Plateau alpine region logging information automatic updating system

Abstract

The invention discloses a plateau alpine region well logging information automatic updating system, relates to the technical field of well logging information, and collects an underground well logging state record set, dynamically adjusts a sampling strategy according to energy consumption and data characteristics, guarantees stable transmission and recombination of key data when a network is abnormal, and performs consistency check and missing completion on the data. According to the system, logging data can be automatically collected, dispatched, transmitted and complemented in the plateau and alpine regions, and through multi-source sensing, self-adaptive sampling, dual-channel communication and intelligent prediction, the real-time monitoring of the logging data can be realized, and the real-time monitoring of the logging data can be realized. The extreme environment problems of low-temperature network disconnection and insufficient power supply are effectively solved, data continuity and response timeliness are guaranteed, remote updating and closed-loop feedback are supported, high robustness and intelligent level are achieved, and the limitation that operation is unstable and response is not timely under the extreme condition in the prior art is broken through.

Description

Technical Field

[0001] This invention relates to the field of well logging information technology, and in particular to an automatic well logging information update system for high-altitude and cold regions. Background Technology

[0002] High-altitude and frigid regions often face challenges such as poor communication, insufficient power supply, and difficulties in manual inspection due to their high altitude, low temperature, complex geological structure, and drastic environmental changes. Conducting geological logging work in these areas, especially for real-time monitoring of dynamic indicators such as downhole temperature, pressure, and conductivity, presents several technical bottlenecks. Traditional logging systems typically rely on fixed-period sampling and single-channel communication, making it difficult to respond promptly to sudden changes in well conditions, ensure the integrity and continuity of logging data, and maintain stable operation for extended periods in extreme environments.

[0003] Currently, Chinese invention patent application number CN202210778865.5 discloses a method, apparatus, device, and storage medium for optimizing the volume of logging data. This data volume optimization method includes: S1, obtaining initial data. The initial data includes multiple initial logging files. S2, determining whether the initial curve identifier set of each initial logging file contains a target curve. S3, for initial logging files containing a target curve: obtaining the position of the target curve based on the initial curve identifier set, and extracting the target curve data from the logging data based on the position. Also, updating the file header information based on the target curve, and merging the updated file header information and the target curve data to obtain a target logging file. S4, obtaining the target data corresponding to the initial data based on the target logging file. The target data includes multiple target logging files.

[0004] The aforementioned technologies are insufficient to achieve continuous acquisition, stable transmission, and intelligent response of logging data in high-altitude and frigid environments. Summary of the Invention

[0005] The technical problem solved by this invention is that existing technologies are unable to achieve continuous acquisition, stable transmission and intelligent response of well logging data in high-altitude and cold environments.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: An automatic update system for well logging information in high-altitude and cold regions includes a multi-source sensing and acquisition module, an adaptive scheduling module, a robust communication module, an intelligent fusion processing module, an edge prediction and control module, and a remote update feedback module. The multi-source sensing and acquisition module is used to acquire downhole logging status records; The adaptive scheduling module is used to dynamically adjust the sampling strategy based on energy consumption and data characteristics; The robust communication module is used to ensure the stable transmission and reassembly of critical data in the event of network anomalies; The intelligent fusion processing module is used to perform consistency checks and missing data completion. The edge prediction control module is used to identify abnormal events and generate emergency control strategies. The remote update feedback module is used to build a closed-loop management system for task issuance and system status feedback.

[0007] Preferably, the multi-source sensing and acquisition module includes a downhole data acquisition unit and a surface environment acquisition unit: The downhole data acquisition unit is used to acquire downhole temperature logging data, downhole pressure logging data, conductivity logging data and wellbore condition logging data. The wellbore condition logging data includes images of wellbore structural changes and surface crack identification results, and generates a downhole logging condition record set by combining the sampling timestamp. The surface environment acquisition unit is used to acquire wind speed logging data, radiation intensity logging data, surface temperature logging data, surface humidity logging data, and frost heave risk indicators. The frost heave risk indicators are calculated based on the combined changes in ground temperature and humidity, and the corresponding spatiotemporal coordinates and sensor health status are recorded simultaneously to construct a surface environment monitoring data stream.

[0008] Preferably, the adaptive scheduling module includes an energy consumption control unit and an event triggering unit: The energy consumption control unit is used to adjust the sampling interval, logging data resolution and standby cycle of the logging equipment according to the current battery power, equipment power consumption curve and ambient temperature change trend, and output an adaptive low power consumption operation strategy. The event triggering unit is used to establish a multi-condition triggering rule set based on the mutation rate of downhole temperature logging data, the gradient change amplitude of downhole pressure logging data, and the disturbance degree of conductivity logging data. It monitors the fluctuation characteristics of downhole logging data. When any parameter in the multi-condition triggering rule set exceeds the dynamic threshold range within a set time window, it outputs a high-frequency sampling status signal and maintains a fast response acquisition frequency during the duration of the event.

[0009] Preferably, the robust communication module includes a dual-channel communication unit and a data reassembly unit: The dual-channel communication unit is used to dynamically determine whether the current network channel is interrupted according to the real-time monitoring mechanism of the communication status. If the LoRa communication quality is continuously lower than the set threshold or the number of key logging data transmission failures exceeds the limit, the unit outputs a signal to switch the satellite communication link. The key logging data includes high-frequency logging data during the event triggering period, edge prediction results, and equipment operation status identifiers. The data reassembly unit is used to locally cache and identify the integrity of fragmented logging data received under complex terrain and unstable network conditions, construct a data fragment index queue, check and merge out-of-order received fragments through timestamp and sequence number fields, introduce a data integrity verification process, remove redundant and duplicate fragments and correct transmission errors.

[0010] Preferably, the data reassembly unit is further configured to assign a unique timestamp and segment sequence number index to each cached data packet during the process of receiving the downhole logging status record set. The timestamp is used to record the data arrival time, and the segment sequence number is used to identify the position of the logging data in the original complete data. During the data reassembly process, a reassembly mapping queue is constructed according to the timestamp sorting and sequence number alignment rules to identify out-of-order, lost, and duplicate segments.

[0011] Preferably, the intelligent fusion processing module includes a consistency verification unit and a missing completion unit: The consistency verification unit is used to compare and analyze the trends of downhole temperature logging data, downhole pressure logging data, conductivity logging data and environmental monitoring logging data of the corresponding time period at the wellhead, and to construct an index consistency mapping relationship. By detecting the deviation between the fluctuation amplitude of downhole temperature logging data, downhole pressure logging data, conductivity logging data and environmental monitoring logging data of the corresponding time period at the wellhead within a preset specific period and the fitted trend, potential anomalies or measurement drift behaviors are identified and the deviation behaviors are marked. The missing data completion unit is used to construct a multivariate time-series evolution path based on the logging data of the preceding and following time periods after identifying the discontinuity or missing segments of the logging data. It calculates the reasonable logging data trajectory of the missing interval by combining the slope of temperature change, the rate of pressure change, and the trend of conductivity change, and corrects the inference curve by using a sliding window method to generate a complete logging data sequence.

[0012] Preferably, the missing data completion unit constructs a time series comparison model of well logging data before and after the missing segment, extracts the slope of temperature change, the rate of pressure change, and the trend of conductivity change, dynamically calculates the local change pattern using a sliding window method, and combines the change trajectory corresponding to similar geological structures and sensor behavior in the historical well logging sample library to determine the evolution direction and numerical estimation range of the current missing segment, and generates the completion result.

[0013] Preferably, the edge prediction control module includes an anomaly prediction unit and a response strategy unit: The anomaly prediction unit continuously receives and processes real-time data streams from downhole temperature logging data, downhole pressure logging data, and conductivity logging data. Based on multivariate trend analysis and anomaly identification algorithms, it detects abnormal fluctuation patterns in the real-time data streams and determines whether any abnormal events exist. These abnormal events include: If the pressure increase exceeds the preset pressure threshold, it is judged as a blowout risk; If the temperature drop lasts for a longer period than a preset time threshold, it is considered a risk of freezing. If the change in conductivity per unit time is greater than the preset change threshold, it is judged as a risk of freezing and blockage. Generate early warning results for abnormal events, the early warning results including early warning identifiers and time location information; The response strategy unit is used to automatically generate corresponding control strategies based on the early warning results and historical response rule base. The control strategies include adjusting the current sampling mode, changing the communication path, and activating the emergency power module to ensure the complete transmission of logging data, and issuing control strategy instructions to the equipment execution end.

[0014] Preferably, after receiving the warning result from the edge prediction unit through the historical response rule base, the response strategy unit matches the real-time data stream with the historical event response records, selects the corresponding response action combination, which includes sampling mode switching, communication path change, power consumption adjustment and fault reminder uploading, and synchronizes the selected response action combination to the local controller for execution.

[0015] Preferably, the remote update feedback module includes a task distribution unit and a feedback closed-loop unit: The task issuing unit is used to receive parameter setting tasks generated by the remote platform. The parameter setting tasks include sampling plan adjustment instructions, event triggering rule revision instructions, and communication link switching strategy instructions. The feedback closed-loop unit is used to generate a feedback data packet based on the system operation log, sampling completion status, logging data return success rate and prediction deviation magnitude. The feedback data packet includes the remaining power of the equipment, communication stability score, logging data coverage and model prediction error report. The remote platform adjusts the task content according to the feedback results and outputs the task content adjustment signal.

[0016] The beneficial effects of this invention are as follows: The system of this invention can realize the automatic acquisition, scheduling, transmission and completion of well logging data in high-altitude and cold regions. Through multi-source sensing, adaptive sampling, dual-channel communication and intelligent prediction, it can effectively cope with extreme environmental problems such as low temperature network outage and power shortage, ensure data continuity and timely response, support remote updates and closed-loop feedback, and has high robustness and intelligence level, breaking through the limitations of existing technologies in unstable operation and untimely response under extreme conditions. Attached Figure Description

[0017] Figure 1 This is a basic flowchart of an automatic update system for well logging information in high-altitude and cold regions, provided as an embodiment of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] Example, refer to Figure 1 This paper presents an automatic update system for well logging information in high-altitude and cold regions, comprising a multi-source sensing and acquisition module, an adaptive scheduling module, a robust communication module, an intelligent fusion processing module, an edge prediction control module, and a remote update feedback module. The multi-source sensing and acquisition module is used to collect downhole logging status records.

[0020] The adaptive scheduling module is used to dynamically adjust the sampling strategy based on energy consumption and data characteristics.

[0021] Robust communication modules are used to ensure the stable transmission and reassembly of critical data in the event of network anomalies.

[0022] The intelligent fusion processing module is used to perform consistency checks and missing data completion.

[0023] The edge prediction control module is used to identify abnormal events and generate emergency control strategies.

[0024] The remote update feedback module is used to build a closed-loop management system for task distribution and system status feedback.

[0025] The multi-source sensing and acquisition module includes a downhole data acquisition unit and a surface environment acquisition unit: The downhole data acquisition unit is used to collect downhole temperature logging data, downhole pressure logging data, conductivity logging data, and wellbore condition logging data. The wellbore condition logging data includes images of wellbore structural changes and surface crack identification results, and generates a downhole logging condition record set by combining the sampling timestamps.

[0026] The downhole data acquisition unit enables high-frequency sensing and structured recording of core parameters such as downhole temperature, pressure, conductivity, and wellbore condition. It can identify downhole anomalies and construct a downhole logging status record set by combining timestamps, providing a data foundation for anomaly detection and evolution analysis.

[0027] The surface environment acquisition unit is used to acquire wind speed logging data, radiation intensity logging data, surface temperature logging data, surface humidity logging data, and frost heave risk indicators. The frost heave risk indicators are calculated based on the combined changes in ground temperature and humidity, and the corresponding spatiotemporal coordinates and sensor health status are recorded at the same time to construct a surface environment monitoring data stream.

[0028] The surface environment acquisition unit enables dynamic acquisition of external environmental variables such as surface wind speed, radiation intensity, ground temperature, and humidity. It constructs a frost heave risk index based on the joint changes in ground temperature and humidity, while recording spatial location and sensor health status to form a complete surface environment monitoring data stream. This provides a basis for tracing the causes of well logging data fluctuations and modeling the impact of external environments.

[0029] The multi-source sensing and acquisition module collects key logging data from both downhole and surface environments simultaneously, building a multi-dimensional logging data foundation for high-altitude and cold regions. This provides comprehensive, continuous, and spatiotemporally aligned raw information support for subsequent data scheduling, fusion analysis, and risk prediction.

[0030] The adaptive scheduling module includes an energy consumption control unit and an event triggering unit: The energy consumption control unit is used to adjust the sampling interval, logging data resolution and standby cycle of the logging equipment according to the current battery power, equipment power consumption curve and ambient temperature change trend, and output an adaptive low power consumption operation strategy.

[0031] The energy consumption control unit combines the current battery power, power consumption curve and ambient temperature change trend to dynamically control the sampling interval, data resolution and standby cycle of the logging equipment, and generate a low power consumption operation strategy to ensure that the equipment can operate continuously and stably in extreme environments such as high altitude and cold.

[0032] The event triggering unit is used to establish a multi-condition triggering rule set based on the mutation rate of downhole temperature logging data, the gradient change amplitude of downhole pressure logging data, and the disturbance degree of conductivity logging data. It monitors the fluctuation characteristics of downhole logging data. When any parameter in the multi-condition triggering rule set exceeds the dynamic threshold range within the set time window, it outputs a high-frequency sampling status signal and maintains a fast response acquisition frequency during the duration of the event.

[0033] The event triggering unit constructs a multi-condition triggering mechanism based on temperature mutation rate, pressure gradient change and conductivity disturbance degree, and monitors downhole data fluctuation characteristics in real time. When any parameter exceeds the dynamic threshold, it outputs a high-frequency sampling command to realize rapid response to abnormal events and intensive data acquisition, ensuring that key change processes are fully captured.

[0034] The adaptive scheduling module can intelligently adjust the sampling strategy according to the equipment power and environmental conditions, and automatically switch to high-frequency acquisition mode when there are sudden data fluctuations, so as to achieve both energy efficiency optimization and event response flexibility in well logging data acquisition.

[0035] The robust communication module includes a dual-channel communication unit and a data reassembly unit: The dual-channel communication unit is used to dynamically determine whether the current network channel is interrupted based on the real-time monitoring mechanism of the communication status. If the LoRa communication quality is continuously lower than the set threshold or the number of failures in the transmission of key logging data exceeds the limit, a signal to switch the satellite communication link will be output. The key logging data includes high-frequency logging data during the event triggering period, edge prediction results, and equipment operation status indicators.

[0036] The dual-channel communication unit judges the stability of the LoRa link based on the real-time monitoring mechanism of the communication status. If the backhaul fails continuously or the communication quality is lower than the threshold, it will automatically switch to the satellite communication path and ensure the continuous uploading of key data such as high-frequency logging data, edge prediction results and equipment operating status, thereby maintaining the basic connectivity of the system in complex scenarios.

[0037] The data reassembly unit is used to locally cache and identify the integrity of fragmented logging data received under complex terrain and unstable network conditions, build a data fragment index queue, check and merge out-of-order received fragments through timestamp and sequence number fields, introduce a data integrity verification process, remove redundant and duplicate fragments and correct transmission errors.

[0038] The data reassembly unit is also used to assign a unique timestamp and fragment sequence number index to each cached data packet during the process of receiving the downhole logging status record set. The timestamp is used to record the arrival time of the data, and the fragment sequence number is used to identify the position of the logging data in the original complete data. During the data reassembly process, a reassembly mapping queue is constructed according to the timestamp sorting and sequence number alignment rules to identify out-of-order, missing and duplicate fragments.

[0039] The data reassembly unit caches, indexes, and reassembles the received fragmented logging data. It uses timestamp and sequence number fields to correctly sort and merge out-of-order fragments, uses an integrity verification mechanism to remove redundancy and correct errors, and generates a unique identifier for each data packet to ensure that the logging data is consistent in structure, timing, and logic during transmission, providing a reliable data foundation for subsequent data analysis and trend prediction.

[0040] The robust communication module can ensure the reliable transmission and reconstruction of critical logging data in the event of network instability or interruption. It has the ability to switch communication links and restore data integrity in complex terrains such as high altitudes and cold regions, ensuring that logging information is uninterrupted, unlost, and traceable in extreme environments.

[0041] The intelligent fusion processing module includes a consistency verification unit and a missing information completion unit: The consistency verification unit is used to compare and analyze the trends of downhole temperature logging data, downhole pressure logging data, and conductivity logging data with environmental monitoring logging data at the wellhead for the corresponding time period. It constructs a consistency mapping relationship for the indicators and identifies potential anomalies or measurement drift behaviors by detecting the degree of deviation between the fluctuation amplitude of downhole temperature logging data, downhole pressure logging data, and conductivity logging data and environmental monitoring logging data at the wellhead for the corresponding time period within a preset specific period and the fitted trend. The deviation behaviors are then marked.

[0042] The consistency verification unit performs trend matching and deviation analysis on downhole logging data and wellhead environmental monitoring data, constructs an index consistency mapping relationship, and identifies and marks abnormal drift or measurement error points by comparing the fluctuation amplitude and fitting trend within a specific period, thus providing quality assurance for data cleaning and analysis modeling.

[0043] The missing data completion unit is used to construct a multivariate time-series evolution path based on the logging data of the preceding and following time periods after identifying discontinuities or missing segments in the logging data. It combines the temperature change slope, pressure change rate and conductivity change trend to calculate the reasonable logging data trajectory of the missing interval, and uses a sliding window method to correct the inference curve to generate a complete logging data sequence.

[0044] The missing data completion unit constructs a time-series comparison model of well logging data before and after the missing segment, extracts the temperature change slope, pressure change rate, and conductivity change trend, dynamically calculates the local change pattern using a sliding window method, and combines the change trajectory corresponding to similar geological structures and sensor behavior in the historical well logging sample library to determine the evolution direction and numerical estimation range of the current missing segment, and generates the completion result.

[0045] After detecting discontinuities or missing data in well logging, the missing data completion unit constructs a multivariate time-series path based on the valid data before and after it, extracts the slope of temperature, pressure, and conductivity changes and local dynamic features, combines similar well condition behavior trajectories in the historical sample library, infers the evolution trend and numerical range of the missing segment, and continuously corrects the inference curve through a sliding window to generate a complete well logging data sequence with continuous structure and reasonable physical properties.

[0046] The intelligent fusion processing module ensures the continuity, accuracy, and logical consistency of logging data under extreme environments through consistency checks and missing data completion mechanisms, thereby enhancing the system's reliable support capabilities for anomaly data identification, data repair, and modeling analysis.

[0047] The edge prediction control module includes an anomaly prediction unit and a response strategy unit: The anomaly prediction unit continuously receives and processes real-time data streams from downhole temperature logging data, downhole pressure logging data, and conductivity logging data. Based on multivariate trend analysis and anomaly identification algorithms, it detects abnormal fluctuation patterns in the real-time data streams and determines whether any abnormal events exist. Abnormal events include: If the pressure increase exceeds the preset pressure threshold, it is judged as a blowout risk.

[0048] If the temperature drop lasts for a period of time longer than a preset time threshold, it is considered a risk of freezing.

[0049] If the change in conductivity per unit time is greater than the preset change threshold, it is judged as a risk of freezing.

[0050] Early warning results are generated for abnormal events, including warning indicators and time location information.

[0051] The anomaly prediction unit, based on continuous flow analysis of downhole temperature, pressure, and conductivity logging data, employs multivariate trend analysis and abrupt change identification algorithms to determine the presence of abnormal events in real time: a pressure rise exceeding a threshold is identified as a blowout risk, a sustained temperature drop as a freezing risk, and drastic conductivity fluctuations as a freezing risk. For identified abnormal events, it outputs warning results with early warning indicators and time locations, providing a pre-triggered signal for the response mechanism.

[0052] The response strategy unit automatically generates corresponding control strategies based on the early warning results and historical response rule base. The control strategies include adjusting the current sampling mode, changing the communication path, and activating the emergency power module to ensure the complete transmission of logging data. The control strategy commands are then sent to the equipment execution end.

[0053] After receiving the early warning result from the edge prediction unit, the response strategy unit matches the real-time data stream with historical event response records through the historical response rule base, selects the corresponding response action combination, which includes sampling mode switching, communication path change, power consumption adjustment and fault reminder upload, and synchronizes the selected response action combination to the local controller for execution.

[0054] The response strategy unit automatically matches the most suitable combination of actions based on anomaly prediction results and the historical response rule base, including strategies such as sampling mode switching, communication path changes, power consumption adjustment, and fault reporting, and synchronizes the strategies to the local controller for execution. By learning from and accumulating the response effects of historical events, the unit improves the pertinence and efficiency of the current strategy, ensuring that critical data is not interrupted or lost under abnormal conditions, thereby enhancing the system's robustness and adaptive control capabilities.

[0055] The edge prediction control module, through a real-time prediction and strategy linkage mechanism deployed at the edge, enables early identification and adaptive response to abnormal situations downhole, thereby enhancing the system's early warning and on-site handling capabilities for sudden risks.

[0056] The remote update feedback module includes a task distribution unit and a feedback closed-loop unit: The task distribution unit is used to receive parameter setting tasks generated by the remote platform. The parameter setting tasks include sampling plan adjustment instructions, event trigger rule revision instructions, and communication link switching strategy instructions.

[0057] The task distribution unit can receive parameter setting tasks generated by the remote platform, including instructions such as sampling plan adjustment, event trigger rule update and communication link switching. It can synchronize the task configuration to the local device through the communication interface, effectively supporting the platform's remote control and rapid deployment of logging strategies, and improving configuration flexibility and scheduling response speed.

[0058] The feedback closed-loop unit is used to generate feedback data packets based on system operation logs, sampling completion status, logging data return success rate and prediction deviation magnitude. The feedback data packets include equipment remaining power, communication stability score, logging data coverage and model prediction error report. The remote platform adjusts the task content according to the feedback results and outputs task content adjustment signals.

[0059] The feedback closed-loop unit generates feedback data packets based on system operation logs, sampling execution status, and well logging data upload status. The data packets cover indicators such as remaining equipment power, communication stability score, well logging data coverage, and model prediction error. The remote platform analyzes the current operational performance based on this feedback and adjusts the task content accordingly, achieving a closed-loop feedback mechanism from task issuance to effect tracking, ensuring continuous optimization and stable operation of the well logging system.

[0060] The remote update feedback module enables closed-loop linkage between the logging system and the remote platform for task distribution and feedback. It supports dynamic configuration adjustment and operational status feedback, enhancing the system's remote controllability, adaptive adjustment capabilities, and overall closed-loop management efficiency.

[0061] This invention enhances the comprehensiveness of environmental perception by integrating downhole and surface multi-dimensional logging data through a multi-source sensing acquisition module. An adaptive scheduling module dynamically adjusts the sampling frequency based on power status and anomaly monitoring results, achieving low-power, long-term stable operation. A robust communication module is implemented, constructing a dual-channel transmission mechanism and data reassembly logic to ensure reliable data transmission in the event of network instability or signal interruption. An intelligent fusion processing module, combined with consistency verification and trend completion algorithms, effectively recovers data lost due to interruptions, ensuring the continuity and accuracy of logging data. An edge prediction control module is deployed to analyze well condition changes in real time, proactively identify risks such as blowouts, icing, and freezing, and automatically issue emergency strategies. A remote update feedback module enables remote task distribution, dynamic strategy adjustment, and status feedback closed-loop management, improving the system's intelligent collaborative management level.

[0062] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An automatic updating system for well logging information in high-altitude and cold regions, characterized in that, It includes a multi-source sensing and acquisition module, an adaptive scheduling module, a robust communication module, an intelligent fusion processing module, an edge prediction and control module, and a remote update feedback module. The multi-source sensing and acquisition module is used to acquire downhole logging status records; The adaptive scheduling module is used to dynamically adjust the sampling strategy based on energy consumption and data characteristics; The robust communication module is used to ensure the stable transmission and reassembly of critical data in the event of network anomalies; The intelligent fusion processing module is used to perform consistency checks and missing data completion. The edge prediction control module is used to identify abnormal events and generate emergency control strategies. The remote update feedback module is used to build a closed-loop management system for task issuance and system status feedback.

2. The automatic update system for well logging information in high-altitude and cold regions as described in claim 1, characterized in that, The multi-source sensing and acquisition module includes a downhole data acquisition unit and a surface environment acquisition unit: The downhole data acquisition unit is used to acquire downhole temperature logging data, downhole pressure logging data, conductivity logging data and wellbore condition logging data. The wellbore condition logging data includes images of wellbore structural changes and surface crack identification results, and generates a downhole logging condition record set by combining the sampling timestamp. The surface environment acquisition unit is used to acquire wind speed logging data, radiation intensity logging data, surface temperature logging data, surface humidity logging data, and frost heave risk indicators. The frost heave risk indicators are calculated based on the combined changes in ground temperature and humidity, and the corresponding spatiotemporal coordinates and sensor health status are recorded simultaneously to construct a surface environment monitoring data stream.

3. The automatic updating system for well logging information in high-altitude and cold regions as described in claim 2, characterized in that, The adaptive scheduling module includes an energy consumption control unit and an event triggering unit: The energy consumption control unit is used to adjust the sampling interval, logging data resolution and standby cycle of the logging equipment according to the current battery power, equipment power consumption curve and ambient temperature change trend, and output an adaptive low power consumption operation strategy. The event triggering unit is used to establish a multi-condition triggering rule set based on the mutation rate of downhole temperature logging data, the gradient change amplitude of downhole pressure logging data, and the disturbance degree of conductivity logging data. It monitors the fluctuation characteristics of downhole logging data. When any parameter in the multi-condition triggering rule set exceeds the dynamic threshold range within a set time window, it outputs a high-frequency sampling status signal and maintains a fast response acquisition frequency during the duration of the event.

4. The automatic updating system for well logging information in high-altitude and cold regions as described in claim 3, characterized in that, The robust communication module includes a dual-channel communication unit and a data reassembly unit: The dual-channel communication unit is used to dynamically determine whether the current network channel is interrupted according to the real-time monitoring mechanism of the communication status. If the LoRa communication quality is continuously lower than the set threshold or the number of key logging data transmission failures exceeds the limit, the unit outputs a signal to switch the satellite communication link. The key logging data includes high-frequency logging data during the event triggering period, edge prediction results, and equipment operation status identifiers. The data reassembly unit is used to locally cache and identify the integrity of fragmented logging data received under complex terrain and unstable network conditions, construct a data fragment index queue, check and merge out-of-order received fragments through timestamp and sequence number fields, introduce a data integrity verification process, remove redundant and duplicate fragments and correct transmission errors.

5. The automatic updating system for well logging information in high-altitude and cold regions as described in claim 4, characterized in that, The data reassembly unit is also used to assign a unique timestamp and segment sequence number index to each cached data packet during the process of receiving the downhole logging status record set. The timestamp is used to record the arrival time of the data, and the segment sequence number is used to identify the position of the logging data in the original complete data. During the data reassembly process, a reassembly mapping queue is constructed according to the timestamp sorting and sequence number alignment rules to identify out-of-order, lost and duplicate segments.

6. The automatic updating system for well logging information in high-altitude and cold regions as described in claim 5, characterized in that, The intelligent fusion processing module includes a consistency verification unit and a missing completion unit: The consistency verification unit is used to compare and analyze the trends of downhole temperature logging data, downhole pressure logging data, conductivity logging data and environmental monitoring logging data of the corresponding time period at the wellhead, and to construct an index consistency mapping relationship. By detecting the deviation between the fluctuation amplitude of downhole temperature logging data, downhole pressure logging data, conductivity logging data and environmental monitoring logging data of the corresponding time period at the wellhead within a preset specific period and the fitted trend, potential anomalies or measurement drift behaviors are identified and the deviation behaviors are marked. The missing data completion unit is used to construct a multivariate time-series evolution path based on the logging data of the preceding and following time periods after identifying the discontinuity or missing segments of the logging data. It calculates the reasonable logging data trajectory of the missing interval by combining the slope of temperature change, the rate of pressure change, and the trend of conductivity change, and corrects the inference curve by using a sliding window method to generate a complete logging data sequence.

7. The automatic updating system for well logging information in high-altitude and cold regions as described in claim 6, characterized in that, The missing data completion unit constructs a time-series comparison model of well logging data before and after the missing segment, extracts the slope of temperature change, rate of pressure change, and trend of conductivity change, dynamically calculates the local change pattern using a sliding window method, and combines the change trajectory corresponding to similar geological structures and sensor behavior in the historical well logging sample library to determine the evolution direction and numerical estimation range of the current missing segment, and generates the completion result.

8. The automatic updating system for well logging information in high-altitude and cold regions as described in claim 7, characterized in that, The edge prediction control module includes an anomaly prediction unit and a response strategy unit: The anomaly prediction unit continuously receives and processes real-time data streams from downhole temperature logging data, downhole pressure logging data, and conductivity logging data. Based on multivariate trend analysis and anomaly identification algorithms, it detects abnormal fluctuation patterns in the real-time data streams and determines whether any abnormal events exist. These abnormal events include: If the pressure increase exceeds the preset pressure threshold, it is judged as a blowout risk; If the temperature drop lasts for a longer period than a preset time threshold, it is considered a risk of freezing. If the change in conductivity per unit time is greater than the preset change threshold, it is judged as a risk of freezing and blockage. Generate early warning results for abnormal events, the early warning results including early warning identifiers and time location information; The response strategy unit is used to automatically generate corresponding control strategies based on the early warning results and historical response rule base. The control strategies include adjusting the current sampling mode, changing the communication path, and activating the emergency power module to ensure the complete transmission of logging data, and issuing control strategy instructions to the equipment execution end.

9. The automatic updating system for well logging information in high-altitude and cold regions as described in claim 8, characterized in that, After receiving the early warning result from the edge prediction unit, the response strategy unit matches the real-time data stream with historical event response records through the historical response rule base, selects the corresponding response action combination, which includes sampling mode switching, communication path change, power consumption adjustment and fault reminder uploading, and synchronizes the selected response action combination to the local controller for execution.

10. The automatic updating system for well logging information in high-altitude and cold regions as described in claim 9, characterized in that, The remote update feedback module includes a task distribution unit and a feedback closed-loop unit: The task issuing unit is used to receive parameter setting tasks generated by the remote platform. The parameter setting tasks include sampling plan adjustment instructions, event triggering rule revision instructions, and communication link switching strategy instructions. The feedback closed-loop unit is used to generate a feedback data packet based on the system operation log, sampling completion status, logging data return success rate and prediction deviation magnitude. The feedback data packet includes the remaining power of the equipment, communication stability score, logging data coverage and model prediction error report. The remote platform adjusts the task content according to the feedback results and outputs the task content adjustment signal.

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