajectory to be drilled m meters ahead of the drill bit, and the first designed trajectory is updated according to the first trajectory to be drilled to obtain the second designed trajectory; the second designed trajectory is a point where the end point of the actual drilling trajectory is connected to the starting point of the first trajectory to be drilled, and the end point of the first trajectory to be drilled is connected to the junction point or junction section of the first designed trajectory, and the spatial position and posture of each junction point or junction section are exactly the same; that is, when the geological data collected in real time is different from the geological data before going down to the well, it is necessary to regenerate a new first trajectory to be drilled to adjust the current actual drilling point, and re-update the designed trajectory, and perform a dual adjustment process of the actual drilling trajectory and the designed trajectory; S33: When the geological deviation Δ0 is less than or equal to the threshold ε0, further trajectory determination is performed, that is, the continuous actual drilling trajectory is compared with the first / second designed trajectory to calculate the geometric deviation Δ1.

[0035] Then the trajectory is determined. Specifically, in S4, when the geometric deviation Δ1 exceeds the given threshold ε1, the actual drilling trajectory needs to be adjusted. First, the artificial intelligence prediction model redesigns the trajectory to be drilled m meters in front of the drill bit, requiring that the trajectory to be drilled can be completely connected with the designed trajectory, that is, the spatial position and posture of the junction point are completely consistent. In particular, before the time t for trajectory determination, all geological deviations Δ0 within the time period t are less than or equal to the threshold ε0, then the first designed trajectory is used for comparison and determination with the actual drilling trajectory. When the geological deviation Δ0 at any moment within the time period t is greater than the threshold ε0, the second designed trajectory is used for comparison and determination with the actual drilling trajectory. When there is a calculation of the trajectory to be drilled, the generated trajectory to be drilled and the drilling operation data need to be input into the rotary steering tool control instruction prediction model to generate new steering control instructions. The trajectory to be drilled in this embodiment can be obtained using conventional methods in the industry, or it can be obtained using the rotary steering tool control instruction prediction model. It should be noted that the length of the trajectory to be drilled must be determined by the rotary steering tool control instruction prediction model to ensure the output speed of the decision instruction.

[0036] For details, see Figure 3 In this embodiment, the method for constructing the rotary steering tool control instruction prediction model includes the following steps: Step 1: Obtain 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 steering tool within m meters before the drill bit; Step 2: construct a sample set, using the cleaning and enhancement completed data to construct a sample set, using the drilling operation data as input and the rotary steering operation instruction data as output; Step 3: 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.

[0037] In the construction method of the rotary steerable tool control command prediction model, data selection, preprocessing method and sample set establishment are the most critical and core innovative elements for the output of control commands, while there is diversity in the choice of model.

[0038] In terms of data selection, the drilling operation data only needs to select 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, so as to improve the output speed of the control instructions.

[0039] In order to improve the accuracy of the control instructions, this embodiment also uses different methods to clean and enhance different drilling operation data.

[0040] 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; 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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 judgment 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 judgment interval is deleted.

[0046] 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.

[0047] 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.

[0048] After performing the same processing on the units corresponding to each instruction interval, the cleaning of this type of data is completed. The thresholds of the guide force size and direction in each processing unit can be flexibly adjusted according to the actual effect to achieve the optimal cleaning effect.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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:

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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:

[0062] Table 2 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. New control instructions can be adjusted according to the measured geological data, thereby improving drilling efficiency and wellbore trajectory quality.

[0063] Second embodiment A closed-loop control system for geosteering between wells and ground, see Figure 4 , the control system includes: The measurement data acquisition module M1 is used to obtain downhole continuous attitude measurement data and LWD measurement parameter data; The actual drilling trajectory calculation module M2 is used to obtain the continuous actual drilling trajectory according to the continuous posture measurement data; The geological deviation calculation module M3 continuously compares the geological deviation Δ0 between the LWD measurement parameter data and the geological requirements along the actual drilling trajectory; when the geological deviation Δ0 is greater than the threshold ε0, according to the geological guidance trajectory adjustment principle, the artificial intelligence prediction model is used to automatically design the first trajectory to be drilled m meters ahead of the drill bit, and the first designed trajectory is updated according to the first trajectory to be drilled to obtain the second designed trajectory; The geometric deviation calculation module M4 is used to compare the continuous actual drilling trajectory with the first / second design trajectory to calculate the geometric deviation Δ1 when the geological deviation Δ0 is less than or equal to the threshold ε0; when the geometric deviation Δ1 is greater than the threshold ε1, the second trajectory to be drilled m meters in front of the drill bit is recalculated according to the first / second design trajectory, and the connection between the second trajectory to be drilled and the first / second design trajectory is completely connected; when the geometric deviation Δ1 is less than or equal to the threshold ε1, the drilling parameters and control instructions are kept unchanged, and the control instructions are issued to the rotary steering tool; and continuous attitude measurement data and LWD measurement parameter data are continuously obtained; The rotary steering tool control instruction prediction module M5 is used to input the first trajectory to be drilled or the second trajectory to be drilled, and the drilling operation parameters corresponding to the trajectory to be drilled into the rotary steering tool control instruction prediction model to obtain new control instructions; issue the control instructions to the rotary steering tool, and continuously obtain continuous attitude measurement data and LWD measurement parameter data.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0068] 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.

[0069] 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 closed-loop control method for geosteering between wells and ground, It is characterized in that The control method comprises: Obtain downhole continuous attitude measurement data and LWD measurement parameter data; According to the continuous posture measurement data, a continuous actual drilling trajectory is obtained by fitting; Continuously compare the geological deviation Δ between the LWD measurement parameter data and the geological requirements along the actual drilling trajectory 0 ; When the geological deviation Δ 0 Greater than the threshold ε 0 When the geological deviation Δ 0 Less than or equal to the threshold ε 0 When the continuous actual drilling trajectory is compared with the first / second designed trajectory, the geometric deviation Δ 1 ; When the geometric deviation Δ 1 Greater than the threshold ε 1 When the second trajectory to be drilled m meters ahead of the drill bit is recalculated according to the first / second designed trajectory, the second trajectory to be drilled and the first / second designed trajectory are completely connected at the connection point; When the geometric deviation Δ 1 Less than or equal to the threshold ε 1 When drilling, the drilling parameters and control instructions are kept unchanged, and the control instructions are sent to the rotary steerable tool; and continuous attitude measurement data and LWD measurement parameter data are continuously obtained; The first trajectory to be drilled or the second trajectory to be drilled, and the drilling operation parameters corresponding to the trajectory to be drilled are input into the rotary steering tool control instruction prediction model to obtain a new control instruction; the control instruction is issued to the rotary steering tool, and continuous attitude measurement data and LWD measurement parameter data are continuously obtained.

2. The method for closed-loop control of well-ground geosteering according to claim 1, It is characterized in that The downhole continuous attitude measurement data and LWD measurement parameter data are respectively acquired by a continuous attitude measurement sensor arranged on a rotary steering tool and multiple types of LWD short sections arranged on a cable drill pipe, and each type of LWD short section is arranged at intervals.

3. The closed-loop control method for geosteering between wells and ground according to claim 1, It is characterized in that The LWD sub measures at least azimuthal gamma or azimuthal resistivity.

4. The method for closed-loop control of well-ground geosteering according to claim 1, It is characterized in that The method for constructing the rotary steerable tool control instruction prediction model comprises: 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; A machine learning model with a regression algorithm is selected, and the constructed sample set is divided into a training set, a validation set, and a test set for training, validation, and testing on the machine learning model to obtain a mature artificial intelligence prediction model for control instructions.

5. The closed-loop control method for geosteering between wells and ground according to claim 1, It 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 M of all the measured values ​​in the first sliding window. 1 , we can get the determination interval 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 [M 1 -X*W / Y,M 1 +X*W / Y]; 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.

6. The closed-loop control method for geosteering between wells and ground according to claim 1, It 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 value M of all the deflection force magnitude and direction data under the instruction condition 2 ; According to the fluctuation amplitude of the deflection 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 [M 2 -M 2 *r1,M 2 + M 2 *r1], delete the data outside the judgment interval.

7. The closed-loop control method for geosteering between wells and ground according to claim 1, It 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 M of the corresponding data type in the second sliding window 3 ; According to the fluctuation range of different types of data, the threshold adjustment ratio coefficient r2 is determined. The judgment interval of the drilling pressure, torque, speed, displacement and drilling time data is [M 3 -M 3 *r2,M 3 + M 3 *r2], delete the data outside the judgment interval.

8. The closed-loop control method for geosteering between wells and ground according to claim 1, It 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.

9. The method for closed-loop control of well-ground geosteering according to claim 1, It 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.

10. A closed-loop control system for geosteering between wells and ground. It is characterized in that The control system comprises: A measurement data acquisition module is used to acquire downhole continuous attitude measurement data and LWD measurement parameter data; A real drilling trajectory calculation module, used for fitting the continuous real drilling trajectory according to the continuous posture measurement data; A geological deviation calculation module continuously compares the geological deviation Δ between the LWD measurement parameter data and the geological requirements along the actual drilling trajectory. 0 ; When the geological deviation Δ 0 Greater than the threshold ε 0 When the first drilling trajectory is m meters ahead of the drill bit, the artificial intelligence prediction model is used to automatically design the first drilling trajectory according to the geological guidance trajectory adjustment principle, and the first designed trajectory is updated according to the first drilling trajectory to obtain the second designed trajectory; The geometric deviation calculation module is used to calculate the local geological deviation Δ 0 Less than or equal to the threshold ε 0 When the continuous actual drilling trajectory is compared with the first / second designed trajectory, the geometric deviation Δ 1 ; When the geometric deviation Δ 1 Greater than the threshold ε 1 When the geometric deviation Δ 1 Less than or equal to the threshold ε 1 When drilling, the drilling parameters and control instructions are kept unchanged, and the control instructions are sent to the rotary steerable tool; and continuous attitude measurement data and LWD measurement parameter data are continuously obtained; The rotary steering tool control instruction prediction module is used to input the first trajectory to be drilled or the second trajectory to be drilled, and the drilling operation parameters corresponding to the trajectory to be drilled into the rotary steering tool control instruction prediction model to obtain new control instructions; issue the control instructions to the rotary steering tool, and continuously obtain continuous attitude measurement data and LWD measurement parameter data.

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