A method and system for preventing borehole deviation based on geological feedback information

Abstract

This invention discloses a method and system for preventing borehole deviation during drilling based on geological feedback information, belonging to the field of borehole deviation control technology. The method includes: real-time monitoring of stress changes experienced by the drill bit and generating a stress signal dataset; processing the stress signal dataset to construct a stress change quadrant distribution; generating anomaly triggering results based on anomaly triggering discrimination; if the anomaly triggering discrimination result indicates an anomaly triggered by a heterogeneous stratum, generating a surrounding rock strength quadrant distribution; and performing integrated control of local cutting tooth load adaptive control and global control of the direction adjustment mechanism. This invention solves the technical problems in existing technologies where, during drilling in heterogeneous strata, it is difficult to accurately identify the causes of borehole deviation and there is a lack of effective means to prevent and correct it, leading to poor drilling accuracy and insufficient stability. It achieves effective prevention and correction of drill bit deviation in heterogeneous strata, improving drilling accuracy and stability.

Description

Technical Field

[0001] This invention relates to the field of borehole deviation prevention and control technology, specifically to a borehole drilling method and system based on geological feedback information. Background Technology

[0002] In the field of drilling engineering, with the deepening of underground resource development and infrastructure construction, drilling operations in complex heterogeneous strata are becoming increasingly common. However, existing technologies face many challenges. On the one hand, it is difficult to monitor the stress on the drill bit and changes in the formation in real time and accurately during traditional drilling, resulting in the inability to detect the impact of heterogeneous strata on the drill bit in a timely manner. On the other hand, the lack of efficient hole deviation prevention and correction mechanisms means that once encountering differences in formation strength or drill bit malfunctions, hole deviation is very likely to occur, causing a decrease in drilling accuracy and efficiency, and even leading to engineering accidents, seriously affecting the quality and progress of drilling projects.

[0003] Existing technologies have technical problems when drilling in heterogeneous formations, such as difficulty in accurately identifying the causes of borehole deviation and lack of effective means to prevent and correct borehole deviation, resulting in poor drilling accuracy and insufficient stability. Summary of the Invention

[0004] This application provides a method and system for preventing borehole deviation based on geological feedback information, which is used to address the technical problems in the prior art where it is difficult to accurately identify the causes of borehole deviation and there is a lack of effective means to prevent and correct borehole deviation, resulting in poor drilling accuracy and insufficient stability when drilling in heterogeneous strata.

[0005] In view of the above problems, this application provides a method and system for preventing borehole deviation based on geological feedback information.

[0006] A first aspect of this application provides a method for preventing borehole deviation based on geological feedback information, the method comprising:

[0007] During the drilling process, the stress changes experienced by the drill bit are monitored in real time by a pre-configured stress sensing component, generating a stress signal dataset. The stress signal dataset is processed using a signal processing component, and quadrant points corresponding to the drill bit cross-section are numbered to construct a stress change quadrant distribution. Based on this stress change quadrant distribution, anomaly trigger discrimination for heterogeneous strata and drill bit self-faults is performed, generating anomaly trigger discrimination results. If the anomaly trigger discrimination result indicates a heterogeneous strata trigger anomaly, the surrounding rock strength in different quadrants of the borehole circular cross-section is analyzed based on the stress change quadrant distribution, generating a surrounding rock strength quadrant distribution. Based on the surrounding rock strength quadrant distribution, a fusion control of local cutting tooth load adaptive control and global control of the direction adjustment mechanism is performed.

[0008] A second aspect of this application provides a borehole deviation prevention drilling system based on geological feedback information, the system comprising:

[0009] The system includes a dataset generation module for real-time monitoring of stress changes on the drill bit during drilling using pre-configured stress sensing components, generating a stress signal dataset; a quadrant distribution construction module for processing the stress signal dataset using signal processing components, numbering the corresponding quadrant points on the drill bit cross-section, and constructing a stress change quadrant distribution; a discrimination result generation module for performing anomaly trigger discrimination for heterogeneous strata and drill bit self-faults based on the stress change quadrant distribution, generating anomaly trigger discrimination results; a quadrant distribution generation module for analyzing the surrounding rock strength in different quadrants of the borehole circular cross-section based on the stress change quadrant distribution if the anomaly trigger discrimination result indicates a heterogeneous strata trigger anomaly, generating a surrounding rock strength quadrant distribution; and a fusion control module for performing fusion control of local cutting tooth load adaptive control and global control of the direction adjustment mechanism based on the surrounding rock strength quadrant distribution.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The system monitors stress changes on the drill bit in real time, generating a stress signal dataset. This dataset is then processed using a signal processing component, and quadrant points corresponding to the drill bit cross-section are numbered to construct a stress change quadrant distribution. Anomaly triggering detection is performed for heterogeneous strata and drill bit self-faults, generating anomaly triggering detection results. If the anomaly triggering detection result indicates a heterogeneous strata triggering anomaly, the surrounding rock strength in different quadrants of the borehole cross-section is analyzed based on the stress change quadrant distribution, generating a surrounding rock strength quadrant distribution. Based on this surrounding rock strength quadrant distribution, a fusion control system is implemented combining local cutting tooth load adaptive control and global control of the direction adjustment mechanism. This achieves effective prevention and correction of drill bit deviation in heterogeneous strata, improving drilling accuracy and stability. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the anti-deviation drilling method based on geological feedback information provided in the embodiments of this application;

[0014] Figure 2 A schematic diagram of the anti-deviation drilling system based on geological feedback information provided in this application embodiment.

[0015] Figure labeling: Dataset generation module 10, Quadrant distribution construction module 20, Discrimination result generation module 30, Quadrant distribution generation module 40, Fusion control module 50. Detailed Implementation

[0016] This application provides a method and system for preventing borehole deviation based on geological feedback information, which addresses the technical problems in existing technologies where it is difficult to accurately identify the causes of borehole deviation and there is a lack of effective means to prevent and correct borehole deviation, resulting in poor drilling accuracy and insufficient stability when drilling in heterogeneous strata.

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0018] Example 1, as Figure 1 As shown, this application provides a method for preventing borehole deviation based on geological feedback information, the method comprising:

[0019] Step S100: During the drilling process of the drill bit, the stress changes on the drill bit are monitored in real time by a pre-configured stress sensing component, and a stress signal dataset is generated.

[0020] Specifically, during the drilling process, stress sensors pre-installed on each cutting tooth are closely fitted to the working part of the cutting tooth to capture the stress changes experienced by the drill bit in real time. During drilling, the drill bit continuously cuts the formation, and the cutting teeth are in direct contact with the rock, experiencing forces from different directions and magnitudes. These stress changes are quickly detected by the stress sensors and converted into corresponding electrical signals or other easily processed signal forms. As drilling continues, the stress sensors on each cutting tooth continuously collect data, and a large number of stress change signals are continuously output. These signals are systematically integrated according to preset time intervals and data storage rules, recording detailed information on stress changes at each moment and at each cutting tooth, ultimately generating a stress signal dataset. This dataset accurately reflects the stress changes of different cutting teeth at different drilling times, including key features such as stress magnitude fluctuations, frequency of change, and direction of action. It provides indispensable raw data support for subsequent use of signal processing components to construct stress change quadrant distributions, thereby performing abnormal triggering judgment of heterogeneous formations and drill bit self-faults, and achieving precise control during the drilling process. It plays a decisive role in ensuring the accuracy and stability of the drilling direction.

[0021] Step S200: Process the stress signal dataset using the signal processing component, number the corresponding quadrant points of the drill bit cross section, and construct the stress change quadrant distribution.

[0022] Specifically, after acquiring the stress signal dataset, the signal processing component undertakes the crucial tasks of processing the dataset and constructing key data distributions. 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 signals with smaller amplitudes in the stress signal dataset, increasing signal strength and enabling subsequent processing to more clearly capture signal characteristics. Then, the signal filtering circuit effectively removes noise interference from the signal, which may originate from drilling equipment vibration, electromagnetic interference from the surrounding environment, etc. 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 according to the drill bit's structural characteristics. Generally, the drill bit cross-section is divided into multiple quadrants, such as four or eight quadrants, to allow for more detailed analysis of the stress distribution. For the filtered stress signal, the stress data is classified and collected according to the cutting tooth positions corresponding to different quadrants. Through statistical analysis of the stress data within each quadrant, such as calculating the average, maximum, minimum, and trend of stress, a stress variation quadrant distribution is constructed. This distribution visually demonstrates the differences and variations in stress experienced by the drill bit in different quadrants, helping to accurately locate potential anomalies and their corresponding quadrant regions, thus providing crucial information for subsequent adjustments to drilling control strategies.

[0023] Step S300: Based on the stress change quadrant distribution, perform abnormal triggering discrimination for heterogeneous strata and drill bit self-fault, and generate abnormal triggering discrimination results.

[0024] Specifically, after constructing the stress variation quadrant distribution, the focus is on extracting information from it to determine the source of anomalies, i.e., whether they are caused by heterogeneous geological formations or drill bit malfunctions. First, based on the stress variation quadrant distribution, a preset stress non-uniformity threshold is constructed to identify stress non-uniformity within different quadrants. For stress data at different cutting tooth positions within each quadrant, a real-time stress non-uniformity index is calculated and compared with the preset stress non-uniformity threshold. If the real-time stress non-uniformity index exceeds the threshold range, it indicates a stress anomaly, and the next step of analysis is initiated. Once a stress anomaly is identified, its temporal and spatial characteristics are further compared. On one hand, based on the stress variation quadrant distribution, the stress changes at multiple consecutive time stamps are analyzed to identify the real-time abrupt changes in stress from a normal to an abnormal state, observing whether the stress anomaly appears suddenly or evolves gradually, which helps distinguish different types of anomaly causes. On the other hand, the locations of stress anomalies are connected and identified to generate real-time anomaly location connection features, determining the distribution range and concentration area of ​​stress anomalies on the drill bit cross-section. After completing the dual-path comparison, the preset dual-path anomaly judgment logic is invoked. This logic includes a first differentiation logic based on the time-of-abrupt change characteristics of the heterogeneous strata and the self-fault of the drill bit, and a second differentiation threshold based on the connection characteristics of the anomaly location of the heterogeneous strata and the self-fault of the drill bit. Based on the dual-path comparison results, these two differentiation methods are comprehensively used to accurately analyze and determine whether the anomaly is caused by the heterogeneous strata or by a fault in the drill bit itself, ultimately generating an accurate anomaly triggering judgment result. This result is an important basis for subsequent targeted measures, providing crucial support for analyzing the surrounding rock strength if the anomaly is determined to be triggered by the heterogeneous strata, and for precise control and fault elimination throughout the entire drilling process.

[0025] Step S400: If the abnormal triggering judgment result is a non-homogeneous stratum triggering abnormality, the surrounding rock strength in different quadrants of the borehole circular section is analyzed based on the stress change quadrant distribution to generate a surrounding rock strength quadrant distribution.

[0026] Specifically, an algorithm based on Support Vector Regression (SVR) combined with weight adjustment is used to analyze the surrounding rock strength and generate a quadrant distribution of surrounding rock strength. First, the stress data for each quadrant are standardized to eliminate the influence of dimensions, and features such as the mean stress, standard deviation, and gradient of change are extracted to construct feature vectors. A training set is constructed using historical geological data and corresponding stress features, containing actual measured values ​​of surrounding rock strength under different combinations of stress features. A radial basis function (RBF) is used as the kernel function to construct an SVR model. An optimization algorithm is used to find the optimal parameters (penalty factor C and kernel function parameter γ) so that the model achieves the best fit on the training set, establishing a preliminary relationship between stress features and surrounding rock strength. Considering that different stress features have varying degrees of influence on surrounding rock strength, the weights of each stress feature are determined based on the Analytic Hierarchy Process (AHP). A judgment matrix is ​​constructed based on the experience of geological experts, the weight vectors of each feature are calculated, and consistency checks are performed. These weights are used to weight and correct the SVR model prediction results, making the results more consistent with reality. For each quadrant of the borehole's circular cross-section, its stress characteristics are substituted into the weighted and corrected 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.

[0027] Step S500: Based on the surrounding rock strength quadrant distribution, perform integrated control of local cutting tooth load adaptive control and global control of direction adjustment mechanism.

[0028] Specifically, the key cutting teeth for the local cutting tooth load adaptive control module are determined. Based on a comparison of the cutting tooth's influence on the drill bit's cutting trajectory and orientation indicators with preset thresholds, cutting teeth that significantly impact drilling accuracy are selected as key cutting teeth. These key cutting teeth are the core components for achieving local control. Next, the borehole deviation behavior is analyzed in depth based on the surrounding rock strength quadrant distribution. If the analysis reveals a local borehole deviation, it means the borehole has deviated in a local area. At this point, the local cutting tooth load adaptive control module comes into play. This module includes micro-hydraulic adjustment units located on each key cutting tooth and a key cutting tooth adjustment parameter library. Based on the surrounding rock strength quadrant distribution, it matches a suitable cutting tooth unit cutting force from the key cutting tooth adjustment parameter library, and then precisely adjusts the key cutting teeth through the micro-hydraulic adjustment units to correct the local borehole deviation, allowing the drill bit to return to its normal drilling trajectory in the local area. If the borehole deviation is a global directional deviation, it indicates a deviation in the entire drilling direction. At this point, the global direction control module begins operation. This module contains a library of adjustment parameters for the direction adjustment mechanism. Based on the quadrant distribution of surrounding rock strength, the module selects appropriate adjustment parameters from the library to adjust the direction adjustment mechanism, thereby achieving precise control of the drilling direction and speed to correct global borehole deviation and ensure that the drill bit can drill in the predetermined direction. In actual operation, if the optimal cutting force per unit cutting tooth matched by the local cutting tooth load adaptive control module cannot completely correct the local borehole deviation, or if the adjustment parameters of the direction adjustment mechanism matched by the global direction control module cannot completely correct the global direction borehole deviation, a fitting operation of the control effect is performed to calculate the control deviation. Subsequently, another control module matches control parameters for this control deviation to generate collaborative control parameters, realizing collaborative control of the two modules, further optimizing the drilling process, improving the accuracy and stability of drilling, and effectively ensuring the smooth progress of drilling work.

[0029] In one possible implementation, step S300 further includes:

[0030] Step S310: Based on the stress change quadrant distribution, identify the non-uniformity of stress in different quadrants, perform stress anomaly judgment, and generate stress anomaly judgment result.

[0031] Step S320: If the stress anomaly judgment result shows stress anomaly, perform time feature comparison and spatial feature comparison of stress distribution anomaly based on the stress change quadrant distribution to generate dual-path comparison result.

[0032] Step S330: Invoke the preset dual-path anomaly judgment logic, perform dual-path anomaly trigger analysis based on the dual-path comparison results, complete the anomaly trigger judgment of heterogeneous strata and drill bit self-failure, and generate the anomaly trigger judgment result.

[0033] Specifically, the previously obtained stress variation quadrant distribution data is standardized and preprocessed, and stress data from different sampling times and quadrants are stored in a structured manner to ensure data accuracy and consistency. Then, a preset stress non-uniformity threshold system is constructed, comprehensively considering drill bit design parameters, the mechanical properties of common formations, and stress characteristics of anomalies in past drilling cases. For example, for specific drill bit models and common rock types, a large number of simulation experiments and actual tests are conducted to determine the reasonable range of stress fluctuations in each quadrant under normal drilling conditions. Next, stress non-uniformity indices are calculated for different quadrants. The standard deviation is used to measure the degree of stress dispersion. For stress data at different cutting positions in each quadrant, the mean is calculated, and then the square root of the average of the squares of the differences between each data point and the mean is calculated to obtain the stress standard deviation for that quadrant. This standard deviation is used as the stress non-uniformity index for that quadrant. Then, the calculated stress non-uniformity indices for each quadrant are compared with the preset stress non-uniformity thresholds. If the stress standard deviation of a certain quadrant exceeds the corresponding threshold, or the stress change rate exceeds the reasonable range, it is determined that there is a stress anomaly in that quadrant. Finally, the judgment results of all quadrants are summarized to generate a comprehensive stress anomaly judgment result, clearly indicating which quadrants have stress anomalies, providing a basis for subsequent anomaly cause analysis and handling.

[0034] When the stress anomaly assessment indicates the presence of stress anomalies, 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 are meticulously analyzed. By analyzing the process of stress transitioning from a normal to an abnormal state, the specific time of stress abrupt changes, the magnitude of the abrupt change, and the trend of stress changes before and after the abrupt change are precisely identified as real-time abrupt change characteristics. For example, is it a sudden, large stress jump, or a slow increase over a period followed by a sudden intensification? This helps determine whether the anomaly is caused by sudden external interference or by a gradually accumulating internal problem. For spatial feature comparison, the location of the stress anomaly distribution is emphasized. These anomaly locations are connected and identified, and their distribution pattern and interrelationships on the drill bit cross-section are observed to generate real-time anomaly location connection features. If the anomaly locations are concentrated in a few adjacent quadrants, it may indicate a local problem, such as changes in local formation characteristics or local drill bit wear; if the anomaly locations are scattered across multiple non-adjacent quadrants, it may indicate complex overall formation conditions or a systemic fault in the drill bit. By combining the real-time mutation time features obtained from temporal feature comparison with the real-time anomaly location connection features obtained from spatial feature comparison, a dual-path comparison result is finally generated, providing a key basis for subsequent accurate judgment of whether the anomaly is caused by a heterogeneous stratum or a self-fault of the drill bit.

[0035] The system invokes a pre-defined dual-path anomaly detection logic, which includes discrimination rules for time and spatial characteristics. Then, the obtained dual-path comparison results—the temporal and spatial characteristics of stress distribution anomalies—are input. In spatial characteristic analysis, based on multi-point stress data comparison rules, the distribution of stress anomaly points is compared. If a regional or patchy distribution is observed, consistent with the spatial characteristics of geological changes, it is likely due to the influence of heterogeneous strata; if anomalies are isolated points, they are more consistent with mechanical failure characteristics. In temporal characteristic analysis, through smoothing and trend analysis of the time series, if the stress anomaly shows a slow changing trend, consistent with the typically slow nature of geological changes, it is likely caused by heterogeneous strata; if abrupt changes occur, consistent with the abrupt nature of mechanical failures, it is likely due to a drill bit self-failure. Combining the analysis results of spatial and temporal characteristics, dual-path anomaly trigger analysis is executed, ultimately completing the anomaly trigger discrimination for heterogeneous strata and drill bit self-failure, generating anomaly trigger discrimination results, and providing a reliable basis for subsequent targeted measures.

[0036] In one possible implementation, step S310 further includes:

[0037] Step S311: Based on the structural characteristics of the drill bit, construct a preset stress non-uniformity threshold that satisfies microscopic non-homogeneity.

[0038] Step S312: Based on the stress change quadrant distribution, calculate the stress non-uniformity index for different cutter positions in different quadrants to generate a real-time stress non-uniformity index.

[0039] Step S313: Compare the preset stress non-uniformity threshold with the real-time stress non-uniformity index, perform stress anomaly judgment, and generate the stress anomaly judgment result.

[0040] Specifically, the pre-defined stress non-uniformity threshold that satisfies microscopic heterogeneity is achieved through the following methods. First, using 3D scanning technology, precise structural data of the drill bit is acquired, including the shape, size, and spacing of the cutting teeth, as well as the outline of the drill bit body, to construct a digital model. Then, using finite element analysis software, microscopic mechanical parameters of different strata, such as the elastic modulus, Poisson's ratio, and compressive strength of the rock, are input into the model according to the actual stratum distribution. By simulating the drilling process of the drill bit under different stratum conditions, the stress distribution at different locations is calculated. Multiple simulations are conducted with parameter adjustments to collect a large amount of stress data. Subsequently, statistical analysis is performed on this data, and mathematical statistics methods are used to determine the mean, standard deviation, and other statistical quantities of the stress distribution. Based on this, combined with engineering experience and safety margin requirements, a reasonable stress fluctuation range is set, ultimately constructing the pre-defined stress non-uniformity threshold that satisfies microscopic heterogeneity.

[0041] Stress data at different cutting tooth positions within each quadrant of the stress variation quadrant distribution are extracted. This data contains the magnitude of stress experienced by the cutting teeth during drilling. Next, for each quadrant, the mean stress data for all cutting tooth positions within that quadrant is calculated. Then, the mean stress for that quadrant is subtracted from the stress value at each cutting tooth position, and the difference is squared. The average of these squared differences is then calculated, and the square root of this average is taken to obtain the stress standard deviation for that quadrant. This calculation process is repeated to obtain the stress standard deviations for all quadrants. These stress standard deviations reflect the degree of stress dispersion within each quadrant, i.e., stress non-uniformity. Using these as a real-time stress non-uniformity index provides an accurate quantitative basis for subsequent comparison with preset stress non-uniformity thresholds and for determining whether stress is abnormal.

[0042] The predetermined stress non-uniformity threshold is a reasonable stress fluctuation range constructed based on the structural characteristics of the drilling rig and drill bit and the microscopic heterogeneity of the formation. The real-time stress non-uniformity index, calculated based on the stress variation quadrant distribution, represents the standard deviation of stress in each quadrant, reflecting the degree of stress dispersion in each quadrant during drilling. Next, for each quadrant, the real-time stress non-uniformity index is compared with the corresponding predetermined stress non-uniformity threshold. If the real-time stress non-uniformity index exceeds the predetermined threshold, it indicates that the stress dispersion in that quadrant exceeds the normal range, and a stress anomaly is identified in that quadrant. If the real-time stress non-uniformity index does not exceed the predetermined threshold, it indicates that the stress distribution in that quadrant is within the normal fluctuation range, and no stress anomaly exists. Finally, by synthesizing the judgments for all quadrants, a comprehensive stress anomaly judgment result is generated, clearly indicating which quadrants exhibit stress anomalies, providing crucial evidence for accurately identifying the triggering cause of the anomaly and taking targeted measures.

[0043] In one possible implementation, step S320 further includes:

[0044] Step S321: Based on the stress change quadrant distribution, compare the time features of stress distribution anomalies across multiple consecutive time stamps to identify the real-time abrupt change time features of stress from normal to abnormal.

[0045] Step S322: Identify the connection locations of stress anomalies based on the quadrant distribution of stress changes, and generate real-time anomaly location connection features.

[0046] Step S323: Generate the dual-path alignment result using the real-time mutation time feature and the real-time anomaly location connection feature.

[0047] Specifically, from the vast amount of data recorded in the stress variation quadrant distribution, stress data sequences at multiple consecutive time stamps were selected. These data meticulously record the stress conditions in different quadrants at each time point. Next, a normal stress range was defined, based on data accumulated during past normal drilling and estimated in conjunction with current geological conditions. Subsequently, stress data was analyzed point-by-point in chronological order. During the analysis, close monitoring was maintained to ensure stress values ​​did not exceed the boundaries of the normal range. Once a stress value was found to exceed the normal range, this time point was recorded. Simultaneously, the stress changes before and after this time point were further analyzed, including the rate of stress change (whether it was a slow increase beyond the normal range or a sudden jump), the magnitude of the stress change, and the degree to which it exceeded the normal range. By comprehensively considering this information, the real-time abrupt change time of stress transitioning from a normal to an abnormal state was accurately identified, along with related abrupt change characteristics such as the speed and magnitude of the change. These identified real-time abrupt change time characteristics provide a basis for subsequent anomaly assessment.

[0048] Based on the quadrant distribution of stress changes, the location of stress anomalies is analyzed in depth. First, all stress anomalies are located within the quadrant distribution, corresponding to the areas where drill bits experience abnormal stress during drilling. Next, the spatial relationships between these anomaly locations are analyzed. If certain anomaly locations are close to each other, forming a continuous or adjacent distribution, they are connected for identification. For example, if stress anomalies occur at multiple drill bit locations within several adjacent quadrants, these adjacent anomaly locations are considered as a whole. Through this connection identification, it can be determined whether the anomalies are concentrated in a specific area or dispersed in different locations. Finally, based on the connection identification results, real-time anomaly location connection features are generated, which visually present the spatial distribution and clustering patterns of stress anomalies.

[0049] The obtained real-time abrupt change time features are analyzed to clarify the specific time when stress changes from a normal state to an abnormal state, as well as the speed and magnitude of the abrupt change. Simultaneously, the generated real-time anomaly location connection features are organized to clearly understand the spatial distribution of stress anomalies, including whether the anomaly locations are concentrated in specific areas or more dispersed, and the connections between them. Then, these two sets of features are organically combined. The temporal information of stress anomalies is correlated with the spatial distribution information of anomaly locations to form a comprehensive comparison dataset. For example, if the real-time abrupt change time features show a sudden and significant change in stress within a short period, while the real-time anomaly location connection features indicate that the anomaly locations are concentrated on one side of the drill bit, then the dual-path comparison results will show that a sudden and concentrated stress anomaly occurred on one side of the drill bit at a specific moment. In this way, the generated dual-path comparison results can comprehensively reflect the characteristics of stress anomalies from both temporal and spatial dimensions, providing clear and comprehensive data support for subsequent invocation of preset dual-path anomaly judgment logic and accurate differentiation between anomalies caused by heterogeneous strata and drill bit self-failure.

[0050] In one possible implementation, step S323 further includes:

[0051] The preset dual-path anomaly judgment logic includes a first differentiation logic based on the abrupt change time characteristics of heterogeneous strata and drill bit self-faults, and a second differentiation threshold based on the abnormal location connection characteristics of heterogeneous strata and drill bit self-faults.

[0052] Specifically, the pre-defined dual-path anomaly judgment logic is a refined anomaly discrimination system. It includes a first differentiation logic based on the time characteristics of abrupt changes and a second differentiation threshold based on the location and connectivity characteristics of anomalies, thereby effectively distinguishing whether anomalies originate from heterogeneous strata or from drill bit self-failures. Regarding the first differentiation logic based on the time characteristics of abrupt changes, since the formation of heterogeneous strata is the result of long-term geological processes, its impact on stress gradually manifests over time. When there are non-uniform characteristics such as uneven hardness within the strata, the stress on the drill bit will slowly change as it gradually comes into contact with different types of strata during drilling. The transition from a normal state to an abnormal state may take some time, and the rate of change is relatively slow. For example, as the drill bit gradually moves from a softer stratum to a harder stratum, the stress gradually increases. In this process, the appearance of stress anomalies is often a gradual process. Conversely, drill bit self-failures are usually sudden events. Faults such as sudden chipping of cutting teeth or instantaneous damage to internal mechanical components can cause an instantaneous change in the stress state of the drill bit, resulting in a significant change in stress within a very short time. Therefore, the first differentiation logic establishes rules for judging the source of anomalies based on key characteristics such as the duration and speed of the abrupt change in stress from normal to abnormal. If the stress abrupt change is long and slow, it is more likely that the anomaly is caused by a heterogeneous stratum; if the abrupt change is extremely short and fast, it is more likely to be caused by a self-failure of the drill bit. The second differentiation threshold setting, based on the connection characteristics of the anomaly location, is based on the spatial distribution perspective. Heterogeneous strata have a certain degree of continuity and regionality in space. When the drill bit enters a heterogeneous stratum, affected by the stratum characteristics, the location of stress anomalies is often concentrated in the part where the drill bit contacts a specific stratum area. These anomaly locations will show obvious connections, forming regional anomaly distribution characteristics. For example, when encountering a rock band with high hardness, the stress on the cutting teeth on the side in contact with the rock band will be abnormal during the process of the drill bit passing through the rock band. These anomaly locations will be concentrated on the same side of the drill bit and interconnected. In contrast, the distribution of stress anomalies caused by a self-failure of the drill bit is relatively random. 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. Through numerous real-world cases, an appropriate second discrimination threshold was established. When the tightness of the connection and the distribution range of the anomaly locations exceed this threshold, it more closely resembles the characteristics of anomalies caused by heterogeneous formations; if it is below the threshold, it is more likely that the anomaly is caused by a fault in the drill bit itself. These two parts complement each other, together forming a pre-defined dual-path anomaly judgment logic, providing a basis for accurately identifying the cause of the anomaly.

[0053] In one possible implementation, step S500 further includes:

[0054] Step S510: Determine the key cutting teeth of the local cutting tooth load adaptive control module. The key cutting teeth are those whose influence on the cutting trajectory and orientation index of the drill bit of the drilling rig is greater than a preset threshold.

[0055] Step S520: Based on the surrounding rock strength quadrant distribution, perform pore deviation behavior analysis to generate pore deviation characteristics.

[0056] Step S530: If the hole deviation feature is a local deviation, with the goal of correcting the local deviation, the local cutting tooth load adaptive control module adjusts the unit cutting force of the key cutting tooth.

[0057] Step S540: If the deviation feature is a global deviation, with the goal of correcting the global deviation, the drilling direction and speed are controlled by matching the adjustment parameters of the direction adjustment mechanism using the global direction control module.

[0058] Specifically, when the drilling rig is drilling a short section of standard formation, high-precision trajectory monitoring equipment and orientation measurement instruments are used to record the initial cutting trajectory and orientation data of the drill bit. Then, each cutting tooth is stopped one by one. After stopping one cutting tooth, the drilling rig continues drilling 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 deviation of the cutting trajectory and the change in orientation angle before and after stopping each individual cutting tooth, the impact of each cutting tooth on the cutting trajectory and orientation is quantified. The quantified impact values ​​of all cutting teeth are ranked, and a suitable preset threshold is determined based on engineering experience and drilling accuracy requirements. Cutting teeth whose quantified impact values ​​are greater than the preset threshold are those that have a significant impact on the cutting trajectory and orientation of the drilling rig's drill bit, i.e., the key cutting teeth of the local cutting tooth load adaptive control module.

[0059] To analyze borehole deviation behavior based on the quadrant distribution of surrounding rock strength, the surrounding rock area contacted by the drill bit must first be divided into quadrants. Using equipment such as pressure sensors and acoustic detectors, mechanical parameters such as compressive strength and elastic modulus of the surrounding rock in each quadrant are collected in real time, forming detailed quadrant distribution data of surrounding rock strength. Subsequently, this data is analyzed in depth, comparing the differences in surrounding rock strength between quadrants. If the surrounding rock strength in one or more adjacent quadrants is significantly higher than in other quadrants, the drill bit will experience increased resistance in these high-strength areas during drilling, causing it to deviate towards the lower-strength quadrants. In this case, the quadrant location, deviation angle, and deviation rate of the deviation are recorded. If the surrounding rock strength varies across quadrants but is relatively dispersed, it may lead to irregular shaking and deviation of the drill bit during drilling. Based on the above analysis results, it is determined whether the borehole deviation exhibits a localized concentrated deviation trend or an overall directional deviation. Finally, a borehole feature is generated, containing information such as the borehole location, deviation direction, and deviation degree, providing crucial information for subsequent targeted borehole correction measures.

[0060] A thorough analysis of the specific characteristics of localized hole deviation is conducted, encompassing key information such as the location of the deviation, the angle of deviation, and the rate of deviation. Based on this information, the critical cutting teeth requiring adjustment are precisely identified. Because critical cutting teeth significantly influence the cutting trajectory and orientation of the drill bit, adjusting them can effectively correct the hole deviation. Next, the localized cutting tooth load adaptive control module selects the most suitable adjustment scheme from a pre-set adjustment strategy library based on the hole deviation characteristics. This scheme clarifies the specific value and direction of the unit cutting force required to be adjusted for each critical cutting tooth. Then, through a micro-hydraulic adjustment device installed on the drill bit, the unit cutting force of the critical cutting teeth is precisely adjusted. If the hole deviation is in a certain direction, the unit cutting force of the critical cutting tooth on the opposite side of that direction is increased, or the unit cutting force of the critical cutting tooth in the deviation direction is decreased, thereby changing the force balance of the drill bit in the local area. During the adjustment process, the cutting trajectory and hole deviation of the drill bit are monitored in real time, and the actual hole deviation correction effect is compared with the expected target. If the correction effect is found to be unsatisfactory, the hole deviation characteristics should be quickly re-analyzed, the adjustment scheme should be dynamically adjusted, and the unit cutting force of the key cutting teeth should be optimized and controlled again until the local hole deviation is effectively corrected and the drill bit returns to the normal drilling trajectory.

[0061] When the generated borehole deviation features are determined to be globally oriented deviations, the global direction control module is used to correct these deviations. First, the global direction deviation features are meticulously analyzed to clarify key information such as the specific direction, angle of deviation, and degree of deviation. Next, the global direction control module uses these deviation features to precisely match them with a pre-built adjustment parameter database, identifying the most suitable adjustment parameters for the direction adjustment mechanism under the current borehole deviation conditions. These parameters are derived from extensive simulation experiments and actual drilling experience, covering the optimal adjustment values ​​for the direction adjustment mechanism under different borehole deviation scenarios. After obtaining the matched adjustment parameters, they are transmitted to the direction adjustment mechanism. The direction adjustment mechanism precisely controls the drilling direction and speed of the drill bit based on the received parameters. For example, if the borehole deviation is to the right, the direction adjustment mechanism will fine-tune the drill bit angle, shifting it to the left by a certain amount, and simultaneously adjust the drilling speed appropriately according to the degree of deviation. If the degree of deviation is large, the drilling speed may be appropriately reduced to ensure more accurate borehole correction. During the control process, the drilling direction and speed of the drill bit are continuously monitored in real time, and the actual situation is compared and analyzed with the expected correction target. Once a deviation is found between the actual drilling situation and the expectation, the global direction control module will immediately re-analyze the deviation characteristics, re-match and optimize the adjustment parameters, and dynamically adjust the action of the direction adjustment mechanism to ensure that the drilling direction and speed are always precisely adjusted in the direction of correcting the global deviation until the global deviation is effectively corrected and the drill bit returns to the predetermined correct drilling path.

[0062] In one possible implementation, step S540 further includes:

[0063] Step S541: If the optimal cutting force of the cutting tooth matched by the local cutting tooth load adaptive control module cannot completely correct the local hole deviation or the adjustment parameter of the direction adjustment mechanism matched by the global direction control module cannot completely correct the global direction hole deviation, the control deviation is determined after fitting the control effect, and another control module is used to match the control parameters of the control deviation to generate collaborative control parameters for collaborative control.

[0064] Specifically, when the optimal cutting force per unit of the cutting tooth matched by the local cutting tooth load adaptive control module cannot completely correct the local hole deviation, or the adjustment parameters of the direction adjustment mechanism matched by the global direction control module cannot completely correct the global direction hole deviation, a high-precision sensor array is invoked to collect multi-dimensional data during the drill bit's drilling process in real time, including information such as the real-time force on each cutting tooth, 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, and the control deviation is identified. Then, based on the control module that has not achieved the expected effect, the corresponding other control module is activated. If the local cutting tooth load control fails, the global direction control module quickly intervenes, retrieves successful control schemes under similar working conditions from the historical case database based on the control deviation, and optimizes and adjusts the direction adjustment parameters using a reinforcement learning algorithm; if the global direction control fails to achieve the target, the local cutting tooth load adaptive control module is restarted, and a particle swarm optimization algorithm is used to search and select cutting force per unit of the cutting tooth that is suitable for the current deviation from the cutting force parameter library. Finally, the optimized parameters from the two control modules are fused using a weighted allocation algorithm to generate coordinated control parameters. These parameters are synchronously transmitted to the cutting tooth hydraulic drive system and the direction adjustment mechanism, achieving coordinated dynamic control of the cutting force and drilling direction of the cutting tooth. This ensures that the drill bit gradually returns to the predetermined drilling trajectory, effectively solving the problem of hole deviation.

[0065] In one possible implementation, step S540 further includes:

[0066] Step S542: The local cutting tooth load adaptive control module includes a micro hydraulic adjustment unit located at each key cutting tooth and a key cutting tooth adjustment parameter library. After matching the unit cutting force of the cutting tooth for local deviation holes with the key cutting tooth adjustment parameter library, the module is controlled by the micro hydraulic adjustment unit.

[0067] Step S543: The global direction control module includes an adjustment parameter library for the direction adjustment mechanism, and the adjustment parameters of the global direction offset hole are matched using the adjustment parameter library.

[0068] Specifically, the local cutting tooth load adaptive control module is the core component for accurately addressing local hole deviation problems. It achieves efficient regulation through the coordinated operation of a micro-hydraulic adjustment unit and a key cutting tooth adjustment parameter library. Upon detecting a local hole deviation, the module immediately transmits the specific characteristics of the deviation, such as its quadrant, deviation angle, and deviation rate, to the key cutting tooth adjustment parameter library. This library, built upon numerous real-world engineering cases and simulation experimental data, stores the optimal unit cutting force parameters for each key cutting tooth under different local hole deviation conditions. Based on the input hole deviation characteristics, the module performs rapid searching and matching within the parameter library, selecting the combination of unit cutting force parameters suitable for the current hole deviation condition. After matching, this parameter information is transmitted in real-time to the micro-hydraulic adjustment unit installed on each key cutting tooth. The micro-hydraulic adjustment unit, acting as the actuator, responds quickly upon receiving instructions, adjusting the unit cutting force of the key cutting tooth by precisely controlling the pressure and flow rate of the hydraulic system. For example, if the deviation direction is to the left, it will increase the unit cutting force of the critical cutting tooth on the right or decrease the unit cutting force of the critical cutting tooth on the left, thereby changing the local force distribution of the drill bit and prompting the drill bit to return to the normal drilling trajectory. During the adjustment process, the micro hydraulic adjustment unit will also provide real-time feedback on the force status of the cutting tooth, continuously optimize the control strategy, and provide data support to ensure that the local deviation is corrected efficiently and accurately.

[0069] When a global directional deviation is detected, the global direction control module immediately activates. This module relies on the adjustment parameter library of the direction adjustment mechanism, which is built upon numerous actual drilling engineering cases, simulation experiments under different geological conditions, and in-depth research on various deviation situations. It covers the optimal adjustment parameters corresponding to different types of global directional deviations (such as different deflection angles, deflection directions, and deflection rates). The detected global directional deviation characteristics, including detailed information such as the overall direction of the deviation, the offset angle, and the offset speed, are compared and analyzed with the data in the adjustment parameter library. Through a retrieval algorithm, the adjustment parameters that best match the current global directional deviation situation are quickly matched in the parameter library. Once a match is successful, these adjustment parameters are transmitted to the direction adjustment mechanism to guide it in precisely adjusting the drilling direction of the drill bit, thereby effectively correcting the global directional deviation and ensuring that the drill bit can drill stably along the predetermined path.

[0070] In one possible implementation, step S200 further includes:

[0071] Step S210: The signal processing component includes a signal amplification circuit, a signal filtering circuit, and a data analysis channel.

[0072] Step S220: The signal amplification circuit amplifies the stress signal dataset, the signal filtering circuit filters the amplified signal, and the corresponding quadrant points of the drill bit section are numbered based on the filtered stress signal dataset to construct the stress change quadrant distribution.

[0073] Step S230: The data analysis channel sends data to the data analysis channel to convert the stress change quadrant distribution into the surrounding rock strength quadrant distribution after performing anomaly trigger judgment. The data analysis channel is constructed through historical conversion data.

[0074] Specifically, the signal processing component, as a key hub for data processing, consists of three closely cooperating parts: a signal amplification circuit, a signal filtering circuit, and a data analysis channel. The signal amplification circuit is primarily responsible for enhancing the acquired raw stress signal dataset. Since signals may attenuate due to transmission distance, environmental interference, and other factors during actual acquisition, the amplification chip and specific circuit design in this circuit can increase the signal amplitude and intensity, ensuring accurate subsequent analysis. The signal filtering circuit focuses on purifying the amplified signal, using low-pass, high-pass, or band-pass filtering techniques to remove high-frequency noise, power frequency interference, and other useless information, making the stress signal purer and reducing the impact of interference on the analysis results. The data analysis channel receives the filtered signal, performs in-depth analysis and transformation of the stress change data after anomaly detection, and provides crucial data support for subsequent drilling processes such as borehole deviation analysis and anomaly detection.

[0075] In the signal processing flow, a stress variation quadrant distribution is constructed through signal amplification, signal filtering, and quadrant point numbering. First, the signal amplification circuit receives the acquired raw stress signal dataset. Due to signal attenuation caused by transmission loss and environmental interference under actual working conditions, the signal amplification circuit uses operational amplifiers and other devices to enhance the raw stress signal according to a preset amplification factor, bringing the signal amplitude to a suitable range for analysis. Then, the amplified signal enters the signal filtering circuit. This circuit employs low-pass and band-pass filtering algorithms to effectively filter out unwanted components such as high-frequency noise and power frequency interference, resulting in a purer stress signal. After filtering, points corresponding to each quadrant are numbered sequentially based on the geometric characteristics of the drill bit cross-section, establishing a correspondence between signal data and the spatial position of the drill bit. Finally, based on the numbered filtered stress signal dataset and combined with time-series data, the stress variation of each quadrant point is integrated and visualized, constructing the stress variation quadrant distribution. This distribution visually illustrates the dynamic changes in stress over time in different quadrants of the drill bit during drilling, providing a clear and accurate data foundation for subsequent analysis of stress anomalies and judgment of borehole deviation behavior.

[0076] Once the stress change quadrant distribution data is sent to the data analysis channel, anomaly detection is performed, setting normal range thresholds for various stress change indicators (such as stress change rate, stress difference between adjacent quadrants, etc.). The values ​​of each indicator in the current stress change quadrant distribution are calculated and compared with their corresponding thresholds. If any indicator exceeds the threshold range, it is considered an anomaly, and the specific location and relevant indicator values ​​are recorded for subsequent troubleshooting. If all indicators are within the threshold range, it is considered normal, and the subsequent conversion operation continues. Next, the conversion from the stress change quadrant distribution to the surrounding rock strength quadrant distribution is performed. A support vector regression (SVR)-based model is constructed using historical conversion data. During the construction process, stress change features (such as mean and variance of stress in each quadrant) are extracted from historical data as input, and the corresponding surrounding rock strength is used as output. These data are normalized to eliminate the influence of different feature dimensions. Cross-validation is used to train and fine-tune the SVR model on the training set to determine the optimal kernel function (such as the radial basis function kernel function) and parameter combination. In actual conversion, the features of the current stress change quadrant distribution are input into the trained SVR model. The model will output the corresponding predicted value of the surrounding rock strength based on the learned mapping relationship, thereby obtaining the quadrant distribution of the surrounding rock strength.

[0077] Example 2 is based on the same inventive concept as the anti-deviation drilling method based on geological feedback information in the previous examples, such as... Figure 2 As shown, this application provides an anti-deviation drilling system based on geological feedback information. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0078] The dataset generation module 10 is used to monitor the stress changes of the drill bit in real time during the drilling process of the drill bit through a pre-configured stress sensing component, and generate a stress signal dataset.

[0079] The quadrant distribution construction module 20 is used to process the stress signal dataset using the signal processing component, number the quadrant points corresponding to the drill bit cross section, and construct the stress change quadrant distribution.

[0080] The discrimination result generation module 30 is used to perform abnormal trigger discrimination of heterogeneous strata and drill bit self-fault based on the stress change quadrant distribution, and generate abnormal trigger discrimination results.

[0081] The quadrant distribution generation module 40 is used to generate a surrounding rock strength quadrant distribution based on the stress change quadrant distribution analysis of the surrounding rock strength in different quadrants of the borehole circular section if the abnormal triggering judgment result is a non-homogeneous stratum triggering abnormality.

[0082] The fusion control module 50 is used for fusion control of local cutting tooth load adaptive control and direction adjustment mechanism global control based on the surrounding rock strength quadrant distribution.

[0083] Furthermore, the system is also used for the following functions:

[0084] Based on the stress change quadrant distribution, stress non-uniformity within different quadrants is identified, stress anomaly judgment is performed, and stress anomaly judgment results are generated. If the stress anomaly judgment results indicate stress anomaly, time feature comparison and spatial feature comparison of stress distribution anomaly are performed based on the stress change quadrant distribution, generating dual-path comparison results. The preset dual-path anomaly judgment logic is invoked, and dual-path anomaly trigger analysis is performed according to the dual-path comparison results to complete the anomaly trigger discrimination of heterogeneous strata and drill bit self-failure, generating the anomaly trigger discrimination results.

[0085] Furthermore, the system is also used for the following functions:

[0086] Based on the structural characteristics of the drill bit, a preset stress non-uniformity threshold that satisfies microscopic non-homogeneity is constructed; based on the stress change quadrant distribution, stress non-uniformity indices are calculated for different cutting tooth positions in different quadrants to generate real-time stress non-uniformity indices; by comparing the preset stress non-uniformity threshold with the real-time stress non-uniformity indices, stress anomaly judgment is performed to generate the stress anomaly judgment result.

[0087] Furthermore, the system is also used for the following functions:

[0088] Based on the stress change quadrant distribution, the temporal features of stress distribution anomalies at multiple consecutive time stamps are compared to identify the real-time abrupt change time features of stress from normal to abnormal. Based on the stress change quadrant distribution, the connection features of stress anomaly distribution locations are identified to generate real-time anomaly location connection features. The dual-path comparison result is generated using the real-time abrupt change time features and the real-time anomaly location connection features.

[0089] Furthermore, the system is also used for the following functions:

[0090] The preset dual-path anomaly judgment logic includes a first differentiation logic based on the abrupt change time characteristics of heterogeneous strata and drill bit self-faults, and a second differentiation threshold based on the abnormal location connection characteristics of heterogeneous strata and drill bit self-faults.

[0091] Furthermore, the system is also used for the following functions:

[0092] The key cutting teeth of the local cutting tooth load adaptive control module are identified. These key cutting teeth are those whose influence on the cutting trajectory and orientation of the drill bit exceeds a preset threshold. Based on the surrounding rock strength quadrant distribution, deviation behavior analysis is performed to generate deviation characteristics. If the deviation characteristics are local deviations, the local cutting tooth load adaptive control module is used to adjust the unit cutting force of the key cutting teeth to correct the local deviations. If the deviation characteristics are global deviations, the global direction control module is used to adjust the drilling direction and speed by matching the adjustment parameters of the direction adjustment mechanism.

[0093] Furthermore, the system is also used for the following functions:

[0094] If the optimal cutting force of the cutting tooth matched by the local cutting tooth load adaptive control module cannot completely correct the local hole deviation, or the adjustment parameters of the direction adjustment mechanism matched by the global direction control module cannot completely correct the global direction hole deviation, the control deviation is determined after fitting the control effect. Another control module is used to match the control parameters of the control deviation to generate collaborative control parameters for collaborative control.

[0095] Furthermore, the system is also used for the following functions:

[0096] The local cutting tooth load adaptive control module includes a micro hydraulic adjustment unit located at each key cutting tooth and a key cutting tooth adjustment parameter library. After matching the unit cutting force of the cutting tooth for local deviation with the key cutting tooth adjustment parameter library, the micro hydraulic adjustment unit controls the operation. The global direction control module includes an adjustment parameter library for the direction adjustment mechanism. The adjustment parameter library is used to match the adjustment parameters for global direction deviation.

[0097] Furthermore, the system is also used for the following functions:

[0098] The signal processing component includes a signal amplification circuit, a signal filtering circuit, and a data analysis channel. The signal amplification circuit amplifies the stress signal dataset, and the signal filtering circuit filters the amplified signal. Based on the filtered stress signal dataset, the corresponding quadrant points of the drill bit section are numbered to construct the stress change quadrant distribution. The data analysis channel is used to convert the stress change quadrant distribution into a surrounding rock strength quadrant distribution after anomaly triggering. The data analysis channel is constructed using historical conversion data.

[0099] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0100] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0101] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for preventing borehole deviation 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 by a pre-configured stress sensing component, and a stress signal dataset is generated. The stress signal dataset is processed using a signal processing component, and quadrant points corresponding to the drill bit cross section are numbered to construct a quadrant distribution of stress changes. Based on the stress change quadrant distribution, perform abnormal triggering judgment for heterogeneous strata and drill bit self-failure, and generate abnormal triggering judgment results; If the abnormal triggering judgment result is a non-homogeneous stratum triggering abnormality, the surrounding rock strength in different quadrants of the borehole circular section is analyzed based on the stress change quadrant distribution to generate a surrounding rock strength quadrant distribution. Based on the aforementioned quadrant distribution of surrounding rock strength, a fusion control system is implemented, combining local cutting tooth load adaptive control and global control of the direction adjustment mechanism. Specifically, based on the stress change quadrant distribution, anomaly triggering discrimination is performed for heterogeneous strata and drill bit self-failure, generating anomaly triggering discrimination results, including: Based on the stress change quadrant distribution, stress non-uniformity in different quadrants is identified, stress anomaly judgment is performed, and stress anomaly judgment result is generated. If the stress anomaly judgment result shows stress anomaly, the time feature comparison and spatial feature comparison of stress distribution anomaly are performed based on the stress change quadrant distribution to generate dual-path comparison results; The preset dual-path anomaly judgment logic is invoked, and dual-path anomaly trigger analysis is performed based on the dual-path comparison results to complete the anomaly trigger judgment of heterogeneous strata and drill bit self-failure, and generate the anomaly trigger judgment result; The fusion control based on the surrounding rock strength quadrant distribution, which integrates local cutter load adaptive control and global control of the direction adjustment mechanism, includes: The key cutting teeth of the local cutting tooth load adaptive control module are determined. The key cutting teeth are those whose influence on the cutting trajectory and orientation of the drill bit of the drilling rig is greater than a preset threshold. Based on the surrounding rock strength quadrant distribution, pore deviation behavior analysis is performed to generate pore deviation characteristics; If the hole deviation is a local deviation, with the goal of correcting the local deviation, the local cutting tooth load adaptive control module adjusts the unit cutting force of the key cutting tooth. If the deviation is a global deviation, the drilling direction and speed are controlled by matching the adjustment parameters of the direction adjustment mechanism after the global direction control module is used to correct the global deviation.

2. The anti-deviation drilling method based on geological feedback information as described in claim 1, characterized in that, Based on the stress variation quadrant distribution, stress non-uniformity within 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, a preset stress non-uniformity threshold that satisfies microscopic non-homogeneity is constructed. Based on the stress change quadrant distribution, the stress non-uniformity index is calculated for different tooth positions in different quadrants to generate a real-time stress non-uniformity index. By comparing the preset stress non-uniformity threshold with the real-time stress non-uniformity index, a stress anomaly judgment is performed, and the stress anomaly judgment result is generated.

3. The anti-deviation drilling method based on geological feedback information as described in claim 1, characterized in that, Based on the aforementioned stress change quadrant distribution, temporal and spatial feature comparisons of stress distribution anomalies are performed to generate dual-path comparison results, including: Based on the stress change quadrant distribution, the temporal characteristics of stress distribution anomalies at multiple consecutive time stamps are compared to identify the real-time abrupt change time characteristics of stress from normal to abnormal. Based on the quadrant distribution of stress changes, the connection of stress anomaly distribution locations is identified, and real-time anomaly location connection features are generated. The dual-path alignment result is generated using the real-time mutation time feature and the real-time anomaly location connection feature.

4. The anti-deviation drilling method based on geological feedback information as described in claim 3, characterized in that, The preset dual-path anomaly judgment logic includes a first differentiation logic based on the abrupt change time characteristics of heterogeneous strata and drill bit self-faults, and a second differentiation threshold based on the abnormal location connection characteristics of heterogeneous strata and drill bit self-faults.

5. The anti-deviation drilling method based on geological feedback information as described in claim 1, characterized in that, If the optimal cutting force of the cutting tooth matched by the local cutting tooth load adaptive control module cannot completely correct the local hole deviation, or the adjustment parameters of the direction adjustment mechanism matched by the global direction control module cannot completely correct the global direction hole deviation, the control deviation is determined after fitting the control effect. Another control module is used to match the control parameters of the control deviation to generate collaborative control parameters for collaborative control.

6. The anti-deviation drilling method based on geological feedback information as described in claim 1, characterized in that, The local cutting tooth load adaptive control module includes a micro hydraulic adjustment unit located at each key cutting tooth and a key cutting tooth adjustment parameter library. After matching the unit cutting force of the cutting tooth for local deviation holes with the key cutting tooth adjustment parameter library, the module is controlled by the micro hydraulic adjustment unit. The global direction control module includes an adjustment parameter library for the direction adjustment mechanism, and the adjustment parameters of the global direction offset hole are matched using the adjustment parameter library.

7. The anti-deviation drilling method based on geological feedback information as described in 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 amplifies the stress signal dataset, and the signal filtering circuit filters the amplified signal. Based on the filtered stress signal dataset, the corresponding quadrant points of the drill bit section are numbered to construct the stress change quadrant distribution. The data analysis channel sends data to the data analysis channel to convert the stress change quadrant distribution into the surrounding rock strength quadrant distribution after anomaly triggering. The data analysis channel is constructed using historical conversion data.

8. A borehole deviation prevention drilling system based on geological feedback information, characterized in that, The system is used to implement the anti-deviation drilling method based on geological feedback information as described in any one of claims 1-7, and the system comprises: The dataset generation module is used to monitor the stress changes on the drill bit in real time during the drilling process of the drill bit through a pre-configured stress sensing component, and generate a stress signal dataset. The quadrant distribution construction module is used to process the stress signal dataset using the signal processing component, number the quadrant points corresponding to the drill bit cross section, and construct the stress change quadrant distribution. The discrimination result generation module is used to perform abnormal trigger discrimination of heterogeneous strata and drill bit self-fault based on the stress change quadrant distribution, and generate abnormal trigger discrimination results; The quadrant distribution generation module is used to generate a surrounding rock strength quadrant distribution based on the stress change quadrant distribution analysis of the surrounding rock strength in different quadrants of the borehole circular section if the abnormal triggering judgment result is a non-homogeneous stratum triggering abnormality. The fusion control module is used for fusion control of local cutting tooth load adaptive control and direction adjustment mechanism global control based on the surrounding rock strength quadrant distribution.

Images