Real-time monitoring and data analysis system for deep hole drilling process

Abstract

The invention relates to the technical field of geological exploration, and discloses a deep hole drilling process real-time monitoring and data analysis system, which comprises drilling equipment for deep hole drilling; the sensor module is used for monitoring in real time and transmitting data; the data acquisition module is used for receiving the real-time monitoring data transmitted by the sensor module and performing primary processing, and the data acquisition module specifically comprises the following steps: performing filtering processing on the data through a low-pass filter or a high-pass filter, and the design of the filter is based on Fourier transform or wavelet transform; by monitoring key parameters in the drilling process in real time, the system can feed back the drilling state in real time, so that an operator can quickly adjust the drilling parameters, the drilling process is optimized, the drilling efficiency is remarkably improved, drilling data can be automatically analyzed, bottlenecks and problems in the drilling process can be recognized, improvement suggestions are provided for the operator, and the economic benefit of the operator is improved. And the drilling efficiency is further improved.

Description

Technical Field

[0001] The invention relates to the technical field of geological exploration, and in particular to a real-time monitoring and data analysis system for a deep hole drilling process. Background Art

[0002] In the field of geological exploration, deep hole drilling is an important means of obtaining underground geological information. However, deep hole drilling faces many challenges, such as complex and changeable geological conditions, easy damage to drilling equipment, and difficulty in accurately controlling drilling process parameters. Traditionally, the monitoring and control of drilling operations mainly rely on the experience and intuition of operators, lacking scientific data support and real-time feedback mechanisms. This leads to low drilling efficiency, high safety risks, and difficulty in ensuring drilling quality.

[0003] Specifically, traditional drilling operations have obvious shortcomings in the following aspects:

[0004] Limited drilling efficiency: Due to the inability to monitor key parameters in the drilling process in real time, such as drilling depth, drilling speed, mud pressure, etc., it is difficult for operators to adjust drilling parameters according to real-time conditions, resulting in low drilling efficiency.

[0005] High safety risk: Complex geological conditions may be encountered during the drilling process, such as faults and broken zones. If they cannot be discovered and handled in time, they may lead to safety accidents such as equipment damage and personal injury.

[0006] Unstable drilling quality: Lacking scientific data support and real-time feedback, operators find it difficult to accurately control drilling process parameters, resulting in unstable drilling quality and affecting subsequent geological analysis and resource assessment.

[0007] Insufficient decision-making basis: Traditional drilling operations lack scientific data analysis and early warning mechanisms. It is difficult for operators to make scientific decisions based on historical data and real-time data, which affects the intelligence level of drilling operations.

[0008] In order to solve the above problems, a real-time monitoring and data analysis system for deep hole drilling process is proposed. Summary of the invention

[0009] The present invention proposes a real-time monitoring and data analysis system for a deep hole drilling process to solve the existing technical problems.

[0010] In order to achieve the above object, the present invention provides the following technical solution: a real-time monitoring and data analysis system for deep hole drilling process, comprising:

[0011] Drilling equipment, the drilling equipment is used for deep hole drilling;

[0012] A sensor module, the sensor module is used for real-time monitoring and data transmission;

[0013] The data acquisition module is used to receive the real-time monitoring data transmitted by the sensor module and perform preliminary processing, specifically including the following steps:

[0014] The data is filtered by a low-pass filter or a high-pass filter, the design of the filter is based on Fourier transform or wavelet transform, and the moving average method or exponential smoothing method is used to smooth the data;

[0015] Based on geological conditions, drilling equipment status, and drilling process parameters, a weighted average method is used to calculate a comprehensive risk index, or a machine learning algorithm such as a support vector machine is used to conduct a risk assessment of the drilling process;

[0016] Based on preset rules or experience, the drilling parameters are dynamically adjusted to optimize the drilling process, or genetic algorithms and particle swarm algorithms are used to search for the optimal solution in the drilling parameter space.

[0017] A data processing module, which is used to further process and analyze the data transmitted by the data acquisition module and generate an analysis report and early warning information of the drilling process;

[0018] Display module and alarm module, the display module is used to display real-time monitoring data and analysis reports; the alarm module is used to send out alarm signals in time when abnormal situations occur during the drilling process.

[0019] Preferably, the working process of the sensor module is:

[0020] Real-time monitoring of key parameters during drilling;

[0021] The monitored data is transmitted to the data acquisition module.

[0022] Preferably, the workflow of the display module is:

[0023] Receive real-time monitoring data and analysis reports transmitted by the data processing module;

[0024] Display data and analysis reports in a graphical and visual way, including drilling depth curve, drilling speed curve, and mud pressure curve;

[0025] Provides a user interaction interface for users to view historical data, export reports, and set warning rules.

[0026] Preferably, the specific working process of the alarm module is:

[0027] Receive warning information transmitted by the data processing module;

[0028] Different levels of alarm signals are issued according to the type and severity of the early warning information;

[0029] Record alarm history for users to conduct subsequent analysis and processing.

[0030] Preferably, the alarm signal includes sound alarm, light alarm, text message alarm, and email alarm.

[0031] Preferably, the key parameters include drilling depth, drill pipe torque, drilling speed, mud pressure, mud flow, and drill pipe temperature.

[0032] Preferably, the sensor module includes a distance sensor, a pressure sensor and a temperature sensor. Preferably, the Fourier transform formula is:

[0033]

[0034] Among them, X(k) is the frequency domain signal, which represents the representation of the original signal x(n) in the frequency domain. x(n) is the time domain signal, that is, the original signal, which represents the data monitored in real time during the drilling process, including drill pipe torque, drilling speed, etc. N is the signal length, which represents the number of sampling points of the original signal x(n). k is the frequency domain index, which represents different frequency points in the frequency domain. It is a complex exponential term and is the core part of Fourier transform, which is used to convert time domain signals into frequency domain signals.

[0035] Preferably, the formula of the wavelet transform is:

[0036]

[0037] Among them, W(a,b) is the wavelet transform coefficient, x(t) is the original signal, ψ(t) is the wavelet function, a is the scale parameter, and b is the translation parameter.

[0038] Preferably, the formula of the weighted average method is:

[0039]

[0040] Where R is the comprehensive risk index, which indicates the overall risk level after considering multiple factors; wi is the weight of the i-th factor, which indicates the importance of the factor in the comprehensive risk assessment; ri is the risk value of the i-th factor, which indicates the risk size when the factor is evaluated alone; ∑ is the summation symbol, which indicates the weighted summation of the risk values ​​of all factors.

[0041] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0042] The present invention monitors the key parameters of the drilling process in real time. The system can instantly feedback the drilling status, allowing the operator to quickly adjust the drilling parameters, thereby optimizing the drilling process and significantly improving the drilling efficiency. It can automatically analyze the drilling data, identify bottlenecks and problems in the drilling process, and provide improvement suggestions to the operator to further improve the drilling efficiency. It can monitor abnormal conditions in the drilling process, such as geological anomalies, equipment failures, etc. in real time, and immediately issue early warning signals to enable the operator to promptly discover and deal with potential safety hazards, thereby effectively reducing safety risks. It can also predict safety issues that may arise during the drilling process, provide preventive measures for the operator, and further enhance the safety of drilling operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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.

[0044] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0045] 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.

[0046] The present invention provides Figure 1 A real-time monitoring and data analysis system for deep hole drilling process is shown, providing the following implementation methods:

[0047] Implementation Method 1

[0048] The deep hole drilling process real-time monitoring and data analysis system uses a conventional rotary drilling rig with powerful drilling capabilities. The sensor module includes a distance sensor, a pressure sensor, and a temperature sensor, which are used to monitor the drilling depth, mud pressure, and drill pipe temperature in real time. These sensors transmit data to the data acquisition module via wired or wireless means.

[0049] After receiving the sensor data, the data acquisition module first filters the data through a low-pass filter based on Fourier transform to remove high-frequency noise. Subsequently, the moving average method is used to smooth the data to further reduce data fluctuations. Based on geological conditions, drilling equipment status and drilling process parameters, the data acquisition module uses the weighted average method to calculate the comprehensive risk index to provide real-time risk assessment for operators.

[0050] The data processing module conducts in-depth analysis of the collected data and generates an analysis report on the drilling process, including information on drilling efficiency, equipment status, geological conditions, etc. At the same time, based on preset rules or experience, the data processing module will also generate early warning information, such as excessive mud pressure and excessive drill pipe temperature.

[0051] The display module displays real-time monitoring data and analysis reports in a graphical and visual way, including drilling depth curve, drilling speed curve and mud pressure curve. Users can view historical data, export reports and set warning rules through the interactive interface.

[0052] When an abnormal situation occurs during the drilling process, the alarm module will immediately sound an alarm and a light alarm, and record the alarm history. The operator can take prompt measures based on the alarm information to ensure the safety of the drilling operation.

[0053] Implementation Method 2

[0054] This embodiment is optimized on the basis of the first embodiment. In addition to the distance sensor, pressure sensor and temperature sensor, the sensor module also adds a torque sensor and a flow sensor for real-time monitoring of the drill pipe torque and mud flow. These sensors transmit data to the data acquisition module in real time through a high-performance data transmission protocol.

[0055] In the data processing process, the data acquisition module not only uses a low-pass filter based on Fourier transform, but also introduces a high-pass filter based on wavelet transform to better handle different types of noise. At the same time, the data acquisition module uses the support vector machine algorithm to conduct risk assessment on the drilling process, which improves the accuracy and reliability of risk assessment.

[0056] When generating analysis reports, the data processing module adds analysis of energy consumption and costs during drilling, providing operators with a more comprehensive basis for decision-making. In addition, the data processing module also uses genetic algorithms to search for the optimal solution in the drilling parameter space to further optimize the drilling process.

[0057] The display module adds a 3D visualization function when displaying data, allowing operators to more intuitively understand the status and trend of the drilling process. At the same time, the user interaction interface has been optimized to make the operation easier and more intuitive.

[0058] When the alarm module sends out an alarm signal, in addition to the sound alarm and light alarm, it also adds SMS alarm and email alarm functions, so that operators can receive alarm information anytime and anywhere. In addition, the alarm module also provides detailed recording and analysis functions of the alarm history, which provides strong support for subsequent troubleshooting and improvement.

[0059] Implementation Method 3

[0060] This embodiment provides a real-time monitoring and data analysis system for deep hole drilling process under complex geological conditions. The drilling equipment adopts advanced hydraulic drilling rigs with strong drilling capabilities and adaptability. The sensor module includes a variety of high-precision sensors for real-time monitoring of key parameters in the drilling process.

[0061] The data acquisition module uses advanced filtering and smoothing algorithms in the data processing process to deal with the problem of large data fluctuations under complex geological conditions. At the same time, the data acquisition module also uses deep learning algorithms to conduct risk assessment and prediction of the drilling process, improving the accuracy and reliability of risk assessment.

[0062] When generating analysis reports, the data processing module not only analyzes the basic parameters of the drilling process, but also adds analysis of geological structure and resource distribution, providing strong support for geological exploration and resource evaluation. In addition, the data processing module also uses a particle swarm algorithm to search for the optimal solution in the drilling parameter space to further optimize the drilling process.

[0063] The display module uses virtual reality technology to display data, allowing operators to understand the status and trends of the drilling process in an immersive way. At the same time, the user interface provides rich functions and options, allowing operators to customize display content and alarm rules as needed.

[0064] When sending out alarm signals, the alarm module uses a variety of alarm modes and levels to adapt to situations of different urgency and risk types. At the same time, the alarm module also provides detailed recording and analysis functions for alarm information, providing strong support for subsequent troubleshooting and improvement. In addition, the alarm module can also be connected to the remote monitoring system, so that operators can receive alarm information anytime and anywhere and take corresponding measures.

[0065] Implementation Method 4

[0066] Real-time monitoring and data analysis system for deep hole drilling process, including:

[0067] Drilling equipment, which is used to perform deep hole drilling;

[0068] Sensor module: The sensor module is used for real-time monitoring and data transmission;

[0069] The data acquisition module is used to receive the real-time monitoring data transmitted by the sensor module and perform preliminary processing, which specifically includes the following steps:

[0070] The data is filtered by a low-pass filter or a high-pass filter, the design of the filter is based on Fourier transform or wavelet transform, and the moving average method or exponential smoothing method is used to smooth the data;

[0071] Based on geological conditions, drilling equipment status, and drilling process parameters, a weighted average method is used to calculate a comprehensive risk index, or a machine learning algorithm such as a support vector machine is used to conduct a risk assessment of the drilling process;

[0072] Based on preset rules or experience, the drilling parameters are dynamically adjusted to optimize the drilling process, or genetic algorithms and particle swarm algorithms are used to search for the optimal solution in the drilling parameter space.

[0073] Data processing module, which is used to further process and analyze the data transmitted by the data acquisition module and generate analysis reports and early warning information of the drilling process;

[0074] Display module and alarm module. The display module is used to display real-time monitoring data and analysis reports; the alarm module is used to send out alarm signals in time when abnormal situations occur during the drilling process.

[0075] In addition, in the present invention, regarding the above-mentioned sensor module: the working process of the sensor module is:

[0076] Real-time monitoring of key parameters during drilling;

[0077] The monitored data is transmitted to the data acquisition module.

[0078] In addition, in the present invention, regarding the above-mentioned display module: the working process of the display module is:

[0079] Receive real-time monitoring data and analysis reports transmitted by the data processing module;

[0080] Display data and analysis reports in a graphical and visual way, including drilling depth curve, drilling speed curve, and mud pressure curve;

[0081] Provides a user interaction interface for users to view historical data, export reports, and set warning rules.

[0082] Furthermore, in the present invention, regarding the above-mentioned alarm module: the specific working process of the alarm module is:

[0083] Receive warning information transmitted by the data processing module;

[0084] Different levels of alarm signals are issued according to the type and severity of the early warning information;

[0085] Record alarm history for users to conduct subsequent analysis and processing.

[0086] In addition, in the present invention, regarding the above-mentioned alarm signal: the alarm signal includes sound alarm, light alarm, text message alarm, and email alarm.

[0087] In addition, in the present invention, regarding the above-mentioned key parameters, the key parameters include drilling depth, drill pipe torque, drilling speed, mud pressure, mud flow, and drill pipe temperature.

[0088] Furthermore, in the present invention, regarding the above-mentioned sensor module: the sensor module includes a distance sensor, a pressure sensor and a temperature sensor

[0089] Furthermore, in the present invention, regarding the above-mentioned Fourier transform: the formula of Fourier transform is:

[0090]

[0091] Among them, X(k) is the frequency domain signal, which represents the representation of the original signal x(n) in the frequency domain. x(n) is the time domain signal, that is, the original signal, which represents the data monitored in real time during the drilling process, including drill pipe torque, drilling speed, etc. N is the signal length, which represents the number of sampling points of the original signal x(n). k is the frequency domain index, which represents different frequency points in the frequency domain. It is a complex exponential term and is the core part of Fourier transform, which is used to convert time domain signals into frequency domain signals.

[0092] In addition, in the present invention, regarding the above wavelet transform: the formula of wavelet transform is:

[0093]

[0094] Among them, W(a,b) is the wavelet transform coefficient, x(t) is the original signal, ψ(t) is the wavelet function, a is the scale parameter, and b is the translation parameter.

[0095] In addition, in the present invention, regarding the above-mentioned weighted average method: the formula of the weighted average method is:

[0096]

[0097] Where R is the comprehensive risk index, which indicates the overall risk level after considering multiple factors; wi is the weight of the i-th factor, which indicates the importance of the factor in the comprehensive risk assessment; ri is the risk value of the i-th factor, which indicates the risk size when the factor is evaluated alone; ∑ is the summation symbol, which indicates the weighted summation of the risk values ​​of all factors.

[0098] 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. Real-time monitoring and data analysis system for deep hole drilling process, characterized by: include: Drilling equipment, the drilling equipment is used for deep hole drilling; A sensor module, the sensor module is used for real-time monitoring and data transmission; The data acquisition module is used to receive the real-time monitoring data transmitted by the sensor module and perform preliminary processing, specifically including the following steps: The data is filtered by a low-pass filter or a high-pass filter, the design of the filter is based on Fourier transform or wavelet transform, and the moving average method or exponential smoothing method is used to smooth the data; Based on geological conditions, drilling equipment status, and drilling process parameters, a weighted average method is used to calculate a comprehensive risk index, or a machine learning algorithm such as a support vector machine is used to conduct a risk assessment of the drilling process; Based on preset rules or experience, the drilling parameters are dynamically adjusted to optimize the drilling process, or genetic algorithms and particle swarm algorithms are used to search for the optimal solution in the drilling parameter space. A data processing module, which is used to further process and analyze the data transmitted by the data acquisition module and generate an analysis report and early warning information of the drilling process; Display module and alarm module, the display module is used to display real-time monitoring data and analysis reports; the alarm module is used to send out alarm signals in time when abnormal situations occur during the drilling process.

2. The deep hole drilling process real-time monitoring and data analysis system according to claim 1, characterized in that: The working process of the sensor module is as follows: Real-time monitoring of key parameters during drilling; The monitored data is transmitted to the data acquisition module.

3. The deep hole drilling process real-time monitoring and data analysis system according to claim 1 is characterized in that: The working process of the display module is as follows: Receive real-time monitoring data and analysis reports transmitted by the data processing module; Display data and analysis reports in a graphical and visual way, including drilling depth curve, drilling speed curve, and mud pressure curve; Provides a user interaction interface for users to view historical data, export reports, and set warning rules.

4. The deep hole drilling process real-time monitoring and data analysis system according to claim 1 is characterized in that: The specific working process of the alarm module is as follows: Receive warning information transmitted by the data processing module; Different levels of alarm signals are issued according to the type and severity of the early warning information; Record alarm history for users to conduct subsequent analysis and processing.

5. The deep hole drilling process real-time monitoring and data analysis system according to claim 4 is characterized in that: The alarm signal includes sound alarm, light alarm, text message alarm and email alarm.

6. The deep hole drilling process real-time monitoring and data analysis system according to claim 2, characterized in that: The key parameters include drilling depth, drill pipe torque, drilling speed, mud pressure, mud flow, and drill pipe temperature.

7. The deep hole drilling process real-time monitoring and data analysis system according to claim 1, characterized in that: The sensor module includes a distance sensor, a pressure sensor and a temperature sensor.

8. The deep hole drilling process real-time monitoring and data analysis system according to claim 1, characterized in that: The formula for the Fourier transform is: Among them, X(k) is the frequency domain signal, which represents the representation of the original signal x(n) in the frequency domain. x(n) is the time domain signal, that is, the original signal, which represents the data monitored in real time during the drilling process, including drill pipe torque, drilling speed, etc. N is the signal length, which represents the number of sampling points of the original signal x(n). k is the frequency domain index, which represents different frequency points in the frequency domain. It is a complex exponential term and is the core part of Fourier transform, which is used to convert time domain signals into frequency domain signals.

9. The deep hole drilling process real-time monitoring and data analysis system according to claim 1, characterized in that: The formula of the wavelet transform is: Among them, W(a,b) is the wavelet transform coefficient, x(t) is the original signal, ψ(t) is the wavelet function, a is the scale parameter, and b is the translation parameter.

10. The deep hole drilling process real-time monitoring and data analysis system according to claim 1, characterized in that: The formula of the weighted average method is: Where R is the comprehensive risk index, which indicates the overall risk level after considering multiple factors; wi is the weight of the i-th factor, which indicates the importance of the factor in the comprehensive risk assessment; ri is the risk value of the i-th factor, which indicates the risk size when the factor is evaluated alone; ∑ is the summation symbol, which indicates the weighted summation of the risk values ​​of all factors.

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