A dual-mode while-drilling azimuthal resistivity measurement method, device and related equipment

Abstract

This application provides a dual-mode drilling azimuth resistivity measurement method, apparatus, and related equipment, relating to the field of charging management technology. The technical solution provided in this application accurately locates key geological interfaces and abnormal areas by identifying gradient abrupt events in the resistivity curve through gradient feature identification. Data transmission modes are determined by collecting drilling condition parameters corresponding to gradient abrupt events, ensuring that the transmission strategy matches the actual drilling conditions. Based on drilling condition parameters, gradient abrupt events, and the resistivity curve, a transmission priority score for each data point is calculated, enabling a quantitative assessment of data importance and timeliness. The transmission priority score is compared with a preset percentage threshold to distinguish between first and second data, ensuring that important data is transmitted first while less important data is stored later. The first data is adaptively encoded before transmission, and the second data is stored hierarchically, balancing real-time performance and integrity requirements within limited transmission bandwidth.

Description

Technical Field

[0001] This application relates to the field of charging management technology, specifically to a dual-mode drilling azimuth resistivity measurement method, device, and related equipment. Background Technology

[0002] The azimuth resistivity measurement technology while drilling acquires formation resistivity data in real time at different azimuths around the drill bit using downhole measuring instruments. This data contains important geological information such as the location of formation interfaces and reservoir boundaries, providing a basis for geological steering decisions during the drilling process.

[0003] Currently, resistivity data transmission while drilling primarily utilizes mud pulse transmission, which has extremely limited bandwidth, ranging from a few bits to tens of bits per second. In contrast, downhole measurement instruments can acquire data at rates of hundreds of data sets per second, representing a speed difference of hundreds of times. Traditional methods employ fixed-interval sampling to select a portion of data for real-time transmission, while storing the remaining data downhole for later reading after drilling is completed. This fixed sampling strategy fails to differentiate the importance of data. During critical geological events such as encountering formation interfaces, resistivity data containing important geological features are often delayed in transmission because they are not selected, affecting the timeliness of geological guidance, while a large amount of less important data consumes valuable transmission bandwidth. Therefore, existing technologies struggle to simultaneously meet the requirements for both real-time performance and completeness of resistivity measurement data. Summary of the Invention

[0004] This application provides a dual-mode drilling azimuth resistivity measurement method, apparatus, and related equipment that can meet the requirements of both real-time performance and completeness of resistivity measurement data.

[0005] Firstly, this application provides a dual-mode drilling azimuth resistivity measurement method, the method comprising:

[0006] A multi-directional resistivity dataset is obtained by using a transmit and receive coil group, and spatial gradient features and temporal gradient features are extracted from the directional resistivity dataset.

[0007] Resistivity curves are constructed based on multi-directional resistivity datasets, and gradient abrupt events in the resistivity curves are determined based on spatial gradient characteristics and temporal gradient characteristics.

[0008] Collect drilling condition parameters corresponding to gradient mutation events, and determine the data transmission mode based on the drilling condition parameters. The data transmission mode has preset data transmission indicators and data storage indicators.

[0009] Based on drilling parameters, gradient mutation events, and resistivity curves, calculate the transmission priority score for each data point in the multi-directional resistivity dataset.

[0010] Each transmission priority score is compared with a preset percentage threshold corresponding to the data transmission mode to determine the first data corresponding to the data transmission index and the second data corresponding to the data storage index.

[0011] The first data is transmitted after adaptive encoding, and the second data is stored in layers.

[0012] By employing the above technical solution, the transmitting and receiving coil group acquires multi-directional resistivity datasets and extracts spatial and temporal gradient features, enabling comprehensive capture of the multi-dimensional variation patterns of formation information. Based on these gradient features, gradient abrupt events in the resistivity curve are identified, accurately locating key geological interfaces and anomalous areas. Data transmission patterns are determined by collecting drilling condition parameters corresponding to gradient abrupt events, ensuring the transmission strategy matches the actual drilling conditions. Based on drilling condition parameters, gradient abrupt events, and resistivity curves, transmission priority scores for each data point are calculated, achieving a quantitative assessment of data importance and timeliness. The transmission priority scores are compared with preset percentage thresholds to distinguish between first and second data, ensuring that important data is transmitted first while secondary data is stored later. The first data is adaptively encoded before transmission, and the second data is stored hierarchically, balancing real-time performance and integrity requirements within limited transmission bandwidth.

[0013] Secondly, this application provides a dual-mode drilling azimuth resistivity measurement device, the device comprising:

[0014] The data acquisition module is used to acquire multi-directional resistivity datasets through a group of transmitting and receiving coils, and to extract spatial gradient features and temporal gradient features from the directional resistivity datasets.

[0015] The event recognition module is used to construct resistivity curves based on multi-directional resistivity datasets and to determine gradient abrupt events in the resistivity curves based on spatial gradient features and temporal gradient features.

[0016] The mode determination module is used to collect drilling condition parameters corresponding to gradient mutation events and determine the data transmission mode based on the drilling condition parameters. The data transmission mode is preset with data transmission indicators and data storage indicators.

[0017] The priority calculation module is used to calculate the transmission priority score of each data in the multi-directional resistivity dataset based on drilling operating parameters, gradient mutation events, and resistivity curves.

[0018] The data classification module is used to compare each transmission priority score with a preset percentage threshold corresponding to the data transmission mode, and to determine the first data corresponding to the data transmission index and the second data corresponding to the data storage index.

[0019] The execution module is used to adaptively encode the first data before transmission and to store the second data in layers.

[0020] Thirdly, this application provides a computer storage medium that stores multiple instructions adapted for loading by a processor and executing any of the methods described above.

[0021] Fourthly, this application provides an electronic device including a processor, a memory, and a transceiver. The memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform any of the methods described above.

[0022] In summary, the beneficial effects of the technical solution of this application include:

[0023] By employing the above technical solution, the transmitting and receiving coil group acquires multi-directional resistivity datasets and extracts spatial and temporal gradient features, enabling comprehensive capture of the multi-dimensional variation patterns of formation information. Based on these gradient features, gradient abrupt events in the resistivity curve are identified, accurately locating key geological interfaces and anomalous areas. Data transmission patterns are determined by collecting drilling condition parameters corresponding to gradient abrupt events, ensuring the transmission strategy matches the actual drilling conditions. Based on drilling condition parameters, gradient abrupt events, and resistivity curves, transmission priority scores for each data point are calculated, achieving a quantitative assessment of data importance and timeliness. The transmission priority scores are compared with preset percentage thresholds to distinguish between first and second data, ensuring that important data is transmitted first while secondary data is stored later. The first data is adaptively encoded before transmission, and the second data is stored hierarchically, balancing real-time performance and integrity requirements within limited transmission bandwidth. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a dual-mode drilling azimuth resistivity measurement method according to an embodiment of this application.

[0025] Figure 2 This is a circuit diagram of a dual-mode drilling azimuth resistivity measurement device provided in an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the structure of a dual-mode drilling azimuth resistivity measurement device according to an embodiment of this application;

[0027] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0028] Explanation of reference numerals in the attached figures: 400, electronic device; 401, processor; 402, communication bus; 403, user interface; 404, network interface; 405, memory. Detailed Implementation

[0029] 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.

[0030] In the description of the embodiments of this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0031] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple devices refer to two or more devices, and multiple screen terminals refer to 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.

[0032] Please see Figure 1 This is a flowchart illustrating a dual-mode drilling azimuth resistivity measurement method provided in this application embodiment. This method can be implemented using a computer program, a microcontroller, or run on a dual-mode drilling azimuth resistivity measurement device based on the von Neumann architecture. The computer program can be integrated into the application or run as a standalone utility application. The specific steps of the dual-mode drilling azimuth resistivity measurement method are described in detail below.

[0033] S101: Obtain multi-directional resistivity dataset through the transmitting and receiving coil group, and extract spatial gradient features and temporal gradient features from the directional resistivity dataset;

[0034] The transmit-receive coil group refers to the antenna system used for resistivity measurement while drilling, comprising a transmitting coil and multiple receiving coils, capable of transmitting and receiving electromagnetic signals at different azimuth angles. The multi-azimuth resistivity dataset represents a collection of formation resistivity values ​​measured at multiple azimuth angles, reflecting the electrical distribution characteristics of the formation around the well. Spatial gradient features represent the rate of change of resistivity values ​​between adjacent measurement points along the drilling direction, reflecting the spatial distribution of formation interfaces and geological anomalies. Temporal gradient features refer to the rate of change of resistivity measurements at the same spatial location at different times over time, primarily reflecting dynamic changes such as formation exposure and mud invasion during drilling. Gradient feature extraction refers to the process of calculating and extracting mathematical characteristic parameters that characterize formation changes from the raw resistivity data.

[0035] Specifically, the transmitting and receiving coil array performs a 360-degree omnidirectional scan within a preset time interval, measuring the resistivity of the formation at different azimuth angles using the principle of electromagnetic induction. First, the transmitting coil generates an electromagnetic field of a specific frequency, inducing eddy currents in the formation. The intensity of these eddy currents is inversely proportional to the formation resistivity. The receiving coil detects the secondary electromagnetic field generated by the eddy currents and converts the received electromagnetic signal into resistivity values ​​using a signal processing algorithm. After acquiring multi-azimuth resistivity data, a numerical difference method is used to calculate the spatial gradient characteristics, i.e., calculating the resistivity difference between adjacent measurement points divided by the spatial distance; simultaneously, the resistivity measurements at each spatial location at different times are recorded, and a time series analysis method is used to calculate the temporal gradient characteristics, i.e., the rate of change of resistivity over time.

[0036] In some embodiments, the acquisition of multi-directional resistivity data and gradient feature extraction can be achieved in various ways: Optionally, a rotating coil group design is adopted, in which multiple receiving coils are installed on the logging tool at specific angular intervals, and omnidirectional scanning is achieved by rotating with the drill pipe. The measurement data of each coil at different rotation angles are recorded, and a continuous directional resistivity distribution is generated by angle interpolation algorithm. Then, the resistivity gradient of adjacent measurement points is calculated by finite difference method, and a data sequence with timestamp association is established to track the time change of the same location. This is not limited here.

[0037] S102: Construct resistivity curves based on multi-directional resistivity datasets, and determine gradient abrupt events in the resistivity curves based on spatial gradient characteristics and temporal gradient characteristics.

[0038] The resistivity curve is a continuous graph formed by arranging multi-directional resistivity data according to well depth or time series, used to visually display the changing trend of formation electrical parameters with drilling depth. Gradient abrupt change events represent data points or segments in the resistivity curve where the gradient value changes significantly, typically corresponding to important geological engineering information such as formation interfaces, structural anomalies, or changes in drilling techniques. Abrupt change event identification refers to the process of automatically detecting abnormal change patterns in resistivity data using mathematical algorithms. The spatial gradient threshold represents the critical value for judging whether a spatial gradient has abruptly changed, determined based on the statistical distribution of historical data. The temporal gradient threshold is the critical value for judging whether the temporal gradient is abnormal, reflecting the reasonable range of resistivity changes over time during normal drilling.

[0039] Specifically, firstly, multi-azimuth resistivity data are sorted according to well depth. Data interpolation and filtering techniques are used to construct smooth and continuous resistivity curves, ensuring that the curves accurately reflect the true changing trends of formation electrical properties. Next, gradient abrupt change events are identified. A statistical model of gradient distribution is established based on historical drilling data. Spatial and temporal gradient thresholds are determined using probabilistic statistical methods, typically set to the 95th or 99th quantile of the historical data distribution. Then, the real-time calculated spatial gradient features are compared point-by-point with the spatial gradient thresholds. When the absolute value of the spatial gradient at a data point exceeds the threshold, that point is marked as a spatial abrupt change candidate point. Similarly, temporal gradient features are compared with the temporal gradient thresholds to identify temporal abrupt change candidate points. Finally, the spatial and temporal abrupt change candidate points are merged and deduplicated to form the final gradient abrupt change event list. Each event records its location, gradient magnitude, abrupt change type, and other attribute information.

[0040] In some embodiments, resistivity curve construction and gradient abrupt event identification can be achieved in various ways: Optionally, an adaptive threshold dynamic adjustment strategy is adopted. First, the gradient distribution characteristics of the current data segment are statistically analyzed based on a sliding window, and the local gradient threshold is dynamically calculated to adapt to the changing characteristics of different strata. Then, a multi-scale wavelet analysis method is used to decompose the resistivity curve, detect abrupt signals at different scales, identify gradient abrupt events by the amplitude changes of wavelet coefficients, and finally verify and filter the identification results by combining geological prior knowledge. It is understood that other methods can also be used to identify gradient abrupt events, which are not limited here.

[0041] S103: Collect drilling condition parameters corresponding to gradient mutation events, and determine the data transmission mode based on the drilling condition parameters. The data transmission mode has preset data transmission indicators and data storage indicators.

[0042] Drilling operating parameters refer to various parameters reflecting the current drilling operation status and environmental conditions, including key indicators such as drilling depth, drilling speed, mud circulation pressure, bottom hole temperature, drill pipe rotation speed, uplink data transmission bandwidth, and signal transmission delay. Data transmission mode represents the data processing and transmission strategy determined based on current operating conditions, defining rules such as data transmission priority, encoding method, and transmission frequency. Data transmission indicators represent the selection criteria for data that needs immediate uplink transmission, typically corresponding to high-priority, time-sensitive critical data. Data storage indicators refer to the selection criteria for data temporarily stored in downhole equipment, awaiting subsequent transmission or retrieval after tripping out of the well; these typically include detailed supplementary data and low-priority data.

[0043] Specifically, the process begins by collecting various operational parameters from the drilling monitoring system, including drilling parameters from drilling control, circulation pressure and flow rate from mud circulation, downhole environmental parameters from logging tools, and transmission link status from the communication system. Next, the data transmission mode is determined. Based on the range of values ​​for the collected drilling parameters, the corresponding initial data transmission mode is retrieved from a pre-established mapping table. This table, built upon extensive historical drilling data and expert experience, covers optimal transmission strategies for different combinations of operational conditions. Then, actual data transmission capacity indicators are calculated based on parameters such as current transmission bandwidth, signal strength, and transmission delay. Current transmission performance is quantified using metrics such as bandwidth utilization, transmission success rate, and average transmission latency. Finally, the initial transmission mode is dynamically adjusted based on the calculated transmission capacity indicators. This includes adjusting the percentage thresholds corresponding to data transmission and data storage indicators to ensure the transmission strategy matches the current operational conditions, guaranteeing real-time transmission of critical data while avoiding waste of transmission resources.

[0044] In some embodiments, drilling condition parameter acquisition and transmission mode determination can be achieved in various ways: Optionally, a multi-source data fusion-based condition perception scheme can be adopted, deploying a distributed sensor network to monitor various parameters at the drilling site in real time. Condition information from different systems can be integrated using a data fusion algorithm to establish a time-series database of condition parameters for trend analysis and prediction. Then, a dynamic transmission mode selection strategy can be established based on the current condition and short-term future predictions. The mapping relationship between the transmission mode and the condition parameters can be continuously optimized using a reinforcement learning algorithm to achieve adaptive adjustment of the transmission strategy. It is understood that other methods can also be used to achieve condition perception and transmission mode determination, which are not limited here.

[0045] S104: Calculate the transmission priority score of each data point in the multi-directional resistivity dataset based on drilling condition parameters, gradient mutation events, and resistivity curves.

[0046] The transmission priority score is a numerical indicator that quantifies the importance and urgency of transmission of each resistivity data point. The higher the score, the more priority the data needs to be transmitted.

[0047] Specifically, the process begins by establishing a coupled response relationship between gradient mutation events and drilling operating parameters. By analyzing the information retention time of different types of mutation events under various operating conditions in historical drilling data, a statistical analysis method is used to establish a mapping function from operating parameters to information decay rate. This function typically employs an exponential decay model or a power function model to describe the decay of information value over time. Next, the transmission priority score is calculated. Based on the gradient magnitude corresponding to each data point in the resistivity curve, a normalization method is used to calculate the initial priority score for each data point. Data points with larger gradient magnitudes receive higher initial scores. Then, based on the current drilling operating parameters, the established mapping function is used to calculate the information decay rate corresponding to each gradient mutation event, and subsequently, a decay correction coefficient is calculated. This coefficient reflects the change in data value over time. Finally, the decay correction coefficient and the initial priority score are weighted to obtain the final transmission priority score, considering timeliness, ensuring that both the static importance of the data and dynamic timeliness requirements are considered.

[0048] In some embodiments, transmission priority scores can be calculated in various ways: Optionally, a multi-factor weighted evaluation scheme can be adopted to establish a comprehensive evaluation index system that includes multiple factors such as gradient magnitude, mutation type, geological significance, and operational impact. The weight coefficients of each factor can be determined using the analytic hierarchy process (AHP), and a multi-dimensional priority evaluation matrix can be established for comprehensive scoring. Simultaneously, the correlation and complementarity between data points can be considered, and the overall priority of related data groups can be adjusted. A dynamic weight adjustment mechanism can also be established to dynamically adjust the importance weights of each factor based on actual transmission performance and geological interpretation needs. It is understood that other methods can also be used to calculate transmission priority, and this is not limited here.

[0049] S105: Compare each transmission priority score with the preset percentage threshold corresponding to the data transmission mode, and determine the first data corresponding to the data transmission index and the second data corresponding to the data storage index respectively.

[0050] The preset percentage threshold refers to a predefined dividing point in the data transmission mode, used to classify all data according to transmission priority scores, usually expressed as a percentage, such as the top 30% being high-priority data. The first data represents resistivity data with a high transmission priority score that needs immediate uplink transmission, corresponding to data transmission requirements. The second data represents resistivity data with a relatively low transmission priority score that can be temporarily stored in downhole equipment, corresponding to data storage requirements.

[0051] Specifically, firstly, based on the currently selected data transmission mode, the corresponding preset percentage threshold parameters are obtained. These thresholds are typically determined comprehensively based on factors such as transmission bandwidth limitations, geological interpretation requirements, and data storage capacity. For example, the first 20% of the data is allocated for immediate transmission, the middle 50% for delayed transmission, and the last 30% for local storage. Next, data classification processing is performed, sorting all resistivity data in descending order of transmission priority score to form a priority ranking list. Then, based on the preset percentage thresholds, a dividing point is determined in the ranking list. Data with priority scores higher than the highest priority threshold is classified as the first data category; this data meets the data transmission requirements and needs to be immediately encoded and transmitted uplink. Data with priority scores lower than the highest priority threshold but still possessing some value is classified as the second data category; this data meets the data storage requirements and will be managed in a tiered manner in downhole equipment. Finally, a corresponding processing identifier and transmission queue are assigned to each data category, and data classification records are established for subsequent transmission scheduling and storage management.

[0052] S106: Transmit the first data after adaptive encoding, and store the second data in layers.

[0053] Adaptive coding refers to a technique that automatically selects the optimal coding algorithm based on data characteristics and transmission conditions, maximizing compression efficiency while ensuring data accuracy.

[0054] Specifically, the first data undergoes adaptive encoding. Based on parameters such as current transmission bandwidth, signal quality, and data characteristics, the optimal encoding scheme is automatically selected. For critical data containing abrupt changes, lossless compression algorithms are used to ensure data accuracy, while lossy compression algorithms are employed for data in stable regions to improve transmission efficiency. After encoding, the encoded data is encapsulated into transmission data packets, with necessary header information and checksums added. The data is then transmitted to ground receiving equipment via a mud pulse transmission system or electromagnetic transmission system, and a transmission confirmation and retransmission mechanism is established to ensure data transmission reliability. Simultaneously, the second data undergoes tiered storage management. Data is allocated to different storage levels based on its importance level, access frequency, and storage timeliness. High-importance data is stored in a high-speed cache for fast access, general data is stored in a standard storage area, and low-importance data is stored in a large-capacity storage area. Detailed index records are created for each stored data item, including data location, timestamp, importance level, and associated abrupt change events, facilitating subsequent data retrieval and management.

[0055] The following is combined Figure 2 The specific architecture of the embodiments in the book application will be explained and described. Figure 2This is a circuit diagram of a dual-mode drilling azimuth resistivity measurement device provided in an embodiment of this application.

[0056] The dual-mode downhole azimuth resistivity measurement device shown in the diagram mainly consists of core components such as a power module, main controller, storage system, transmitting system, receiving system, and communication module. The power module provides a stable power supply to the entire measurement device, ensuring reliable operation of each system in the harsh downhole environment. The main controller is the core control unit of the entire device, responsible for coordinating the work of each subsystem, executing measurement control algorithms, and handling data acquisition and transmission tasks. The storage system represents the storage device used for caching and long-term storage of resistivity measurement data, supporting hierarchical storage management. The transmitting system contains five transmitting induction coils T1-T5, used to transmit electromagnetic signals of different frequencies and powers to the formation. T1-T4 are normal cylindrical coils, and coil T5 is at a 45° angle to the main body. The receiving system consists of four receiving induction coils R1-R4, responsible for detecting the electromagnetic signals of the formation response. R1, R2, and R4 are normal cylindrical coils, and coil R3 is at a 45° angle to the main body. The communication module is used to realize data transmission between the downhole measurement device and the surface monitoring system, transmitting data to the surface via mud pulse sub.

[0057] Specifically, the measuring device employs a dual-mode operating mechanism. The ground system can flexibly configure the instrument's operating mode, allowing for either single-storage mode or real-time data transmission mode. In single-storage mode, all measurement data is stored in the storage system, suitable for detailed geological surveys and subsequent data analysis. In real-time data transmission mode, while storing all data, the device transmits some key data to the mud pulse sub via a communication circuit, and then transmits it to the ground system in real time, meeting the needs of real-time geological guidance. The main controller precisely controls the transmitting operation based on the ground system configuration parameters, exciting different transmitting induction coils at different time intervals and operating frequencies to ensure that multi-frequency, multi-azimuth electromagnetic field excitation can obtain rich formation response information. A combination of conventional cylindrical coils (T1-T4 and R1, R2, R4) is used to measure conventional induced resistivity, obtaining basic electrical parameters of the formation. An induction coil at an angle to the instrument (T5 and R3) is used to measure azimuth induced resistivity, detecting resistivity changes around the wellbore and providing crucial information for geological structure interpretation and wellbore trajectory optimization. The receiving system preprocesses the raw data collected by each receiving coil, including signal filtering, amplification, and digitization. The preprocessed data is then transmitted to the main controller for resistivity calculation. The main controller employs an advanced inversion algorithm to convert the received electromagnetic response signal into an accurate resistivity value and determines the data storage and transmission strategy based on the configuration mode.

[0058] Based on the above embodiments, as an optional implementation method, the method of obtaining multi-directional resistivity dataset by transmitting and receiving coil group in step S101 and extracting spatial gradient features and temporal gradient features from the directional resistivity dataset can be specifically implemented through the following steps S201-S203.

[0059] S201: Control the transmitting and receiving coil group to perform multi-directional scanning within a preset time interval, obtain resistivity measurement values ​​corresponding to multiple azimuth angles, and form a multi-directional resistivity dataset.

[0060] The preset time interval represents the pre-set scanning cycle, with the specific value determined based on drilling speed and measurement accuracy requirements. Multi-azimuth scanning refers to the process of the coil group performing electromagnetic measurements sequentially within a 360-degree range at fixed angular steps. The azimuth angle represents the angular offset of the measurement direction relative to the reference direction, typically calibrated in degrees. The resistivity measurement value represents the formation resistivity obtained through the principle of electromagnetic induction, reflecting the formation's electrical conductivity.

[0061] After receiving control commands, the transmitting and receiving coil assembly initiates a scanning program at preset time intervals. The transmitting coil generates an alternating electromagnetic field of a specific frequency, which penetrates the wellbore and enters the surrounding formation. The intensity of the eddy currents induced in the formation is inversely proportional to the formation resistivity, and these eddy currents generate a secondary electromagnetic field. The receiving coil detects the secondary electromagnetic field signal, and the signal processing circuit converts the received electromagnetic signal into a digital signal. The measurement system records the amplitude and phase information of the received signal corresponding to each azimuth angle, and converts the electromagnetic response parameters into resistivity values ​​through an inversion algorithm. The angle encoder records the rotation angle of the coil assembly in real time, ensuring that each resistivity measurement value is associated with accurate azimuth angle information. The data acquisition system organizes all resistivity measurements at all azimuth angles according to timestamps and spatial locations, forming a complete data record containing azimuth angle, resistivity value, measurement depth, and time information.

[0062] S202: Based on the resistivity difference between adjacent measurement points in the multi-directional resistivity dataset, calculate the spatial gradient along the drilling direction to obtain the spatial gradient characteristics;

[0063] Adjacent measurement points represent the two closest resistivity measurement locations along the drilling direction. The resistivity difference refers to the numerical difference between resistivity measurements at adjacent measurement points. The spatial gradient along the drilling direction represents the rate of change of resistivity along the well axis, reflecting the spatial distribution characteristics of formation electrical properties.

[0064] A sequence of resistivity measurement points arranged along the drilling direction is extracted from the multi-azimuth resistivity dataset. For each measurement depth, the resistivity difference between that location and the previous measurement location is calculated. The resistivity difference is divided by the spatial distance between the two measurement points to obtain the spatial gradient value at that location. The spatial distance is determined by the difference in drilling depth and corrected for by the inclination angle of the drilling trajectory. For the same depth location containing multiple azimuth measurements, the spatial gradient of each azimuth is calculated separately, and then a weighted average is performed to obtain the comprehensive spatial gradient value at that location. A sliding window method is used to smooth and filter the calculation results to eliminate the influence of measurement noise. The spatial gradient values ​​of all measurement locations are arranged in depth order to form a complete spatial gradient feature sequence, and the corresponding depth location, calculation time, and gradient magnitude information are recorded.

[0065] S203: Based on the resistivity measurements at the same spatial location at different times, calculate the rate of change of resistivity over time to obtain the time gradient characteristics.

[0066] Here, "same spatial location" refers to the location of a measurement point with the same well depth coordinates. Resistivity measurements at different times represent multiple measurements of that location over a time series. The rate of change of resistivity over time represents the magnitude of the resistivity change per unit time, reflecting dynamic processes such as formation exposure and mud invasion during drilling.

[0067] A time-series database indexed by spatial location is established to store resistivity measurements at different times for each location. For spatial locations with multiple measurement records, the resistivity values ​​are arranged in chronological order to form a time series for that location. The difference between resistivity measurements at adjacent times is calculated, and the difference is divided by the corresponding time interval to obtain the rate of change of resistivity over that time period. For locations with long time series, a linear regression method is used to fit the trend of resistivity change over time, and the slope of the regression line is the average temporal gradient value for that location. The influence of outlier measurements is eliminated, and a median filtering method is used to remove transient interference signals. The temporal gradient values ​​of all spatial locations with time series are summarized to form a temporal gradient feature dataset, which is associated with the corresponding spatial location, start and end times, gradient magnitude, and confidence level information.

[0068] Based on the above embodiments, as an optional implementation method, the method of determining the gradient abrupt event in the resistivity curve according to the spatial gradient characteristics and the temporal gradient characteristics in step S102 can be specifically implemented through the following steps S301-S304.

[0069] S301: Calculate the spatial gradient threshold of the spatial gradient feature and the temporal gradient threshold of the temporal gradient feature respectively. The gradient threshold is determined based on the statistical distribution of historical data.

[0070] The spatial gradient threshold is a critical value used to determine whether a significant abrupt change has occurred in the spatial gradient. When the measured spatial gradient exceeds this threshold, an abnormal spatial change is considered to have occurred. The temporal gradient threshold is a critical value used to determine whether a significant abrupt change has occurred in the temporal gradient, used to identify abnormal changes in resistivity over time. Historical data refers to resistivity measurement data and its corresponding gradient data accumulated from previous drilling operations, encompassing various formation conditions and drilling conditions. Statistical distribution is a mathematical expression describing the frequency of data occurrences across different value ranges. Common statistical distributions include normal distribution and log-normal distribution. Statistical distribution parameters can be used to determine the typical value range of the data and the criteria for identifying outliers.

[0071] In practice, the process begins by extracting a large amount of spatial and temporal gradient data under different geological conditions from historical databases. Statistical analysis is then performed on this historical gradient data to calculate statistical parameters such as the mean, standard deviation, and quantiles. For determining the spatial gradient threshold, the distribution characteristics of historical spatial gradient data are analyzed. Typically, spatial gradients exhibit a relatively stable distribution within normal formations, while significant gradient increases occur at formation interfaces. Spatial gradient values ​​corresponding to confirmed formation interface locations are extracted from historical data. The lower statistical limit of these gradient values ​​is used as a reference benchmark for the spatial gradient threshold. This is combined with the upper statistical limit of gradient values ​​in normal formation segments to ensure that the threshold effectively identifies true formation interfaces while avoiding misinterpreting normal formation fluctuations as abrupt events. For determining the temporal gradient threshold, the resistivity temporal variation patterns caused by various dynamic factors in historical data are analyzed, including the normal temporal gradient fluctuation range caused by changes in drilling fluid performance and wellbore stability, as well as the temporal gradient characteristics corresponding to abnormal events such as formation fluid intrusion. By comparing and analyzing the gradient differences between normal fluctuations and abnormal changes, a statistical quantile that can effectively distinguish between the two is selected as the time gradient threshold. The setting of this threshold needs to balance the identification sensitivity and anti-interference ability, so as to capture important dynamic changes while filtering out measurement noise and normal operating condition fluctuations.

[0072] S302: Compare the spatial gradient features with the corresponding spatial gradient threshold, and identify data points whose spatial gradient exceeds the spatial gradient threshold as candidate points for spatial abrupt changes.

[0073] Among them, spatial abrupt change candidate points refer to measurement data points whose spatial gradient values ​​exceed the spatial gradient threshold. These data points have the potential to become real stratigraphic interfaces or geological anomaly boundaries and need further verification and confirmation.

[0074] In practice, all calculated spatial gradient data points are traversed. For each data point, its corresponding spatial gradient magnitude is extracted and then compared with a spatial gradient threshold. When the spatial gradient magnitude of a data point is greater than the spatial gradient threshold, the data point is marked as a candidate for spatial abrupt change, and the well depth, azimuth information, and gradient magnitude of that point are recorded. In multi-azimuth scan data, different azimuths within the same well depth segment may exhibit different gradient responses. The spatial gradients of all azimuths are compared against thresholds. When the spatial gradient of at least one azimuth in a well depth segment exceeds the threshold, that well depth segment is marked as having a spatial abrupt change. To improve the reliability of identification, the distribution characteristics of the candidate spatial abrupt change points are further analyzed. If the gradients of multiple adjacent azimuths simultaneously exceed the threshold, it indicates the presence of a circumferentially continuous formation interface at that location, indicating high reliability. If only a few azimuths exceed the threshold, it may indicate a local geological anomaly or measurement interference. All identified spatial mutation candidate points are arranged in order of well depth to form a spatial mutation candidate point sequence. This sequence records all possible formation interface locations along the drilling direction, providing a basis for determining the spatial dimension for subsequent gradient mutation events.

[0075] S303: Compare the temporal gradient features with the corresponding temporal gradient thresholds to identify data points whose temporal gradients exceed the temporal gradient thresholds as candidate points for temporal abrupt changes.

[0076] Among them, time abrupt change candidate points refer to measurement data points whose time gradient values ​​exceed the time gradient threshold. The spatial locations corresponding to these data points have undergone significant resistivity changes in the time dimension, which may reflect changes in formation dynamics or measurement environment.

[0077] In practice, all calculated time gradient data points are traversed. For each data point, its corresponding time gradient amplitude is extracted, reflecting the rate of resistivity change over time. This time gradient amplitude is compared with a time gradient threshold. When the time gradient amplitude of a data point is greater than the threshold, the data point is marked as a candidate for time abrupt change. Since the time gradient reflects a dynamic process, it is necessary to distinguish between time gradient anomalies caused by different reasons. For the marked candidate time abrupt changes, the trend of their time gradient changes is further analyzed. A continuously increasing time gradient usually corresponds to a continuous intrusion process of formation fluids, while a pulse-like time gradient abrupt change corresponds to an instantaneous change in drilling conditions. The spatial location, occurrence time, gradient amplitude, and trend of each candidate time abrupt change are recorded, and the correlation between the candidate time abrupt change and the candidate spatial abrupt change is analyzed. When both spatial gradient abrupt changes and time gradient abrupt changes occur at a certain location, it indicates that there is both a formation interface and a dynamic fluid exchange process at that location. This composite abrupt change is of great significance for reservoir drilling encounters and oil-water interface identification. All candidate points of temporal mutation are sorted according to their occurrence time to form a sequence of candidate points of temporal mutation. This sequence provides a time dimension basis for determining subsequent gradient mutation events.

[0078] S304: Identify spatial or temporal abrupt change candidate points as gradient abrupt change events in the resistivity curve.

[0079] In practice, a unified index for spatial and temporal abrupt change candidate points is first established. For any spatial abrupt change candidate point, it is directly identified as a gradient abrupt change event, as spatial gradient abrupt changes directly reflect significant changes in formation resistivity along the drilling direction. These changes typically correspond to actual formation interfaces or lithological boundaries. Similarly, temporal abrupt change candidate points are also identified as gradient abrupt change events, as temporal gradient abrupt changes reflect the dynamic changes in formation resistivity, which are valuable for identifying formation fluid activity and assessing reservoir characteristics. When merging spatial and temporal abrupt change candidate points, the overlap in spatial location is checked. If a spatial location is simultaneously marked as both a spatial and temporal abrupt change candidate point, it is marked as a composite gradient abrupt change event and assigned a higher importance level. For candidate points with only a single-dimensional abrupt change characteristic, their reliability is evaluated based on their gradient amplitude and duration. Candidate points with larger gradient amplitudes and longer durations are assigned higher reliability scores. All confirmed gradient mutation events are sorted according to the well depth where they occurred to form a gradient mutation event sequence. Each event record contains detailed information such as event type, spatial location, time of occurrence, gradient magnitude, and azimuth range involved. This event sequence provides key geological interpretation basis for subsequent data transmission priority calculation.

[0080] Based on the above embodiments, as an optional implementation method, the method of determining the data transmission mode according to the drilling condition parameters in step S103 can be specifically implemented through the following steps S401-S403.

[0081] S401: Based on the range of drilling condition parameters, find the initial data transmission mode corresponding to the drilling condition parameters from the mapping table;

[0082] The operating condition parameter range is a series of intervals divided according to the numerical values ​​of drilling operating conditions. Each interval corresponds to a specific drilling operation state and data transmission environment. The initial data transmission mode is a baseline transmission scheme obtained from the mapping table based on the current drilling operating conditions. This mode specifies the basic strategy and parameter settings for data transmission.

[0083] Specifically, the current drilling parameters are first acquired, including real-time measurements of multiple parameters such as drilling speed, drilling fluid displacement, and downhole temperature. Then, the acquired drilling parameters are compared with preset parameter ranges in a mapping table to determine the interval to which the current parameter belongs. The mapping table stores multiple sets of correspondences between parameter ranges and data transmission modes, each pre-defined based on transmission channel quality, data generation rate, and transmission bandwidth limitations under specific operating conditions. During the search process, the current drilling speed is compared with the speed range threshold in the mapping table to determine the speed's interval. Simultaneously, other parameters such as drilling fluid displacement and downhole temperature undergo the same interval determination. Once the intervals for all operating parameters are determined, the initial data transmission mode corresponding to these interval combinations is located in the mapping table. This initial data transmission mode includes basic transmission parameters such as data compression level, transmission frequency, and data packet size, providing a baseline scheme for subsequent dynamic adjustments based on actual transmission capacity.

[0084] S402: Calculate the current data transmission capacity index based on drilling operating parameters. The data transmission capacity index represents the amount of data that can be transmitted per unit time.

[0085] Among them, the data transmission capability index is a comprehensive parameter that quantitatively describes the transmission performance of downhole data transmission under the current operating conditions. This index reflects the actual available bandwidth and data throughput capacity of the transmission channel.

[0086] Specifically, data transmission capability indicators are calculated by analyzing the impact of current drilling operating parameters on the data transmission channel. First, the signal propagation speed and attenuation characteristics under drilling fluid pulse transmission mode are determined based on drilling fluid displacement parameters. A larger drilling fluid displacement results in faster pulse signal propagation speed but also more severe attenuation. Then, the signal transmission distance is calculated based on well depth parameters, and the energy loss and delay time during signal transmission are calculated in conjunction with the propagation characteristics of the drilling fluid. Next, the working efficiency of the transmission equipment is evaluated based on downhole temperature and pressure parameters. High temperature and high pressure environments reduce the processing speed and signal transmission power of electronic equipment. After obtaining the above influencing factors, the shortest time interval required for a single data transmission is calculated. This time interval is obtained by summing the signal transmission time, signal propagation delay, and signal reception confirmation time. Dividing the unit time by the time interval required for a single transmission yields the number of transmissions that can be completed per unit time. Multiplying this by the data packet size for a single transmission yields the amount of data that can be transmitted per unit time. This data amount serves as a data transmission capability indicator, directly reflecting the actual transmission bandwidth under current operating conditions and providing a quantitative basis for subsequent adjustments to the data transmission mode.

[0087] S403: Adjust the initial data transmission mode according to the data transmission capability indicators to obtain the data transmission mode.

[0088] Specifically, the data transmission capability index is first compared with the preset standard transmission capability of the initial data transmission mode, and the difference ratio between the two is calculated. When the actual transmission capability is lower than the preset standard, the data transmission requirement needs to be reduced to adapt to the limited transmission bandwidth. Specific adjustment methods include reducing the data sampling frequency, increasing the data compression level, and increasing the transmission priority threshold. Reducing the data sampling frequency reduces the amount of data generated per unit time by extending the time interval between two adjacent measurements; increasing the data compression level reduces the data volume by using algorithms with higher compression ratios; and increasing the transmission priority threshold reduces the amount of data that needs to be transmitted immediately by increasing the data screening criteria for real-time transmission. When the actual transmission capability is higher than the preset standard, data quality and transmission integrity can be improved. Specific adjustment methods include increasing the data sampling frequency to obtain more detailed formation information, reducing the data compression level to retain more original data details, and decreasing the transmission priority threshold to increase the range of data transmitted in real time. During the adjustment process, it is necessary to maintain coordination among various parameters to ensure that the adjusted data generation rate matches the data transmission capability, ultimately forming a data transmission mode adapted to the current drilling conditions.

[0089] Based on the above embodiments, as an optional implementation method, the method of calculating the transmission priority score of each data in the multi-directional resistivity dataset according to drilling operating parameters, gradient mutation events and resistivity curves in S104 can be implemented through the following steps S501-S502.

[0090] S501: Establish the coupling response relationship between gradient mutation events and drilling condition parameters. The coupling response relationship characterizes the information decay rate of the same gradient mutation event under different drilling conditions.

[0091] The coupling response relationship is a functional relationship describing the interaction between the characteristics of gradient mutation events and drilling condition parameters. This relationship quantifies the degree of influence of different drilling conditions on the information expression of gradient mutation events. The information decay rate refers to the rate at which the geological information contained in the gradient mutation event gradually weakens over time or as the drilling conditions change. A higher decay rate indicates stronger information timeliness and a need for faster transmission.

[0092] Specifically, a coupled response relationship is established by analyzing the information retention characteristics of gradient abrupt events in historical drilling data under different drilling conditions. First, identified gradient abrupt events and their corresponding drilling condition parameters, including drilling speed, drilling fluid flow rate, and downhole temperature, are extracted from historical data. Then, the information value changes of each gradient abrupt event over different time periods after its occurrence are analyzed. The information value is measured by the degree of impact of the event on subsequent drilling decisions. For fast drilling conditions, the drill bit quickly crosses the formation interface, and the formation location information corresponding to the gradient abrupt event loses its guiding significance in a short time, resulting in a high information decay rate. For slow drilling conditions, the drill bit stays near the formation interface for a longer period, and the information of the gradient abrupt event remains valuable for a longer time, resulting in a low information decay rate. Simultaneously, the impact of drilling fluid flow rate on information decay is analyzed. Under high flow rate conditions, severe wellbore erosion and rapid changes in formation resistivity measurements due to drilling fluid intrusion easily mask the characteristics of gradient abrupt events, leading to a high information decay rate. Based on the statistical data of information attenuation rate under different combinations of operating parameters, a mapping function from operating parameters to attenuation rate is established. This function constitutes a mathematical expression of the coupling response relationship, which is used to calculate the transmission priority of each gradient mutation event under a specific operating condition.

[0093] Based on the above embodiments, as an optional implementation method, the method of establishing the coupling response relationship between gradient mutation events and drilling operating parameters in S501 can be specifically implemented through the following steps S5011-S5013.

[0094] S5011: Information retention time of different types of historical gradient mutation events in statistical historical data under different drilling conditions;

[0095] Historical gradient mutation events refer to recorded abnormal changes in resistivity gradients extracted from historical drilling operation data. These events include two main types: spatial gradient mutation events and temporal gradient mutation events. Information retention time refers to the duration for which the geological information contained in a gradient mutation event remains effective in guiding drilling decisions; beyond this duration, the information value significantly decreases.

[0096] Specifically, historical gradient mutation events are first classified according to mutation type: spatial gradient mutation events are grouped into one category, temporal gradient mutation events into another, and events possessing both characteristics are separately classified as composite mutation events. Then, each category of mutation events is further grouped according to the numerical range of key operating parameters such as drilling rate, drilling fluid discharge, and downhole temperature, with each group representing a typical drilling operating condition combination. For each historical gradient mutation event within each group of operating conditions, the information retention time is determined by analyzing the time point at which geologists make drilling parameter adjustment decisions after the event occurs. Specifically, the time difference between the occurrence time of the gradient mutation event and the time of the last drilling parameter adjustment based on the event information is found; this time difference is the information retention time of the event under that specific operating condition. Statistical analysis is performed on the information retention times of multiple mutation events of the same type under the same operating condition combination, and the average value is calculated as the typical information retention time for that type of mutation event under that operating condition. After completing the statistical analysis of the information retention times of all types of mutation events under all operating condition combinations, a complete mapping dataset of mutation event types, operating parameter combinations, and information retention times is formed.

[0097] S5012: Calculate the corresponding information decay rate based on the information retention time, and establish a mapping function from drilling operating parameters to the information decay rate. The information decay rate is inversely proportional to the information retention time.

[0098] Specifically, for each combination of operating parameters and the corresponding information retention time for each type of abrupt event, the information decay rate is calculated by taking its reciprocal. A shorter retention time results in a larger reciprocal value and a higher decay rate, while a longer retention time results in a smaller reciprocal value and a lower decay rate. After calculating the information decay rate for all operating condition combinations, multiple sets of data points corresponding to the operating parameters and the information decay rate are obtained. Then, multiple operating parameters such as drilling speed, drilling fluid discharge, and downhole temperature are used as independent variables, and the information decay rate is used as the dependent variable. A continuous function relationship between the operating parameters and the information decay rate is fitted using multiple regression analysis. During the fitting process, an appropriate function form is selected to accurately reflect the influence trend and weight of each operating parameter on the information decay rate. The fitted function is validated using partially retained historical data as a test set to verify the degree of agreement between the predicted information decay rate and the actual statistical value, ensuring sufficient accuracy and generalization ability. After successful validation, the fitted function serves as the mapping function from drilling operating parameters to the information decay rate.

[0099] S5013: Based on drilling operating parameters, use a mapping function to calculate the information decay rate of gradient mutation events and establish a coupled response relationship.

[0100] Specifically, the process begins by acquiring real-time measurements of various parameters under the current drilling conditions, including drilling speed, drilling fluid flow rate, downhole temperature, and downhole pressure. Then, it identifies whether the detected gradient mutation event is a spatial gradient mutation, a temporal gradient mutation, or a composite mutation, thus determining its event type. The current operating condition parameter values ​​and the event type are used as inputs and substituted into a mapping function for calculation. The mapping function outputs the corresponding information decay rate value based on the combination of input operating condition parameters. This information decay rate quantitatively describes how quickly the information value of the current gradient mutation event decreases over time under the existing drilling conditions. This calculation process is repeated for all detected gradient mutation events to obtain a unique information decay rate for each event under the current operating conditions. A correlation is established between the gradient mutation event and its corresponding information decay rate, forming a coupled response relationship between the gradient mutation event and the drilling condition parameters. This coupled response relationship comprehensively describes the differences in the timeliness of information from different gradient mutation events under different drilling conditions.

[0101] S502: Based on the coupling response relationship and resistivity curve, calculate the transmission priority score corresponding to each data point in the multi-directional resistivity dataset.

[0102] Specifically, the process begins by traversing all data points in the multi-azimuth resistivity dataset. For each data point, it is determined whether it corresponds to a gradient abrupt change event. For data points belonging to gradient abrupt change events, the information decay rate corresponding to the current drilling parameters is queried from the coupling response relationship. Gradient abrupt change events with higher information decay rates are assigned higher base priority scores. Then, priority is adjusted based on the gradient amplitude of the data point in the resistivity curve. A larger gradient amplitude indicates a clearer formation interface or a more significant geological anomaly, thus increasing the priority score of the data point accordingly. For composite data points involving both spatial and temporal gradient abrupt change events, the priority increments corresponding to both types of abrupt changes are superimposed, giving the composite abrupt change data a higher transmission priority. For ordinary data points not belonging to gradient abrupt change events, base priority scores are assigned based on their position and numerical characteristics in the resistivity curve. Data points in the stable resistivity segment are assigned lower priority scores, while those in the slowly changing resistivity segment are assigned medium priority scores. The freshness of the data is also considered during the allocation process, with newly acquired data having higher priority than historical cached data. Finally, a corresponding transmission priority score is generated for each data point in the dataset, and these scores serve as the basis for data transmission scheduling.

[0103] Based on the above embodiments, as an optional implementation method, the method of calculating the transmission priority score corresponding to each data in the multi-directional resistivity dataset based on the coupling response relationship and resistivity curve in S502 can be specifically implemented through the following steps S5021-S5023.

[0104] S5021: Calculate the initial priority score for each data point based on the gradient magnitude corresponding to each data point in the resistivity curve.

[0105] The gradient magnitude refers to the intensity of the resistivity change at a specific data point in the resistivity curve. This value is obtained by calculating the resistivity difference between the data point and its adjacent data points. The initial priority score is a basic priority value calculated solely based on the gradient characteristics of the data point itself. This score reflects the importance of the data point in the resistivity curve.

[0106] Specifically, for each data point, the resistivity measurement value of that point and its adjacent data points are first obtained. The absolute values ​​of the resistivity differences between this data point and the previous data point and the next data point are calculated, and the larger of the two absolute values ​​is taken as the gradient magnitude of that data point. Then, a conversion rule is established from gradient magnitude to initial priority score. The larger the gradient magnitude, the more drastic the resistivity change at that location, and the more important the corresponding geological information, thus assigning a higher initial priority score. In the specific conversion process, the gradient magnitude is divided into multiple numerical intervals, each corresponding to a basic priority level. Data points whose gradient magnitude falls in higher intervals receive a higher basic priority level. Above the basic level, further adjustments are made based on the specific location of the gradient magnitude within its interval. The closer the gradient magnitude is to the upper limit of the interval, the more priority score is added to the basic level. For data points identified as gradient abrupt events, additional priority bonuses are added on top of the above calculations. Spatial gradient abrupt events receive a first-level bonus, temporal gradient abrupt events receive a second-level bonus, and composite gradient abrupt events receive both levels of bonus. After all data points have been calculated, each data point receives its corresponding initial priority score.

[0107] S5022: Calculate the attenuation correction coefficient based on the information attenuation rate in the coupling response relationship;

[0108] The attenuation correction coefficient is an adjustment parameter calculated based on the information attenuation rate. This coefficient is used to quantify the impact of time factors on data transmission priority.

[0109] Specifically, firstly, the timestamp of each gradient mutation event and the current timestamp are obtained, and the time difference between the two timestamps is calculated. This time difference represents the elapsed time of the gradient mutation event. Then, this time difference is combined with the corresponding information decay rate for calculation. The product of the information decay rate and the elapsed time reflects the cumulative decay of the information value of the gradient mutation event. A decay correction coefficient is calculated based on the cumulative decay rate. The larger the cumulative decay rate, the greater the loss of information value, and the larger the corresponding decay correction coefficient. This coefficient is used to subsequently reduce the transmission priority of the data. For newly occurring gradient mutation events, the elapsed time is close to zero. Even if the information decay rate is high, the cumulative decay rate is still small, the decay correction coefficient is small, and a high transmission priority is maintained. For gradient mutation events that occurred earlier, the elapsed time is larger, the cumulative decay rate increases significantly, the decay correction coefficient increases, and its transmission priority is reduced. A mapping relationship between the cumulative decay rate and the decay correction coefficient is established. This mapping relationship ensures that the decay correction coefficient can accurately reflect the impact of time factors on information value. For all data points in the resistivity curve that belong to gradient mutation events, their respective decay correction coefficients are calculated based on the information decay rate and the elapsed time of their corresponding gradient mutation events.

[0110] S5023: The initial priority score is dynamically adjusted according to the attenuation correction coefficient to obtain the time-adjusted transmission priority score.

[0111] Specifically, for data points belonging to gradient abrupt events, the initial priority score and corresponding attenuation correction coefficient are extracted. Priority is reduced by subtracting the product of the attenuation correction coefficient and the adjustment intensity parameter from the initial priority score. The adjustment intensity parameter controls the impact of time decay on priority. This subtraction ensures that the transmission priority of data points gradually decreases over time and with information decay, reflecting the impact of information timeliness. For ordinary data points not belonging to gradient abrupt events, their initial priority score remains unchanged or is only slightly adjusted based on the data acquisition time. Newly acquired data receives a time freshness bonus on top of the initial priority. During the adjustment process, upper and lower limits are set for the priority score to ensure that the adjusted score remains within a reasonable range, avoiding negative values ​​or exceeding the maximum priority range. After dynamic adjustment of all data points, each data point receives a time-adjusted transmission priority score, which comprehensively reflects the geological importance and time urgency of the data point. The adjusted transmission priority scores are stored in the attribute fields of the data points. The subsequent data transmission scheduling module sorts the data to be transmitted according to these priority scores. Data with higher priority scores is transmitted first, while data with lower priority scores is transmitted later or temporarily stored in the downhole buffer, thus realizing intelligent transmission scheduling based on data value and timeliness.

[0112] The following are embodiments of the apparatus of this application, which can be used to execute the embodiments of the method of this application. For details not disclosed in the embodiments of the apparatus of this application, please refer to the embodiments of the method of this application.

[0113] Please see Figure 3 This illustration shows a schematic diagram of a dual-mode drilling azimuth resistivity measurement device provided in an exemplary embodiment of this application. This device can be implemented as all or part of a whole through software, hardware, or a combination of both. The dual-mode drilling azimuth resistivity measurement device includes:

[0114] The data acquisition module is used to acquire multi-directional resistivity datasets through a group of transmitting and receiving coils, and to extract spatial gradient features and temporal gradient features from the directional resistivity datasets.

[0115] The event recognition module is used to construct resistivity curves based on multi-directional resistivity datasets and to determine gradient abrupt events in the resistivity curves based on spatial gradient features and temporal gradient features.

[0116] The mode determination module is used to collect drilling condition parameters corresponding to gradient mutation events and determine the data transmission mode based on the drilling condition parameters. The data transmission mode is preset with data transmission indicators and data storage indicators.

[0117] The priority calculation module is used to calculate the transmission priority score of each data in the multi-directional resistivity dataset based on drilling operating parameters, gradient mutation events, and resistivity curves.

[0118] The data classification module is used to compare each transmission priority score with a preset percentage threshold corresponding to the data transmission mode, and to determine the first data corresponding to the data transmission index and the second data corresponding to the data storage index.

[0119] The execution module is used to adaptively encode the first data before transmission and to store the second data in layers.

[0120] This application also provides a computer storage medium that can store multiple instructions. The instructions are adapted to be loaded by a processor and executed as described in the above embodiments of the dual-mode drilling azimuth resistivity measurement method. For the specific execution process, please refer to the detailed description of the embodiments, which will not be repeated here.

[0121] Please see Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 400 may include: at least one processor 401, at least one network interface 404, a user interface 403, a memory 405, and at least one communication bus 402.

[0122] The communication bus 402 is used to enable communication between these components.

[0123] The user interface 403 may include a display screen and a camera.

[0124] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0125] The processor 401 may include one or more processing cores. The processor 401 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 405, and by calling data stored in memory 405. Optionally, the processor 401 may be implemented using at least one hardware form of digital signal processing, field-programmable gate array, or programmable logic array. The processor 401 may integrate one or more of the following: a central processing unit (CPU), a graphics processing unit (GPU), and a 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 401.

[0126] The memory 405 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 405 may include a non-transitory computer-readable medium. The memory 405 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 405 may also be at least one storage device located remotely from the aforementioned processor 401. Figure 4 As shown, the memory 405, 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 dual-mode drilling azimuth resistivity measurement method.

[0127] exist Figure 4In the electronic device 400 shown, the user interface 403 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 401 can be used to call an application program stored in the memory 405 for a dual-mode drilling azimuth resistivity measurement method. When executed by one or more processors, the electronic device performs one or more methods as described in the above embodiments.

[0128] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more methods as described in the above embodiments.

[0129] It should be noted that, for the sake of simplicity, the foregoing method embodiments 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, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0130] 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.

[0131] In the several 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 apparatus, 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.

[0132] 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, depending on actual needs.

[0133] 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.

[0134] 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.

[0135] 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. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and practical application disclosed herein. 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.

Claims

1. A dual-mode drilling azimuth resistivity measurement method, characterized in that, The method includes: A multi-directional resistivity dataset is obtained by using a transmitting and receiving coil group, and the spatial gradient features and temporal gradient features in the directional resistivity dataset are extracted. Resistivity curves are constructed based on the multi-directional resistivity dataset, and gradient abrupt events in the resistivity curves are determined based on the spatial gradient features and the temporal gradient features. The drilling condition parameters corresponding to the gradient mutation event are collected, and the data transmission mode is determined based on the drilling condition parameters. The data transmission mode is preset with data transmission indicators and data storage indicators. Based on the drilling condition parameters, the gradient mutation event, and the resistivity curve, calculate the transmission priority score corresponding to each data point in the multi-directional resistivity dataset. The step of calculating the transmission priority score corresponding to each data point in the multi-directional resistivity dataset based on the drilling condition parameters, the gradient abrupt event, and the resistivity curve includes: Establish a coupling response relationship between the gradient mutation event and the drilling condition parameters, wherein the coupling response relationship characterizes the information attenuation rate of the same gradient mutation event under different drilling conditions; based on the coupling response relationship and the resistivity curve, calculate the transmission priority score corresponding to each data in the multi-directional resistivity dataset; The process of establishing the coupled response relationship between the gradient mutation event and the drilling condition parameters includes: The information retention time of different types of historical gradient mutation events in historical data under different drilling conditions is statistically analyzed; the corresponding information decay rate is calculated based on the information retention time, and a mapping function from the drilling condition parameters to the information decay rate is established, wherein the information decay rate is inversely proportional to the information retention time; based on the drilling condition parameters, the information decay rate of the gradient mutation event is calculated using the mapping function, and a coupled response relationship is established. The calculation of the transmission priority score corresponding to each data point in the multi-directional resistivity dataset based on the coupling response relationship and the resistivity curve includes: Based on the gradient magnitude corresponding to each data point in the resistivity curve, calculate the initial priority score corresponding to each data point; calculate the attenuation correction coefficient based on the information attenuation rate in the coupling response relationship; dynamically adjust the initial priority score based on the attenuation correction coefficient to obtain the time-adjusted transmission priority score. Each of the transmission priority scores is compared with a preset percentage threshold corresponding to the data transmission mode to determine the first data corresponding to the data transmission index and the second data corresponding to the data storage index. The first data is transmitted after adaptive encoding, and the second data is stored in layers.

2. The method according to claim 1, characterized in that, The process of acquiring a multi-azimuth resistivity dataset through a transmitting and receiving coil group, and extracting spatial and temporal gradient features from the azimuth resistivity dataset, includes: The transmitting and receiving coil group is controlled to perform multi-directional scanning within a preset time interval to obtain resistivity measurement values ​​corresponding to multiple azimuth angles and form a multi-directional resistivity dataset. Based on the resistivity difference between adjacent measurement points in the multi-directional resistivity dataset, the spatial gradient along the drilling direction is calculated to obtain the spatial gradient characteristics. The resistivity change rate over time is calculated based on resistivity measurements at the same spatial location at different times, thus obtaining the time gradient characteristic.

3. The method according to claim 1, characterized in that, The step of determining gradient abrupt events in the resistivity curve based on the spatial gradient features and the temporal gradient features includes: The spatial gradient threshold of the spatial gradient feature and the temporal gradient threshold of the temporal gradient feature are calculated respectively, and the gradient thresholds are determined based on the statistical distribution of historical data; The spatial gradient features are compared with the corresponding spatial gradient thresholds to identify data points whose spatial gradients exceed the spatial gradient thresholds, which are then used as candidate points for spatial abrupt changes. The time gradient features are compared with the corresponding time gradient thresholds to identify data points whose time gradients exceed the time gradient thresholds, which are then used as candidate points for time abrupt changes. The spatial mutation candidate point or the temporal mutation candidate point is determined as the gradient mutation event in the resistivity curve.

4. The method according to claim 1, characterized in that, The step of determining the data transmission mode based on the drilling condition parameters includes: Based on the range of operating parameters in which the drilling operating parameters are located, the initial data transmission mode corresponding to the drilling operating parameters is found from the mapping table; The current data transmission capability index is calculated based on the drilling condition parameters, and the data transmission capability index represents the amount of data that can be transmitted per unit time. The initial data transmission mode is adjusted according to the data transmission capability index to obtain the data transmission mode.

5. A dual-mode drilling azimuth resistivity measurement device, characterized in that, The device includes: The data acquisition module is used to acquire a multi-directional resistivity dataset through a transmitting and receiving coil group, and to extract the spatial gradient features and temporal gradient features from the directional resistivity dataset. An event recognition module is used to construct a resistivity curve based on the multi-directional resistivity dataset, and to determine gradient abrupt events in the resistivity curve based on the spatial gradient features and the temporal gradient features. The mode determination module is used to collect drilling condition parameters corresponding to the gradient mutation event and determine the data transmission mode based on the drilling condition parameters. The data transmission mode is preset with data transmission indicators and data storage indicators. The priority calculation module is used to calculate the transmission priority score corresponding to each data point in the multi-directional resistivity dataset based on the drilling condition parameters, the gradient mutation event, and the resistivity curve. The calculation of the transmission priority score based on the drilling condition parameters, the gradient mutation event, and the resistivity curve includes: establishing a coupling response relationship between the gradient mutation event and the drilling condition parameters, wherein the coupling response relationship characterizes the information attenuation rate of the same gradient mutation event under different drilling conditions; calculating the transmission priority score corresponding to each data point in the multi-directional resistivity dataset based on the coupling response relationship and the resistivity curve; establishing the coupling response relationship between the gradient mutation event and the drilling condition parameters includes: statistically analyzing different types of historical gradient mutation events in historical data. Information retention time under different drilling conditions; information decay rate calculated based on the information retention time, and a mapping function established from the drilling condition parameters to the information decay rate, wherein the information decay rate is inversely proportional to the information retention time; information decay rate of gradient mutation event calculated using the mapping function based on the drilling condition parameters, and coupling response relationship established; the step of calculating the transmission priority score corresponding to each data point in the multi-directional resistivity dataset based on the coupling response relationship and the resistivity curve includes: calculating the initial priority score corresponding to each data point based on the gradient magnitude corresponding to each data point in the resistivity curve; calculating the decay correction coefficient based on the information decay rate in the coupling response relationship; dynamically adjusting the initial priority score based on the decay correction coefficient to obtain the time-adjusted transmission priority score; The data classification module is used to compare each of the transmission priority scores with a preset percentage threshold corresponding to the data transmission mode, and to determine the first data corresponding to the data transmission index and the second data corresponding to the data storage index. An execution module is used to adaptively encode the first data and then transmit it, and to store the second data in layers.

6. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions, which are adapted to be loaded by a processor and executed as described in any one of claims 1 to 4.

7. An electronic device, characterized in that, The device includes a processor, a memory, and a transceiver. The memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 4.

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