Anti-deviation hole drilling method and system based on geological feedback information

Abstract

The invention discloses an anti-deviation hole drilling method and system based on geological feedback information, and relates to the technical field of drilling anti-deviation regulation, and the method comprises the steps: monitoring the stress change of a drill bit of a drilling machine in real time, and generating a stress signal data set; processing the stress signal data set, and constructing stress change quadrant distribution; generating an abnormal trigger discrimination result based on the abnormal trigger discrimination; if the abnormal triggering judgment result is that heterogeneous stratum triggering is abnormal, surrounding rock strength quadrant distribution is generated; and fusion control of local cutting tooth load self-adaptive control and direction adjusting mechanism global control is carried out. The technical problems that in the prior art, when drilling is conducted in the heterogeneous stratum, hole deviation inducements are difficult to accurately recognize, effective hole deviation prevention and correction means are lacked, the drilling precision is poor, and the stability is insufficient are solved, effective prevention and correction of hole deviation of the drill bit of the drilling machine under the heterogeneous stratum are achieved, and the drilling efficiency is improved. And the drilling precision and stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of drilling deviation prevention and control, and in particular to a method and system for preventing deviation of a hole based on geological feedback information. Background Art

[0002] In the field of drilling engineering, with the in-depth development of underground resources and infrastructure construction, drilling operations in complex heterogeneous formations are becoming increasingly common. However, existing technologies face many challenges. On the one hand, it is difficult to accurately monitor the drill bit stress and formation changes in real time during traditional drilling, resulting in the inability to promptly detect the impact of heterogeneous formations on the drill bit. On the other hand, there is a lack of efficient prevention and correction mechanisms for deviations in the hole. Once the formation strength varies or the drill bit itself fails, deviations in the hole are very likely to occur, resulting in reduced drilling accuracy and efficiency, and even causing engineering accidents, seriously affecting the quality and progress of the drilling project.

[0003] The existing technology has technical problems such as difficulty in accurately identifying the causes of deviation holes when drilling in heterogeneous formations and lack of effective means to prevent and correct deviation holes, resulting in poor drilling accuracy and insufficient stability. Summary of the Invention

[0004] The present application provides a method and system for preventing deviation hole drilling based on geological feedback information, which is used to solve the technical problems in the prior art of difficulty in accurately identifying the causes of deviation holes and lack of effective means of preventing and correcting deviation holes when drilling in heterogeneous formations, resulting in poor drilling accuracy and insufficient stability.

[0005] In view of the above problems, the present application provides an anti-deviation hole drilling method and system based on geological feedback information.

[0006] In a first aspect of the present application, a method for preventing deviation hole drilling based on geological feedback information is provided, the method comprising: During the drilling process of the drill bit, the stress changes of the drill bit are monitored in real time through the pre-configured stress sensing component to generate a stress signal data set; the stress signal data set is processed by the signal processing component, and the quadrant points corresponding to the drill bit cross section are numbered to construct a stress change quadrant distribution; based on the stress change quadrant distribution, abnormal triggering judgment of heterogeneous formations and drill bit self-faults is performed to generate an abnormal triggering judgment result; if the abnormal triggering judgment result is a heterogeneous formation triggering abnormality, the surrounding rock strength of different quadrants of the borehole circular cross section is analyzed based on the stress change quadrant distribution to generate a surrounding rock strength quadrant distribution; based on the surrounding rock strength quadrant distribution, fusion control of local pick load adaptive control and global control of the direction adjustment mechanism is performed.

[0007] A second aspect of the present application provides an anti-deviation hole drilling system based on geological feedback information, the system comprising: A data set generation module is used to monitor the stress changes of the drill bit in real time through a pre-configured stress sensing component during the drilling process of the drill bit, and generate a stress signal data set; a quadrant distribution construction module is used to process the stress signal data set using a signal processing component, and number the quadrant points corresponding to the drill bit cross section to construct a stress change quadrant distribution; a discrimination result generation module is used to perform abnormal triggering discrimination of heterogeneous formations and drill bit self-faults based on the stress change quadrant distribution, and generate an abnormal triggering discrimination result; a quadrant distribution generation module is used to analyze the surrounding rock strength of different quadrants of the borehole circular cross section based on the stress change quadrant distribution if the abnormal triggering discrimination result is a heterogeneous formation triggering abnormality, and generate a surrounding rock strength quadrant distribution; a fusion control module is used to perform fusion control of local pick load adaptive control and global control of the direction adjustment mechanism based on the surrounding rock strength quadrant distribution.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: The system monitors stress changes in the drill bit in real time to generate a stress signal dataset. The system processes this stress signal dataset using a signal processing component, numbers the corresponding quadrant points on the drill bit cross section, and constructs a stress change quadrant distribution. It then identifies abnormal triggering for heterogeneous formations and drill bit self-failures, generating an abnormal triggering identification result. If the abnormal triggering identification result indicates a heterogeneous formation triggering anomaly, the system analyzes the surrounding rock strength in different quadrants of the borehole circular cross section based on the stress change quadrant distribution to generate a surrounding rock strength quadrant distribution. Based on this surrounding rock strength quadrant distribution, it performs a fusion control of local pick load adaptive control and global control of the direction adjustment mechanism. This system effectively prevents and corrects drill bit deviation in heterogeneous formations, improving drilling accuracy and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a flow chart of a method for preventing deviation hole drilling based on geological feedback information provided in an embodiment of the present application; Figure 2 Schematic diagram of the structure of the anti-deviation hole drilling system based on geological feedback information provided in an embodiment of the present application.

[0011] Description of the accompanying drawings: data set generation module 10, quadrant distribution construction module 20, discrimination result generation module 30, quadrant distribution generation module 40, fusion control module 50. DETAILED DESCRIPTION

[0012] This application provides a method and system for preventing deviation hole drilling based on geological feedback information, which is used to solve the technical problems in the existing technology that when drilling in heterogeneous formations, it is difficult to accurately identify the causes of deviation holes and lack effective means of preventing and correcting deviation holes, resulting in poor drilling accuracy and insufficient stability.

[0013] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0014] Example 1, as Figure 1 As shown, the present application provides a method for preventing deviation hole drilling based on geological feedback information, the method comprising: Step S100: During the drilling process of the drill bit of the drilling rig, the stress changes of the drill bit of the drilling rig are monitored in real time by a pre-configured stress sensing component to generate a stress signal data set.

[0015] Specifically, during the drilling operation of the drill bit, the stress sensors pre-installed on each pick fit tightly against the working part of the pick, capturing in real time the stress changes that the drill bit is subjected to during drilling. During drilling, the drill bit continuously cuts the formation, and the picks are in direct contact with the formation rock, subjecting them to forces from different directions and magnitudes. The stress changes caused by these forces are quickly sensed by the stress sensors and converted into corresponding electrical signals or other signal forms that are easy to process. As drilling continues, the stress sensors on each pick continue to collect data, and a large number of stress change signals are continuously output. These signals are orderly integrated according to the preset time intervals and data storage rules, recording detailed information on the stress changes at each moment and each pick, and ultimately generating a stress signal data set. This dataset accurately reflects the stress changes of different picks at different drilling moments, including key characteristics such as stress fluctuations, change frequency, and direction of action. It provides indispensable raw data support for the subsequent use of signal processing components to construct a stress change quadrant distribution, and then perform abnormal triggering judgment of heterogeneous formations and drill bit self-faults, as well as to achieve precise control during the drilling process. It plays a decisive role in ensuring the accuracy and stability of the drilling direction.

[0016] Step S200: Processing the stress signal data set using a signal processing component, numbering the quadrant points corresponding to the drill bit cross section, and constructing a stress change quadrant distribution.

[0017] Specifically, after acquiring the stress signal dataset, the signal processing component is responsible for processing it and constructing a key data distribution. The signal processing component consists of a signal amplification circuit, a signal filtering circuit, and a data analysis channel. First, the signal amplification circuit amplifies the smaller amplitude signals in the stress signal dataset, enhancing the signal strength and enabling subsequent processing to more clearly capture the signal characteristics. Subsequently, the signal filtering circuit begins operation, effectively removing noise interference from the signal. This noise may come from vibrations from the drilling rig equipment, electromagnetic interference from the surrounding environment, and other factors. After filtering, the quality of the stress signal is significantly improved. After signal preprocessing, the quadrant points corresponding to the drill bit cross section are numbered based on the structural characteristics of the drill bit. Generally, the drill bit cross section is divided into multiple quadrants, such as four or eight, to facilitate more detailed analysis of the stress distribution. For the filtered stress signal, the stress data is categorized and aggregated according to the pick position corresponding to each quadrant. Through statistical analysis of the stress data within each quadrant, such as calculating the mean, maximum, minimum, and trend of stress, a quadrant distribution of stress variation is constructed. This distribution intuitively demonstrates the differences and changing patterns of stress on the drill bit in different quadrants, helping to accurately locate possible abnormal conditions and their corresponding quadrant areas, thereby providing a key basis for subsequent adjustments to the drilling control strategy.

[0018] Step S300: performing abnormal triggering discrimination of heterogeneous formations and drill bit self-faults based on the stress change quadrant distribution, and generating an abnormal triggering discrimination result.

[0019] Specifically, after constructing the stress variation quadrant distribution, the focus is on mining information from it to identify the source of anomalies, specifically determining whether the anomaly is caused by inhomogeneous formations or a drill bit malfunction. First, based on the stress variation quadrant distribution, a preset stress nonuniformity threshold is established to identify stress nonuniformity within different quadrants. A real-time stress nonuniformity index is calculated for stress data at different pick positions within each quadrant and compared with the preset stress nonuniformity threshold. If the real-time stress nonuniformity index exceeds the threshold, a stress anomaly is present, initiating the next step of analysis. Once a stress anomaly is identified, the temporal and spatial characteristics of the anomaly are further compared. First, based on the stress variation quadrant distribution, stress variations at multiple consecutive time stamps are analyzed to identify the real-time temporal signatures of the transition from normal to abnormal stress states. This allows for the identification of sudden onset or gradual evolution of stress anomalies, which helps distinguish between different types of anomaly causes. Second, connections are identified between locations where stress anomalies occur, generating real-time connection features of the anomaly locations to determine the distribution range and concentration areas of the stress anomaly across the drill bit cross-section. After completing the dual-path comparison, the system invokes a pre-set dual-path anomaly determination logic. This logic includes a first-level distinction between heterogeneous formations and drill bit failures based on the temporal characteristics of the mutation, and a second-level distinction threshold based on the connectivity characteristics of the anomaly locations. Based on the dual-path comparison results, these two distinctions are combined to accurately determine whether the anomaly is caused by heterogeneous formations or a drill bit failure, ultimately generating an accurate anomaly trigger determination. This result serves as an important basis for subsequent targeted measures, enabling analysis of surrounding rock strength if heterogeneous formations are identified as the trigger, as well as providing critical support for precise control and troubleshooting throughout the drilling process.

[0020] Step S400: If the abnormal triggering judgment result is that the abnormality is triggered by heterogeneous formations, the surrounding rock strength in different quadrants of the borehole circular cross section is analyzed based on the stress change quadrant distribution to generate a surrounding rock strength quadrant distribution.

[0021] Specifically, an algorithm based on support vector regression (SVR) combined with weight adjustment was used to analyze surrounding rock strength and generate a quadrant distribution of surrounding rock strength. Stress data for each quadrant was first standardized to eliminate dimensionality effects. Stress characteristics such as the mean, standard deviation, and gradient were extracted to construct a feature vector. A training set containing actual measurements of surrounding rock strength under different stress feature combinations was constructed using historical geological data and corresponding stress features. A radial basis function (RBF) kernel function was used to construct an SVR model. An optimization algorithm was used to find the optimal parameters (penalty factor C and kernel function parameter γ) to achieve the best fit for the model on the training set and establish a preliminary relationship between stress features and surrounding rock strength. Considering the varying impacts of different stress features on surrounding rock strength, the weights of each stress feature were determined using the analytic hierarchy process (AHP). A judgment matrix was constructed based on the experience of geological experts, and the weight vectors for each feature were calculated and tested for consistency. These weights were used to weight the SVR model predictions, ensuring they more closely matched actual conditions. For each quadrant of the borehole's circular cross-section, its stress characteristics are substituted into the weighted, modified SVR model to calculate the surrounding rock strength value for each quadrant. Finally, these strength values ​​are organized according to quadrant order to generate an intuitive quadrant distribution of surrounding rock strength, providing an accurate basis for subsequent drilling control.

[0022] Step S500: performing fusion control of local pick load adaptive control and direction adjustment mechanism global control based on the surrounding rock strength quadrant distribution.

[0023] Specifically, the key picks of the local pick load adaptive control module are determined. Based on the comparison of the pick's impact on the drilling rig's drill bit cutting trajectory and directional indicators with the preset threshold, the picks that have a greater impact on drilling accuracy are selected as key picks. These key picks are the core components for achieving local control. Next, the deviation hole behavior is deeply analyzed based on the surrounding rock strength quadrant distribution. If the analysis shows that the deviation hole feature is a local deviation hole, it means that the borehole deviates in the local area. At this time, the local pick load adaptive control module comes into play. The module contains a micro-hydraulic adjustment unit located at each key pick and a key pick adjustment parameter library. According to the surrounding rock strength quadrant distribution, the appropriate unit cutting force of the pick is matched from the key pick adjustment parameter library, and then the key pick is precisely controlled by the micro-hydraulic adjustment unit to correct the local deviation hole and restore the normal drilling trajectory of the drill bit in the local area. If the deviation hole feature is a global direction deviation hole, it indicates that the entire drilling direction has deviated. At this time, the global direction control module starts working. This module contains a library of adjustment parameters for the direction adjustment mechanism. The module selects appropriate adjustment parameters from the library based on the quadrant distribution of the surrounding rock strength and adjusts the direction adjustment mechanism to achieve precise control of the drilling direction and speed to correct the global deviation hole and ensure that the drill bit can drill in the predetermined direction. In actual operation, if the optimal unit cutting force of the pick matched by the local pick load adaptive control module cannot completely correct the local deviation hole, or the adjustment parameters of the direction adjustment mechanism matched by the global direction control module cannot completely correct the global direction deviation hole, a fitting operation of the control effect is performed to calculate the control deviation. Subsequently, another control module is used to match the control parameters for the control deviation, generate collaborative control parameters, and realize collaborative control of the two modules, further optimize the drilling process, improve the accuracy and stability of drilling, and effectively ensure the smooth progress of the drilling work.

[0024] In one possible implementation, step S300 further includes: Step S310: Based on the stress change quadrant distribution, stress non-uniformity in different quadrants is identified, stress anomaly judgment is performed, and a stress anomaly judgment result is generated.

[0025] Step S320: If the stress anomaly judgment result shows stress anomaly, a time feature comparison and a space feature comparison of the stress distribution anomaly are performed based on the stress change quadrant distribution to generate a two-way comparison result.

[0026] Step S330: calling the preset dual-path abnormality judgment logic, performing dual-path abnormality trigger analysis according to the dual-path comparison result, completing the abnormality trigger judgment of the heterogeneous formation and the drill bit self-fault, and generating the abnormality trigger judgment result.

[0027] Specifically, the previously acquired stress variation quadrant distribution data is normalized and preprocessed, and stress data from different sampling times and quadrants are structured and stored to ensure data accuracy and consistency. A preset stress nonuniformity threshold system is then constructed, taking into account the drill bit's design parameters, the mechanical properties of common formations, and stress characteristics associated with anomalies in past drilling cases. For example, for a specific drill bit model and common rock types, extensive simulation experiments and field tests are conducted to determine the acceptable range of stress fluctuations in each quadrant under normal drilling conditions. Next, stress nonuniformity indices are calculated for each quadrant. The standard deviation is used to measure stress dispersion. The mean of the stress data at different pick positions within each quadrant is calculated. The square root of the average of the sum of the squared differences between each data point and the mean is then taken to determine the stress standard deviation for that quadrant. This standard deviation serves as the stress nonuniformity indicator for that quadrant. The calculated stress nonuniformity indices for each quadrant are then compared with the preset stress nonuniformity threshold. If the stress standard deviation for a quadrant exceeds the corresponding threshold, or if the stress change rate exceeds the acceptable range, a stress anomaly is determined for that quadrant. Finally, the judgment results of all quadrants are summarized to generate a comprehensive stress anomaly judgment result, which clearly points out which quadrants have stress anomalies, providing a basis for subsequent abnormal cause analysis and treatment.

[0028] When stress anomaly detection results indicate a stress anomaly, a dual-path feature comparison is performed. For temporal feature comparison, based on the quadrant distribution of stress changes, stress data from multiple consecutive time stamps is carefully analyzed. By analyzing the transition from normal to abnormal stress states, the specific moment of the stress abrupt change, the magnitude of the abrupt change, and the stress change trends before and after the abrupt change are precisely identified. For example, is the stress abrupt change a sudden, large jump, or a slow increase followed by a sudden intensification? This helps determine whether the anomaly is a sudden external disturbance or an internal problem caused by gradual accumulation. For spatial feature comparison, the focus is on the locations where stress anomalies occur. These locations are connected and identified, and their distribution patterns and relationships on the drill bit cross section are observed to generate real-time anomaly location connection features. If anomalies are concentrated in a few adjacent quadrants, this may indicate a local problem, such as a change in formation properties or localized wear of the drill bit. If anomalies are dispersed across multiple, non-adjacent quadrants, this may indicate complex formation conditions or a systemic failure of the drill bit. The real-time mutation time features obtained by temporal feature comparison and the real-time abnormal position connection features obtained by spatial feature comparison are combined to finally generate a two-way comparison result, which provides a key basis for the subsequent accurate judgment of whether the abnormality is caused by heterogeneous formations or drill bit self-failure.

[0029] The pre-set dual-path anomaly detection logic, which includes rules for distinguishing temporal and spatial characteristics, is invoked. The resulting dual-path comparison results, namely the temporal and spatial characteristics of the stress distribution anomaly, are then input into the logic. In spatial analysis, the distribution of stress anomalies is compared based on multi-point stress data comparison rules. If a regional or patchy distribution is consistent with the spatial characteristics of geological change, it is likely due to heterogeneous strata. If an anomaly is an isolated point, it is more consistent with mechanical failure. In temporal analysis, through time series smoothing and trend analysis, if the stress anomaly exhibits a slow-changing trend, consistent with the generally slow geological change, it is likely caused by heterogeneous strata. If a sudden change occurs, it is consistent with the characteristic of mechanical failures, which are often sudden, and is likely due to drill bit failure. Combining the results of the spatial and temporal analysis, dual-path anomaly trigger analysis is performed, ultimately distinguishing between anomaly triggers due to heterogeneous strata and drill bit failure. The generated anomaly trigger determination provides a reliable basis for subsequent targeted measures.

[0030] In one possible implementation, step S310 further includes: Step S311: Based on the structural characteristics of the drill bit, a preset stress non-uniformity threshold that satisfies microscopic heterogeneity is constructed.

[0031] Step S312: Calculating stress non-uniformity indices at different pick positions in different quadrants based on the stress change quadrant distribution to generate real-time stress non-uniformity indices.

[0032] Step S313: comparing the preset stress non-uniformity threshold with the real-time stress non-uniformity index, performing stress anomaly judgment, and generating the stress anomaly judgment result.

[0033] Specifically, the establishment of a preset stress inhomogeneity threshold that meets microscopic heterogeneity requirements is achieved through the following methods. First, using 3D scanning technology, precise structural data of the drill bit is obtained, including the shape, size, and spacing of the cutters, as well as the contours of the drill body, to construct a digital model. Then, using finite element analysis software, micromechanical parameters of different formations, such as the rock's elastic modulus, Poisson's ratio, and compressive strength, are input into the model based on the actual formation distribution. By simulating the drill bit's drilling process under different formation conditions, the stress distribution at different locations is calculated. Multiple simulations with adjusted parameters are performed to collect a large amount of stress data. This data is then statistically analyzed, and mathematical statistics methods are used to determine statistical quantities such as the mean and standard deviation of the stress distribution. Based on this, combined with engineering experience and safety margin requirements, a reasonable stress fluctuation range is set, ultimately establishing a preset stress inhomogeneity threshold that meets microscopic heterogeneity requirements.

[0034] The stress data of different pick positions in each quadrant are extracted from the stress change quadrant distribution. These data contain the stress magnitude that the pick bears during the drilling process. Then, for each quadrant, the mean of the stress data of all pick positions in the quadrant is calculated. After that, the stress value of each pick position is subtracted from the stress mean of the quadrant, and the difference is squared. The average of these squared differences is then calculated, and finally the square root of this average is taken to obtain the stress standard deviation of the quadrant. The above calculation process is repeated to obtain the stress standard deviation of all quadrants. These stress standard deviations can reflect the degree of discreteness of stress in each quadrant, that is, the non-uniformity of stress. They are used as real-time stress non-uniformity indicators, providing an accurate quantitative basis for subsequent comparison with the preset stress non-uniformity threshold and judgment of whether the stress is abnormal.

[0035] The preset stress nonuniformity threshold defines a reasonable stress fluctuation range based on the structural characteristics of the drill bit and the microscopic heterogeneity of the formation. The real-time stress nonuniformity index (RSI) is the standard deviation of stress in each quadrant, calculated based on the quadrant distribution of stress variations. It reflects the degree of stress dispersion in each quadrant during drilling. Next, for each quadrant, the real-time stress nonuniformity index is compared with the corresponding preset stress nonuniformity threshold. If the real-time stress nonuniformity index exceeds the preset threshold, the stress dispersion in that quadrant exceeds the normal range, and a stress anomaly is determined in that quadrant. If the real-time stress nonuniformity index does not exceed the preset threshold, the stress distribution in that quadrant is within the normal fluctuation range, and no stress anomaly exists. Finally, the judgment results for all quadrants are combined to generate a comprehensive stress anomaly judgment result, clearly indicating which quadrants have stress anomalies. This provides a key basis for accurately identifying the triggering cause of the anomaly and taking targeted measures.

[0036] In one possible implementation, step S320 further includes: Step S321: comparing the time characteristics of stress distribution anomalies of multiple consecutive time stamps based on the stress change quadrant distribution, and identifying the real-time mutation time characteristics of stress from normal to abnormal.

[0037] Step S322: performing connection identification of abnormal stress distribution locations based on stress change quadrant distribution, and generating real-time abnormal location connection features.

[0038] Step S323: generating the two-way comparison result based on the real-time mutation time feature and the real-time abnormal position connection feature.

[0039] Specifically, from the massive amount of data recorded by the quadrant distribution of stress changes, stress data sequences with multiple consecutive timestamps are selected. This data records the stress conditions in different quadrants at each time point in detail. Next, a normal stress range is established, based on data accumulated during past normal drilling and estimated based on current geological conditions. The stress data is then analyzed point by point in chronological order. During the analysis, close attention is paid to whether stress values ​​exceed the normal range. Whenever a stress value exceeds the normal range, the time point is recorded. Furthermore, the stress changes before and after this time point are further analyzed, including the rate of stress change (whether it gradually rises beyond the normal range or suddenly jumps), the magnitude of the stress change, and the degree to which it exceeds the normal range. By comprehensively considering this information, the real-time sudden change time when stress transitions from normal to abnormal state, as well as the associated sudden change characteristics, such as the speed and magnitude of the change, are accurately identified. These identified real-time sudden change time characteristics provide a basis for subsequent anomaly assessment.

[0040] Based on the stress change quadrant distribution, the locations of stress anomalies are analyzed in depth. First, all stress anomaly locations are located in the stress change quadrant distribution. These locations correspond to the areas where the drill bit picks are subjected to abnormal stress during drilling. Next, the spatial relationship between these abnormal locations is analyzed. If certain abnormal locations are close to each other in space, forming a continuous or adjacent distribution state, they are connected for identification. For example, if stress anomalies appear at multiple pick positions in several adjacent quadrants, these adjacent abnormal locations are considered as a whole. Through this connection identification, it is possible to determine whether the abnormal locations are concentrated in a specific area or dispersed in different locations. Finally, based on the results of the connection identification, a real-time abnormal location connection feature is generated. This feature can intuitively present the spatial distribution pattern and aggregation pattern of stress anomalies.

[0041] The resulting real-time mutation time features are then analyzed to identify the specific moment when stress transitioned from normal to abnormal, as well as the speed and magnitude of the mutation. Simultaneously, the generated real-time anomaly location connectivity features are organized to clearly understand the spatial distribution of the stress anomaly, including whether the anomaly locations are concentrated in specific areas or more dispersed, and the connectivity between them. These two sets of features are then organically combined, correlating the temporal information of the stress anomaly with the spatial distribution of the anomaly locations to form a comprehensive comparison dataset. For example, if the real-time mutation time features indicate a sudden and significant stress change within a short period of time, while the real-time anomaly location connectivity features indicate that the anomaly is concentrated on a single side of the drill bit, the two-way comparison results will indicate a sudden and concentrated stress anomaly on that side of the drill bit at a specific moment. In this way, the generated two-way comparison results comprehensively reflect the characteristics of the stress anomaly from both temporal and spatial dimensions, providing clear and comprehensive data support for subsequent invocation of the pre-set two-way anomaly judgment logic to accurately distinguish anomalies caused by heterogeneous formations and drill bit failures.

[0042] In one possible implementation, step S323 further includes: The preset dual-path abnormality judgment logic includes a first distinguishing logic of heterogeneous formations and drill bit self-failures based on mutation time characteristics and a second distinguishing threshold of heterogeneous formations and drill bit self-failures based on abnormal position connection characteristics.

[0043] Specifically, the pre-set dual-path anomaly detection logic is a refined anomaly discrimination system. It includes a first-level differentiation logic based on the time signature of the sudden change and a second-level differentiation threshold based on the connectivity of the anomaly's location. This system effectively distinguishes whether anomalies stem from heterogeneous formations or drill bit failures. Regarding the first differentiation logic based on the time signature of the sudden change, since heterogeneous formations are the result of long-term geological processes, their impact on stress will gradually manifest over time. When the formation contains uneven softness and hardness, the stress of the drill bit will slowly change as the drill bit gradually contacts different formations during drilling. The transition from normal to abnormal state may take time, and the rate of sudden change is relatively slow. For example, as the drill bit gradually enters a harder formation from a softer formation, stress will gradually increase. During this process, the onset of stress anomalies is often gradual. In contrast, drill bit failures are often sudden. Failures such as the sudden breakage of a pick or the transient damage of internal mechanical components can cause a sudden change in the stress conditions of the drill bit, resulting in a large sudden change in stress within a very short period of time. Therefore, the first differentiation logic establishes logical rules for identifying the source of anomalies based on key characteristics such as the duration and speed of the stress transition from normal to abnormal. If the stress mutation duration is long and the speed is slow, the anomaly is more likely to be caused by heterogeneous formations. If the mutation duration is extremely short and the speed is fast, it is more likely to be caused by a drill bit failure. The second differentiation threshold, based on the connectivity of anomaly locations, is set based on spatial distribution. Heterogeneous formations exhibit a certain degree of spatial continuity and regionality. When a drill bit penetrates heterogeneous formations, influenced by formation characteristics, stress anomalies tend to be concentrated where the drill bit contacts a specific formation area. These anomaly locations exhibit distinct connections, forming a regional anomaly distribution pattern. For example, when encountering a hard rock zone, the drill bit will experience stress anomalies on the side of the cutter in contact with the rock zone as it passes through it. These anomalies are concentrated on the same side of the drill bit and are interconnected. Stress anomalies caused by drill bit failures, on the other hand, are more randomly distributed. Because the fault occurs within the drill bit itself and is unrelated to the formation distribution, the anomaly locations may be scattered across different parts of the drill bit, lacking a clear, regular connection. By analyzing a large number of real-world cases, an appropriate second discrimination threshold was established. When indicators such as the tightness of the connections and the distribution range of the anomaly locations exceed this threshold, the anomaly is more consistent with heterogeneous formations. If it falls below the threshold, the anomaly is more likely to be caused by a drill bit fault. These two components complement each other, forming a pre-defined dual-path anomaly judgment logic that provides a basis for accurately identifying the cause of the anomaly.

[0044] In one possible implementation, step S500 further includes: Step S510: determining a key pick of a local pick load adaptive control module, wherein the key pick is a pick having an impact index on the cutting trajectory and orientation of the drill bit greater than a preset threshold.

[0045] Step S520: Based on the surrounding rock strength quadrant distribution, perform biased hole behavior analysis to generate biased hole characteristics.

[0046] Step S530: If the biased hole feature is a local biased hole, with the goal of correcting the local biased hole, the local pick load adaptive control module is used to control the unit cutting force of the key pick.

[0047] Step S540: If the deviation hole feature is a global directional deviation hole, with the goal of correcting the global deviation hole, the global directional control module is used to perform adjustment parameter matching of the direction adjustment mechanism and then control the drilling direction and speed.

[0048] Specifically, when the drill rig is drilling normally into a short section of standard formation, high-precision trajectory monitoring equipment and directional measuring instruments are used to record the initial cutting trajectory and orientation data of the drill bit. Then, the work of each pick is stopped one by one. After stopping a pick each time, the drill rig is allowed to continue drilling into a short section of the same type of formation, and the cutting trajectory and orientation data of the drill bit are recorded again. By comparing the cutting trajectory deviation and orientation angle change of the drill bit before and after each stop of a single pick, the impact of each pick on the cutting trajectory and orientation is quantified. The quantitative impact values ​​of all picks are sorted, and a suitable preset threshold is determined based on engineering experience and the requirements for drilling accuracy. Those picks with quantitative impact values ​​greater than the preset threshold are the picks that have a greater impact on the cutting trajectory and orientation of the drill bit, that is, the key picks of the local pick load adaptive control module.

[0049] To analyze the behavior of deviated drilling based on the quadrant distribution of surrounding rock strength, the surrounding rock area contacted by the drill bit is first divided into quadrants. Using pressure sensors, acoustic wave detectors, and other equipment, mechanical parameters such as the compressive strength and elastic modulus of the surrounding rock within each quadrant are collected in real time to generate detailed quadrant distribution data on surrounding rock strength. This data is then analyzed in depth to compare the differences in surrounding rock strength between quadrants. If the surrounding rock strength in one or several adjacent quadrants is significantly higher than that in other quadrants, the drill bit experiences increased resistance in these high-strength areas during drilling, causing it to deviate toward the lower-strength quadrant. Information such as the quadrant location, deviation angle, and deviation rate of the deviated drilling are recorded. If the surrounding rock strength varies across quadrants but is relatively discrete, this may cause the drill bit to experience irregular wobbling and deflection during drilling. Based on these analysis results, it is determined whether the deviated drilling exhibits a trend of localized, concentrated deflection or a global, directional deflection. Ultimately, a deviated drilling signature is generated, containing information such as the location, direction, and degree of deflection, providing a key basis for subsequent targeted corrective measures.

[0050] The specific characteristics of localized hole deviations are thoroughly analyzed, encompassing key information such as the location, angle, and rate of deviation. Based on this information, the key picks requiring adjustment are precisely identified. Because key picks significantly influence the drill bit's cutting trajectory and orientation, adjusting them effectively corrects the deviations. Next, the local pick load adaptive control module selects the most appropriate adjustment strategy from a library of preset adjustment strategies based on the deviation characteristics. This strategy specifies the specific value and direction of the unit cutting force required for each key pick. The unit cutting force of each key pick is then precisely adjusted using a micro-hydraulic adjustment device installed on the drill bit. If the deviation is in a certain direction, the unit cutting force of the key pick opposite that direction is increased, or the unit cutting force of the key pick in the direction of deviation is reduced, thereby altering the force balance in the local area of ​​the drill bit. During the adjustment process, the drill bit's cutting trajectory and hole deviation are monitored in real time, and the actual correction results are compared with the expected targets. If it is found that the correction effect does not meet expectations, quickly re-analyze the deviation hole characteristics, dynamically adjust the adjustment plan, and optimize and control the unit cutting force of the key picks again until the local deviation hole is effectively corrected and the drill bit returns to the normal drilling trajectory.

[0051] When the generated deviation hole characteristics are determined to be global directional deviation holes, the global directional control module operates with the goal of correcting the global deviation. First, the global directional deviation hole characteristics are carefully analyzed to determine key information such as the specific direction, deviation angle, and degree of deviation. Next, the global directional control module accurately matches these deviation hole characteristics with a pre-built adjustment parameter database to identify the most appropriate adjustment parameters for the directional control mechanism for the current deviation hole condition. These parameters, derived from extensive simulation experiments and actual drilling experience, cover the optimal adjustment values ​​for the directional control mechanism under various deviation hole conditions. After obtaining the matching adjustment parameters, they are transmitted to the directional control mechanism. Based on the received parameters, the directional control mechanism precisely controls the drilling direction and speed of the drill bit. For example, if the hole direction is deviated to the right, the directional control mechanism will fine-tune the drill bit angle to the left by a certain amount. It will also adjust the drilling speed appropriately based on the degree of deviation. If the degree of deviation is significant, the drilling speed may be appropriately reduced to ensure more accurate correction of the deviation hole. During the control process, the drill bit's drilling direction and speed are continuously monitored in real time, and the actual situation is compared and analyzed with the expected correction target. If a deviation is detected between the actual drilling situation and the expected situation, the global direction control module will immediately re-analyze the deviation characteristics, re-match and optimize the adjustment parameters, and dynamically adjust the direction adjustment mechanism to ensure that the drilling direction and speed are always precisely adjusted to correct the global deviation until the global deviation is effectively corrected and the drill bit returns to the predetermined corrective drilling path.

[0052] In one possible implementation, step S540 further includes: Step S541: If the optimal unit cutting force of the pick matched by the local pick load adaptive control module cannot completely correct the local deviation hole or the direction adjustment mechanism adjustment parameters matched by the global direction control module cannot completely correct the global direction deviation hole, the control deviation is determined after performing the fitting of the control effect, and the control parameters of the control deviation are matched with another control module to generate collaborative control parameters for collaborative control.

[0053] Specifically, when the optimal cutter-specific cutting force matched by the local pick load adaptive control module fails to fully correct the local hole deviation, or when the directional control mechanism adjustment parameters matched by the global directional control module are insufficient to fully correct the global directional hole deviation, a high-precision sensor array is invoked to collect multidimensional data from the drill bit during drilling, including real-time forces on each cutter, drill bit attitude angle, and drilling displacement trajectory. Subsequently, a cubic spline interpolation algorithm is used to fit the actual collected data with the preset ideal drilling data. By calculating the mean square error between the two, the degree of deviation between the current control and the ideal state is quantified, thus clarifying the control deviation. Next, depending on the control module that failed to achieve the expected effect, the corresponding control module is activated. If the local pick load control fails, the global directional control module quickly intervenes. Based on the control deviation, it retrieves successful control solutions under similar working conditions from a historical case database and optimizes the directional control parameters using a reinforcement learning algorithm. If the global directional control fails to achieve the target, the local pick load adaptive control module is reactivated and a particle swarm optimization algorithm is used to search and select the cutter-specific cutting force parameters that match the current deviation from the cutting force parameter library. Finally, a weighted distribution algorithm combines the optimized parameters of the two control modules to generate coordinated control parameters. These parameters are synchronously transmitted to the pick hydraulic drive system and the direction adjustment mechanism, enabling coordinated dynamic control of the pick cutting force and drilling direction, ensuring that the drill bit gradually returns to the intended drilling trajectory and effectively resolving the problem of off-center drilling.

[0054] In one possible implementation, step S540 further includes: Step S542: the local pick load adaptive control module includes a micro hydraulic adjustment unit located at each key pick and a key pick adjustment parameter library, and controls the unit cutting force of the pick of the local offset hole after matching the key pick adjustment parameter library through the micro hydraulic adjustment unit.

[0055] Step S543: the global direction control module includes a regulation parameter library of the direction regulation mechanism, and uses the regulation parameter library to match the regulation parameters of the global direction deviation hole.

[0056] Specifically, the local pick load adaptive control module is the core component for accurately dealing with the problem of local deviation holes. It achieves efficient control through the coordinated operation of the micro-hydraulic adjustment unit and the key pick adjustment parameter library. When a local deviation hole is detected, the specific characteristics of the deviation hole, such as the quadrant in which the deviation hole is located, the deviation angle, the deviation rate and other data information, are immediately transmitted to the key pick adjustment parameter library. This parameter library is constructed based on a large number of actual engineering cases and simulation experimental data. It stores the optimal unit cutting force parameters of each key pick corresponding to different local deviation hole conditions. Based on the input deviation hole characteristics, a rapid search and matching is performed in the parameter library to screen out the unit cutting force parameter combination of the pick that is suitable for the current deviation hole condition. After the matching is completed, these parameter information is transmitted in real time to the micro-hydraulic adjustment unit installed on each key pick. As an actuator, the micro-hydraulic adjustment unit responds quickly after receiving the command and adjusts the unit cutting force of the key pick by precisely controlling the pressure and flow of the hydraulic system. For example, if the hole deviates to the left, the unit cutting force on the key pick on the right side will be increased, or the unit cutting force on the key pick on the left side will be reduced, thereby changing the local force distribution on the drill bit and causing the drill bit to return to the normal drilling trajectory. During the adjustment process, the micro-hydraulic adjustment unit also provides real-time feedback on the pick force status, continuously optimizing the control strategy and providing data support to ensure that the local deviation is corrected efficiently and accurately.

[0057] When a global directional deviation is detected, the global directional control module immediately activates. This module relies on the directional control mechanism's adjustment parameter library, which is constructed based on numerous actual drilling projects, simulation experiments under various geological conditions, and in-depth research on various deviation scenarios. It contains the optimal adjustment parameters for different types of global directional deviations (such as different deviation angles, deviation directions, and deviation rates). The detected global directional deviation characteristics, including detailed information such as the overall direction, deviation angle, and deviation rate of the deviation, are compared and analyzed with the data in the adjustment parameter library. A search algorithm quickly matches the adjustment parameters in the parameter library that best match the current global directional deviation. Once a match is successful, these adjustment parameters are transmitted to the directional control mechanism, guiding it to precisely adjust the drill bit's drilling direction, thereby effectively correcting the global directional deviation and ensuring stable drilling along the predetermined path.

[0058] In one possible implementation, step S200 further includes: Step S210: The signal processing component includes a signal amplification circuit, a signal filtering circuit and a data analysis channel.

[0059] Step S220: the signal amplification circuit performs signal amplification processing on the stress signal data set, the signal filtering circuit performs filtering processing on the amplified signal, and the quadrant points corresponding to the drill bit cross section are numbered based on the filtered stress signal data set to construct the stress change quadrant distribution.

[0060] Step S230: The data analysis channel is sent to the data analysis channel for converting the stress change quadrant distribution into the surrounding rock strength quadrant distribution after abnormal triggering judgment. The data analysis channel is constructed by historical conversion data.

[0061] Specifically, the signal processing component, serving as a key hub for data processing, consists of three closely coordinated components: a signal amplification circuit, a signal filtering circuit, and a data analysis channel. The signal amplification circuit is primarily responsible for enhancing the collected raw stress signal data set. Since the signal may attenuate due to factors such as transmission distance and environmental interference during the actual acquisition process, the circuit's amplifier chip and specific circuit design can enhance the signal's amplitude and strength, ensuring that the signal can be accurately analyzed later. The signal filtering circuit focuses on purifying the amplified signal, using low-pass, high-pass, or band-pass filtering techniques to remove useless information such as high-frequency noise and power frequency interference, making the stress signal purer and reducing the impact of interference on the analysis results. The data analysis channel is responsible for receiving the filtered signal and, after anomaly triggering, conducting in-depth analysis and conversion of the stress change data, providing critical data support for subsequent deviation hole analysis and anomaly judgment during the drilling process.

[0062] In the signal processing process, a quadrant distribution of stress variation is constructed through signal amplification, signal filtering, and quadrant point numbering. First, the signal amplification circuit receives the collected raw stress signal dataset. Because signals are susceptible to attenuation due to factors such as transmission loss and environmental interference in real-world operating conditions, the signal amplification circuit uses components such as operational amplifiers to enhance the raw stress signal at a preset amplification factor, bringing the signal amplitude within a suitable range for analysis. The amplified signal then enters the signal filtering circuit. This circuit employs algorithms such as low-pass and band-pass filtering to effectively remove unwanted components such as high-frequency noise and power frequency interference, resulting in a purer stress signal. After filtering, the points corresponding to each quadrant are sequentially numbered based on the geometric characteristics of the drill bit's cross-section, establishing a correspondence between the signal data and the drill bit's spatial position. Finally, based on the numbered filtered stress signal dataset and combined with time series data, the stress variations at each quadrant point are integrated and visualized to construct a quadrant distribution of stress variation. This distribution intuitively demonstrates the dynamic changes of stress over time in different quadrants of the drill bit during the drilling process, providing a clear and accurate data basis for subsequent analysis of stress anomalies and judgment of deviation hole behavior.

[0063] After the stress change quadrant distribution data is sent to the data analysis channel, anomaly trigger detection is performed, and normal range thresholds are set for various stress change indicators (such as the stress change rate and the stress difference between adjacent quadrants). The values ​​of each indicator in the current stress change quadrant distribution are calculated and compared with the corresponding thresholds. If any indicator exceeds the threshold range, it is considered an anomaly, and the specific location and relevant indicator value are recorded for subsequent troubleshooting. If all indicators are within the threshold range, it is considered normal and the subsequent conversion process continues. Next, the stress change quadrant distribution is converted to the surrounding rock strength quadrant distribution. A support vector regression (SVR) model is constructed using the historical conversion data. During the construction process, stress change characteristics (such as the mean and variance of each quadrant stress) are extracted from the historical data as input, and the corresponding surrounding rock strength is used as output. This data is normalized to eliminate the influence of different feature dimensions. The SVR model is trained and parameterized on the training set using cross-validation to determine the optimal kernel function (such as the radial basis kernel) and parameter combination. During the actual conversion, the characteristics of the current stress change quadrant distribution are input into the trained SVR model. The model will output the corresponding surrounding rock strength prediction value based on the learned mapping relationship, thereby obtaining the surrounding rock strength quadrant distribution.

[0064] Example 2, based on the same inventive concept as the anti-deviation hole drilling method based on geological feedback information in the previous embodiment, Figure 2 As shown, the present application provides an anti-deviation hole drilling system based on geological feedback information. The system and method embodiments in the present application are based on the same inventive concept. The system includes: The data set generation module 10 is used to monitor the stress changes of the drill bit of the drill rig in real time through the pre-configured stress sensing component during the drilling process of the drill bit of the drill rig, and generate a stress signal data set.

[0065] The quadrant distribution construction module 20 is used to process the stress signal data set using a signal processing component, number the quadrant points corresponding to the drill cross section, and construct a stress change quadrant distribution.

[0066] The discrimination result generating module 30 is configured to perform abnormal trigger discrimination of heterogeneous formations and drill bit self-faults based on the stress change quadrant distribution, and generate an abnormal trigger discrimination result.

[0067] The quadrant distribution generating module 40 is used to analyze the surrounding rock strength in different quadrants of the borehole circular cross section based on the stress change quadrant distribution if the abnormal triggering judgment result is that the heterogeneous formation triggers the abnormality, and generate the surrounding rock strength quadrant distribution.

[0068] The fusion control module 50 is used for performing fusion control of the local pick load adaptive control and the direction adjustment mechanism global control based on the surrounding rock strength quadrant distribution.

[0069] Furthermore, the system is also used for the following functions: Based on the stress change quadrant distribution, stress non-uniformity in different quadrants is identified, stress anomaly judgment is performed, and a stress anomaly judgment result is generated; if the stress anomaly judgment result shows stress anomaly, time feature comparison and spatial feature comparison of the stress distribution anomaly are performed based on the stress change quadrant distribution to generate a two-way comparison result; the preset two-way anomaly judgment logic is called, and a two-way anomaly trigger analysis is performed according to the two-way comparison result to complete the abnormal trigger judgment of the heterogeneous formation and the drill bit self-fault, and generate the abnormal trigger judgment result.

[0070] Furthermore, the system is also used for the following functions: Based on the structural characteristics of the drill bit, a preset stress non-uniformity threshold that satisfies microscopic heterogeneity is constructed; based on the stress change quadrant distribution, stress non-uniformity indicators at different pick positions in different quadrants are calculated to generate a real-time stress non-uniformity indicator; the preset stress non-uniformity threshold is compared with the real-time stress non-uniformity indicator, and a stress anomaly judgment is performed to generate the stress anomaly judgment result.

[0071] Furthermore, the system is also used for the following functions: Based on the stress change quadrant distribution, the time characteristics of stress distribution anomalies of multiple consecutive time stamps are compared to identify the real-time mutation time characteristics of stress from normal to abnormal; based on the stress change quadrant distribution, the connection of the stress abnormal distribution position is identified to generate the real-time abnormal position connection characteristics; the two-way comparison result is generated using the real-time mutation time characteristics and the real-time abnormal position connection characteristics.

[0072] Furthermore, the system is also used for the following functions: The preset dual-path abnormality judgment logic includes a first distinguishing logic of heterogeneous formations and drill bit self-failures based on mutation time characteristics and a second distinguishing threshold of heterogeneous formations and drill bit self-failures based on abnormal position connection characteristics.

[0073] Furthermore, the system is also used for the following functions: Determine the key pick of the local pick load adaptive control module, the key pick being the pick whose cutting trajectory and directional influence index on the drill bit of the drilling rig is greater than a preset threshold; based on the quadrant distribution of the surrounding rock strength, perform biased hole behavior analysis to generate biased hole characteristics; if the biased hole characteristics are local biased holes, with the goal of correcting the local biased holes, control the unit cutting force of the pick on the key pick through the local pick load adaptive control module; if the biased hole characteristics are global directional biased holes, with the goal of correcting the global biased holes, use the global directional control module to perform adjustment parameter matching of the directional adjustment mechanism and then control the drilling direction and speed.

[0074] Furthermore, the system is also used for the following functions: If the optimal unit cutting force of the pick matched by the local pick load adaptive control module cannot completely correct the local deviation hole or the direction adjustment mechanism adjustment parameters matched by the global direction control module cannot completely correct the global direction deviation hole, the control deviation is determined after performing the fitting of the control effect, and the control parameters of the control deviation are matched with another control module to generate collaborative control parameters for collaborative control.

[0075] Furthermore, the system is also used for the following functions: The local pick load adaptive control module includes a micro-hydraulic adjustment unit located at each key pick and a key pick adjustment parameter library, which is used to match the unit cutting force of the pick of the local biased hole with the key pick adjustment parameter library and then controlled by the micro-hydraulic adjustment unit; the global direction control module includes an adjustment parameter library of the direction adjustment mechanism, which is used to match the adjustment parameters of the global direction biased hole.

[0076] Furthermore, the system is also used for the following functions: The signal processing component includes a signal amplification circuit, a signal filtering circuit and a data analysis channel; the signal amplification circuit performs signal amplification processing on the stress signal data set, and the signal filtering circuit performs filtering processing on the amplified signal. The quadrant points corresponding to the drill cross section are numbered based on the filtered stress signal data set to construct the stress change quadrant distribution; the data analysis channel is sent to the data analysis channel for converting the stress change quadrant distribution into the surrounding rock strength quadrant distribution after abnormal triggering judgment. The data analysis channel is constructed through historical conversion data.

[0077] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0079] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. Anti-deviation hole drilling method based on geological feedback information, characterized in that: include: During the drilling process of the drill bit, the stress changes on the drill bit are monitored in real time through the pre-configured stress sensing component to generate a stress signal data set; Processing the stress signal data set using a signal processing component, numbering the quadrant points corresponding to the drill bit cross section, and constructing a stress change quadrant distribution; Perform abnormal triggering discrimination of heterogeneous formations and drill bit self-faults based on the stress change quadrant distribution, and generate an abnormal triggering discrimination result; If the abnormal triggering judgment result is that the abnormality is triggered by heterogeneous formations, the surrounding rock strength in different quadrants of the borehole circular cross section is analyzed based on the stress change quadrant distribution to generate a surrounding rock strength quadrant distribution; Based on the quadrant distribution of surrounding rock strength, a fusion control of local pick load adaptive control and a global control of a direction adjustment mechanism is performed.

2. The anti-deviation hole drilling method based on geological feedback information according to claim 1, characterized in that: Based on the stress change quadrant distribution, abnormal trigger discrimination of heterogeneous formations and drill bit self-faults is performed to generate abnormal trigger discrimination results, including: Identifying stress non-uniformity in different quadrants based on the stress change quadrant distribution, performing stress anomaly judgment, and generating a stress anomaly judgment result; If the stress anomaly judgment result shows stress anomaly, performing a time feature comparison and a space feature comparison of the stress distribution anomaly based on the stress change quadrant distribution to generate a two-way comparison result; The preset dual-path abnormality judgment logic is called, and a dual-path abnormality trigger analysis is performed according to the dual-path comparison result to complete the abnormality trigger judgment of the heterogeneous formation and the drill bit self-fault, and generate the abnormality trigger judgment result.

3. The anti-deviation hole drilling method based on geological feedback information according to claim 2, characterized in that: Based on the stress change quadrant distribution, stress non-uniformity in different quadrants is identified, stress anomaly judgment is performed, and stress anomaly judgment results are generated, including: Based on the structural characteristics of the drill bit of the drilling rig, a preset stress non-uniformity threshold that satisfies microscopic heterogeneity is established; Calculating stress non-uniformity indices at different pick positions in different quadrants based on the stress change quadrant distribution to generate real-time stress non-uniformity indices; The preset stress non-uniformity threshold is compared with the real-time stress non-uniformity index, a stress anomaly judgment is performed, and the stress anomaly judgment result is generated.

4. The anti-deviation hole drilling method based on geological feedback information according to claim 2, characterized in that: Based on the stress change quadrant distribution, a temporal feature comparison and a spatial feature comparison of stress distribution anomalies are performed to generate a two-way comparison result, including: Compare the time characteristics of stress distribution anomalies of multiple consecutive time stamps based on the stress change quadrant distribution to identify the real-time mutation time characteristics of stress from normal to abnormal; Identify connections at locations with abnormal stress distribution based on stress change quadrant distribution and generate real-time abnormal location connection features; The two-way comparison result is generated using the real-time mutation time feature and the real-time abnormal position connection feature.

5. The anti-deviation hole drilling method based on geological feedback information according to claim 4, characterized in that: The preset dual-path abnormality judgment logic includes a first distinguishing logic of heterogeneous formations and drill bit self-failures based on mutation time characteristics and a second distinguishing threshold of heterogeneous formations and drill bit self-failures based on abnormal position connection characteristics.

6. The anti-deviation hole drilling method based on geological feedback information according to claim 1, characterized in that: The fusion control of local pick load adaptive control and direction adjustment mechanism global control based on the surrounding rock strength quadrant distribution includes: Determine a key pick of a local pick load adaptive control module, wherein the key pick is a pick having an impact on the cutting trajectory and orientation of the drill bit greater than a preset threshold; Based on the quadrant distribution of surrounding rock strength, biased hole behavior analysis is performed to generate biased hole characteristics; If the deviation hole characteristic is a local deviation hole, with the goal of correcting the local deviation hole, the local pick load adaptive control module is used to control the unit cutting force of the key pick; If the deviation hole feature is a global direction deviation hole, with the goal of correcting the global deviation hole, the global direction control module is used to perform adjustment parameter matching of the direction adjustment mechanism and then control the drilling direction and speed.

7. The anti-deviation hole drilling method based on geological feedback information according to claim 6, characterized in that: If the optimal unit cutting force of the pick matched by the local pick load adaptive control module cannot completely correct the local deviation hole or the direction adjustment mechanism adjustment parameters matched by the global direction control module cannot completely correct the global direction deviation hole, the control deviation is determined after performing the fitting of the control effect, and the control parameters of the control deviation are matched with another control module to generate collaborative control parameters for collaborative control.

8. The anti-deviation hole drilling method based on geological feedback information according to claim 6, characterized in that: The local pick load adaptive control module includes a micro hydraulic adjustment unit located at each key pick and a key pick adjustment parameter library, and the key pick adjustment parameter library is used to match the unit cutting force of the pick of the local offset hole and then control it through the micro hydraulic adjustment unit; The global direction control module includes an adjustment parameter library of the direction adjustment mechanism, and uses the adjustment parameter library to perform adjustment parameter matching of the global direction deviation hole.

9. The anti-deviation hole drilling method based on geological feedback information according to claim 1, characterized in that: The signal processing component includes a signal amplification circuit, a signal filtering circuit and a data analysis channel; The signal amplification circuit performs signal amplification processing on the stress signal data set, the signal filtering circuit performs filtering processing on the amplified signal, and the quadrant points corresponding to the drill bit cross section are numbered based on the filtered stress signal data set to construct the stress change quadrant distribution; The data analysis channel is sent to the data analysis channel for converting the stress change quadrant distribution into the surrounding rock strength quadrant distribution after abnormal triggering judgment. The data analysis channel is constructed by historical conversion data. 10.Anti-deviation hole drilling system based on geological feedback information, characterized in that: The system is used to implement the anti-deviation hole drilling method based on geological feedback information according to any one of claims 1 to 9, and the system includes: A data set generation module is used to monitor the stress changes of the drill bit in real time through a pre-configured stress sensing component during the drilling process of the drill bit, and generate a stress signal data set; A quadrant distribution construction module is used to process the stress signal data set using a signal processing component, number the quadrant points corresponding to the drill bit cross section, and construct a stress change quadrant distribution; a discrimination result generating module, configured to perform abnormal trigger discrimination of heterogeneous formations and drill bit self-faults based on the stress change quadrant distribution, and generate an abnormal trigger discrimination result; A quadrant distribution generation module is used to analyze the surrounding rock strength in different quadrants of the borehole circular cross section based on the stress change quadrant distribution if the abnormal trigger judgment result is a heterogeneous formation trigger abnormality, and generate a surrounding rock strength quadrant distribution; A fusion control module is used for fusion control of local pick load adaptive control and global control of the direction adjustment mechanism based on the quadrant distribution of the surrounding rock strength.

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