Complex stratum high-precision vertical settlement observation deep hole drilling process

Abstract

The invention relates to the technical field of geological exploration, and discloses a complex stratum high-precision vertical settlement observation deep hole drilling process which comprises the steps of stratum pretreatment and grouting reinforcement, real-time monitoring of the position and posture of a drill bit and optimization of drilling parameters, intelligent prediction of the drilling parameters through a machine learning algorithm, and deep hole drilling. Real-time monitoring of drilling tool and geological changes and parameter adjustment, fault identification and processing suggestion, drilling quality inspection, wall surface strengthening treatment, and long-term monitoring and maintenance based on the Internet of Things; according to the technology, the drilling precision, efficiency and safety are improved, long-term stability of the drilling quality is ensured, through drill bit positioning and track optimization, the positioning precision is improved, the position and track of the drill bit can be predicted more accurately, errors are reduced, drilling parameters can be adjusted in real time according to the stratum characteristics, and the drilling efficiency is improved; by optimizing the drill hole wall surface strengthening treatment technology and process parameters, the stability and bearing capacity of the drill hole wall surface can be enhanced, and the risks of collapse and deformation of the drill hole are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of geological drilling, and in particular to a high-precision vertical settlement observation deep hole drilling process for complex strata. 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 still remain at the level of static optimization, that is, drilling strategies are formulated based on 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, a high-precision vertical settlement observation deep hole drilling process for complex formations is proposed to solve the above problems. Summary of the invention

[0004] The present invention proposes a complex stratum high-precision vertical settlement observation deep hole drilling process to solve the existing problems.

[0005] In order to achieve the above-mentioned object, the present invention provides the following technical solution: a high-precision vertical settlement observation deep hole drilling process for complex strata, comprising the following steps:

[0006] S1: Pre-treat the stratum, use the grouting pressure calculation algorithm based on the geomechanical model, calculate the grouting material ratio and grouting pressure according to the stratum stability and bearing capacity requirements, and then perform grouting reinforcement. Through the adaptive sealing algorithm, according to the real-time monitoring data of the borehole wall, dynamically adjust the type and application method of the sealing material to protect the wall and plug the leak;

[0007] S2: Using 3D positioning algorithm and posture recognition algorithm to monitor the 3D position and posture of the drill bit in real time;

[0008] Based on real-time monitoring data, trajectory prediction and optimization algorithms are used to dynamically adjust drilling parameters and drill bit trajectory control strategies;

[0009] Intelligently predict and optimize drilling parameters based on formation characteristics and drilling requirements.

[0010] S3: Uses machine learning algorithms, including support vector machines and random forests, to intelligently predict and optimize drilling parameters by combining historical drilling data and current formation characteristics;

[0011] S4: Use the drill bit to perform coring and drilling during the drilling process, monitor the working status of the drill bit and the geological changes in the borehole in real time, and automatically adjust the working parameters of the drill bit or replace the drill bit according to the monitoring results;

[0012] S5: Real-time monitoring of the geological conditions and drilling parameters in the borehole, automatic identification and diagnosis of various faults and abnormal conditions during the drilling process, and corresponding treatment suggestions;

[0013] S6: Conduct quality inspection and acceptance of drilling holes;

[0014] S7: Implement borehole wall strengthening treatment to improve the stability and bearing capacity of the borehole wall;

[0015] S8: Based on the Internet of Things technology and remote monitoring technology, a long-term monitoring and maintenance system for drilling is established. Using real-time data monitoring and analysis algorithms, real-time monitoring and data analysis of drilling are carried out to promptly identify and deal with potential problems.

[0016] Preferably, the monitoring data includes wall crack width and leakage amount.

[0017] Preferably, the calculation formula of S2 is:

[0018] X, Y, Z, α, β, γ = g(t), where X, Y, Z are the three-dimensional coordinates of the drill bit, α, β, γ are the attitude angles of the drill bit, and t is time.

[0019] Preferably, the drilling parameters include drilling speed and drilling pressure.

[0020] Preferably, the model formula of S3 is:

[0021] y=h(x), where y is the predicted drilling parameters and x is the input features such as formation characteristics and drilling requirements.

[0022] Preferably, the calculation formula in S1 is:

[0023] P = f(σ, μ, c, φ, h), where P is the grouting pressure, σ is the formation stress, μ is the formation Poisson's ratio, c is the formation cohesion, φ is the formation internal friction angle, and h is the grouting depth.

[0024] Preferably, the S5 specifically comprises the steps of:

[0025] Deep learning algorithms, including convolutional neural networks and recurrent neural networks, are used to analyze and process massive amounts of data during the drilling process in real time, and to automatically identify faults and abnormalities. The model formula is: C=F(D), where C is the identified fault or abnormality, and D is the data during the drilling process.

[0026] According to the identified faults and abnormal conditions, the fault diagnosis and processing suggestion algorithm is used to give corresponding processing suggestions. The algorithm includes a fault diagnosis module and a processing suggestion generation module

[0027] Preferably, the S6 specifically comprises the steps of:

[0028] High-precision measuring instruments are used to measure and check the verticality, hole diameter and hole depth of the borehole. Measurement data evaluation algorithms are used to process and analyze the measurement data to evaluate whether the drilling quality meets the design requirements.

[0029] Preferably, the S7 specifically comprises the steps of:

[0030] The wall strengthening material selection algorithm is used to select the appropriate wall strengthening material according to the formation characteristics and the actual situation of the borehole wall. The wall strengthening process optimization algorithm is used to optimize the wall strengthening process parameters according to the selected wall strengthening material and the actual situation of the borehole wall.

[0031] Preferably, the process parameters include curing time and curing temperature.

[0032] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0033] The present invention improves positioning accuracy through drill bit positioning and trajectory optimization, can more accurately predict the position and trajectory of the drill bit, reduce errors, can adjust drilling parameters in real time according to formation characteristics, and improve drilling efficiency. It can reduce unnecessary energy consumption and waste by intelligently predicting and optimizing drilling parameters through deep learning algorithms. It can monitor and predict drilling parameters in real time, and promptly discover potential safety hazards to avoid accidents. It can automatically adjust drilling tool parameters according to real-time monitoring data to adapt to different formations and drilling requirements, and improve drilling efficiency. It can enhance the stability and bearing capacity of the borehole wall and reduce the risk of borehole collapse and deformation by optimizing the borehole wall strengthening treatment technology and process parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] 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.

[0035] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0036] 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.

[0037] The present invention provides Figure 1 A high-precision vertical settlement observation deep hole drilling process for complex formations is shown, and the following implementation methods are provided:

[0038] Implementation method 1:

[0039] S1: Ground pretreatment and grouting reinforcement

[0040] The grouting pressure calculation algorithm based on the geomechanical model is adopted. According to the formation stress σ=30MPa, Poisson's ratio μ=0.25, cohesion c=20kPa, internal friction angle φ=30° and grouting depth h=50m, the grouting pressure P=f(σ, μ, c, φ, h)=2.5MPa is calculated.

[0041] Mix the grouting materials to ensure that the grouting pressure requirements are met, and carry out grouting reinforcement.

[0042] Through the adaptive sealing algorithm, the type of sealing material and the method of application are dynamically adjusted according to the real-time monitoring of the crack width (≤0.5mm) and leakage volume (≤0.1L / min) of the borehole wall to achieve wall protection and leakage plugging.

[0043] S2: Drill bit positioning and trajectory optimization

[0044] Using the three-dimensional positioning algorithm and posture recognition algorithm, the three-dimensional coordinates X, Y, Z and posture angles α, β, γ of the drill bit are monitored in real time, and their relationship with time t is g(t).

[0045] According to the real-time monitoring data, the trajectory prediction and optimization algorithm is used to dynamically adjust the drilling speed v=1m / h and drilling pressure F=50kN, as well as the drill bit trajectory control strategy to ensure drilling accuracy.

[0046] Intelligently predict and optimize drilling parameters based on formation characteristics and drilling requirements to improve drilling efficiency.

[0047] S3: Intelligent prediction and optimization of drilling parameters

[0048] A machine learning algorithm (support vector machine SVM) is used to intelligently predict and optimize drilling parameters in combination with historical drilling data and current formation characteristics, and a model y=h(x) is established, where y is the predicted drilling parameter and x is input features such as formation characteristics and drilling requirements.

[0049] S4: Drilling tool monitoring and parameter adjustment

[0050] During the drilling process, the working status of the drill tool and the geological changes in the borehole, including rotation speed, torque and vibration, are monitored in real time.

[0051] According to the monitoring results, the working parameters of the drilling tool are automatically adjusted, such as reducing the rotation speed to avoid drill sticking, or replacing the drilling tool to adapt to different formations.

[0052] S5: Fault identification and troubleshooting suggestions

[0053] A deep learning algorithm (convolutional neural network CNN) is used to analyze and process massive data in the drilling process in real time, automatically identify faults and abnormal conditions, and establish a model C=F(D), where C is the identified fault or abnormal condition and D is the data in the drilling process.

[0054] According to the identified fault type, the fault diagnosis and processing suggestion algorithm is used to give corresponding processing suggestions, such as adjusting drilling parameters or stopping the machine for inspection.

[0055] S6: Drilling quality inspection and acceptance

[0056] High-precision measuring instruments are used to measure and check the verticality, hole diameter and hole depth of the drilled holes.

[0057] The measurement data evaluation algorithm is used to process and analyze the measurement data to evaluate whether the drilling quality meets the design requirements, such as verticality deviation ≤ 0.5% and aperture deviation ≤ 2mm.

[0058] S7: Drilling wall strengthening treatment

[0059] The wall reinforcement material selection algorithm is used to select appropriate wall reinforcement materials according to the actual conditions of the formation and the borehole wall.

[0060] The wall strengthening process optimization algorithm is used to optimize the wall strengthening process parameters, such as the curing time t = 24h and the curing temperature T = 60°C, according to the selected wall strengthening material and the actual situation of the drill hole wall.

[0061] S8: Long-term monitoring and maintenance of boreholes

[0062] Based on Internet of Things technology and remote monitoring technology, a long-term drilling monitoring and maintenance system is established.

[0063] Real-time data monitoring and analysis algorithms are used to conduct real-time monitoring and data analysis of boreholes, so as to promptly detect and deal with potential problems such as formation displacement or borehole wall leakage.

[0064] Implementation method 2:

[0065] Based on the previous implementation method, some optimizations and adjustments have been made:

[0066] S1: Ground pretreatment and grouting reinforcement

[0067] The grouting pressure calculation algorithm is optimized to consider more formation parameters, such as formation moisture and temperature, to calculate the grouting pressure more accurately.

[0068] Improve the proportion of grouting materials and grouting technology to improve the grouting reinforcement effect.

[0069] Adopting more advanced adaptive sealing algorithm and real-time monitoring data of borehole wall, more accurate wall protection and leak plugging can be achieved.

[0070] S2: Drill bit positioning and trajectory optimization

[0071] Upgrade the three-dimensional positioning algorithm and posture recognition algorithm to improve the drill bit positioning accuracy and trajectory prediction accuracy.

[0072] Optimize drilling parameter adjustment strategies and achieve faster drilling parameter adjustments based on real-time monitoring data.

[0073] S3: Intelligent prediction and optimization of drilling parameters

[0074] The random forest algorithm is introduced and combined with the support vector machine algorithm to improve the accuracy and stability of intelligent prediction and optimization of drilling parameters.

[0075] S4: Drilling tool monitoring and parameter adjustment

[0076] Add drill tool vibration monitoring and drill tool wear prediction functions to improve the comprehensiveness and accuracy of drill tool monitoring.

[0077] Optimize the drilling tool parameter adjustment strategy and achieve more intelligent drilling tool parameter adjustment according to the drilling tool working status and geological changes.

[0078] S5: Fault identification and troubleshooting suggestions

[0079] The recurrent neural network algorithm is introduced to perform real-time analysis and processing of time series data during the drilling process to improve the accuracy and timeliness of fault identification.

[0080] Optimize the fault diagnosis and processing suggestion algorithm to provide more reasonable processing suggestions based on the fault type and severity.

[0081] S6: Drilling quality inspection and acceptance

[0082] Introducing more advanced measurement techniques and evaluation algorithms to improve the accuracy and reliability of drilling quality inspection.

[0083] Based on the drilling quality inspection results, formulate targeted corrective measures and acceptance standards.

[0084] S7: Drilling wall strengthening treatment

[0085] Optimize the wall strengthening material selection algorithm and wall strengthening process optimization algorithm to improve the effect and efficiency of wall strengthening treatment.

[0086] Introduce new materials and new technologies to improve the stability and bearing capacity of the borehole wall.

[0087] S8: Long-term monitoring and maintenance of boreholes

[0088] Upgrade Internet of Things technology and remote monitoring technology to improve the stability and reliability of the long-term monitoring and maintenance system for drilling.

[0089] Strengthen data analysis and processing capabilities, promptly identify and address potential problems, and ensure long-term stable operation of drilling.

[0090] Implementation method three:

[0091] S1: Ground pretreatment and grouting reinforcement

[0092] In response to the complex and changeable geological environment, a more adaptable grouting pressure calculation algorithm has been developed. This algorithm can comprehensively consider multiple factors such as geological humidity, temperature, pressure, and groundwater flow to ensure the stability and reliability of the grouting reinforcement effect.

[0093] Introducing environmentally friendly grouting materials can reduce pollution to the environment while improving the compressive strength and durability of the grouting materials.

[0094] An intelligent grouting system is used to automatically adjust the grouting pressure and grouting speed according to real-time monitoring data to ensure the safety and efficiency of the grouting reinforcement process.

[0095] S2: Drill bit positioning and trajectory optimization

[0096] Upgrade the three-dimensional positioning system and posture recognition system to improve the positioning accuracy and trajectory prediction accuracy of the drill bit in complex formations.

[0097] The introduction of geological radar technology can monitor the stratigraphic structure and geological changes in real time, providing more accurate data support for drill trajectory optimization.

[0098] Optimize the drilling parameter adjustment algorithm to achieve more intelligent and efficient drilling parameter adjustment based on formation characteristics and drilling requirements.

[0099] S3: Intelligent prediction and optimization of drilling parameters

[0100] By introducing deep learning algorithms and combining historical drilling data with real-time formation monitoring data, more accurate intelligent prediction and optimization of drilling parameters can be made.

[0101] Develop an intelligent optimization platform for drilling parameters to achieve real-time monitoring, prediction and optimization of drilling parameters, and improve drilling efficiency and safety.

[0102] S4: Drilling tool monitoring and parameter adjustment

[0103] Introduce high-precision sensors and Internet of Things technology to monitor the working status of drilling tools and geological changes in real time, and improve the accuracy and real-time performance of drilling tool monitoring.

[0104] Develop an intelligent drilling tool parameter adjustment system to automatically adjust drilling tool parameters such as rotation speed, torque and drilling speed according to real-time monitoring data to adapt to different formations and drilling requirements.

[0105] S5: Fault identification and troubleshooting suggestions

[0106] Intelligent fault diagnosis technologies, such as convolutional neural networks and recurrent neural networks in deep learning, are introduced to enable real-time identification and diagnosis of faults during the drilling process.

[0107] Develop a fault handling suggestion system to provide targeted handling suggestions based on the fault type and severity, such as shutting down for inspection, replacing drilling tools, or adjusting drilling parameters.

[0108] S6: Drilling quality inspection and acceptance

[0109] High-precision measurement technology and automated testing equipment are introduced to conduct comprehensive inspection and acceptance of the verticality, hole diameter, hole depth and hole wall quality of the drilled holes.

[0110] Develop a drilling quality assessment system to quantitatively evaluate the drilling quality based on the measurement data to ensure that the drilling quality meets the design requirements.

[0111] S7: Drilling wall strengthening treatment

[0112] For soft and broken strata in complex strata, more effective borehole wall strengthening treatment technologies are developed, such as chemical grouting, cement grouting and anchor support.

[0113] An intelligent wall strengthening system is introduced to automatically select strengthening materials and process parameters based on real-time monitoring data to improve the stability and bearing capacity of the borehole wall.

[0114] S8: Long-term monitoring and maintenance of boreholes

[0115] Establish a long-term monitoring and maintenance system for drilling, and use Internet of Things technology and remote monitoring technology to conduct real-time monitoring and data analysis of drilling.

[0116] Develop a drilling maintenance management system, formulate targeted maintenance plans and measures based on monitoring data and analysis results, and ensure long-term and stable operation of the drilling.

[0117] Implementation method 4:

[0118] S1: Ground pretreatment and grouting reinforcement

[0119] Optimize the grouting material ratio and grouting process to reduce the grouting reinforcement cost while ensuring that the grouting reinforcement effect meets the design requirements.

[0120] Introduce economical grouting materials and equipment to reduce material and equipment costs in the grouting reinforcement process.

[0121] S2: Drill bit positioning and trajectory optimization

[0122] Optimize the configuration and layout of the drill bit positioning system and posture recognition system to reduce system costs while improving positioning accuracy and trajectory prediction accuracy.

[0123] Introduce low-cost, high-precision geological radar technology to reduce formation monitoring costs and improve drilling efficiency.

[0124] S3: Intelligent prediction and optimization of drilling parameters

[0125] Develop an intelligent prediction and optimization platform for drilling parameters based on cloud computing and big data technologies to reduce platform construction and operation costs.

[0126] Utilize historical drilling data and real-time formation monitoring data to intelligently predict and optimize drilling parameters, reducing energy consumption and labor costs during the drilling process.

[0127] S4: Drilling tool monitoring and parameter adjustment

[0128] Introduce low-power, high-precision sensors and Internet of Things technology to reduce the cost of drilling tool monitoring.

[0129] Develop an intelligent adjustment system for drilling tool parameters to realize automatic adjustment and optimization of drilling tool parameters and reduce drilling tool wear and replacement costs.

[0130] S5: Fault identification and troubleshooting suggestions

[0131] Introduce intelligent fault diagnosis technology to reduce fault identification and diagnosis costs.

[0132] Develop a fault handling suggestion system to improve fault handling efficiency and accuracy and reduce fault handling costs.

[0133] S6: Drilling quality inspection and acceptance

[0134] Introduce automated testing equipment and efficient measurement technology to reduce the cost of drilling quality inspection.

[0135] Develop a drilling quality assessment system to improve the accuracy and efficiency of drilling quality assessment and reduce acceptance costs.

[0136] S7: Drilling wall strengthening treatment

[0137] Optimize the borehole wall strengthening treatment technology and process parameters to reduce the strengthening treatment cost.

[0138] Introduce economical strengthening materials and equipment to reduce material and equipment costs during the strengthening process.

[0139] S8: Long-term monitoring and maintenance of boreholes

[0140] Establish a long-term drilling monitoring and maintenance system based on Internet of Things technology and remote monitoring technology to reduce monitoring and maintenance costs.

[0141] Develop a drilling maintenance management system to realize the intelligence and automation of drilling maintenance, reduce maintenance costs and labor costs. At the same time, through long-term monitoring and maintenance, the service life and stability of the drilling can be improved, bringing long-term economic and social benefits to the project.

[0142] Implementation method five:

[0143] A high-precision vertical settlement observation deep hole drilling process for complex strata, comprising the following steps:

[0144] S1: Pre-treat the stratum, use the grouting pressure calculation algorithm based on the geomechanical model, calculate the grouting material ratio and grouting pressure according to the stratum stability and bearing capacity requirements, and then perform grouting reinforcement. Through the adaptive sealing algorithm, according to the real-time monitoring data of the borehole wall, the monitoring data includes the wall crack width and leakage, dynamically adjust the type and application method of the sealing material, and perform wall protection and leakage plugging. The calculation formula is:

[0145] P = f(σ, μ, c, φ, h), where P is the grouting pressure, σ is the formation stress, μ is the formation Poisson's ratio, c is the formation cohesion, φ is the formation internal friction angle, and h is the grouting depth;

[0146] S2: Using the three-dimensional positioning algorithm and posture recognition algorithm, the three-dimensional position and posture of the drill bit are monitored in real time. The calculation formula is:

[0147] X, Y, Z, α, β, γ = g(t), where X, Y, Z are the three-dimensional coordinates of the drill bit, α, β, γ are the attitude angles of the drill bit, and t is time;

[0148] Based on real-time monitoring data, trajectory prediction and optimization algorithms are used to dynamically adjust drilling parameters and drill bit trajectory control strategies;

[0149] Intelligently predict and optimize drilling parameters based on formation characteristics and drilling requirements.

[0150] S3: Using machine learning algorithms, including support vector machines and random forests, combined with historical drilling data and current formation characteristics, to intelligently predict and optimize drilling parameters. The model formula is:

[0151] y = h(x), where y is the predicted drilling parameters and x is the input features such as formation properties and drilling requirements;

[0152] S4: Use the drill bit to perform coring and drilling during the drilling process, monitor the working status of the drill bit and the geological changes in the borehole in real time, and automatically adjust the working parameters of the drill bit or replace the drill bit according to the monitoring results;

[0153] S5: Deep learning algorithms, including convolutional neural networks and recurrent neural networks, are used to analyze and process massive amounts of data during the drilling process in real time, and to automatically identify faults and abnormalities. The model formula is: C = F(D), where C is the identified fault or abnormality, and D is the data during the drilling process.

[0154] According to the identified faults and abnormal conditions, the fault diagnosis and processing suggestion algorithm is used to give corresponding processing suggestions.

[0155] S6: Use high-precision measuring instruments to measure and check the verticality, hole diameter and hole depth of the borehole, and use measurement data evaluation algorithms to process and analyze the measurement data to evaluate whether the drilling quality meets the design requirements;

[0156] S7: using a wall strengthening material selection algorithm to select appropriate wall strengthening materials according to the formation characteristics and the actual conditions of the borehole wall, and using a wall strengthening process optimization algorithm to optimize the wall strengthening process parameters according to the selected wall strengthening materials and the actual conditions of the borehole wall, the process parameters include curing time and curing temperature;

[0157] S8: Based on the Internet of Things technology and remote monitoring technology, a long-term monitoring and maintenance system for drilling is established. Using real-time data monitoring and analysis algorithms, real-time monitoring and data analysis of drilling are carried out to promptly identify and deal with potential problems.

[0158] 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 high-precision vertical settlement observation deep hole drilling process for complex strata, characterized in that: The steps include: S1: Pre-treat the stratum, use the grouting pressure calculation algorithm based on the geomechanical model, calculate the grouting material ratio and grouting pressure according to the stratum stability and bearing capacity requirements, and then perform grouting reinforcement. Through the adaptive sealing algorithm, according to the real-time monitoring data of the borehole wall, dynamically adjust the type and application method of the sealing material to protect the wall and plug the leak; S2: Using 3D positioning algorithm and posture recognition algorithm to monitor the 3D position and posture of the drill bit in real time; Based on real-time monitoring data, trajectory prediction and optimization algorithms are used to dynamically adjust drilling parameters and drill bit trajectory control strategies; Intelligently predict and optimize drilling parameters based on formation characteristics and drilling requirements. S3: Uses machine learning algorithms, including support vector machines and random forests, to intelligently predict and optimize drilling parameters by combining historical drilling data and current formation characteristics; S4: Use the drill bit to perform coring and drilling during the drilling process, monitor the working status of the drill bit and the geological changes in the borehole in real time, and automatically adjust the working parameters of the drill bit or replace the drill bit according to the monitoring results; S5: Real-time monitoring of the geological conditions and drilling parameters in the borehole, automatic identification and diagnosis of various faults and abnormal conditions during the drilling process, and corresponding treatment suggestions; S6: Conduct quality inspection and acceptance of drilling holes; S7: Implement borehole wall strengthening treatment to improve the stability and bearing capacity of the borehole wall; S8: Based on the Internet of Things technology and remote monitoring technology, a long-term monitoring and maintenance system for drilling is established. Using real-time data monitoring and analysis algorithms, real-time monitoring and data analysis of drilling are carried out to identify and deal with potential problems.

2. A high-precision vertical settlement observation deep hole drilling process for complex strata according to claim 1, characterized in that: The monitoring data include wall crack width and leakage amount.

3. The high-precision vertical settlement observation deep hole drilling process for complex strata according to claim 1 is characterized by: The calculation formula of S2 is: X, Y, Z, α, β, γ = g(t), where X, Y, Z are the three-dimensional coordinates of the drill bit, α, β, γ are the attitude angles of the drill bit, and t is time.

4. The high-precision vertical settlement observation deep hole drilling process for complex strata according to claim 1 is characterized by: The drilling parameters include drilling speed and drilling pressure.

5. The high-precision vertical settlement observation deep hole drilling process for complex strata according to claim 1 is characterized by: The model formula of S3 is: y=h(x), where y is the predicted drilling parameters and x is the input features such as formation characteristics and drilling requirements.

6. The high-precision vertical settlement observation deep hole drilling process for complex strata according to claim 1 is characterized by: The calculation formula in S1 is: P = f(σ, μ, c, φ, h), where P is the grouting pressure, σ is the formation stress, μ is the formation Poisson's ratio, c is the formation cohesion, φ is the formation internal friction angle, and h is the grouting depth.

7. The high-precision vertical settlement observation deep hole drilling process for complex strata according to claim 1 is characterized by: The S5 specifically includes the steps of: Deep learning algorithms, including convolutional neural networks and recurrent neural networks, are used to analyze and process massive amounts of data during the drilling process in real time, and to automatically identify faults and abnormalities. The model formula is: C=F(D), where C is the identified fault or abnormality, and D is the data during the drilling process. According to the identified faults and abnormal conditions, the fault diagnosis and processing suggestion algorithm is used to give corresponding processing suggestions.

8. The high-precision vertical settlement observation deep hole drilling process for complex strata according to claim 1 is characterized by: The S6 specifically comprises the steps of: High-precision measuring instruments are used to measure and check the verticality, hole diameter and hole depth of the borehole. Measurement data evaluation algorithms are used to process and analyze the measurement data to evaluate whether the drilling quality meets the design requirements.

9. The high-precision vertical settlement observation deep hole drilling process for complex strata according to claim 1 is characterized by: The S7 specifically includes the steps of: The wall strengthening material selection algorithm is used to select the appropriate wall strengthening material according to the formation characteristics and the actual situation of the borehole wall. The wall strengthening process optimization algorithm is used to optimize the wall strengthening process parameters according to the selected wall strengthening material and the actual situation of the borehole wall.

10. The high-precision vertical settlement observation deep hole drilling process for complex strata according to claim 9, characterized in that: The process parameters include curing time and curing temperature.

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