Self-adaptive adjustment method for drilling strategy of deep and difficult-to-incline multi-layer geologic structure

Abstract

The invention relates to the technical field of geological exploration, and discloses a deep difficult-to-skew multilayer geological structure drilling strategy self-adaptive adjustment method, which comprises the following steps of: acquiring multi-source information by using a multi-source data fusion analysis module, and generating a geological structure model through satellite remote sensing data, unmanned aerial vehicle aerial photography data and seismic wave detection data multi-source information; formulating a preliminary drilling strategy according to the geological structure model; a machine learning algorithm is introduced, a drilling strategy is optimized, and the adaptability and accuracy of the strategy are improved; through multi-source data fusion analysis, geologic structure features can be more comprehensively known, a scientific basis is provided for formulation of a drilling strategy, the drilling strategy is optimized through machine learning, drilling parameters can be dynamically adjusted according to real-time monitoring data, the adaptability and accuracy of the drilling strategy are improved, the drilling risk and cost are reduced, and the drilling efficiency is improved. The real-time monitoring and adjusting system can find potential problems and risks in the drilling process in time, and drilling accidents are avoided or reduced.

Description

Technical Field

[0001] The invention relates to the technical field of geological exploration, and in particular to a method for adaptively adjusting drilling strategies for deep, difficult-to-incline, and multi-layer geological structures. Background Art

[0002] In geological exploration and mineral resource development, drilling operations in deep, difficult-to-incline, multi-layer geological structures have always been a very challenging field. Such geological structures usually contain complex rock stratification, fault zones, weak interlayers, and rock interlayers of different properties. These characteristics make drilling operations face extremely high technical difficulties and safety risks. Traditional drilling methods often rely on empirical judgment and fixed drilling strategies, which are difficult to adapt to complex and changeable geological conditions. Especially in deep, difficult-to-incline, multi-layer geological structures, due to factors such as large stratum inclination, uneven rock hardness, and high groundwater levels, accidents such as drill jamming, drill breakage, and well wall collapse are prone to occur during the drilling process, seriously affecting drilling efficiency and safety.

[0003] In order to improve the efficiency and safety of drilling operations, domestic and foreign scholars and engineers have conducted a lot of research and improvement on drilling strategies in recent years. However, most of the existing drilling strategies are still at the level of static optimization, that is, drilling strategies are formulated according to prior geological exploration data, and limited adjustments are made during the drilling process. This method is often difficult to achieve ideal drilling results when facing deep, difficult and multi-layer geological structures. Therefore, an adaptive adjustment method for drilling strategies in deep, difficult and multi-layer geological structures is proposed to solve the above problems. Summary of the invention

[0004] The present invention proposes a method for adaptively adjusting drilling strategies for deep, difficult-to-incline, multi-layer geological structures to solve the existing problems.

[0005] In order to achieve the above object, the present invention provides the following technical solution: a method for adaptively adjusting drilling strategy for deep, difficult and multi-layer geological structures, comprising the following steps:

[0006] S1: Use the multi-source data fusion analysis module to collect multi-source information, and generate a geological structure model through satellite remote sensing data, drone aerial photography data, and seismic wave detection data;

[0007] S2: Develop preliminary drilling strategies based on the geological structure model;

[0008] S3: Introduce machine learning algorithms to optimize drilling strategies and improve their adaptability and accuracy;

[0009] S4: During the drilling process, the real-time monitoring and adjustment module is used to monitor geological parameters and drilling parameters in real time, and the drilling strategy is adjusted in real time according to the monitoring data;

[0010] S5: Set the safety threshold during the drilling process. When the real-time monitoring data exceeds the safety threshold, a safety warning is triggered and corresponding countermeasures are taken;

[0011] S6: Introduce adaptive learning algorithms to continuously optimize drilling strategies and safety warning mechanisms based on data feedback during the drilling process.

[0012] Preferably, the S1 specifically comprises the steps of:

[0013] S11: Collect multi-source information such as satellite remote sensing data, drone aerial photography data, and seismic wave detection data;

[0014] S12: Preprocess multi-source data, including data cleaning, denoising, and normalization, to improve data quality;

[0015] S13: Use data fusion algorithms to fuse multi-source data to form a comprehensive geological structure model;

[0016] S14: Identify complex areas such as difficult-to-drill layers, inclined layers, and multi-layer structures in geological structures, and use geostatistical methods to evaluate the distribution of geological parameters in complex areas.

[0017] Preferably, the S2 specifically comprises the steps of:

[0018] S21: Determine the drilling direction based on the geological structure model to avoid directly crossing difficult-to-drill layers or inclined layers;

[0019] S22: Set the drilling depth and drilling speed according to geological parameters such as formation pressure and rock strength;

[0020] S23: Use geomechanical models to predict the torque and drilling efficiency during drilling. The formula is as follows:

[0021] T=f_1(R,P,S,\sigma_r,\sigma_t,\mu)\][E=f_2(T,V,D,\rho)\];

[0022] Among them, \(T\) is the predicted torque, \(R\) is the drill bit radius, \(P\) is the bit pressure, \(S\) is the drilling fluid flow rate, \(\sigma_r\) is the radial strength of the rock, \(\sigma_t\) is the tangential strength of the rock, \(\mu\) is the friction coefficient, \(E\) is the drilling efficiency, \(V\) is the drilling speed, \(D\) is the borehole diameter, and \(\rho\) is the rock density.

[0023] Preferably, the S3 specifically comprises the steps of:

[0024] S31: constructing a feature vector including geological parameters and drilling process parameters;

[0025] S32: Using machine learning algorithms to train drilling strategy optimization models;

[0026] S33; optimizing the preliminary drilling strategy based on the trained model to improve the adaptability and accuracy of the strategy;

[0027] Preferably, the S4 specifically comprises the steps of:

[0028] S41: Real-time monitoring of geological parameters, such as formation dip, rock strength changes, etc.;

[0029] S42: Real-time monitoring of drilling parameters, such as torque, drilling speed, drilling fluid pressure, etc.;

[0030] S42: When the monitoring data deviates from the preset range, an alarm is triggered and the drilling strategy is automatically adjusted, such as adjusting the drilling speed, drilling pressure, etc.

[0031] Preferably, the S5 specifically comprises the steps of:

[0032] S51: Set reasonable safety thresholds based on geological parameters and drilling parameters;

[0033] S52: When the real-time monitoring data exceeds the safety threshold, a safety warning is triggered;

[0034] S53: Take appropriate response measures based on the warning type, such as suspending drilling, adjusting drilling strategies, strengthening support, etc.

[0035] Preferably, the S6 specifically comprises the steps of:

[0036] S61: Using reinforcement learning algorithms, including Q-learning and Deep Q-Network, to continuously optimize drilling strategies based on data feedback during the drilling process;

[0037] S62: Dynamically adjust the safety threshold according to the data changes during the drilling process to improve the accuracy of the safety warning mechanism;

[0038] S63: Using adaptive learning algorithms to continuously accumulate drilling experience and improve the intelligence and adaptability of drilling strategies

[0039] Preferably, the geological parameters include rock strength and formation pressure, and the drilling process parameters include drill bit type and drilling speed.

[0040] Preferably, the data fusion algorithm includes Kalman filtering and Bayesian network.

[0041] Preferably, the machine learning algorithm includes support vector machine SVM, random forest RF or neural network NN.

[0042] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0043] Through multi-source data fusion analysis, the present invention can more comprehensively understand the geological structure characteristics, provide a scientific basis for the formulation of drilling strategies, and optimize drilling strategies through machine learning. It can dynamically adjust drilling parameters according to real-time monitoring data, improve the adaptability and accuracy of drilling strategies, and reduce drilling risks and costs. The real-time monitoring and adjustment system can promptly detect potential problems and risks in the drilling process, avoid or reduce the occurrence of drilling accidents, and the adaptive learning optimization mechanism can continuously accumulate drilling experience and knowledge, providing reference and reference for future drilling projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0045] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The present invention provides Figure 1 The method for adaptively adjusting the drilling strategy for deep and difficult to deflect multi-layer geological structures shown in the figure provides the following implementation methods: Specific implementation method 1

[0049] S1: Multi-source data fusion analysis, the specific steps are as follows:

[0050] S11: Collect remote sensing images from satellites, high-definition images taken by drones, and seismic wave detection data.

[0051] S12: Use data cleaning techniques to remove noise and invalid data, and ensure data consistency through normalization.

[0052] S13: Kalman filtering algorithm is used to fuse multi-source data to construct an accurate geological structure model.

[0053] S14: Use geostatistical methods such as Kriging interpolation to identify difficult-to-drill layers, inclined layers, and multi-layer structures in geological structures, and evaluate the distribution of geological parameters in these areas.

[0054] S2: Develop a preliminary drilling strategy, with the following steps:

[0055] S21: Based on the geological structure model, plan the drilling path to ensure that difficult-to-drill and inclined layers are avoided.

[0056] S22: Set the drilling depth and drilling speed according to the formation pressure and rock strength to ensure the stability of the drilling process.

[0057] S23: Use the geomechanical model to predict the torque and drilling efficiency during the drilling process. The formula is as described above, and the drilling parameters are adjusted according to the prediction results.

[0058] S3: Machine learning optimizes drilling strategy, with the following steps:

[0059] S31: Construct feature vectors, including rock strength, formation pressure, drill bit type, drilling speed, etc.

[0060] S32: Use support vector machine (SVM) algorithm to train the drilling strategy optimization model.

[0061] S33: Based on the trained model, the preliminary drilling strategy is optimized to improve the adaptability and accuracy of the strategy.

[0062] S4: Real-time monitoring and adjustment, the specific steps are as follows:

[0063] S41: Real-time monitoring of geological parameters such as formation inclination, rock strength changes, etc.

[0064] S42: Monitor drilling parameters such as torque, drilling speed, and drilling fluid pressure.

[0065] S43: When the monitoring data deviates from the preset range, an alarm is triggered and the drilling strategy is automatically adjusted, such as reducing the drilling speed or increasing the drilling pressure.

[0066] S5: Safety warning and response measures, the specific steps are as follows:

[0067] S51: Setting safety thresholds according to geological parameters and drilling parameters.

[0068] S52: When the real-time monitoring data exceeds the safety threshold, a safety warning is triggered.

[0069] S53: Take appropriate response measures based on the warning type, such as suspending drilling, adjusting drilling strategies or strengthening support.

[0070] S6: Adaptive learning optimization, the specific steps are as follows:

[0071] S61: Use Q-learning algorithm to continuously optimize drilling strategy based on data feedback during drilling process.

[0072] S62: Dynamically adjust the security threshold according to data changes to improve the accuracy of the security warning mechanism.

[0073] S63: Use adaptive learning algorithms to accumulate drilling experience and improve the intelligence and adaptability of drilling strategies. Specific implementation method 2

[0075] S1: Multi-source data fusion analysis, the specific steps are as follows:

[0076] S11: Collect satellite remote sensing data, drone aerial photography data and seismic wave detection data.

[0077] S12: Preprocess the data, including data cleaning, denoising and normalization.

[0078] S13: Use Bayesian network algorithm to fuse multi-source data and construct geological structure model.

[0079] S14: Use geostatistical methods to identify complex areas and evaluate the distribution of geological parameters.

[0080] S2: Develop a preliminary drilling strategy, with the following steps:

[0081] S21: Planning drilling paths based on geological structure models.

[0082] S22: Set the drilling depth and drilling speed according to the formation pressure and rock strength.

[0083] S23: Use geomechanical models to predict the torque and drilling efficiency during the drilling process, and adjust the drilling parameters based on the prediction results.

[0084] S3: Machine learning optimizes drilling strategy, with the following steps:

[0085] S31: construct a feature vector including geological parameters and drilling process parameters.

[0086] S32: The random forest (RF) algorithm is used to train the drilling strategy optimization model.

[0087] S33: Optimize the preliminary drilling strategy based on the trained model.

[0088] S4: Real-time monitoring and adjustment, the specific steps are as follows:

[0089] S41: Real-time monitoring of geological parameters and drilling parameters.

[0090] S42: When the monitoring data deviates from the preset range, an alarm is triggered and the drilling strategy is automatically adjusted.

[0091] S5: Safety warning and response measures, the specific steps are as follows:

[0092] S51: Set reasonable safety thresholds.

[0093] S52: When the real-time monitoring data exceeds the safety threshold, a safety warning is triggered.

[0094] S53: Take appropriate response measures based on the type of warning.

[0095] S6: Adaptive learning optimization, the specific steps are as follows:

[0096] S61: Use the Deep Q-Network algorithm to optimize drilling strategies based on data feedback during the drilling process.

[0097] S62: Dynamically adjust the safety threshold to improve the accuracy of the safety warning mechanism.

[0098] S63: Use adaptive learning algorithms to improve the intelligence and adaptability of drilling strategies. Specific implementation method three

[0100] S1: Multi-source data fusion analysis, the specific steps are as follows:

[0101] S11: Collect satellite remote sensing data, drone aerial photography data and seismic wave detection data.

[0102] S12: Preprocess the data to improve data quality.

[0103] S13: Use data fusion algorithms (such as Kalman filtering or Bayesian networks) to fuse multi-source data and construct a geological structure model.

[0104] S14: Identify complex areas in geological structures and evaluate the distribution of geological parameters.

[0105] S2: Develop a preliminary drilling strategy, with the following steps:

[0106] S21: Planning drilling paths based on geological structure models.

[0107] S22: Set the drilling depth and drilling speed according to the formation pressure and rock strength.

[0108] S23: Use geomechanical models to predict the torque and drilling efficiency during the drilling process, and adjust the drilling parameters based on the prediction results.

[0109] S3: Machine learning optimizes drilling strategy, with the following steps:

[0110] S31: Construct feature vector.

[0111] S32: Using a neural network (NN) algorithm to train a drilling strategy optimization model.

[0112] S33: Optimize the preliminary drilling strategy based on the trained model.

[0113] S4: Real-time monitoring and adjustment, the specific steps are as follows:

[0114] S41: Real-time monitoring of geological parameters and drilling parameters.

[0115] S42: When the monitoring data deviates from the preset range, an alarm is triggered and the drilling strategy is automatically adjusted.

[0116] S5: Safety warning and response measures, the specific steps are as follows:

[0117] S51: Set reasonable safety thresholds.

[0118] S52: When the real-time monitoring data exceeds the safety threshold, a safety warning is triggered.

[0119] S53: Take appropriate response measures according to the type of warning, such as suspending drilling, strengthening support, etc.

[0120] S6: Adaptive learning optimization, the specific steps are as follows:

[0121] S61: Use reinforcement learning algorithms (such as Q-learning or Deep Q-Network) to optimize drilling strategies based on data feedback during the drilling process.

[0122] S62: Dynamically adjust the security threshold according to data changes.

[0123] S63: Use adaptive learning algorithms to improve the intelligence and adaptability of drilling strategies and accumulate drilling experience. Specific implementation method four

[0125] S1: Multi-source data fusion analysis, the specific steps are as follows:

[0126] S11: In addition to traditional satellite remote sensing data, drone aerial photography data and seismic wave detection data, geological exploration drilling data are also introduced, which provide direct information on underground rocks and soil layers.

[0127] S12: In the data preprocessing stage, in addition to cleaning and denoising, data enhancement techniques such as data augmentation and data transformation are also used to improve the generalization ability of the model.

[0128] S13: Use deep learning algorithms (such as convolutional neural networks (CNNs)) to fuse multi-source data and build a more sophisticated geological structure model.

[0129] S14: Use geostatistics and machine learning algorithms to conduct deeper analysis of complex areas, such as using clustering algorithms to identify the distribution of different stratigraphic types.

[0130] S2: Develop a preliminary drilling strategy, with the following steps:

[0131] S21: Based on the geological structure model, the drilling path is displayed using 3D visualization technology to plan the drilling direction more intuitively.

[0132] S22: Based on the formation pressure and rock strength, combined with the geomechanical model, formulate dynamic drilling depth and drilling speed strategies to adapt to the changes in different formations.

[0133] S23: Introduce an expert system to further optimize the drilling strategy by combining the experience and knowledge of geologists.

[0134] S3: Machine learning optimizes drilling strategy, with the following steps:

[0135] S31: Construct a more comprehensive feature vector, including geological parameters, drilling process parameters and drilling history data.

[0136] S32: Use ensemble learning methods (such as gradient boosting tree GBDT) to train the drilling strategy optimization model to improve the accuracy and stability of the model.

[0137] S33: According to the trained model, the preliminary drilling strategy is iteratively optimized until the optimal solution is reached.

[0138] S4: Real-time monitoring and adjustment, the specific steps are as follows:

[0139] S41: Introduce Internet of Things technology to realize remote monitoring and real-time data transmission of drilling equipment.

[0140] S42: Establish a real-time monitoring and early warning system. When the monitoring data is abnormal, the alarm is automatically triggered and the drilling strategy is adjusted immediately.

[0141] S43: Use data mining technology to analyze real-time monitoring data and identify potential problems and risks in the drilling process.

[0142] S5: Safety warning and response measures, the specific steps are as follows:

[0143] S51: Based on real-time monitoring data and historical data, establish a security risk assessment model and set more accurate security thresholds.

[0144] S52: When the real-time monitoring data exceeds the safety threshold, a multi-level warning mechanism is triggered, including primary warning, intermediate warning and emergency warning.

[0145] S53: According to the warning level, take corresponding response measures, such as suspending drilling, adjusting drilling parameters, strengthening support, etc., and activate the emergency plan.

[0146] S6: Adaptive learning optimization, the specific steps are as follows:

[0147] S61: Use deep learning algorithms (such as recurrent neural networks (RNNs)) to analyze and learn data from the drilling process and continuously optimize drilling strategies.

[0148] S62: Dynamically adjust safety thresholds and early warning mechanisms based on data changes during the drilling process to improve the accuracy and timeliness of safety warnings.

[0149] S63: Establish a drilling experience database to continuously accumulate drilling experience and knowledge to provide reference for future drilling projects. Specific implementation method five

[0151] S1: Multi-source data fusion analysis, the specific steps are as follows:

[0152] S11: Collect various types of data, including satellite remote sensing data, drone aerial photography data, seismic wave detection data, geological exploration drilling data, and geological literature data.

[0153] S12: Use data cleaning and preprocessing techniques to remove noise and redundant information in the data and improve data quality.

[0154] S13: Use multi-source data fusion algorithms (such as Bayesian networks or deep learning algorithms) to fuse data from different sources and construct a unified geological structure model.

[0155] S14: Use geostatistical methods and machine learning algorithms to verify and optimize geological structure models to ensure the accuracy and reliability of the models.

[0156] S2: Develop a preliminary drilling strategy, with the following steps:

[0157] S21: Based on the geological structure model, use the geographic information system (GIS) to plan the drilling path to ensure the rationality and effectiveness of the drilling path.

[0158] S22: Develop drilling depth and drilling speed strategies based on geological parameters such as formation pressure and rock strength, combined with the performance parameters of drilling equipment.

[0159] S23: Use geomechanical models and numerical simulation technology to predict and analyze parameters such as torque and drilling efficiency during the drilling process, providing a scientific basis for the formulation of drilling strategies.

[0160] S3: Machine learning optimizes drilling strategy, with the following steps:

[0161] S31: constructing a feature vector, including geological parameters, drilling process parameters, drilling history data, and performance parameters of drilling equipment.

[0162] S32: Use advanced machine learning algorithms (such as deep learning algorithms, reinforcement learning algorithms, etc.) to train drilling strategy optimization models.

[0163] S33: Based on the trained model, the preliminary drilling strategy is iteratively optimized to continuously improve the adaptability and accuracy of the drilling strategy.

[0164] S4: Real-time monitoring and adjustment, the specific steps are as follows:

[0165] S41: Use IoT technology and sensor technology to monitor geological parameters and drilling parameters in real time during the drilling process.

[0166] S42: Establish a real-time monitoring and early warning system. When the monitoring data is abnormal, the alarm is automatically triggered and the drilling strategy is adjusted immediately.

[0167] S43: Use data mining and machine learning techniques to analyze and mine real-time monitoring data to identify potential problems and risks in the drilling process and provide a scientific basis for adjusting drilling strategies.

[0168] S5: Safety warning and response measures, the specific steps are as follows:

[0169] S51: Based on real-time monitoring data and historical data, establish a security risk assessment model and set reasonable security thresholds.

[0170] S52: When the real-time monitoring data exceeds the safety threshold, the safety warning mechanism is triggered and corresponding response measures are taken, such as suspending drilling, adjusting drilling parameters, strengthening support, etc.

[0171] S53: Establish emergency plans and emergency response mechanisms to ensure that emergencies can be responded to and handled quickly and effectively in emergency situations.

[0172] S6: Adaptive learning optimization, the specific steps are as follows:

[0173] S61: Use machine learning algorithms and reinforcement learning algorithms to analyze and learn data from the drilling process, and continuously optimize drilling strategies and safety warning mechanisms.

[0174] S62: Dynamically adjust safety thresholds and early warning mechanisms based on data changes during the drilling process to improve the accuracy and timeliness of safety warnings.

[0175] S63: Establish a drilling experience database and drilling knowledge map to continuously accumulate drilling experience and knowledge, provide reference and reference for future drilling projects, and promote the continuous innovation and development of drilling technology.

[0176] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for adaptively adjusting drilling strategies for deep, difficult and multi-layer geological structures, characterized by: The steps include: S1: Use the multi-source data fusion analysis module to collect multi-source information, and generate a geological structure model through satellite remote sensing data, drone aerial photography data, and seismic wave detection data; S2: Develop preliminary drilling strategies based on the geological structure model; S3: Introduce machine learning algorithms to optimize drilling strategies and improve their adaptability and accuracy; S4: During the drilling process, the real-time monitoring and adjustment module is used to monitor geological parameters and drilling parameters in real time, and the drilling strategy is adjusted in real time according to the monitoring data; S5: Set the safety threshold during the drilling process. When the real-time monitoring data exceeds the safety threshold, a safety warning is triggered and corresponding countermeasures are taken; S6: Introduce adaptive learning algorithms to continuously optimize drilling strategies and safety warning mechanisms based on data feedback during the drilling process.

2. The method for adaptively adjusting drilling strategies for deep, difficult and multi-layer geological structures according to claim 1 is characterized by: The S1 specifically comprises the steps of: S11: Collect multi-source information such as satellite remote sensing data, drone aerial photography data, and seismic wave detection data; S12: Preprocess multi-source data, including data cleaning, denoising, and normalization, to improve data quality; S13: Use data fusion algorithms to fuse multi-source data to form a comprehensive geological structure model; S14: Identify complex areas such as difficult-to-drill layers, inclined layers, and multi-layer structures in geological structures, and use geostatistical methods to evaluate the distribution of geological parameters in complex areas.

3. The method for adaptively adjusting drilling strategies for deep, difficult and multi-layer geological structures according to claim 1 is characterized by: The S2 specifically includes the steps of: S21: Determine the drilling direction based on the geological structure model to avoid directly crossing difficult-to-drill layers or inclined layers; S22: Set the drilling depth and drilling speed according to geological parameters such as formation pressure and rock strength; S23: Use geomechanical models to predict the torque and drilling efficiency during drilling. The formula is as follows: T=f_1(R,P,S,\sigma_r,\sigma_t,\mu)\][E=f_2(T,V,D,\rho)\]; Among them, \(T\) is the predicted torque, \(R\) is the drill bit radius, \(P\) is the bit pressure, \(S\) is the drilling fluid flow rate, \(\sigma_r\) is the radial strength of the rock, \(\sigma_t\) is the tangential strength of the rock, \(\mu\) is the friction coefficient, \(E\) is the drilling efficiency, \(V\) is the drilling speed, \(D\) is the borehole diameter, and \(\rho\) is the rock density.

4. The method for adaptively adjusting drilling strategies for deep, difficult and multi-layer geological structures according to claim 1 is characterized by: The S3 specifically includes the steps of: S31: constructing a feature vector including geological parameters and drilling process parameters; S32: Using machine learning algorithms to train drilling strategy optimization models; S33; Based on the trained model, the preliminary drilling strategy is optimized to improve the adaptability and accuracy of the strategy.

5. The method for adaptively adjusting drilling strategy for deep, difficult and multi-layer geological structures according to claim 1 is characterized by: The S4 specifically comprises the steps of: S41: Real-time monitoring of geological parameters, such as formation dip, rock strength changes, etc.; S42: Real-time monitoring of drilling parameters, such as torque, drilling speed, drilling fluid pressure, etc.; S42: When the monitoring data deviates from the preset range, an alarm is triggered and the drilling strategy is automatically adjusted, such as adjusting the drilling speed, drilling pressure, etc.

6. The method for adaptively adjusting drilling strategies for deep, difficult and multi-layer geological structures according to claim 1, characterized in that: The S5 specifically includes the steps of: S51: Set reasonable safety thresholds based on geological parameters and drilling parameters; S52: When the real-time monitoring data exceeds the safety threshold, a safety warning is triggered; S53: Take appropriate response measures based on the warning type, such as suspending drilling, adjusting drilling strategies, strengthening support, etc.

7. The method for adaptively adjusting drilling strategies for deep, difficult and multi-layer geological structures according to claim 1 is characterized by: The S6 specifically comprises the steps of: S61: Using reinforcement learning algorithms, including Q-learning and Deep Q-Network, to continuously optimize drilling strategies based on data feedback during the drilling process; S62: Dynamically adjust the safety threshold according to the data changes during the drilling process to improve the accuracy of the safety warning mechanism; S63: Use adaptive learning algorithms to continuously accumulate drilling experience and improve the intelligence and adaptability of drilling strategies.

8. The method for adaptively adjusting drilling strategy for deep, difficult-to-slope, multi-layer geological structures according to any one of claims 4 to 6, characterized in that: The geological parameters include rock strength and formation pressure, and the drilling process parameters include drill bit type and drilling speed.

9. The method for adaptively adjusting drilling strategy for deep, difficult and multi-layer geological structures according to claim 2, characterized in that: The data fusion algorithm includes Kalman filtering and Bayesian network.

10. The method for adaptively adjusting drilling strategy for deep, difficult and multi-layer geological structures according to claim 4, characterized in that: The machine learning algorithm includes support vector machine SVM, random forest RF or neural network NN.

Images