Winch panel system, data processing method and device for winch panel system

Abstract

The application provides a winch panel system, a data processing method and equipment of the winch panel system, and relates to the technical field of well logging monitoring. The winch panel system comprises a data acquisition unit which outputs digitized wellhead tension data, depth pulse data and cable head tension data. The winch panel system further comprises an industrial control host connected with the data acquisition unit and running on an operating system based on a Linux kernel. A double-process processing architecture is deployed in the industrial control host, comprising a background acquisition process and a graphical user interface process which are independent of each other. The background acquisition process and the graphical user interface process communicate through a shared memory module. The background acquisition process is configured to listen to a communication interface by using an IO multiplexing mechanism, receive data, process the data to obtain well logging analysis results, and write the well logging analysis results into the shared memory module in real time. The graphical user interface process periodically reads the well logging analysis results from the shared memory module for rendering, generates visualized data, and can ensure the stability of the system.

Description

Technical Field

[0001] This application relates to the field of well logging monitoring technology, specifically to a winch panel system, a data processing method for the winch panel system, and equipment. Background Technology

[0002] In oil well logging operations, the winch panel is the core hub connecting downhole instruments and the surface system, responsible for real-time acquisition of key parameters such as depth and tension, and monitoring operational safety. Existing winch panel systems typically use Windows systems and deploy a single-process, single-threaded software architecture. This architecture simultaneously handles high-frequency, real-time data acquisition tasks and computationally intensive, resource-intensive graphical interface rendering tasks within the same process. Due to the competition and contention for system resources between these two types of tasks, the data acquisition thread is often blocked, response is delayed, or even crashes. In severe cases, this can lead to system failure, creating blind spots in the monitoring of logging depth and tension data, and posing operational risks.

[0003] Meanwhile, the system's stability deficiencies also limit the improvement of its data processing capabilities. Although the current system incorporates multi-source sensor signals such as wellhead tension and cable tension, it struggles to reliably perform operational safety monitoring and achieve intelligent fault identification and accurate classification based on the instability of a single-process architecture. Therefore, the limitations of the single-process architecture in resource scheduling restrict the stability of the system's long-term continuous operation and hinder the evolution of its ability to achieve high-precision measurement and intelligent diagnosis using multi-source sensor data. Summary of the Invention

[0004] This application provides a winch panel system, a data processing method for the winch panel system, and a device that can ensure system stability and achieve high-precision measurement and safety monitoring.

[0005] The technical solution of this application embodiment is as follows:

[0006] In a first aspect, embodiments of this application provide a winch panel system, the system comprising:

[0007] The data acquisition unit is configured to connect to the wellhead tension meter, cable encoder, and downhole instrument string, respectively acquiring wellhead tension signal, depth pulse signal, and cable head tension signal, performing analog-to-digital conversion, and outputting digital wellhead tension data, depth pulse data, and cable head tension data;

[0008] The industrial control host is configured to connect to the data acquisition unit via a communication interface and runs on a Linux kernel-based operating system;

[0009] The industrial control host is equipped with a dual-process processing architecture, which includes an independent background acquisition process and a graphical user interface process. The background acquisition process and the graphical user interface process communicate with each other through a shared memory module.

[0010] The background acquisition process is configured to use an I / O multiplexing mechanism to listen to the communication interface, receive the wellhead tension data, depth pulse data and cable head tension data output by the data acquisition unit, and use the wellhead tension data to perform real-time compensation calculation on the depth pulse data to generate corrected depth data. It also performs trend calculation based on the wellhead tension data and the cable head tension data to generate a cable status identifier, constructs a logging analysis result based on the corrected depth data and the cable status identifier, and writes the logging analysis result into the shared memory module in real time.

[0011] The graphical user interface process is configured to build a human-computer interaction interface and periodically read the well logging analysis results from the shared memory module for rendering to generate visualized well logging parameter curves and values.

[0012] In the above technical solution, the system includes a data acquisition unit and an industrial control host. The data acquisition unit is configured to connect to a wellhead tension meter, a cable encoder, and a downhole instrument string, respectively acquiring wellhead tension signals, depth pulse signals, and cable head tension signals, performing analog-to-digital conversion, and outputting digitized wellhead tension data, depth pulse data, and cable head tension data. This unified access and digitization of multi-source heterogeneous sensor data provides the raw data foundation for subsequent high-precision calculations. The industrial control host is configured to connect to the data acquisition unit via a communication interface and runs on a Linux kernel-based operating system. The industrial control host deploys a dual-process processing architecture, including independent background acquisition processes and a graphical user interface process. The background acquisition process and the graphical user interface process communicate through a shared memory module. By running on a Linux kernel, the process space is isolated, achieving decoupling between acquisition and display, thereby ensuring the stability of system operation and fundamentally eliminating resource occupation issues caused by processes. The system addresses the risks of failure and operational safety monitoring. The background data acquisition process utilizes an I / O multiplexing mechanism to monitor the communication interface, receiving wellhead tension data, depth pulse data, and cable head tension data from the data acquisition unit. It then uses the wellhead tension data to perform real-time compensation calculations on the depth pulse data to generate corrected depth data. Based on the trend calculations of the wellhead tension data and cable head tension data, it generates cable status identifiers. Based on the corrected depth data and cable status identifiers, it constructs logging analysis results, which are written to the shared memory module in real-time. The background acquisition process receives data, performs data parsing and correction, improving the accuracy of data measurement and thus enhancing the accuracy of safety monitoring. The graphical user interface (GUI) process is configured to build a human-machine interface and periodically reads logging analysis results from the shared memory module for rendering, generating visualized logging parameter curves and values. The shared memory connects decoupled processes, ensuring that the curves displayed on the screen are synchronized with the real-time downhole conditions. This process guarantees system stability, achieving high-precision measurement and safety monitoring under stable system conditions.

[0013] In some embodiments of this application, the background acquisition process integrates a data parsing logic architecture, which includes a depth correction module. The depth correction module includes:

[0014] The spatiotemporal synchronization submodule is configured to perform time-series alignment of the wellhead tension data and the cable head tension data based on the depth pulse data to obtain aligned wellhead tension data, aligned cable head tension data, and aligned depth pulse data.

[0015] The first correction submodule is configured to calculate the average value of the aligned wellhead tension data and the aligned cable head tension data to obtain the average tension value;

[0016] The second correction submodule multiplies the average tension value with the aligned depth pulse data, and calculates the elastic elongation by comparing the result of the multiplication with the cable elasticity coefficient, wherein the cable elasticity coefficient is the product of the cable's elastic modulus and its cross-sectional area.

[0017] The third correction submodule adds the aligned depth pulse data and the elastic elongation to obtain the corrected depth data.

[0018] In some embodiments of this application, the data parsing logic architecture further includes a security analysis module, which includes:

[0019] The smoothing submodule is configured to perform mean filtering on the wellhead tension data and the cable head tension data using a preset sliding window to obtain filtered wellhead tension data and filtered cable head tension data.

[0020] The sensitivity calculation submodule is configured to calculate the mean and standard deviation of tension within the current time window based on filtered wellhead tension data, and to construct a dynamic alarm threshold according to a preset sensitivity adjustment coefficient, wherein the sensitivity adjustment coefficient represents the relationship between the current instrument depth and the cable.

[0021] The abnormal triggering submodule is configured to generate a tension abnormality trigger signal when the filtered wellhead tension data exceeds the dynamic alarm threshold and the current depth change rate is non-zero.

[0022] The differential calculation submodule is configured to calculate the correlation between the changing trends of the filtered wellhead tension data and the filtered cable head tension data based on the tension anomaly trigger signal.

[0023] The identifier generation submodule is configured to determine the cable status identifier based on the correlation of the change trend.

[0024] In some embodiments of this application, the industrial control host is further equipped with an external storage device detection module, which includes:

[0025] The monitoring trigger submodule is configured to trigger a preset archiving script when a mobile device is detected being inserted, and to retrieve the stored historical logging data file in the shared memory;

[0026] The file copying submodule is configured to copy a preset number of historical logging data files generated over a preset period of time to a specified directory on the mobile device.

[0027] Secondly, embodiments of this application provide a data processing method for a winch panel system, operating on the winch panel system as described in any of the first aspects above, the method comprising:

[0028] The system collects wellhead tension signals from the wellhead tension gauge, depth pulse signals from the cable encoder, and cable head tension signals from the downhole instrument string. It then performs analog-to-digital conversion on the wellhead tension signals, depth pulse signals, and cable head tension signals to output digitized wellhead tension data, depth pulse data, and cable head tension data.

[0029] Under the dual-process processing architecture, the communication interface is monitored using an IO multiplexing mechanism to receive the wellhead tension data, the depth pulse data, and the cable head tension data output by the data acquisition unit;

[0030] The wellhead tension data is used to perform real-time compensation calculation on the depth pulse data to generate corrected depth data. The wellhead tension data and the cable head tension data are used to perform trend calculation to generate cable status identifiers. The well logging analysis results are constructed based on the corrected depth data and the cable status identifiers and are written to the shared memory module in real time.

[0031] A human-computer interaction interface is constructed, and the well logging analysis results are periodically read from the shared memory module for rendering to generate visualized well logging parameter curves and values.

[0032] In some embodiments of this application, the step of using the wellhead tension data to perform real-time compensation calculations on the depth pulse data to generate corrected depth data includes:

[0033] The wellhead tension data and the cable head tension data are time-aligned based on the depth pulse data to obtain aligned wellhead tension data, aligned cable head tension data, and aligned depth pulse data.

[0034] The average tension value is obtained by averaging the aligned wellhead tension data and the aligned cable head tension data.

[0035] The average tension value is multiplied by the aligned depth pulse data, and the result of the multiplication is compared with the cable elasticity coefficient to calculate the elastic elongation, wherein the cable elasticity coefficient is the product of the cable's elastic modulus and its cross-sectional area.

[0036] The aligned depth pulse data and the elastic elongation are added together to obtain the corrected depth data.

[0037] In some embodiments of this application, the step of generating a cable status identifier by performing trend calculation based on the wellhead tension data and the cable head tension data includes:

[0038] The wellhead tension data and the cable head tension data are subjected to mean filtering using a preset sliding window to obtain filtered wellhead tension data and filtered cable head tension data.

[0039] Based on the filtered wellhead tension data, the mean and standard deviation of tension within the current time window are calculated, and a dynamic alarm threshold is constructed according to a preset adjustment sensitivity coefficient, wherein the adjustment sensitivity coefficient represents the relationship between the current instrument depth and the cable.

[0040] When the filtered wellhead tension data is detected to exceed the dynamic alarm threshold and the current depth change rate is non-zero, a tension anomaly trigger signal is generated.

[0041] Based on the tension anomaly trigger signal, calculate the correlation between the changing trends of the filtered wellhead tension data and the filtered cable head tension data;

[0042] Based on the correlation of the changing trends, the cable status identifier is determined.

[0043] In some embodiments of this application, determining the cable status identifier based on the correlation of the change trend includes:

[0044] If the correlation of the change trend is that the filtered wellhead tension data shows an upward trend and the filtered cable head tension data shows a synchronous upward trend, then it is an instrument jamming fault. The instrument jamming fault and the corresponding first-level alarm command are encapsulated to obtain the cable status identifier.

[0045] If the correlation of the changing trend is that the filtered wellhead tension data shows an upward trend, and the filtered cable head tension data shows a constant or downward trend, then it is a cable sticking fault. The cable sticking fault and the corresponding secondary alarm command are encapsulated to obtain the cable status identifier.

[0046] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, a user interface, a communication bus, and a network interface. The processor, the memory, the user interface, and the network interface are respectively connected to the communication bus. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method described in any one of the second aspects.

[0047] Fourthly, embodiments of this application provide a computer-readable storage medium storing instructions that, when executed, perform any of the methods described in the second aspect above.

[0048] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0049] 1. The system utilizes an industrial control host connected to the data acquisition unit via a communication interface, running on a Linux kernel-based operating system. The industrial control host employs a dual-process architecture, comprising an independent background acquisition process and a graphical user interface (GUI) process. These processes communicate via a shared memory module. The Linux kernel-based operation isolates the process spaces, decoupling acquisition and display, thus ensuring system stability and fundamentally eliminating system failures and operational safety monitoring risks caused by resource conflicts between processes. The background acquisition process is configured to utilize I / O multiplexing to listen to the communication interface, receiving wellhead tension data, depth pulse data, and cable head tension data output from the data acquisition unit, and utilizing... Real-time compensation calculations are performed on depth pulse data using wellhead tension data to generate corrected depth data. Trend calculations are then performed based on the wellhead tension data and cable head tension data to generate cable status identifiers. Well logging analysis results are constructed based on the corrected depth data and cable status identifiers, and these results are written to a shared memory module in real time. Data is received, parsed, and corrected in the background to improve the accuracy of data measurement, thereby enhancing the accuracy of safety monitoring. A graphical user interface (GUI) process is configured to build a human-machine interface and periodically reads well logging analysis results from the shared memory module for rendering, generating visualized well logging parameter curves and values. The shared memory connects decoupled processes to ensure that the curves displayed on the screen are synchronized with the real-time downhole conditions. This process ensures system stability and achieves high-precision measurement and safety monitoring under stable conditions. Therefore, it effectively solves the problem of meeting the high-frequency and high-precision requirements of algal bloom monitoring in related technologies.

[0050] 2. The wxWidgets 3.3.1 open-source graphics library was selected as the development platform and the LGPL license agreement was adopted to eliminate commercial risks. It is open-source and friendly and does not affect the implementation of functions, thus solving the compliance issues of the existing solutions.

[0051] 3. By adopting a dual-process design and select multiplexed I / O mechanism, combined with shared memory inter-process communication, the existing single-process and ordinary serial port communication are replaced, realizing high-concurrency data processing and high-speed data interaction, and improving the system's real-time response capability and data processing efficiency.

[0052] 4. By automatically archiving and automatically exporting data to mobile devices via USB flash drives, the existing basic storage function is replaced, which improves the flexibility and convenience of data management and facilitates subsequent data playback analysis and system integration.

[0053] 5. Under the established stable system, accurate data monitoring is achieved by compensating for depth pulse data and filtering, triggering early warnings, and calibrating the status of wellhead tension data and cable head tension, thereby improving the accuracy of safety monitoring. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the structure of a winch panel system provided in one embodiment of this application;

[0055] Figure 2 This is a schematic diagram of the data processing flow of a winch panel system provided in one embodiment of this application;

[0056] Figure 3 This is a schematic flowchart of a data processing method for a winch panel system provided in one embodiment of this application;

[0057] Figure 4 This is a schematic flowchart of a data processing method for a winch panel system provided in another embodiment of this application;

[0058] Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0059] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0060] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0061] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0062] In related technologies, hardware solutions are built using Windows systems on general-purpose computer devices (such as personal laptops). However, the oilfield environment is complex, with factors such as vibration, dust, and voltage fluctuations. Personal laptops have insufficient anti-interference capabilities and durability, making them susceptible to environmental influences. Furthermore, they do not employ a targeted high-concurrency data processing architecture, resulting in limited real-time response capabilities in scenarios involving simultaneous acquisition of multiple parameters and high-frequency data transmission.

[0063] Based on this, embodiments of this application provide a winch panel system, a data processing method for the winch panel system, an electronic device, and a readable storage medium. The winch panel system includes a data acquisition unit and an industrial control host. The data acquisition unit is configured to connect to a wellhead tension meter, a cable encoder, and a downhole instrument string, respectively acquiring wellhead tension signals, depth pulse signals, and cable head tension signals, performing analog-to-digital conversion, and outputting digitized wellhead tension data, depth pulse data, and cable head tension data. This unified access and digitization of multi-source heterogeneous sensor data provides the raw data foundation for subsequent high-precision calculations. The industrial control host is configured to connect to the data acquisition unit via a communication interface and runs on a Linux kernel-based operating system. The industrial control host deploys a dual-process processing architecture, including an independent background acquisition process and a graphical user interface process. The background acquisition process and the graphical user interface process communicate through a shared memory module. By running on a Linux kernel, the process space is isolated, achieving decoupling between acquisition and display, thereby ensuring system operation. Stability is fundamentally ensured to eliminate system failures and operational safety monitoring risks caused by resource consumption between processes. The background acquisition process is configured to use an I / O multiplexing mechanism to listen to the communication interface, receiving wellhead tension data, depth pulse data, and cable head tension data output from the data acquisition unit. It then uses the wellhead tension data to perform real-time compensation calculations on the depth pulse data to generate corrected depth data. Based on the trend calculations of the wellhead tension data and cable head tension data, it generates cable status identifiers. Based on the corrected depth data and cable status identifiers, it constructs the logging analysis results, which are written to the shared memory module in real time. The background acquisition process receives data, performs data parsing and correction processing, improving the accuracy of data measurement and thus the accuracy of safety monitoring. The graphical user interface process is configured to build a human-machine interface and periodically reads logging analysis results from the shared memory module for rendering, generating visualized logging parameter curves and values. The shared memory connects decoupled processes, ensuring that the curves displayed on the screen are synchronized with the real-time downhole conditions. This process guarantees system stability, achieving high-precision measurement and safety monitoring under stable system conditions.

[0064] It should be noted that this winch panel system is used in the oil and gas industry and is suitable for field logging operations in oil fields. Specifically, it is used for the acquisition, processing, display, and storage of key parameters such as tension and depth during the logging process.

[0065] The technical solutions provided in the embodiments of this application will be further described below with reference to the accompanying drawings.

[0066] Reference Figure 1 , Figure 1 This is a schematic diagram of the winch panel system provided in this embodiment. The winch panel system includes a data acquisition unit, which uses a 5752DAPS data acquisition board. It connects to a wellhead tension gauge, a cable encoder, and a downhole instrument string via a docking port interface in the well logging system. This downhole instrument string consists of various sensors used to monitor the downhole environment. Wellhead tension and cable head tension signals are transmitted via cable, and depth pulse signals are transmitted via the cable encoder. The data acquisition unit internally employs a high-precision analog-to-digital converter (ADC), such as a 24-bit Sigma-Delta ADC, to acquire analog voltage at a sampling rate of 1kHz. The voltage signals are converted into wellhead tension data, depth pulse data, and cable head tension data, and then output as digitized data. This unified access and digitization of multi-source heterogeneous sensor data provides the raw data foundation for subsequent high-precision calculations.

[0067] The wellhead tension signal represents the total mechanical load currently borne by the surface winch system. It is an analog voltage signal sensed by tension sensors (usually strain gauge sensors) installed at the ground or top pulleys. After being converted by an analog-to-digital converter, it is numerically equal to the sum of the weight of the downhole instrument string in the fluid, the weight of the cable lowered into the well, and the friction force from the well wall. This is the most direct basis for determining whether the cable is overloaded. The depth pulse signal is a discrete electrical pulse sequence generated by an encoder mechanically connected to the winch drum or metering wheel. Each encoder rotation generates a fixed number of pulses, each pulse corresponding to a tiny physical displacement of the cable on the surface. The digitized depth pulse data represents how many meters of cable the winch has actually released or retrieved, providing the basis for subsequent graphical displays. The cable head tension data is the actual tension borne at the connection between the cable and the instrument. After analog-to-digital conversion, the digitized cable head tension data represents the current actual load on the instrument string and is the core data for distinguishing complex downhole operating conditions.

[0068] The winch panel system includes an industrial control host, which is an all-in-one industrial control machine with abundant interfaces, offering plug-and-play functionality without additional configuration. It connects to the data acquisition unit via a communication interface running a Linux kernel-based operating system. This communication interface is an RS232 interface, and the Linux kernel operating system uses the Ubuntu 24.04.2LTS open-source Linux operating system, replacing the existing Windows system on personal laptops. This not only improves system stability and security but also supports switching to domestic Linux systems such as Kylin, adapting to domestic requirements and solving the problems of poor stability and high security risks in existing systems.

[0069] During well logging, graphics rendering is a resource-intensive operation that consumes a significant amount of graphics card and CPU resources, especially when replaying historical data. If a single process is used, interface lag can directly freeze the data acquisition loop. Therefore, the industrial control host is equipped with a dual-process architecture, which includes an independent background acquisition process and a graphical user interface process. By setting different processes, the background acquisition process and the graphical user interface are decoupled and made independent of each other.

[0070] To ensure data consistency between the background data acquisition process and the graphical user interface (GUI), the two processes communicate via a shared memory module, enabling high-speed data sharing. Furthermore, through memory isolation, even if the GUI process freezes or crashes due to excessive graphics load or graphics card driver issues, the background data acquisition process can still survive independently.

[0071] like Figure 2 As shown, the background acquisition process writes data into the shared memory module in real time. The graphical user interface reads the corresponding data and displays it according to a preset cycle. In the shared memory mode, the data only needs to be written to the memory once, and the reader can access it directly with almost zero latency. This achieves microsecond-level data synchronization latency, ensuring that the curves displayed on the screen are almost synchronized with the real-time situation downhole, and that what is displayed is what is happening at the moment.

[0072] The background data acquisition process is configured to monitor the communication interface using an I / O multiplexing mechanism. This mechanism, specifically the select multiplexed I / O mechanism, enables concurrent monitoring of multiple serial port devices. Unlike traditional polling, the I / O multiplexing mechanism only wakes up the process when data arrives, significantly reducing CPU idle time and ensuring microsecond-level real-time response. The select multiplexed I / O mechanism allows simultaneous reception of wellhead tension data, depth pulse data, and cable head tension data from the data acquisition unit, providing a foundation for subsequent analysis, correction, and labeling.

[0073] The received data is not simply forwarded; the background acquisition process integrates a data parsing logic architecture. It uses wellhead tension data to perform real-time compensation calculations on depth pulse data to generate corrected depth data. It also performs trend calculations based on wellhead tension data and cable head tension data to generate cable status indicators, thereby improving the accuracy of monitoring data and ultimately enhancing the accuracy of operational safety monitoring.

[0074] In one embodiment, the data parsing logic architecture includes a deep correction module, which comprises a spatiotemporal synchronization submodule, a first correction submodule, a second correction submodule, and a third correction submodule. The spatiotemporal synchronization submodule is connected to the first correction submodule, the first correction submodule is connected to the second correction submodule, and the second correction submodule is connected to the third correction submodule.

[0075] The spatiotemporal synchronization submodule is configured to perform time-series alignment of wellhead tension data and cable head tension data based on depth pulse data, resulting in aligned wellhead tension data, aligned cable head tension data, and aligned depth pulse data. Specifically, the cable head tension meter is located thousands of meters downhole, and the signal needs to be transmitted via cable modulation, resulting in significant delays. Without alignment, the calculated tension would be distorted. The spatiotemporal synchronization submodule eliminates the "misalignment" of data on the time axis caused by differences in geographical location and signal transmission paths between different sensors, ensuring that all data involved in the calculation are aligned to the same physical moment. First, a circular buffer based on the depth pulse timestamp is established, determined according to the cable transmission delay time, which is tailored to different cable models. Based on the transmitted wellhead tension data and depth pulse data at the current time t, the system backtracks in the buffer to extract the cable head tension data at time t. This cable head tension data is actually the data generated at time t minus the delay time, or after waiting for the delay time, the current wellhead data and the lagging cable head data are matched. This achieves temporal alignment of the wellhead tension data, depth pulse data, and cable head tension data, resulting in wellhead tension data, cable head tension data, and depth pulse data aligned at the same time. This process eliminates spatiotemporal asynchronous errors, ensuring the physical accuracy of subsequent average tension calculations, especially guaranteeing the dynamic response accuracy of depth correction during variable-speed logging or when encountering stuck conditions.

[0076] The first correction submodule is configured to calculate the average tension value by averaging the aligned wellhead tension data and the aligned cable head tension data. Specifically, in engineering mechanics, for a linearly distributed axial load, the deformation effect is equivalent to a constant force of magnitude equal to the "average of the beginning and end" acting on the rod. At a certain moment, the statistical mean of the equivalent tension acting on the entire cable is calculated by adding the aligned wellhead tension data and the aligned cable head tension data, and then dividing by 2 to obtain the average tension value. Compared to using only the wellhead tension (which is too high) or only the cable head tension (which is too low) for calculation, using the average value can more accurately reflect the overall stress state of the cable, thereby significantly improving the estimation accuracy of elastic elongation.

[0077] The second correction submodule is configured to multiply the average tension value by the aligned depth pulse data, and then calculate the elastic elongation by comparing the result with the cable elastic modulus, where the cable elastic modulus is the product of the cable's elastic modulus and its cross-sectional area. Specifically, the elastic elongation is the additional length increase of a cable due to elastic deformation caused by external forces, such as its own weight and instrument tension during logging. The average tension value calculated in the first correction submodule is multiplied by the aligned depth pulse data, and the result is divided by the cable elastic modulus to obtain the elastic elongation. Within the elastic limit, the deformation of an object is directly proportional to the force, directly proportional to the length, and inversely proportional to the stiffness. By using the average tension value for elastic quantification, the cable elongation in deep well operations can reach 20-30 meters, and the calculation of the elastic elongation can reduce errors.

[0078] The elastic modulus and cross-sectional area of ​​the cable can be determined according to the cable type, as detailed in the cable instruction manual. The cable's elastic coefficient is obtained by calculating the product; this constant reflects the cable's stiffness.

[0079] The third correction submodule is configured to add the aligned depth pulse data and the elastic elongation to obtain the corrected depth data. Specifically, the encoder can only measure "how many revolutions the drum made," i.e., how many meters of cable were deployed. However, downhole, due to gravity and tension, the cable is under taut tension, and the instrument's position downhole is the sum of the length of the cable deployed to the surface and the length stretched on the cable. Based on the elastic elongation obtained from the second correction submodule, the depth pulse data and the elastic elongation are added to obtain the corrected depth data. This ensures that the depth value recorded on the logging curve in the subsequent graphical interface matches the actual formation depth, and also avoids "invalid perforation" or "mistaken layer extraction" due to depth errors, greatly improving the success rate and economic benefits of oil and gas well development.

[0080] In one embodiment, the data parsing logic architecture further includes a security analysis module, which includes a smoothing submodule, a sensitivity calculation submodule, an anomaly triggering submodule, a differential calculation submodule, and an identifier generation submodule. The smoothing submodule is connected to the sensitivity calculation submodule, the sensitivity calculation submodule is connected to the anomaly triggering submodule, the anomaly triggering submodule is connected to the differential calculation submodule, and the differential calculation submodule is connected to the identifier generation submodule.

[0081] The smoothing submodule is configured to perform mean filtering on the wellhead tension data and cable head tension data using a preset sliding window, resulting in filtered wellhead tension data and filtered cable head tension data. Specifically, the preset sliding window is a window queue of a preset fixed length, which can be 50. New data enters the tail of the queue, and old data is moved out of the head. Since high-frequency noise has a significant impact on the trend of calculated data changes, for example, when subsequent trend calculations are performed using differentiation, the derivative of high-frequency noise is often infinite. If the derivative is directly calculated without first performing smoothing filtering, tiny electromagnetic interference pulses from the sensor will be calculated as huge slopes, causing the system to incorrectly believe that the tension is changing drastically, thus reducing monitoring accuracy. Therefore, mean filtering is performed on the wellhead tension data and cable head tension data within the sliding window, with the window length denoted as N. For each sampling time t, the data set of the current time and the previous N-1 times is taken, and the arithmetic mean is calculated. The mean is then calculated for both the wellhead tension data and the cable head tension data to obtain the filtered wellhead tension data and filtered cable head tension data. By smoothing the signal, non-physical interference signals are effectively filtered out, providing a clean and stable data source for subsequent trend analysis and reducing the possibility of misjudgment.

[0082] The sensitivity calculation submodule is configured to calculate the mean and standard deviation of the tension within the current time window based on filtered wellhead tension data. It then constructs a dynamic alarm threshold based on a preset sensitivity adjustment coefficient, which represents the relationship between the current instrument depth and the cable. Specifically, within a sliding window, the mean and standard deviation of the wellhead tension data are calculated, and the sensitivity adjustment coefficient is called. This coefficient is positively correlated with the well depth; the deeper the well, the longer the cable, and the greater the natural elastic fluctuation, the larger the coefficient (e.g., 3 or 5). Conversely, for shallower wells, the coefficient is smaller (e.g., 2 or 3). The product of the mean tension, standard deviation, and sensitivity adjustment coefficient is added to the dynamic alarm threshold. This dynamic alarm threshold is obtained from the dynamic wellhead tension data and sensitivity adjustment coefficient within the current window, thus it also changes dynamically. Compared to fixed thresholds, which cannot simultaneously adapt to shallow wells (hundreds of meters) and deep wells (several kilometers), dynamic thresholds more closely reflect the data fluctuation range, achieving adaptive protection across the entire well section.

[0083] The anomaly trigger submodule is configured to generate a tension anomaly trigger signal when the filtered wellhead tension data exceeds the dynamic alarm threshold and the current depth change rate is non-zero. Specifically, high tension alone does not necessarily indicate a fault; the condition of depth change is introduced to confirm that the winch is indeed operating, eliminating human interference in a stationary state. When the filtered wellhead tension data exceeds the dynamic alarm threshold and the current depth change rate is non-zero, an anomaly is detected, generating a tension anomaly trigger signal. This signal is a Boolean value, serving as a switch to initiate the fault analysis process for anomaly analysis, while also eliminating invalid interference from non-operational states. The depth change rate is calculated by using the depth pulse data and the derivative at a corresponding moment.

[0084] Specifically, if the wellhead tension exceeds the dynamic alarm threshold and the current depth change rate is zero, it may be due to interference from road conditions, which is considered normal. If the wellhead tension does not exceed the dynamic alarm threshold, it is considered normal. This allows for accurate identification of any abnormalities for timely analysis.

[0085] The differential calculation submodule is configured to calculate the correlation between the changing trends of filtered wellhead tension data and filtered cable head tension data based on the tension anomaly trigger signal. Specifically, when the anomaly trigger submodule detects an anomaly signal, it calculates the changing directions of wellhead tension and cable head tension. For the time before and after the trigger, it performs differential calculations on the filtered tension data and the filtered cable head tension data to obtain the corresponding slope changes and the correlation of changing trends, providing a basis for subsequent judgment based on the changing trends.

[0086] The identifier generation submodule is configured to determine the cable status identifier based on the correlation of changing trends. Specifically, if the correlation of changing trends shows that the filtered wellhead tension data is increasing and the filtered cable head tension data is increasing synchronously, it indicates that the force is transmitted seamlessly from the ground to the instrument end, reflecting that the instrument string itself is stuck, which is an instrument jamming fault. A preset fault type table is read, which includes fault types and corresponding alarm commands. The alarm command is a combination of an indication of the existence of a fault and a control command for that fault. The instrument jamming fault and its corresponding first-level alarm command are concatenated to obtain the cable status identifier. If the correlation of changing trends shows that the filtered wellhead tension data is increasing while the filtered cable head tension data remains unchanged or decreases, it is a cable adhesion fault. The alarm command is retrieved by querying the fault type table, and the cable adhesion fault and its corresponding second-level alarm command are concatenated to obtain the cable status identifier. Through the above analysis and calculation, the risk of instrument falling into the well is greatly reduced.

[0087] In one embodiment, well logging analysis results are constructed based on corrected depth data and cable status identifiers, and these results are written to a shared memory module in real time. Specifically, the background acquisition process uses the `shm_open` and `mmap` system calls to create a memory region, defining a structure containing real-time data such as depth, tension, and alarm status, and writes it sequentially according to the acquisition time. The data stored in the shared memory module includes the original acquired data, processed data, generated cable status identifiers, alarm commands, and well logging analysis results. It should be noted that the corrected depth data and cable status identifiers are combined in depth-representation order to form the well logging analysis results. A region is allocated in memory to store the well logging analysis results, facilitating rapid data retrieval and rendering. The background process, acting as a "producer," writes data using a mutex-protected method, while the graphical user interface process, acting as a "consumer," reads data in read-only mode.

[0088] The graphical user interface (GUI) process, configured to build a human-machine interface, utilizes the wxWidgets graphics library, providing cross-platform GUI support. It uses timed data refresh to update the display and rendering, facilitating operator viewing and control. It periodically reads well logging analysis results from the shared memory module for rendering, generating visualized well logging parameter curves and values. It can also read raw acquired data for monitoring. The 50ms interval achieves microsecond-level data synchronization latency, ensuring that the curves displayed on the screen are almost synchronized with the real-time downhole conditions, allowing the operator to see exactly what is happening now.

[0089] In one embodiment, the industrial control host also deploys an external storage device detection module, which includes a monitoring trigger submodule and a file copying submodule. The monitoring trigger submodule is configured to listen for USB bus events using the Linux kernel's udev mechanism or netlink sockets. When a mobile device is detected being inserted and the file system is successfully mounted, a preset shell script is automatically triggered. This shell script acts as an automatic archiving script, retrieving historical logging data files stored in shared memory. This eliminates the need for operators to enter cumbersome mount commands in the Linux command line, achieving "plug and play" and reducing the computer skills required of operators. The external storage device can be a USB flash drive. Logging data files are typically large, and there are numerous historical files; full copying would be time-consuming and easily fill the USB flash drive. The file copying submodule is configured to execute the `cp` or `rsync` command via a script to copy a preset number of historical logging data files generated over a preset time period to a specified directory on the USB flash drive. The time period can be the period closest to the current time, and the preset data can be 10 data files. Automatic export via USB flash drive replaces existing basic storage functions, improving the flexibility and convenience of data management and facilitating subsequent data playback analysis and system integration. Through reasonable hardware and software design and efficient data processing mechanism, the system achieves accurate acquisition and visual management of key parameters during well logging, ensuring system stability and enabling high-precision measurement and safety monitoring.

[0090] Reference Figure 3 , Figure 3 This is a schematic flowchart of a data processing method for a winch panel system provided in an embodiment of this application. The data processing method for the winch panel system is applied to the winch panel system and is executed by a processor in an electronic device or a readable storage medium. The data processing method for the winch panel system includes steps S100, S200, S300, and S400.

[0091] Step S100: Collect the wellhead tension signal from the wellhead tension gauge, the depth pulse signal from the cable encoder, and the cable head tension signal from the downhole instrument string. Perform analog-to-digital conversion on the wellhead tension signal, the depth pulse signal, and the cable head tension signal to output digitized wellhead tension data, depth pulse data, and cable head tension data.

[0092] In one embodiment, the data acquisition unit employs a 5752DAPS data acquisition board, connecting to a wellhead tension gauge, a cable encoder, and a downhole instrument string via a docking port interface in the well logging system. This downhole instrument string consists of various sensors used to monitor the downhole environment. Wellhead tension and cable head tension signals are transmitted via cable, while depth pulse signals are transmitted via the cable encoder. The data acquisition unit internally uses a high-precision analog-to-digital converter (ADC), such as a 24-bit Sigma-Delta ADC, to acquire analog voltage signals at a 1kHz sampling rate. The voltage signals are converted into wellhead tension data, depth pulse data, and cable head tension data, which are then output as digitized data. This unified access and digitization of multi-source heterogeneous sensor data provides the raw data foundation for subsequent high-precision calculations.

[0093] In step S200, under the dual-process processing architecture, the communication interface is monitored using the IO multiplexing mechanism to receive wellhead tension data, depth pulse data and cable head tension data output by the data acquisition unit.

[0094] In one embodiment, the industrial control host is an integrated industrial control machine running on a Linux kernel-based operating system, with a dual-process processing architecture deployed internally. Under this dual-process architecture, an I / O multiplexing mechanism is used to monitor the communication interface. This I / O multiplexing mechanism is a select-based multiplexed I / O mechanism, enabling concurrent monitoring of multiple serial port devices. Unlike traditional polling, the I / O multiplexing mechanism only wakes up the process when data arrives, significantly reducing CPU idle time and ensuring microsecond-level real-time response. The select-based multiplexed I / O mechanism can simultaneously receive wellhead tension data, depth pulse data, and cable head tension data output from the data acquisition unit, providing a foundation for subsequent analysis, correction, and labeling.

[0095] In step S300, the depth pulse data is compensated in real time using the wellhead tension data to generate corrected depth data. The trend is calculated based on the wellhead tension data and cable head tension data to generate a cable status identifier. The logging analysis results are constructed based on the corrected depth data and the cable status identifier, and the logging analysis results are written to the shared memory module in real time.

[0096] In one embodiment, wellhead tension data is used to perform real-time compensation calculations on depth pulse data to generate corrected depth data, including but not limited to the following steps:

[0097] Step S310: Perform time-series alignment of the wellhead tension data and cable head tension data based on depth pulse data to obtain aligned wellhead tension data, aligned cable head tension data, and aligned depth pulse data.

[0098] Specifically, to eliminate the "misalignment" of data on the time axis caused by differences in geographical location and signal transmission paths between different sensors, and to ensure that all data involved in the calculation are aligned to the same physical moment, a circular buffer based on the depth pulse timestamp is first established. This buffer is determined based on the cable transmission delay, which varies depending on the cable type. Using the wellhead tension data and depth pulse data at the current transmission time t, the buffer is backtracked to extract the cable head tension data at time t. This cable head tension data is actually generated at time t minus the delay time. Alternatively, after waiting for the delay time, the current wellhead data and the lagging cable head data are matched, thus achieving temporal alignment of the wellhead tension data, depth pulse data, and cable head tension data. This results in wellhead tension data, aligned cable head tension data, and aligned depth pulse data aligned to the same moment. This process eliminates spatiotemporal asynchronous errors, ensuring the physical accuracy of subsequent average tension calculations, especially guaranteeing the dynamic response accuracy of depth correction during variable-speed logging or when encountering stuck conditions.

[0099] Step S320: Calculate the average tension value by averaging the aligned wellhead tension data and the aligned cable head tension data.

[0100] Specifically, at a certain moment, the statistical mean of the equivalent tension acting on the entire cable is calculated. This is done by adding the aligned wellhead tension data and the aligned cable head tension data together and then dividing by 2 to obtain the average tension. Compared to using only the wellhead tension (which is too high) or only the cable head tension (which is too low) for calculation, using the mean value can more accurately reflect the overall stress state of the cable, thereby significantly improving the accuracy of elongation estimation.

[0101] Step S330: Multiply the average tension value by the aligned depth pulse data, and calculate the elastic elongation by comparing the result of the multiplication with the cable elasticity coefficient. The cable elasticity coefficient is the product of the cable's elastic modulus and its cross-sectional area.

[0102] Specifically, the extra length increase of a cable due to elastic deformation caused by external forces, such as its own weight and instrument tension during well logging. The average tension calculated in step S320 is multiplied by the aligned depth pulse, and the result is divided by the cable's elastic modulus to obtain the elastic elongation. Within the elastic limit, the deformation of an object is directly proportional to the force, directly proportional to the length, and inversely proportional to the stiffness. By quantifying elasticity using the average tension, the cable elongation in deep well operations can reach 20-30 meters. Calculating the elastic elongation reduces errors. The cable's elastic modulus and cross-sectional area can be determined according to the cable type; refer to the cable instruction manual. The cable's elastic modulus, a constant reflecting its stiffness, is obtained by calculating the product.

[0103] Step S340: The aligned depth pulse data and the elastic elongation are added together to obtain the corrected depth data.

[0104] Specifically, the encoder can only measure "how many revolutions the drum has made," i.e., how many meters of cable have been deployed. However, downhole, due to gravity and tension, the cable is under taut tension, and the instrument's position downhole is determined by the length of the cable deployed to the surface plus the length stretched on the cable. Based on the elastic elongation obtained in step S330, the depth pulse data and the elastic elongation are added together to obtain the corrected depth data. This ensures that the depth value recorded on the logging curve in the subsequent graphical interface matches the actual formation depth, and also avoids "invalid perforation" or "mistaken layer extraction" due to depth errors, greatly improving the success rate and economic benefits of oil and gas well development.

[0105] like Figure 4 As shown, based on the wellhead tension data and cable head tension data, trend calculations are performed to generate cable status indicators, including but not limited to the following steps:

[0106] Step S350: The wellhead tension data and cable head tension data are subjected to mean filtering using a preset sliding window to obtain filtered wellhead tension data and filtered cable head tension data.

[0107] Specifically, the preset sliding window is a window queue of a preset fixed length, which can be 50. New data enters the tail of the queue, and old data is moved out of the head. Since high-frequency noise has a significant impact on the calculated data trend, mean filtering is applied to the wellhead tension data and cable head tension data within the sliding window. The window length is denoted as N. For each sampling time t, the data set of the current time and the previous N-1 times is taken, and the arithmetic mean is calculated. This mean calculation is performed on both the wellhead tension data and the cable head tension data to obtain the filtered wellhead tension data and the filtered cable head tension data. Through smoothing, non-physical interference signals are effectively filtered out, providing a clean and stable data source for subsequent trend analysis and reducing the possibility of misjudgment.

[0108] Step S360: Based on the filtered wellhead tension data, calculate the mean and standard deviation of tension within the current time window, and construct a dynamic alarm threshold according to the preset adjustment sensitivity coefficient. The adjustment sensitivity coefficient represents the relationship between the current instrument depth and the cable.

[0109] Specifically, within the sliding window, the mean and standard deviation of the wellhead tension data are calculated, and an adjustment sensitivity coefficient is applied. This adjustment sensitivity coefficient is positively correlated with the well depth; the deeper the well and the longer the cable, the greater the natural elastic fluctuation, and the larger the adjustment sensitivity coefficient (e.g., 3 or 5). For shallower wells, the adjustment sensitivity coefficient is smaller (e.g., 2 or 3). The product of the tension mean, standard deviation, and adjustment sensitivity coefficient is added to the tension mean to construct the dynamic alarm threshold. This dynamic alarm threshold is obtained from the dynamic wellhead tension data and adjustment sensitivity coefficient within the current window, thus the dynamic alarm threshold also changes dynamically. Compared to fixed thresholds, which cannot simultaneously adapt to the working conditions of shallow wells (hundreds of meters) and deep wells (thousands of meters), the dynamic threshold is closer to the data fluctuation range, achieving adaptive protection throughout the entire well section.

[0110] Step S370: When the filtered wellhead tension data is detected to exceed the dynamic alarm threshold and the current depth change rate is non-zero, a tension anomaly trigger signal is generated.

[0111] Specifically, high tension alone does not necessarily indicate a fault. The condition of depth change is introduced to confirm that the winch is indeed operating, eliminating human interference in a stationary state. When the filtered wellhead tension data exceeds the dynamic alarm threshold and the current depth change rate is non-zero, an anomaly is detected, generating a tension anomaly trigger signal. This signal is a Boolean value that acts as a switch to initiate the fault analysis process, performing anomaly analysis and eliminating invalid interference from non-operational states. The depth change rate is calculated by using the depth pulse data and the derivative at a specific moment.

[0112] When the wellhead tension exceeds the dynamic alarm threshold and the current depth change rate is zero, the disturbance caused by road surface conditions is likely normal. If the wellhead tension does not exceed the dynamic alarm threshold, it is considered normal. This allows for accurate identification of any abnormalities for timely analysis.

[0113] Step S380: Based on the tension anomaly trigger signal, calculate the correlation between the changing trends of the filtered wellhead tension data and the filtered cable head tension data.

[0114] Specifically, when the abnormal triggering submodule detects an abnormal signal, it calculates the direction of change of wellhead tension and cable head tension. For the time before and after the trigger, it performs differential calculations on the filtered tension data and the filtered cable head tension data to obtain the corresponding slope changes and obtains the correlation of the change trend, which provides a basis for subsequent judgment based on the change trend.

[0115] Step S390: Determine the cable status identifier based on the correlation of the change trend.

[0116] Specifically, the cable status indicator is determined based on the correlation of the change trend, including but not limited to the following steps:

[0117] Step S391: If the correlation of the changing trend is that the filtered wellhead tension data shows an upward trend and the filtered cable head tension data shows a synchronous upward trend, then it is an instrument jamming fault. The instrument jamming fault and the corresponding first-level alarm command are encapsulated to obtain the cable status identifier.

[0118] In some possible embodiments of this application, if the correlation between the changing trends is such that the filtered wellhead tension data shows an upward trend, and the filtered cable head tension data shows a synchronous upward trend, it indicates that the force is transmitted seamlessly from the ground to the instrument end, reflecting that the instrument string itself is stuck, which is an instrument jamming fault. A preset fault type table is read, which includes fault types and corresponding alarm commands. The alarm command is a combination of an indication of a fault and a control command for that fault. The instrument jamming fault and the corresponding first-level alarm command are concatenated to obtain the cable status identifier. Through the above analysis and calculation, the risk of instrument falling into the well is greatly reduced.

[0119] Step S392: If the correlation of the changing trend is that the filtered wellhead tension data shows an upward trend, and the filtered cable head tension data shows a constant or downward trend, then it is a cable sticking fault. The cable sticking fault and the corresponding secondary alarm command are encapsulated to obtain the cable status identifier.

[0120] In some possible embodiments of this application, if the correlation of the changing trends is such that the filtered wellhead tension data shows an upward trend while the filtered cable head tension data shows a constant or downward trend, then it is a cable adhesion fault. By querying the fault type table, an alarm command is obtained. The cable adhesion fault and the corresponding secondary alarm command are concatenated to obtain the cable status identifier. Through the above analysis and calculation, the risk of instrument falling into the well is greatly reduced.

[0121] In one embodiment, a memory region is created using the `shm_open` and `mmap` system calls. A structure is defined containing real-time data such as depth, tension, and alarm status, and this data is written sequentially according to the acquisition time. The data stored in the shared memory module includes the original acquired data, processed data, generated cable status identifiers, alarm commands, and well logging analysis results. It should be noted that the corrected depth data and cable status identifiers are combined in depth-representation order to form the well logging analysis results. A region is allocated in memory to store these results, facilitating rapid data retrieval and rendering. The background process acts as a "producer," writing data using a mutex lock for protection, while the graphical user interface process acts as a "consumer," reading data in read-only mode.

[0122] Step S400: Construct a human-computer interaction interface and periodically read well logging analysis results from the shared memory module for rendering to generate visualized well logging parameter curves and values.

[0123] In one embodiment, the wxWidgets graphics library is used to construct the human-computer interaction interface. The wxWidgets library provides cross-platform graphical user interface support, utilizing timed data refresh to update the display and rendering, facilitating operator viewing and control. The system periodically reads logging analysis results from the shared memory module for rendering, generating visualized logging parameter curves and values. It can also read raw acquired data to view the status. A 50ms cycle is used, achieving microsecond-level data synchronization latency, ensuring that the curves displayed on the screen are almost synchronized with the real-time downhole conditions, allowing the operator to see exactly what is happening now. Through reasonable hardware and software design and an efficient data processing mechanism, accurate acquisition and visualized management of key parameters during the logging process are achieved, ensuring system stability and enabling high-precision measurement and safety monitoring.

[0124] It should also be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0125] This application also discloses an electronic device. (See reference...) Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0126] The communication bus 502 is used to enable communication between these components.

[0127] The user interface 503 may include a display screen and a camera. Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0128] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0129] The processor 501 may include one or more processing cores. The processor 501 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 505, and by calling data stored in memory 505. Optionally, the processor 501 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array. The processor 501 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and Modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 501.

[0130] The memory 505 may include random access memory (RAM) or read-only memory. Optionally, the memory 505 may include a non-transitory computer-readable storage medium. The memory 505 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 505 may also be at least one storage device located remotely from the aforementioned processor 501. (Refer to...) Figure 5 The memory 505, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a data processing method of a winch panel system.

[0131] exist Figure 5In the illustrated electronic device 500, the user interface 503 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 501 can be used to call an application program stored in the memory 505 for a data processing method of a winch panel system. When executed by one or more processors 501, the electronic device 500 performs one or more methods as described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0132] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0133] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0134] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0135] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0136] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0137] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will readily conceive of those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0138] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A winch panel system, characterized in that, The system includes: The data acquisition unit is configured to connect to the wellhead tension meter, cable encoder, and downhole instrument string, respectively acquiring wellhead tension signal, depth pulse signal, and cable head tension signal, performing analog-to-digital conversion, and outputting digital wellhead tension data, depth pulse data, and cable head tension data; The industrial control host is configured to connect to the data acquisition unit via a communication interface and runs on a Linux kernel-based operating system; The industrial control host is equipped with a dual-process processing architecture, which includes an independent background acquisition process and a graphical user interface process. The background acquisition process and the graphical user interface process communicate with each other through a shared memory module. The background acquisition process is configured to use an I / O multiplexing mechanism to monitor the communication interface, simultaneously receive the wellhead tension data, depth pulse data, and cable head tension data output by the data acquisition unit, and use the wellhead tension data to perform real-time compensation calculation on the depth pulse data to generate corrected depth data. A trend calculation is performed based on the wellhead tension data and the cable head tension data to generate a cable status identifier. A well logging analysis result is constructed based on the corrected depth data and the cable status identifier, and the well logging analysis result is written to the shared memory module in real time. The I / O multiplexing mechanism is a select multi-channel I / O multiplexing mechanism to concurrently monitor multiple serial port devices. The graphical user interface process is configured to build a human-computer interaction interface and periodically read the well logging analysis results from the shared memory module for rendering to generate visualized well logging parameter curves and values. The background data acquisition process integrates a data parsing logic architecture, which includes a depth correction module. The depth correction module includes: The spatiotemporal synchronization submodule is configured to perform time-series alignment of the wellhead tension data and the cable head tension data based on the depth pulse data to obtain aligned wellhead tension data, aligned cable head tension data, and aligned depth pulse data. The first correction submodule is configured to calculate the average value of the aligned wellhead tension data and the aligned cable head tension data to obtain the average tension value; The second correction submodule multiplies the average tension value with the aligned depth pulse data, and calculates the elastic elongation by comparing the result of the multiplication with the cable elasticity coefficient, wherein the cable elasticity coefficient is the product of the cable's elastic modulus and its cross-sectional area. The third correction submodule adds the aligned depth pulse data and the elastic elongation to obtain the corrected depth data; The data parsing logic architecture also includes a security analysis module, which includes: The smoothing submodule is configured to perform mean filtering on the wellhead tension data and the cable head tension data using a preset sliding window to obtain filtered wellhead tension data and filtered cable head tension data. The sensitivity calculation submodule is configured to calculate the mean and standard deviation of tension within the current time window based on the filtered wellhead tension data, and to construct a dynamic alarm threshold by adding the mean tension to the product of the standard deviation and the adjusted sensitivity coefficient. The adjusted sensitivity coefficient represents the relationship between the current instrument depth and the cable. The abnormal triggering submodule is configured to generate a tension abnormality trigger signal when the filtered wellhead tension data exceeds the dynamic alarm threshold and the current depth change rate is non-zero. The differential calculation submodule is configured to calculate the correlation between the changing trends of the filtered wellhead tension data and the filtered cable head tension data based on the tension anomaly trigger signal. The identifier generation submodule is configured to determine the cable status identifier based on the correlation of the change trend; The process of determining the cable status identifier based on the correlation of the changing trends includes: if the correlation of the changing trends is that the filtered wellhead tension data shows an upward trend and the filtered cable head tension data shows a synchronous upward trend, then it is an instrument jamming fault. The instrument jamming fault and the corresponding first-level alarm command are encapsulated to obtain the cable status identifier. If the correlation of the changing trend is that the filtered wellhead tension data shows an upward trend, and the filtered cable head tension data shows a constant or downward trend, then it is a cable sticking fault. The cable sticking fault and the corresponding secondary alarm command are encapsulated to obtain the cable status identifier.

2. The system according to claim 1, characterized in that, The industrial control host is also equipped with an external storage device detection module, which includes: The monitoring trigger submodule is configured to trigger a preset archiving script when a mobile device is detected being inserted, and to retrieve the stored historical logging data file in the shared memory; The file copying submodule is configured to copy a preset number of historical logging data files generated over a preset period of time to a specified directory on the mobile device.

3. A data processing method for a winch panel system, characterized in that, Operating in the winch panel system as described in any one of claims 1-2, the method comprises: The system collects wellhead tension signals from the wellhead tension gauge, depth pulse signals from the cable encoder, and cable head tension signals from the downhole instrument string. It then performs analog-to-digital conversion on the wellhead tension signals, depth pulse signals, and cable head tension signals to output digitized wellhead tension data, depth pulse data, and cable head tension data. In the dual-process processing architecture, the communication interface is monitored using an IO multiplexing mechanism, and the wellhead tension data, depth pulse data and cable head tension data output by the data acquisition unit are received simultaneously. The IO multiplexing mechanism is a select multiple IO multiplexing mechanism to monitor multiple serial port devices concurrently. The wellhead tension data is used to perform real-time compensation calculation on the depth pulse data to generate corrected depth data. The wellhead tension data and the cable head tension data are used to perform trend calculation to generate cable status identifiers. The well logging analysis results are constructed based on the corrected depth data and the cable status identifiers and are written to the shared memory module in real time. A human-computer interaction interface is constructed, and the well logging analysis results are periodically read from the shared memory module for rendering to generate visualized well logging parameter curves and values; The step of using the wellhead tension data to perform real-time compensation calculations on the depth pulse data to generate corrected depth data includes: The wellhead tension data and the cable head tension data are time-aligned based on the depth pulse data to obtain aligned wellhead tension data, aligned cable head tension data, and aligned depth pulse data. The average tension value is obtained by averaging the aligned wellhead tension data and the aligned cable head tension data. The average tension value is multiplied by the aligned depth pulse data, and the result of the multiplication is compared with the cable elasticity coefficient to calculate the elastic elongation, wherein the cable elasticity coefficient is the product of the cable's elastic modulus and its cross-sectional area. The aligned depth pulse data and the elastic elongation are added together to obtain the corrected depth data; The step of generating a cable status identifier by performing trend calculations based on the wellhead tension data and the cable head tension data includes: The wellhead tension data and the cable head tension data are subjected to mean filtering using a preset sliding window to obtain filtered wellhead tension data and filtered cable head tension data. Based on the filtered wellhead tension data, the mean and standard deviation of tension within the current time window are calculated, and a dynamic alarm threshold is constructed according to a preset adjustment sensitivity coefficient, wherein the adjustment sensitivity coefficient represents the relationship between the current instrument depth and the cable. When the filtered wellhead tension data is detected to exceed the dynamic alarm threshold and the current depth change rate is non-zero, a tension anomaly trigger signal is generated. Based on the tension anomaly trigger signal, calculate the correlation between the changing trends of the filtered wellhead tension data and the filtered cable head tension data; If the correlation of the change trend is that the filtered wellhead tension data shows an upward trend and the filtered cable head tension data shows a synchronous upward trend, then it is an instrument jamming fault. The instrument jamming fault and the corresponding first-level alarm command are encapsulated to obtain the cable status identifier. If the correlation of the changing trend is that the filtered wellhead tension data shows an upward trend, and the filtered cable head tension data shows a constant or downward trend, then it is a cable sticking fault. The cable sticking fault and the corresponding secondary alarm command are encapsulated to obtain the cable status identifier.

4. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, a communication bus, and a network interface. The processor, the memory, the user interface, and the network interface are respectively connected to the communication bus. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in claim 3.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in claim 3.

Images