Method and apparatus for drilling parameter measurement

Abstract

The present application relates to the technical field of drilling parameter measurement method, and is a method and equipment for drilling parameter measurement.The method for drilling parameter measurement comprises sensor deployment, data acquisition and transmission, real-time data analysis, intelligent decision support, parameter adjustment and feedback.The present application realizes rapid and accurate measurement of drilling parameters by integrating various high-precision sensors and real-time data analysis technology, improves the measurement accuracy and speed of drilling parameters, monitors the quality of drilling fluid and underground water in real time, adjusts drilling technology in a timely manner, reduces environmental pollution and environmental impact, provides scientific decision support for drilling operation through an intelligent decision support system, improves decision efficiency, and improves drilling efficiency and safety, and adopts big data processing technology and artificial intelligence algorithm to rapidly analyze and process massive data, extract valuable information, and enhance data processing capability.

Description

Technical Field

[0001] This invention relates to the field of drilling parameter measurement methods, and is a method and equipment for measuring drilling parameters. Background Technology

[0002] Drilling parameters refer to the collective parameters of drilling rigs during the drilling process, including drilling pressure, drill pipe rotation speed, drilling flow rate, rig speed, and drilling torque. When designing drilling operations, the drilling rig speed is first determined. Then, the drill bit type is selected based on the rock properties. The drilling pressure and rotation speed are determined according to the established drilling speed, rock conditions, and drill bit, and the torque is calculated. Finally, the flow rate and flow rate of the well-washing fluid are calculated based on the amount of rock cuttings that can be broken up.

[0003] Existing drilling parameter measurement methods mainly include static methods (such as measurement while drilling tools) and dynamic methods (such as drilling fluid flow characteristic analysis). Each of these methods has its advantages and disadvantages, but common problems include: 1) The contradiction between measurement accuracy and speed: Static methods have high accuracy but slow measurement speed, while dynamic methods have fast measurement speed but relatively low accuracy and high requirements for drilling fluid properties and operating techniques; 2) Equipment limitations: Some methods (such as measurement while drilling tools) are limited by the performance and stability of the equipment itself, making it difficult to obtain accurate data under complex geological conditions; 3) Environmental impact: Drilling fluids and lubricants used in the drilling process may pollute groundwater, affecting the accuracy of water chemical parameter measurements; 4) Complex data processing: Some methods obtain large and complex amounts of data, requiring efficient data processing and analysis techniques to extract valuable information.

[0004] Chinese patent application CN114458296A discloses a method and system for measuring borehole depth. The method includes: receiving distance data xt uploaded in real time by a ranging device; receiving data m of the number of drill rods currently on the pressure carriage; receiving the number of rod connection commands n; reading data b of the drill frame height, c of the drill rig chassis height, a of the drill rod length, p of the drill rod short connector length, q of the drill bit length, and r of the pressure carriage thickness; and calculating the borehole depth H according to a formula. The invention also provides a borehole depth measurement system, including a ranging device, a control host, and a display screen. This invention can monitor the borehole depth while the rotary drilling rig is drilling, and display the depth data on the screen in real time for easy operator observation. When the drilling depth reaches the design requirements, the system automatically stops the drilling rig and alerts the operator, improving the accuracy of drilling operations, ensuring drilling quality, and providing strong technical support for remote and unmanned operation of the drilling rig. This patent application is only for measuring borehole depth. Summary of the Invention

[0005] This invention provides a method and equipment for measuring drilling parameters, which overcomes the shortcomings of the prior art and has the advantages of fast measurement speed, high measurement accuracy, and strong data processing capability.

[0006] One of the technical solutions of the present invention is achieved through the following measures: a method for measuring drilling parameters, comprising: Step 1: Sensor deployment. High-precision sensors are deployed on drilling tools to monitor various parameters during the drilling process in real time. Step two, data acquisition and transmission: The high-precision sensor communicates with the data receiving end of the ground data processing center, transmitting the data acquired by the high-precision sensor to the ground data processing center in real time. During the data transmission process, data compression and encryption technologies are used to ensure the security and efficiency of data transmission. Step 3: Real-time data analysis. Using big data processing technology or artificial intelligence algorithms, the real-time collected data is quickly analyzed and processed to identify abnormal situations in the drilling process. Step four, intelligent decision support: Based on real-time data analysis results, the system automatically generates drilling parameter adjustment suggestions, and combines geological models and drilling experience to generate decision-making schemes. The intelligent decision support system provides scientific decision support for drilling operations. Step 5: Parameter adjustment and feedback. Based on the suggestions of the intelligent decision support system, the drilling parameters are adjusted in real time, the drilling effect after adjustment is monitored, and the adjustment strategy is continuously optimized through the feedback mechanism.

[0007] The following are further optimizations and / or improvements to one of the above-mentioned technical solutions: The aforementioned high-precision sensors include at least pressure sensors, temperature sensors, flow rate sensors, acceleration sensors, ground acoustic sensors, and electromagnetic wave sensors.

[0008] The above data collection and transmission includes the following steps: Step 201, Requirements Analysis: Clarify the types of data to be collected and the role of the collected data in subsequent analysis; Step 202: Determine the data source. Based on the purpose and needs of data collection, determine the data source. Step 203: Design a data acquisition scheme and formulate a detailed data acquisition plan, including at least the acquisition method, acquisition frequency, and acquisition time. Step 204: Perform data collection by using appropriate tools and techniques. Step 205, data cleaning, preprocessing the collected raw data, including at least deduplication, outlier removal and error correction, to ensure the accuracy and reliability of the data; Step 206: Select a transmission method. Choose a suitable transmission method based on the characteristics of the data and transmission requirements. Step 207: Configure transmission parameters and set the parameters of the transmission protocol; Step 208, Data Encoding and Encryption: Encode and encrypt the transmitted data. Step 209, Data transmission and reception: The processed data is sent to the target location using the selected transmission method; Step 210, Data Verification and Storage: Verify the received data to ensure its integrity and accuracy. If any errors or loss are found, repair or retransmission should be carried out promptly. After verification, store the data in the target location.

[0009] In the process of transmitting data collected by high-precision sensors to the ground data processing center, it is necessary to determine the data layer's transmission method, configure transmission parameters, and perform data encoding and encryption.

[0010] Data sources must include at least high-precision sensors.

[0011] Data transmission methods include Ethernet, fiber optic Wi-Fi, Bluetooth, 4G / 5G, etc.

[0012] The parameters of a transmission protocol include transmission rate, transmission distance, and transmission stability.

[0013] The above real-time data analysis includes the following steps: Step 301, Select the appropriate analysis tool: Select the appropriate real-time data analysis tool according to the analysis needs. Real-time data analysis tools include real-time data visualization tools, real-time data query and analysis engines, and algorithm types. Step 302, Perform analysis: Use the above tools to conduct in-depth analysis of real-time data and extract patterns and trends from the data; Step 303, Data Visualization: Present the analysis results in an intuitive and easy-to-understand way; Step 304, Decision Support: Provide decision support suggestions based on the analysis results; Step 305, Effect Evaluation: Evaluate the effect of real-time data analysis; Step 306, Continuous Optimization: Based on the evaluation results and changes in business needs, continuously optimize the process and methods of real-time data analysis.

[0014] Artificial intelligence algorithms include at least: decision trees, random forests, logistic regression, SVM, Naive Bayes, K-nearest neighbors, K-means, Adaboost, neural networks, and Markov models.

[0015] Big data processing technologies include at least: 1) Distributed storage system: Hadoop is currently the most popular distributed storage system. It can distribute data across multiple servers, improving data reliability and scalability.

[0016] 2) Distributed computing framework: Spark is a high-performance distributed computing framework that can perform fast data processing and analysis on large-scale datasets.

[0017] 3) Data mining and machine learning algorithms: Commonly used data mining and machine learning algorithms include clustering, classification, regression and association rule mining, which can help discover hidden patterns and rules from big data.

[0018] 4) Data visualization tools: Data visualization tools such as Ableau and Power BI can display the results of big data processing in intuitive charts and graphs, helping users to better understand and analyze data.

[0019] The aforementioned intelligent decision support includes the following steps: Step 401, Modeling: Based on the results of data analysis, establish the corresponding decision-making model; Step 402, Decision Support Algorithm: Utilize artificial intelligence and machine learning technologies to implement an intelligent decision support algorithm that automatically processes input data and generates decision suggestions or prediction results; Step 403, Human-Computer Interaction: Design a human-computer interaction interface that allows users to easily input decision-making questions, view decision results, and obtain necessary explanations and suggestions; Step 404, Comprehensive Evaluation: Taking into account the output results of the decision support system, user experience, and business rule factors, conduct a comprehensive evaluation of the decision-making scheme; Step 405, Execution and Feedback: Implement the decision-making plan and collect feedback information during the execution process; Step 406, Effectiveness Evaluation: Evaluate the effectiveness of the intelligent decision support system and understand its performance in solving practical problems; Step 407, System Optimization: Based on the evaluation results and user feedback, optimize and improve the system; Step 408, Iterative Updates: As the business environment and data change, continuously iterate and update the intelligent decision support system to ensure that it can adapt to new decision-making problems and challenges.

[0020] The second technical solution of the present invention is achieved through the following measures: a device comprising: Sensor Deployment Unit: Sensor deployment involves deploying high-precision sensors on drilling tools to monitor various parameters during the drilling process in real time; Data Acquisition and Transmission Unit: This unit handles data acquisition and transmission, establishing a communication connection between the high-precision sensor and the data receiving terminal at the ground data processing center (e.g., via high-speed transmission methods such as fiber optic cables). It transmits the data acquired by the high-precision sensor to the ground data processing center in real time. During data transmission, data compression and encryption technologies are employed to ensure the security and efficiency of data transmission. Real-time data analysis unit: Real-time data analysis utilizes big data processing technology or artificial intelligence algorithms to quickly analyze and process data collected in real time, and identify abnormal situations in the drilling process; Intelligent Decision Support Unit: Based on real-time data analysis results, the intelligent decision support system automatically generates drilling parameter adjustment suggestions and, combined with geological models and drilling experience, generates decision-making schemes. The intelligent decision support system provides scientific decision support for drilling operations. Parameter Adjustment and Feedback Unit: Based on the suggestions of the intelligent decision support system, the drilling parameters are adjusted in real time, the drilling effect after adjustment is monitored, and the adjustment strategy is continuously optimized through the feedback mechanism.

[0021] The following are further optimizations and / or improvements to the second technical solution of the above invention: The aforementioned data acquisition and transmission unit includes: Requirements Analysis Module: Requirements analysis clarifies the types of data to be collected and the role of the collected data in subsequent analysis; Data Source Determination Module: Determine the data source based on the purpose and requirements of data collection; Data Acquisition Scheme Design Module: Design a data acquisition scheme and develop a detailed data acquisition plan, including at least the acquisition method, acquisition frequency, and acquisition time. Implement the data acquisition module: Implement data acquisition by using appropriate tools and techniques to collect data; Data cleaning module: Data cleaning preprocesses the collected raw data. Preprocessing includes at least deduplication, outlier removal, and error correction to ensure the accuracy and reliability of the data. Select Transmission Method Module: Select the transmission method based on the characteristics of the data and transmission requirements; Configuring transmission parameters module: Configure transmission parameters and set the parameters of the transmission protocol; Data encoding and encryption module: This module encodes and encrypts the transmitted data. Data sending and receiving module: This module sends and receives processed data to the target location using the selected transmission method. Data verification and storage module: This module verifies the received data to ensure its integrity and accuracy. If any errors or loss are found, the data should be repaired or retransmitted promptly. Once the data is verified to be correct, it should be stored in the target location.

[0022] The aforementioned real-time data analysis unit includes: Analysis Tools Module: Selecting the Appropriate Analysis Tools: Choose the appropriate real-time data analysis tools based on your analysis needs. Real-time data analysis tools include real-time data visualization tools, real-time data query and analysis engines, and algorithm types. Execution Analysis Module: Execution Analysis: Utilizes the above tools to perform in-depth analysis of real-time data, extracting patterns and trends from the data; Data visualization module: Data visualization presents analysis results in an intuitive and easy-to-understand way; Decision Support Module: Decision support provides decision support suggestions based on the analysis results; Effectiveness Evaluation Module: Effectiveness Evaluation: Evaluates the effectiveness of real-time data analysis; Continuous Optimization Module: Continuous optimization: Based on the evaluation results and changes in business needs, continuously optimize the processes and methods of real-time data analysis.

[0023] The aforementioned intelligent decision support unit includes: Modeling Module: Modeling: Based on the results of data analysis, establish corresponding decision-making models; Decision Support Algorithm Module: Decision Support Algorithm: Utilizing artificial intelligence and machine learning technologies, this module implements intelligent decision support algorithms that automatically process input data and generate decision suggestions or prediction results. Human-Computer Interaction Module: Human-Computer Interaction: Design a human-computer interaction interface that allows users to easily input decision-making questions, view decision results, and obtain necessary explanations and suggestions; Comprehensive Evaluation Module: Comprehensive Evaluation: Taking into account the output results of the decision support system, user experience, and business rule factors, the module comprehensively evaluates the decision-making scheme. Execution and Feedback Module: Execution and Feedback: Executes the decision-making plan and collects feedback information during the execution process; Effectiveness Evaluation Module: Effectiveness Evaluation: Evaluate the effectiveness of the intelligent decision support system and understand its performance in solving practical problems; System Optimization Module: System Optimization: Based on the evaluation results and user feedback, optimize and improve the system; Iterative Update Module: Iterative Update: As the business environment and data change, the intelligent decision support system is continuously iterated and updated to ensure that it can adapt to new decision-making problems and challenges.

[0024] Compared with the prior art, the beneficial effects of this invention are as follows: 1) Improve the accuracy and speed of drilling parameter measurement: By integrating multiple high-precision sensors and real-time data analysis technology, achieve rapid and accurate measurement of drilling parameters; 2) Reduce environmental impact: By monitoring the quality of drilling fluid and groundwater in real time, drilling processes can be adjusted in a timely manner to reduce environmental pollution; 3) Improve decision-making efficiency: Intelligent decision support systems provide scientific decision support for drilling operations, improving drilling efficiency and safety; 4) Enhance data processing capabilities: Employ big data processing technologies and artificial intelligence algorithms to rapidly analyze and process massive amounts of data and extract valuable information; 5) Enable remote monitoring: Through remote data transmission and monitoring technology, remote monitoring and management of the drilling process can be achieved, reducing the workload and safety risks of on-site personnel. Attached Figure Description

[0025] Appendix Figure 1 This is a flowchart of the method for measuring drilling parameters according to the present invention; Appendix Figure 2 This is a flowchart illustrating the data acquisition and transmission process described in this invention. Appendix Figure 3 This is a flowchart illustrating the real-time data analysis process described in this invention. Appendix Figure 4 This is a flowchart illustrating the intelligent decision support process for the device described in this invention. Detailed Implementation

[0026] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0027] The present invention will be further described below with reference to embodiments: Example 1: As Figure 1 As shown, a method for measuring drilling parameters includes: Step 1: Sensor deployment. High-precision sensors are deployed on drilling tools to monitor various parameters during the drilling process in real time. Step two, data acquisition and transmission: The high-precision sensor establishes a communication connection with the data receiving end of the ground data processing center (e.g., through high-speed transmission methods such as fiber optic cables) to transmit the data acquired by the high-precision sensor to the ground data processing center in real time. During the data transmission process, data compression and encryption technologies are used to ensure the security and efficiency of data transmission. Step 3: Real-time data analysis. Using big data processing technology or artificial intelligence algorithms, the real-time collected data is quickly analyzed and processed to identify abnormal situations in the drilling process. Step four, intelligent decision support: Based on real-time data analysis results, the system automatically generates drilling parameter adjustment suggestions, and combines geological models and drilling experience to generate decision-making schemes. The intelligent decision support system provides scientific decision support for drilling operations. Step 5: Parameter adjustment and feedback. Based on the suggestions of the intelligent decision support system, the drilling parameters are adjusted in real time, the drilling effect after adjustment is monitored, and the adjustment strategy is continuously optimized through the feedback mechanism.

[0028] Example 2: As an optimization of the above embodiment, the high-precision sensor includes at least a pressure sensor, a temperature sensor, a flow rate sensor, an acceleration sensor, a ground acoustic sensor, and an electromagnetic wave sensor.

[0029] A pressure sensor is a device that senses pressure signals and converts them into usable output electrical signals according to a certain rule. It typically consists of a pressure-sensitive element and a signal processing unit. The pressure-sensitive element measures the pressure and converts it into a perceptible electrical signal; the signal processing unit amplifies and converts the electrical signal for subsequent use. The working principle of a pressure sensor is mainly based on the deformation of the sensitive element, and the circuit generates an electrical signal corresponding to the pressure level. When pressure is applied to the sensitive element, it deforms. This deformation is proportional to the pressure level. The electronic components in the sensitive element convert this deformation and generate a corresponding electrical signal. By detecting the electrical signal, the pressure sensor can output data related to the pressure level.

[0030] A temperature sensor is a sensor that can sense temperature and convert it into a usable output signal. It uses two conductors of different materials to form a closed loop. When there is a temperature gradient between the two ends, a current will flow through the loop. At this time, there is an electromotive force between the two ends—thermoelectric electromotive force. This phenomenon of generating electromotive force due to temperature difference is called Seebeck effect. It is made based on the principle that the thermal motion inside an object radiates electromagnetic waves in all directions, including infrared rays in the band of 0.75μm to 100μm.

[0031] A flow velocity sensor is an instrument used to measure the velocity of liquids or gases. Flow velocity sensors calculate flow velocity by measuring the speed at which fluid flows in a pipe. They utilize the temperature difference generated when fluid passes through a hot wire to measure flow velocity; the fluid flow carries away heat from the wire, and the flow velocity can be calculated by measuring the rate of heat loss. Based on the principle of fluid kinetic energy, flow velocity is determined by measuring the pressure difference generated within the device. Common flow velocity sensors include Pitot tubes, horn tubes, and Rankine tubes. Flow velocity can also be measured using the propagation speed of ultrasound in fluids. Ultrasonic sensors emit ultrasonic pulses, and the flow velocity is determined by measuring the propagation time of the ultrasound waves in the fluid. Flow velocity can also be measured based on the angular velocity generated by fluid passing through a waterwheel or turbine; the fluid flow causes the turbine to rotate, and the flow velocity can be calculated by measuring the turbine's rotational speed.

[0032] An accelerometer is a sensor that measures acceleration. It typically consists of a mass block, a damper, an elastic element, a sensing element, and an adaptation circuit. The working principle of an accelerometer is based on Newton's second law, which states that force equals mass multiplied by acceleration. When the sensor is subjected to acceleration, the mass block inside it generates inertial force, causing the sensing element to deform or displace. By measuring this deformation or displacement and converting it through appropriate circuitry, an electrical signal output proportional to the acceleration can be obtained.

[0033] A ground acoustic sensor is a device used to measure and monitor ground vibrations or sound waves. The working principle of a ground acoustic sensor is based on Faraday's law of induction, which states that when the magnetic field changes, an induced electromotive force is generated in a conductor. In a ground acoustic sensor, when the ground vibrates or sound waves propagate, it causes slight vibrations in the surrounding medium, which in turn leads to changes in the magnetic field inside the sensor. This change in the magnetic field is captured by the sensor and converted into an electrical signal output, thereby realizing the measurement and monitoring of ground vibrations or sound waves.

[0034] An electromagnetic wave sensor is a device that can detect and measure electromagnetic waves. It utilizes the principle of interaction between electromagnetic waves and objects to obtain information related to the target object by measuring the characteristics of electromagnetic waves. The sensor generates electromagnetic waves of a certain frequency through a transmitting antenna and sends signals to the target being detected. When the electromagnetic waves encounter the target object, they are reflected and return to the receiving antenna. The receiving antenna captures these reflected signals, which are then transmitted to a signal processor for analysis and processing. The signal processor measures the time and intensity of the reflected signals and converts them into properties and characteristics of the target object.

[0035] Example 3: As Figure 2 As shown, as an optimization of the above embodiment, data acquisition and transmission include the following steps: Step 201, Requirements Analysis: Clarify the types of data to be collected and the role of the collected data in subsequent analysis; Step 202: Determine the data source. Based on the purpose and needs of data collection, determine the data source. Step 203: Design a data acquisition scheme and formulate a detailed data acquisition plan, including at least the acquisition method, acquisition frequency, and acquisition time. Step 204: Perform data acquisition using appropriate tools (such as high-precision sensors) and techniques; Step 205, data cleaning, preprocessing the collected raw data, including at least deduplication, outlier removal and error correction, to ensure the accuracy and reliability of the data; Step 206: Select a transmission method. Choose a suitable transmission method based on the characteristics of the data and transmission requirements. Step 207: Configure transmission parameters and set the parameters of the transmission protocol; Step 208, Data Encoding and Encryption: Encode and encrypt the transmitted data. Step 209, Data transmission and reception: The processed data is sent to the target location using the selected transmission method; Step 210, Data Verification and Storage: Verify the received data to ensure its integrity and accuracy. If any errors or loss are found, repair or retransmission should be carried out promptly. After verification, store the data in the target location.

[0036] Example 4: Figure 3 As shown, as an optimization of the above embodiment, real-time data analysis includes the following steps: Step 301, Select the appropriate analysis tool: Select the appropriate real-time data analysis tool according to the analysis needs. Real-time data analysis tools include real-time data visualization tools, real-time data query and analysis engines, and algorithm types. Step 302, Perform analysis: Use the above tools to conduct in-depth analysis of real-time data and extract patterns and trends from the data; Step 303, Data Visualization: Present the analysis results in an intuitive and easy-to-understand way; Step 304, Decision Support: Provide decision support suggestions based on the analysis results; Step 305, Effect Evaluation: Evaluate the effect of real-time data analysis; Step 306, Continuous Optimization: Based on the evaluation results and changes in business needs, continuously optimize the process and methods of real-time data analysis.

[0037] Example 5: Figure 4 As shown, as an optimization of the above embodiment, intelligent decision support includes the following steps: Step 401, Modeling: Based on the results of data analysis, establish the corresponding decision-making model; Step 402, Decision Support Algorithm: Utilize artificial intelligence and machine learning technologies to implement an intelligent decision support algorithm that automatically processes input data and generates decision suggestions or prediction results; Step 403, Human-Computer Interaction: Design a human-computer interaction interface that allows users to easily input decision-making questions, view decision results, and obtain necessary explanations and suggestions; Step 404, Comprehensive Evaluation: Taking into account the output results of the decision support system, user experience, and business rule factors, conduct a comprehensive evaluation of the decision-making scheme; Step 405, Execution and Feedback: Implement the decision-making plan and collect feedback information during the execution process; Step 406, Effectiveness Evaluation: Evaluate the effectiveness of the intelligent decision support system and understand its performance in solving practical problems; Step 407, System Optimization: Based on the evaluation results and user feedback, optimize and improve the system; Step 408, Iterative Updates: As the business environment and data change, continuously iterate and update the intelligent decision support system to ensure that it can adapt to new decision-making problems and challenges.

[0038] Example 6: A device comprising: Sensor Deployment Unit: Sensor deployment involves deploying high-precision sensors on drilling tools to monitor various parameters during the drilling process in real time; Data Acquisition and Transmission Unit: This unit handles data acquisition and transmission, establishing a communication connection between the high-precision sensor and the data receiving terminal at the ground data processing center (e.g., via high-speed transmission methods such as fiber optic cables). It transmits the data acquired by the high-precision sensor to the ground data processing center in real time. During data transmission, data compression and encryption technologies are employed to ensure the security and efficiency of data transmission. Real-time data analysis unit: Real-time data analysis utilizes big data processing technology or artificial intelligence algorithms to quickly analyze and process data collected in real time, and identify abnormal situations in the drilling process; Intelligent Decision Support Unit: Based on real-time data analysis results, the intelligent decision support system automatically generates drilling parameter adjustment suggestions and, combined with geological models and drilling experience, generates decision-making schemes. The intelligent decision support system provides scientific decision support for drilling operations. Parameter Adjustment and Feedback Unit: Based on the suggestions of the intelligent decision support system, the drilling parameters are adjusted in real time, the drilling effect after adjustment is monitored, and the adjustment strategy is continuously optimized through the feedback mechanism.

[0039] Example 7: As an optimization of Example 6 above, the data acquisition and transmission unit includes: Requirements Analysis Module: Requirements analysis clarifies the types of data to be collected and the role of the collected data in subsequent analysis; Data Source Determination Module: Determine the data source based on the purpose and requirements of data collection; Data Acquisition Scheme Design Module: Design a data acquisition scheme and develop a detailed data acquisition plan, including at least the acquisition method, acquisition frequency, and acquisition time. Implement the data acquisition module: Implement data acquisition by using appropriate tools and techniques to collect data; Data cleaning module: Data cleaning preprocesses the collected raw data. Preprocessing includes at least deduplication, outlier removal, and error correction to ensure the accuracy and reliability of the data. Select Transmission Method Module: Select the transmission method based on the characteristics of the data and transmission requirements; Configuring transmission parameters module: Configure transmission parameters and set the parameters of the transmission protocol; Data encoding and encryption module: This module encodes and encrypts the transmitted data. Data sending and receiving module: This module sends and receives processed data to the target location using the selected transmission method. Data verification and storage module: This module verifies the received data to ensure its integrity and accuracy. If any errors or loss are found, the data should be repaired or retransmitted promptly. Once the data is verified to be correct, it should be stored in the target location.

[0040] Example 8: As an optimization of Example 6 above, the real-time data analysis unit includes: Analysis Tools Module: Selecting the Appropriate Analysis Tools: Choose the appropriate real-time data analysis tools based on your analysis needs. Real-time data analysis tools include real-time data visualization tools, real-time data query and analysis engines, and algorithm types. Execution Analysis Module: Execution Analysis: Utilizes the above tools to perform in-depth analysis of real-time data, extracting patterns and trends from the data; Data visualization module: Data visualization presents analysis results in an intuitive and easy-to-understand way; Decision Support Module: Decision support provides decision support suggestions based on the analysis results; Effectiveness Evaluation Module: Effectiveness Evaluation: Evaluates the effectiveness of real-time data analysis; Continuous Optimization Module: Continuous optimization: Based on the evaluation results and changes in business needs, continuously optimize the processes and methods of real-time data analysis.

[0041] Example 9: As an optimization of Example 6 above, the intelligent decision support unit includes: Modeling Module: Modeling: Based on the results of data analysis, establish corresponding decision-making models; Decision Support Algorithm Module: Decision Support Algorithm: Utilizing artificial intelligence and machine learning technologies, this module implements intelligent decision support algorithms that automatically process input data and generate decision suggestions or prediction results. Human-Computer Interaction Module: Human-Computer Interaction: Design a human-computer interaction interface that allows users to easily input decision-making questions, view decision results, and obtain necessary explanations and suggestions; Comprehensive Evaluation Module: Comprehensive Evaluation: Taking into account the output results of the decision support system, user experience, and business rule factors, the module comprehensively evaluates the decision-making scheme. Execution and Feedback Module: Execution and Feedback: Executes the decision-making plan and collects feedback information during the execution process; Effectiveness Evaluation Module: Effectiveness Evaluation: Evaluate the effectiveness of the intelligent decision support system and understand its performance in solving practical problems; System Optimization Module: System Optimization: Based on the evaluation results and user feedback, optimize and improve the system; Iterative Update Module: Iterative Update: As the business environment and data change, the intelligent decision support system is continuously iterated and updated to ensure that it can adapt to new decision-making problems and challenges.

[0042] The above technical features constitute various embodiments of the present invention, which have strong adaptability and implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the needs of different situations.

Claims

1. A method for measuring drilling parameters, characterized in that, include: Step 1: Sensor deployment. High-precision sensors are deployed on drilling tools to monitor various parameters during the drilling process in real time. Step 2, data acquisition and transmission: The high-precision sensor communicates with the data receiving end of the ground data processing center to transmit the data acquired by the high-precision sensor to the ground data processing center in real time. Step 3: Real-time data analysis. Using big data processing technology or artificial intelligence algorithms, the real-time collected data is quickly analyzed and processed to identify abnormal situations in the drilling process. Step four, intelligent decision support: Based on real-time data analysis results, the system automatically generates drilling parameter adjustment suggestions, and combines geological models and drilling experience to generate decision-making schemes. The intelligent decision support system provides scientific decision support for drilling operations. Step 5: Parameter adjustment and feedback. Based on the suggestions of the intelligent decision support system, the drilling parameters are adjusted in real time, the drilling effect after adjustment is monitored, and the adjustment strategy is continuously optimized through the feedback mechanism.

2. The method for measuring drilling parameters according to claim 1, characterized in that, High-precision sensors include at least pressure sensors, temperature sensors, flow rate sensors, acceleration sensors, ground acoustic sensors, and electromagnetic wave sensors.

3. The method for measuring drilling parameters according to claim 1 or 2, characterized in that, Data acquisition and transmission include the following steps: Step 201, Requirements Analysis: Clarify the types of data to be collected and the role of the collected data in subsequent analysis; Step 202: Determine the data source. Based on the purpose and needs of data collection, determine the data source. Step 203: Design a data acquisition scheme and formulate a detailed data acquisition plan, including at least the acquisition method, acquisition frequency, and acquisition time. Step 204: Perform data collection by using appropriate tools and techniques. Step 205, data cleaning, preprocessing the collected raw data, including at least deduplication, outlier removal and error correction. Step 206: Select a transmission method. Choose a suitable transmission method based on the characteristics of the data and transmission requirements. Step 207: Configure transmission parameters and set the parameters of the transmission protocol; Step 208, Data Encoding and Encryption: Encode and encrypt the transmitted data. Step 209, Data transmission and reception: The processed data is sent to the target location using the selected transmission method; Step 210, Data Verification and Storage: Verify the received data to ensure its integrity and accuracy. After verification, store the data in the target location.

4. The method for measuring drilling parameters according to claim 3, characterized in that, Real-time data analysis includes the following steps: Step 301, Select the appropriate analysis tool: Select the appropriate real-time data analysis tool according to the analysis needs. Real-time data analysis tools include real-time data visualization tools, real-time data query and analysis engines, and algorithm types. Step 302, Perform analysis: Use the above tools to conduct in-depth analysis of real-time data and extract patterns and trends from the data; Step 303, Data Visualization: Present the analysis results in an intuitive and easy-to-understand way; Step 304, Decision Support: Provide decision support suggestions based on the analysis results; Step 305, Effect Evaluation: Evaluate the effect of real-time data analysis; Step 306, Continuous Optimization: Based on the evaluation results and changes in business needs, continuously optimize the process and methods of real-time data analysis.

5. The method for measuring drilling parameters according to claim 1, 2, or 4, characterized in that, Intelligent decision support includes the following steps: Step 401, Modeling: Based on the results of data analysis, establish the corresponding decision-making model; Step 402, Decision Support Algorithm: Utilize artificial intelligence and machine learning technologies to implement an intelligent decision support algorithm that automatically processes input data and generates decision suggestions or prediction results; Step 403, Human-Computer Interaction: Design a human-computer interaction interface that allows users to easily input decision-making questions, view decision results, and obtain necessary explanations and suggestions; Step 404, Comprehensive Evaluation: Taking into account the output results of the decision support system, user experience, and business rule factors, conduct a comprehensive evaluation of the decision-making scheme; Step 405, Execution and Feedback: Implement the decision-making plan and collect feedback information during the execution process; Step 406, Effectiveness Evaluation: Evaluate the effectiveness of the intelligent decision support system and understand its performance in solving practical problems; Step 407, System Optimization: Based on the evaluation results and user feedback, optimize and improve the system; Step 408, Iterative Updates: As the business environment and data change, continuously iterate and update the intelligent decision support system to ensure that it can adapt to new decision-making problems and challenges.

6. A device, characterized in that, include: Sensor Deployment Unit: Sensor deployment involves deploying high-precision sensors on drilling tools to monitor various parameters during the drilling process in real time; Data Acquisition and Transmission Unit: This unit handles data acquisition and transmission, establishing a communication connection between the high-precision sensor and the data receiving terminal at the ground data processing center. It transmits the data acquired by the high-precision sensor to the ground data processing center in real time. Real-time data analysis unit: Real-time data analysis utilizes big data processing technology or artificial intelligence algorithms to quickly analyze and process data collected in real time, and identify abnormal situations in the drilling process; Intelligent Decision Support Unit: Based on real-time data analysis results, the intelligent decision support system automatically generates drilling parameter adjustment suggestions and, combined with geological models and drilling experience, generates decision-making schemes. The intelligent decision support system provides scientific decision support for drilling operations. Parameter Adjustment and Feedback Unit: Based on the suggestions of the intelligent decision support system, the drilling parameters are adjusted in real time, the drilling effect after adjustment is monitored, and the adjustment strategy is continuously optimized through the feedback mechanism.

7. The device according to claim 6, characterized in that, The data acquisition and transmission unit includes: Requirements Analysis Module: Requirements analysis clarifies the types of data to be collected and the role of the collected data in subsequent analysis; Data Source Determination Module: Determine the data source based on the purpose and requirements of data collection; Data Acquisition Scheme Design Module: Design a data acquisition scheme and develop a detailed data acquisition plan, including at least the acquisition method, acquisition frequency, and acquisition time. Implement the data acquisition module: Implement data acquisition by using appropriate tools and techniques to collect data; Data cleaning module: Data cleaning preprocesses the collected raw data. Preprocessing includes at least deduplication, outlier removal, and error correction. Select Transmission Method Module: Select the transmission method based on the characteristics of the data and transmission requirements; Configuring transmission parameters module: Configure transmission parameters and set the parameters of the transmission protocol; Data encoding and encryption module: This module encodes and encrypts the transmitted data. Data sending and receiving module: This module sends and receives processed data to the target location using the selected transmission method. Data verification and storage module: This module verifies the received data to ensure its integrity and accuracy. Once the data is verified to be correct, it stores the data in the target location.

8. The device according to claim 6 or 7, characterized in that, The real-time data analysis unit includes: Analysis Tools Module: Selecting the Appropriate Analysis Tools: Choose the appropriate real-time data analysis tools based on your analysis needs. Real-time data analysis tools include real-time data visualization tools, real-time data query and analysis engines, and algorithm types. Execution Analysis Module: Execution Analysis: Utilizes the above tools to perform in-depth analysis of real-time data, extracting patterns and trends from the data; Data visualization module: Data visualization presents analysis results in an intuitive and easy-to-understand way; Decision Support Module: Decision support provides decision support suggestions based on the analysis results; Effectiveness Evaluation Module: Effectiveness Evaluation: Evaluates the effectiveness of real-time data analysis; Continuous Optimization Module: Continuous optimization: Based on the evaluation results and changes in business needs, continuously optimize the processes and methods of real-time data analysis.

9. The device according to claim 6 or 7, characterized in that, The intelligent decision support unit includes: Modeling Module: Modeling: Based on the results of data analysis, establish corresponding decision-making models; Decision Support Algorithm Module: Decision Support Algorithm: Utilizing artificial intelligence and machine learning technologies, this module implements intelligent decision support algorithms that automatically process input data and generate decision suggestions or prediction results. Human-Computer Interaction Module: Human-Computer Interaction: Design a human-computer interaction interface that allows users to easily input decision-making questions, view decision results, and obtain necessary explanations and suggestions; Comprehensive Evaluation Module: Comprehensive Evaluation: Taking into account the output results of the decision support system, user experience, and business rule factors, the module comprehensively evaluates the decision-making scheme. Execution and Feedback Module: Execution and Feedback: Executes the decision-making plan and collects feedback information during the execution process; Effectiveness Evaluation Module: Effectiveness Evaluation: Evaluate the effectiveness of the intelligent decision support system and understand its performance in solving practical problems; System Optimization Module: System Optimization: Based on the evaluation results and user feedback, optimize and improve the system; Iterative Update Module: Iterative Update: As the business environment and data change, the intelligent decision support system is continuously iterated and updated to ensure that it can adapt to new decision-making problems and challenges.

10. The device according to claim 8, characterized in that, The intelligent decision support unit includes: Modeling Module: Modeling: Based on the results of data analysis, establish corresponding decision-making models; Decision Support Algorithm Module: Decision Support Algorithm: Utilizing artificial intelligence and machine learning technologies, this module implements intelligent decision support algorithms that automatically process input data and generate decision suggestions or prediction results. Human-Computer Interaction Module: Human-Computer Interaction: Design a human-computer interaction interface that allows users to easily input decision-making questions, view decision results, and obtain necessary explanations and suggestions; Comprehensive Evaluation Module: Comprehensive Evaluation: Taking into account the output results of the decision support system, user experience, and business rule factors, the module comprehensively evaluates the decision-making scheme. Execution and Feedback Module: Execution and Feedback: Executes the decision-making plan and collects feedback information during the execution process; Effectiveness Evaluation Module: Effectiveness Evaluation: Evaluate the effectiveness of the intelligent decision support system and understand its performance in solving practical problems; System Optimization Module: System Optimization: Based on the evaluation results and user feedback, optimize and improve the system; Iterative Update Module: Iterative Update: As the business environment and data change, the intelligent decision support system is continuously iterated and updated to ensure that it can adapt to new decision-making problems and challenges.

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