Oilfield downhole data transmission method and system

Abstract

The application provides an oil field downhole data transmission method and system, which is suitable for the field of data processing technology. The method comprises the following steps: obtaining current oil field downhole channel change information according to oil field downhole depth information, current oil field borehole working condition monitoring information, current oil field downhole environment information, current oil field downhole link operation information, historical oil field borehole working condition monitoring information, historical oil field downhole environment information and historical oil field downhole link operation information; and generating oil field downhole data transmission parameter information according to the current oil field downhole channel change information, current oil field downhole data to be transmitted, an oil field downhole data transmission parameter optimization model and an oil field downhole data transmission parameter set, so as to perform oil field downhole data transmission processing. The application accurately adapts to the complex and changeable working conditions, environment and channel state of the oil field downhole, improves the accuracy, efficiency and stability of the oil field downhole data transmission, and meets the downhole data transmission requirements of deep and complex structure wells.

Description

Technical Field

[0001] This application belongs to the field of data processing technology, and in particular relates to methods and systems for transmitting data from oilfield downholes. Background Technology

[0002] As oil and gas extraction expands to deeper and more complex well structures, downhole data transmission in oilfields, as the core link connecting downhole monitoring units and surface control platforms, has evolved from traditional low-speed telemetry to high-speed, bidirectional, and intelligent technologies.

[0003] In existing technologies, downhole operating parameters such as pressure, temperature, and flow rate, as well as environmental parameters, are typically collected via pre-installed cables or wireless sub-junctions. The data is then transmitted to the surface via the drill string or fluid medium. The surface system receives the data, analyzes it, and stores it, thus achieving basic monitoring of the downhole conditions in the oilfield.

[0004] However, in existing technologies, signal attenuation and interference are severe under complex well conditions, resulting in high data transmission packet loss rate, low accuracy, low bandwidth utilization, and large transmission delay. Summary of the Invention

[0005] In view of this, the embodiments of this application provide a method and system for data transmission in oilfield wells, which aims to solve the problems of poor time-varying adaptability of well channels, weak signal anti-interference ability in complex environments, and insufficient bandwidth utilization in the prior art.

[0006] The first aspect of this application provides a method for transmitting data from downhole oilfields, including: Acquire oilfield downhole depth information, current oilfield wellbore condition monitoring information, current oilfield downhole environment information, current oilfield downhole link operation information, current oilfield downhole data to be transmitted, historical oilfield wellbore condition monitoring information, historical oilfield downhole environment information, historical oilfield downhole link operation information, and oilfield downhole data transmission parameter optimization model; Based on the oilfield well depth information, current oilfield wellbore condition monitoring information, current oilfield well environment information, current oilfield well link operation information, historical oilfield wellbore condition monitoring information, historical oilfield well environment information, and historical oilfield well link operation information, we obtain oilfield well condition change information, oilfield well environment change information, and oilfield well link operation change information. Based on the information on changes in oilfield downhole operating conditions, changes in oilfield downhole environment, and changes in oilfield downhole link operation, the current oilfield downhole channel change information is obtained. Based on the current oilfield downhole channel change information, the current oilfield downhole data to be transmitted, the oilfield downhole data transmission parameter optimization model, and the preset oilfield downhole data transmission parameter set, multiple oilfield downhole data transmission parameter information is generated to perform oilfield downhole data transmission processing through the multiple oilfield downhole data transmission parameter information.

[0007] A second aspect of this application provides an oilfield downhole data transmission system, comprising: The information acquisition module is used to acquire oilfield downhole depth information, current oilfield wellbore condition monitoring information, current oilfield downhole environment information, current oilfield downhole link operation information, current oilfield downhole data to be transmitted, historical oilfield wellbore condition monitoring information, historical oilfield downhole environment information, historical oilfield downhole link operation information, and oilfield downhole data transmission parameter optimization model. The oilfield downhole change information generation module is used to obtain oilfield downhole condition change information, oilfield downhole environment change information, and oilfield downhole link operation change information based on the oilfield downhole depth information, current oilfield wellbore condition monitoring information, current oilfield downhole environment information, current oilfield downhole link operation information, historical oilfield wellbore condition monitoring information, historical oilfield downhole environment information, and historical oilfield downhole link operation information. The current oilfield downhole channel change information generation module is used to obtain the current oilfield downhole channel change information based on the oilfield downhole working condition change information, oilfield downhole environment change information, and oilfield downhole link operation change information. The oilfield downhole data transmission parameter information generation module is used to generate multiple oilfield downhole data transmission parameter information based on the current oilfield downhole channel change information, the current oilfield downhole data to be transmitted, the oilfield downhole data transmission parameter optimization model, and the preset oilfield downhole data transmission parameter set, so as to perform oilfield downhole data transmission processing through the multiple oilfield downhole data transmission parameter information.

[0008] A third aspect of this application provides a terminal device, which includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the oilfield downhole data transmission method described in the first aspect above.

[0009] A fourth aspect of this application provides a computer-readable storage medium, comprising: storing a computer program, wherein when executed by a processor, the computer program implements the steps of the oilfield downhole data transmission method described in the first aspect above.

[0010] Compared with the prior art, the beneficial effects of the embodiments of this application are as follows: This application accurately adapts to the complex and ever-changing working conditions, environment and channel status of oilfield downholes, realizes dynamic optimization of transmission parameters, generates transmission parameter combinations adapted to different scenarios, so that oilfield downhole data transmission can adapt to the time-varying characteristics of downhole channels, thereby improving the accuracy, efficiency and stability of oilfield downhole data transmission, solving the problems of fixed transmission parameters and poor channel adaptability in the prior art, and meeting the downhole data transmission needs of deep and complex structure wells. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram illustrating the implementation process of the oilfield downhole data transmission method provided in Embodiment 1 of this application; Figure 2 This is a schematic diagram illustrating the implementation process of the oilfield downhole data transmission method provided in Embodiment 2 of this application; Figure 3 This is a schematic diagram illustrating the implementation process of the oilfield downhole data transmission method provided in Embodiment 3 of this application; Figure 4 This is a schematic diagram illustrating the implementation process of the oilfield downhole data transmission method provided in Embodiment 4 of this application; Figure 5 This is a schematic diagram illustrating the implementation process of the oilfield downhole data transmission method provided in Embodiment 5 of this application; Figure 6 This is a schematic diagram illustrating the implementation process of the oilfield downhole data transmission method provided in Embodiment Six of this application; Figure 7 This is a schematic diagram illustrating the implementation process of the oilfield downhole data transmission method provided in Embodiment 7 of this application; Figure 8 This is a schematic diagram of the structure of the oilfield downhole data transmission system provided in the embodiments of this application; Figure 9 This is a schematic diagram of the terminal device provided in the embodiments of this application. Detailed Implementation

[0013] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0014] To illustrate the technical solution described in this application, specific embodiments are provided below.

[0015] Figure 1 A flowchart illustrating the implementation of the oilfield downhole data transmission method provided in Embodiment 1 of this application is shown, and detailed below: Step S101: Obtain oilfield downhole depth information, current oilfield wellbore condition monitoring information, current oilfield downhole environment information, current oilfield downhole link operation information, current oilfield downhole data to be transmitted, historical oilfield wellbore condition monitoring information, historical oilfield downhole environment information, historical oilfield downhole link operation information, and oilfield downhole data transmission parameter optimization model.

[0016] In this embodiment, the oilfield downhole depth information refers to the vertical distance from the downhole monitoring point to the surface wellhead. This information is used to distinguish the channel transmission characteristics at different depths, such as the signal attenuation difference between deep downhole (depth greater than 3000 meters) and shallow downhole (depth less than 1000 meters). This information can be acquired in real time by a downhole depth sensor. Current oilfield wellbore operating condition monitoring information may include current wellbore diameter shrinkage deformation information, current wellbore pressure fluctuation information, and current wellbore rock stability information. Specifically, the current wellbore diameter shrinkage deformation information can range from 0-50 mm, the current wellbore pressure fluctuation information can range from 10-50 MPa, and the current wellbore rock stability information can range from 0-1.0 (the closer the value is to 1, the stronger the stability). This information can be acquired in real time by a downhole wellbore monitoring sensor array. The current downhole environmental information of the oilfield can include the current downhole temperature field distribution information, the current downhole fluid composition information, and the current downhole corrosive medium concentration information. The specific values ​​of the current downhole temperature field distribution information can be 25-150℃, the specific values ​​of the current downhole fluid composition information can be crude oil content of 80%-98% and water content of 2%-20%, and the specific values ​​of the current downhole corrosive medium concentration information can be 0-500ppm. These values ​​can be collected synchronously through the downhole environment monitoring module. The current downhole link operation information of the oilfield can include the current downhole wireless link signal-to-noise ratio information, the current downhole wired communication link loss information, and the current downhole relay node working status information. The specific values ​​of the current downhole wireless link signal-to-noise ratio information can be -10dB to 20dB, the specific values ​​of the current downhole wired communication link loss information can be 0-30dB, and the specific values ​​of the current downhole relay node working status information can be normal (1) or abnormal (0). These values ​​can be captured in real time by the downhole link monitoring unit. Currently, the data to be transmitted from downhole wells in the oilfield refers to the raw and preprocessed data collected by various downhole monitoring devices, including operating condition data and environmental data. The specific data volume can range from 100KB to 10MB per transmission, and can be aggregated and obtained through downhole data acquisition terminals. Historical oilfield wellbore operating condition monitoring information refers to data related to wellbore diameter reduction deformation, wellbore pressure fluctuations, and wellbore rock stability recorded over a past period (e.g., 3 months). This data is used to compare and analyze the changing patterns of operating conditions. The system can continuously record and update the previous day's data daily to generate historical records. Historical oilfield downhole environmental information refers to data related to downhole temperature field distribution, downhole fluid composition, and downhole corrosive medium concentration recorded over a past period (e.g., 3 months). This data is used to analyze environmental change trends. The system can continuously record and dynamically update historical records. Historical oilfield downhole link operation information can refer to data related to the signal-to-noise ratio of downhole wireless links, downhole wired communication link loss, and working status of downhole relay nodes recorded within a past period (such as 3 months). This information is used to explore link operation patterns and can be continuously recorded and updated by the system to generate historical records.The oilfield downhole data transmission parameter optimization model can adopt a Transformer+LSTM hybrid deep learning model to dynamically optimize transmission parameters based on various downhole state information. This can be achieved by training a downhole channel state-transmission parameter dataset, combining a CNN pre-trained model (mapping transmission parameter features) and an attention mechanism. Temporal feature extraction and feature fusion are introduced during the training process to achieve accurate optimization of transmission parameters.

[0017] In this embodiment, oilfield downhole depth information can be acquired through downhole depth and pressure gauges, fiber optic depth sensors, magnetostrictive liquid level and depth instruments, and downhole drilling depth measurement instruments. Current oilfield wellbore condition monitoring information can be obtained through downhole pressure transmitters, oilfield downhole explosion-proof pressure instruments, downhole caliper logging tools, downhole deformation monitoring instruments, formation stress sensors, and dedicated instruments for wellbore integrity monitoring. Current oilfield downhole environmental information can be obtained through downhole explosion-proof temperature sensors, multi-point distributed downhole temperature measurement instruments, and oilfield downhole oil and gas component analyzers. The downhole fluid composition detection instrument, crude oil water content online monitoring instrument, downhole corrosion rate monitoring instrument, and medium ion concentration detection instrument measure the current oilfield downhole link operation information. The current oilfield downhole link operation information can be obtained by downhole communication signal tester, wireless link signal-to-noise ratio analyzer, wired link loss tester, and downhole relay node status monitoring terminal. The current oilfield downhole data to be transmitted is collected by downhole pressure instrument, downhole temperature instrument, downhole flow instrument, downhole liquid level instrument, intrinsically safe explosion-proof wired-to-wireless acquisition terminal and downhole data aggregation acquisition instrument.

[0018] Step S102: Based on the oilfield downhole depth information, current oilfield wellbore condition monitoring information, current oilfield downhole environment information, current oilfield downhole link operation information, historical oilfield wellbore condition monitoring information, historical oilfield downhole environment information, and historical oilfield downhole link operation information, obtain oilfield downhole condition change information, oilfield downhole environment change information, and oilfield downhole link operation change information.

[0019] In this embodiment, the downhole depth information of the oilfield can first be normalized to generate a depth feature vector, which serves as the core identifier for distinguishing the transmission environment of different downhole areas. Then, by combining the current oilfield wellbore condition monitoring information with historical oilfield wellbore condition monitoring information, the difference calculation and trend analysis are used to compare the current and historical data on wellbore diameter shrinkage deformation, wellbore pressure fluctuation, and wellbore rock stability. For example, if the current wellbore diameter shrinkage deformation increases by 10 mm compared to the historical period and the wellbore pressure fluctuation increases by 5 MPa compared to the historical period, it is determined that there is a significant change in the operating conditions. Then, the downhole operating condition change information of the oilfield is generated. This information can specifically include the change range (0-100%) and the change trend (increasing / decreasing / stable). Then, by combining current and historical downhole environmental information, and comparing and analyzing data on downhole temperature field distribution, fluid composition, and corrosive medium concentration, for example, if the current downhole temperature is 20°C higher than the historical average or the corrosive medium concentration is 100 ppm higher, then an environmental change is determined, generating downhole environmental change information. This information can specifically include the magnitude of the environmental change (0-100%) and the type of change (gradual / abrupt). Next, by combining current and historical downhole link operation information, and comparing data on downhole wireless link signal-to-noise ratio, wired communication link loss, and downhole relay node operating status, for example, if the current wireless link signal-to-noise ratio is 5 dB lower than the historical average or the link loss is 8 dB higher, then a change in link operation status is determined, generating downhole link operation change information. This information can specifically include the magnitude of the link change (0-100%) and the degree of impact (slight / moderate / severe). Among them, the relevant thresholds used for difference calculation and trend analysis can be preset by humans. The specific values ​​can be 10% for the change in working conditions, 15% for the change in environment, and 20% for the change in links. If the corresponding threshold is exceeded, it is determined that there is a significant change.

[0020] Step S103: Based on the oilfield downhole operating condition change information, oilfield downhole environment change information, and oilfield downhole link operation change information, obtain the current oilfield downhole channel change information.

[0021] In this embodiment, it is understood that changes in oilfield downhole operating conditions and environment directly affect the transmission characteristics of the downhole channel, while changes in link operation directly reflect the real-time status of the channel. Based on a pre-defined channel feature mapping matrix, information on changes in oilfield downhole operating conditions, changes in the oilfield downhole environment, and changes in oilfield downhole link operation can be collaboratively matched. This channel feature mapping matrix can be a 3×3 matrix with elements ranging from 0 to 1, used to characterize the correlation between the three types of change information and channel characteristics. For example, if the oilfield downhole operating condition change information is "wellbore rock stability decreases by 20% (moderate change)," the oilfield downhole environment change information is "downhole temperature increases by 15°C (gradual change)," and the oilfield downhole link operation change information is "wireless link signal-to-noise ratio decreases by 8dB (moderate impact)," then by matching the mapping matrix, it can be determined that channel attenuation increases and anti-interference capability decreases. Then, by combining the channel change patterns corresponding to similar historical combinations of changes, the current mapping result is dynamically calibrated. For example, historical data shows that "moderate change in operating conditions + gradual change in environment + moderate impact on the link" corresponds to a 12dB increase in channel attenuation. This historical pattern is then used to optimize the current judgment result, thereby generating current downhole channel change information for the oilfield. This information can specifically include core components such as channel attenuation level (0-50dB), channel interference intensity (0-100%), and channel transmission rate fluctuation (0-50%), providing a precise basis for subsequent transmission parameter optimization. The historical pattern matching threshold used for dynamic calibration can be preset, specifically set to a similarity threshold of 80%. That is, when the similarity between the current combination of changes and a certain historical combination reaches 80% or more, the channel change pattern of that historical combination is referenced.

[0022] Step S104: Based on the current oilfield downhole channel change information, the current oilfield downhole data to be transmitted, the oilfield downhole data transmission parameter optimization model, and the preset oilfield downhole data transmission parameter set, generate multiple oilfield downhole data transmission parameter information, so as to perform oilfield downhole data transmission processing through the multiple oilfield downhole data transmission parameter information.

[0023] In this embodiment, the preset set of oilfield downhole data transmission parameters can be manually preset. Specifically, it can include the value range and basic configuration of various transmission parameters such as transmission rate (100kbps-10Mbps), signal modulation method (QPSK, 8PSK, 16QAM), channel multiplexing type (time division multiplexing, frequency division multiplexing), relay transmission power (10-50dBm), and communication frequency band (2.4GHz, 5GHz, Sub-6GHz). These parameters can be set in advance based on the transmission requirements of different downhole scenarios. The oilfield downhole data transmission parameter optimization model can adopt a Transformer+LSTM hybrid deep learning model to optimize transmission parameters according to the current channel state and the characteristics of the data to be transmitted. First, the current downhole channel change information in the oilfield can be encoded to extract core features such as channel attenuation, interference intensity, and transmission rate fluctuations, generating a current downhole channel state feature vector. Then, feature analysis can be performed on the current downhole data to be transmitted, determining data priority (high / medium / low) and data type (operating data / environmental data). For example, operating data is given high priority, and environmental data is given medium priority. The current downhole channel state feature vector and the features of the data to be transmitted are then input into the oilfield downhole data transmission parameter optimization model. Based on a pre-defined downhole transmission parameter feature extraction rule, specifically "prioritizing the extraction of core features such as transmission rate, modulation method, and relay power, with weights of 40%, 30%, and 30% respectively," spectral feature information of the downhole transmission parameters is generated according to the pre-defined set of oilfield downhole data transmission parameters. Subsequently, by leveraging the model's self-attention mechanism, the correlation between channel state features and transmission parameter features is explored. The model is then trained and optimized in real-time to obtain a trained optimized model for oilfield downhole data transmission parameters. Based on the current downhole channel state feature vector, the features of the data to be transmitted, and the trained model, multiple oilfield downhole data transmission parameter information adapted to the current scenario are generated. For example, for high-priority data, a parameter combination of "transmission rate 5Mbps, modulation scheme 16QAM, relay transmit power 30dBm, communication frequency band 5GHz" is generated; for medium-priority data, a parameter combination of "transmission rate 2Mbps, modulation scheme 8PSK, relay transmit power 20dBm, communication frequency band 2.4GHz" is generated. This allows for differentiated data transmission through multiple parameter combinations, improving transmission accuracy and efficiency. The threshold used for data priority determination can be preset manually, with specific values ​​such as high-priority data volume ≥60%, medium-priority data volume 30%-60%, and low-priority data volume <30%.

[0024] In this embodiment, preferably, to address the problems of inconsistent interfaces, varying protocols, and chaotic wireless network standards among various downhole instruments in oilfields, resulting in fragmented real-time data acquisition and a lack of inherently secure, independently controllable, stable, and reliable wireless data acquisition solutions, this application can adopt the open-source HarmonyOS software bus architecture to provide a unified data transmission architecture for a unified protocol. The wired-to-wireless conversion module for downhole instruments can be based on the open-source HarmonyOS L0 system, and the oilfield downhole data acquisition gateway module can adopt the L2 standard open-source HarmonyOS system. It is understood that this gateway module has the ability to stably access multiple StarShutter terminals and supports cascading expansion, making it adaptable to downhole applications. The system addresses the need for large-scale networking of multiple instruments. Simultaneously, it utilizes StarFlash technology to provide wireless transmission services. Understandably, compared to commonly used downhole Bluetooth and WiFi wireless communication technologies, StarFlash is better suited to extreme downhole oilfield scenarios. Its SLE mode consumes only 60% of Bluetooth's power, while increasing receiver sensitivity by approximately 7dB at the same power consumption, far superior to the high power consumption characteristics of WiFi. This significantly extends the battery life of downhole battery-powered instruments. The SLB mode's one-way air interface latency can be as low as 20 microseconds, and the SLE mode latency can be controlled to within 20ms, far lower than Bluetooth's typical latency of 100 to 200ms and WiFi's typical latency of over 50ms. This meets the needs of real-time monitoring of production parameters and rapid early warning of anomalies in oilfield downhole operations. Furthermore, the StarSignal technology incorporates 5G polar code technology and a centralized scheduling architecture, achieving a transmission reliability of up to 99.999%. Its anti-interference capability is 7dB stronger than Bluetooth and WiFi, effectively resisting strong electromagnetic interference generated by downhole motors, frequency converters, and other equipment, avoiding data packet loss and mistransmission. It can support up to 4096 devices simultaneously in a network, more than 10 times the connection capability of Bluetooth. It also supports Mesh relay technology to solve the problems of signal attenuation and blind spots in downhole depths of thousands of meters and in confined spaces. It can combine the independent and controllable characteristics of the open-source HarmonyOS system with the transmission advantages of StarSignal technology, along with an oilfield downhole data transmission parameter optimization model, to achieve safe, stable, and efficient transmission of downhole data. At the same time, through the wired-to-wireless conversion module, downhole instruments that originally used wired methods such as HART, RS485, and AI with non-standard protocols can be converted into wireless instrument interfaces with unified protocols, thereby solving the problem of fragmented data acquisition, ensuring the inherent security of hardware and the independent controllability of system software, and forming a complete wireless data acquisition and transmission solution adapted to oilfield downhole scenarios.

[0025] The oilfield downhole data transmission method provided in this application accurately adapts to the complex and ever-changing working conditions, environment, and channel status of oilfield downholes, realizes dynamic optimization of transmission parameters, and generates transmission parameter combinations adapted to different scenarios. This enables oilfield downhole data transmission to adapt to the time-varying characteristics of downhole channels, thereby improving the accuracy, efficiency, and stability of oilfield downhole data transmission. This solves the problems of fixed transmission parameters and poor channel adaptability in the prior art, and meets the downhole data transmission needs of deep and complex structure wells.

[0026] Figure 2 The flowchart illustrating the implementation of the oilfield downhole data transmission method provided in Embodiment 2 of this application is shown. The difference between this method and Embodiment 1 is that step S104 specifically includes: Step S201: Based on the preset oilfield downhole data transmission parameter feature extraction rules, generate multiple oilfield downhole data transmission parameter feature information according to the preset oilfield downhole data transmission parameter set.

[0027] In this embodiment, the preset feature extraction rules for oilfield downhole data transmission parameters can be manually preset. Specifically, it can refer to a standardized process for processing oilfield downhole data transmission parameters through orthogonal frequency division multiplexing, spectrum analysis, and feature filtering. Specific values ​​can be "using 1024-point orthogonal frequency division multiplexing transformation, 512th-order spectrum analysis, and a 30dB feature filtering threshold." The preset set of oilfield downhole data transmission parameters can also be manually preset. Specifically, it can include the range and basic configuration of various transmission parameters such as transmission rate (100kbps-10Mbps), signal modulation method (QPSK, 8PSK, 16QAM), channel multiplexing type (time division multiplexing, frequency division multiplexing), relay transmission power (10-50dBm), and communication frequency band (2.4GHz, 5GHz, Sub-6GHz). These can be pre-set based on the transmission requirements of different downhole scenarios. Multiple sets of transmission parameter configurations can be selected from a preset set of oilfield downhole data transmission parameters. Then, orthogonal frequency division multiplexing transformation is performed on each set of transmission parameters to obtain time-frequency distribution characteristics. Then, core features such as the spectral peak and bandwidth occupancy of the transmission parameters are extracted through spectrum analysis. Finally, feature filtering is used to remove redundant interference features and generate the oilfield downhole data transmission parameter feature information corresponding to each set of transmission parameters, which serves as one of the input features for subsequent oilfield downhole data transmission parameter optimization model training.

[0028] Step S202: Perform feature encoding processing on the current oilfield downhole channel change information to generate current oilfield downhole channel change feature information.

[0029] In this embodiment, the current downhole channel change information in the oilfield includes core information such as channel attenuation (0-50dB), channel interference intensity (0-100%), and channel transmission rate fluctuation (0-50%). The feature encoding process is as follows: First, the real-time acquired channel attenuation, channel interference intensity, and channel transmission rate fluctuation data are converted into structured values, such as channel attenuation of 20dB, channel interference intensity of 30%, and channel transmission rate fluctuation of 15%. Then, these values ​​are input into the CNN pre-trained model to generate corresponding feature description data, such as "moderate channel attenuation, weak interference, and small transmission rate fluctuation". Subsequently, the feature description data can be input into the Transformer encoder in the downhole data transmission parameter optimization model of the oilfield. After processing through the self-attention mechanism, the context vector is extracted as the current downhole channel change feature information of the oilfield. This feature vector can map the correlation between the channel change state and the transmission parameter features, providing channel-dimensional input for the training of the downhole data transmission parameter optimization model of the oilfield. Among them, the oilfield downhole data transmission parameter optimization model can adopt the Transformer+LSTM hybrid deep learning model. It can be achieved by training the downhole channel state-transmission parameter dataset, combining the CNN pre-trained model (mapping transmission parameter features) and attention mechanism. Temporal feature extraction and feature fusion are introduced during the training process to achieve accurate optimization of transmission parameters.

[0030] Step S203: Based on the current oilfield downhole channel change characteristic information and multiple oilfield downhole data transmission parameter characteristic information, the oilfield downhole data transmission parameter optimization model is trained to generate a trained oilfield downhole data transmission parameter optimization model.

[0031] In this embodiment, the oilfield downhole data transmission parameter optimization model can employ a Transformer+LSTM hybrid deep learning model. The training process involves first using the encoder in a CNN pre-trained model to map multiple generated oilfield downhole data transmission parameter feature information to a low-dimensional potential transmission parameter feature vector. Then, the low-dimensional potential transmission parameter feature vector and the current oilfield downhole channel change feature information are input into the Transformer+LSTM hybrid deep learning model. During model training, a temporal feature extraction mechanism is introduced to capture the dynamic correlation between channel changes and transmission parameters. Then, an attention mechanism is used to strengthen the weights of key features such as channel attenuation and interference intensity. The error between the predicted transmission parameters and the actual optimal transmission parameters is calculated. The model parameters are updated through backpropagation, and the training process is repeated until the error converges, resulting in the trained oilfield downhole data transmission parameter optimization model. The threshold used to determine error convergence can be preset, specifically set to an error value ≤ 0.05. When the model training error reaches this threshold, the model training is considered complete.

[0032] Step S204: Based on the multiple oilfield downhole data transmission parameter feature information, the current oilfield downhole data to be transmitted, and the trained oilfield downhole data transmission parameter optimization model, generate multiple oilfield downhole data transmission parameter information, so as to perform oilfield downhole data transmission processing through the multiple oilfield downhole data transmission parameter information.

[0033] In this embodiment, the current downhole data to be transmitted in the oilfield can be first analyzed to determine the data priority (high / medium / low) and data type (operating condition data / environmental data). The threshold used for determining the data priority can be preset by humans. Specifically, the value can be that the proportion of high priority data is ≥60%, the proportion of medium priority data is 30%-60%, and the proportion of low priority data is <30%. For example, operating condition data is high priority, environmental data is medium priority, and auxiliary monitoring data is low priority. Then, the characteristic information of multiple oilfield downhole data transmission parameters and the characteristic information of the current oilfield downhole data to be transmitted are input into the trained oilfield downhole data transmission parameter optimization model. The model is guided by the priority and type of the current oilfield downhole data to be transmitted, and combines the characteristic information of multiple oilfield downhole data transmission parameters to explore the combination rules of transmission parameters that are suitable for different data types and priorities. In this way, multiple oilfield downhole data transmission parameter information adapted to the current scenario is generated. For example, for high-priority working condition data, the parameter combination of "transmission rate 5Mbps, modulation mode 16QAM, relay transmission power 30dBm, communication frequency band 5GHz" is generated; for medium-priority environmental data, the parameter combination of "transmission rate 2Mbps, modulation mode 8PSK, relay transmission power 20dBm, communication frequency band 2.4GHz" is generated; and for low-priority auxiliary monitoring data, the parameter combination of "transmission rate 100kbps, modulation mode QPSK, relay transmission power 10dBm, communication frequency band Sub-6GHz" is generated. In this way, differentiated data transmission is achieved through multiple different parameter combinations, thereby improving the accuracy, efficiency and stability of oilfield downhole data transmission.

[0034] The oilfield downhole data transmission method provided in this application enables the oilfield downhole data transmission parameter optimization model to accurately learn the correlation between channel changes and transmission parameters. This allows the generated multiple oilfield downhole data transmission parameter information to fit the current downhole channel state and the data to be transmitted, thereby improving the targeting and reliability of oilfield downhole data transmission and adapting to the dynamic transmission scenarios of deep and complex well structures. This solves the problems of poor transmission parameter adaptability and low transmission efficiency in the prior art.

[0035] Figure 3 The flowchart illustrating the implementation of the oilfield downhole data transmission method provided in Embodiment 3 of this application is shown. The difference between this method and Embodiment 2 is that step S203 specifically includes: Step S301: Based on the current oilfield downhole channel change feature information, a reference vector is extracted to obtain multiple current oilfield downhole channel change feature reference vector information.

[0036] In this embodiment, the current oilfield downhole channel change feature information is a feature vector generated by the Transformer encoder in the oilfield downhole data transmission parameter optimization model. The oilfield downhole data transmission parameter optimization model can adopt a Transformer+LSTM hybrid deep learning model, which can be implemented by training a downhole channel state-transmission parameter dataset, combined with a CNN pre-trained model (mapping transmission parameter features) and an attention mechanism. Multiple reference vectors of different dimensions can be separated from the current oilfield downhole channel change feature information. Each reference vector corresponds to a core feature of channel change, such as channel attenuation degree, channel interference intensity, and channel transmission rate fluctuation. Then, the feature vector of each dimension is normalized to remove redundant interference, generating multiple current oilfield downhole channel change feature reference vectors. The specific dimension of each reference vector can be 128, used to accurately represent the change features of different dimensions of the channel.

[0037] Step S302: The multiple current oilfield downhole channel change feature reference vector information and the preset current oilfield downhole channel change feature reference vector map are merged to obtain multiple current oilfield downhole channel change feature reference vector merged information.

[0038] In this embodiment, the preset current oilfield downhole channel change feature benchmark vector map can be manually preset. Specifically, it can refer to the set of channel change feature benchmark vectors corresponding to different downhole scenarios (shallow wells, deep wells, and complex structure wells) in the database. Specifically, it can contain 1000 sets of benchmark vectors for different scenarios, covering the full range of scenarios with channel attenuation of 0-50dB, channel interference intensity of 0-100%, and channel transmission rate fluctuation of 0-50%. Multiple current oilfield downhole channel change feature benchmark vectors can be concatenated row-wise with the benchmark vectors of the same scenario in the preset current oilfield downhole channel change feature benchmark vector map. Feature fusion processing is then performed on the concatenated vectors to remove duplicate features and enhance the representational ability of core features, thereby generating multiple current oilfield downhole channel change feature benchmark vector merged information. Each merged information corresponds to a complete fusion vector of channel change features and scenario reference features.

[0039] Step S303: Based on the multiple current oilfield downhole channel change feature benchmark vector merging information, the preset oilfield downhole channel change feature query vector, the preset oilfield downhole channel change feature key vector, and the preset oilfield downhole channel change feature value vector, obtain the weight information of multiple current oilfield downhole channel change feature vectors.

[0040] In this embodiment, the preset oilfield downhole channel change feature query vector, the preset oilfield downhole channel change feature key vector, and the preset oilfield downhole channel change feature value vector can be preset manually. They can all adopt the learnable parameter vector in the self-attention mechanism. Specifically, they can all be 128-dimensional vectors with vector element values ​​ranging from 0 to 1. Multiple current oilfield downhole channel change feature baseline vectors can be merged and multiplied by preset oilfield downhole channel change feature query vectors, preset oilfield downhole channel change feature key vectors, and preset oilfield downhole channel change feature value vectors to obtain query vectors, key vectors, and value vectors. Then, the dot product of the query vector and the key vector is calculated, and this dot product is subjected to dimensionality reduction processing to avoid gradient vanishing, resulting in an attention score matrix, which is the correlation information of the current oilfield downhole channel change feature vectors. The correlation information is then normalized to obtain the weight information of multiple current oilfield downhole channel change feature vectors. This weight information is used to measure the importance of different channel change feature dimensions. For example, the weight of channel attenuation is 0.4, the weight of channel interference intensity is 0.3, and the weight of channel transmission rate fluctuation is 0.3.

[0041] Step S304: Generate multiple current oilfield downhole channel change feature vector information based on the weight information of the multiple current oilfield downhole channel change feature vectors and the multiple current oilfield downhole channel change feature reference vector information.

[0042] In this embodiment, multiple current oilfield downhole channel change feature vector weights and multiple current oilfield downhole data transmission parameter feature information can be weighted and summed. During the weighted summation, the weight of each channel change feature dimension corresponds to the corresponding feature dimension of the transmission parameter. For example, the channel attenuation weight corresponds to the relay transmission power feature in the transmission parameter, and the channel interference strength weight corresponds to the modulation mode feature in the transmission parameter. By weighting and summing, the influence of key features is strengthened and redundant feature interference is removed, thereby generating multiple current oilfield downhole channel change feature vectors. These vectors can accurately map the correlation between channel change features and transmission parameter features, providing more accurate input for subsequent oilfield downhole data transmission parameter optimization model training.

[0043] Step S305: Based on the multiple current oilfield downhole channel change feature vector information and multiple oilfield downhole data transmission parameter feature information, the oilfield downhole data transmission parameter optimization model is trained to generate a trained oilfield downhole data transmission parameter optimization model.

[0044] In this embodiment, the oilfield downhole data transmission parameter optimization model can employ a Transformer+LSTM hybrid deep learning model. This model can be implemented by training a downhole channel state-transmission parameter dataset, combined with a CNN pre-trained model (mapping transmission parameter features) and an attention mechanism. First, the encoder in the CNN pre-trained model maps multiple oilfield downhole data transmission parameter feature information to a low-dimensional potential transmission parameter feature vector. Then, this low-dimensional potential transmission parameter feature vector is concatenated with multiple current oilfield downhole channel change feature vectors to form a fused feature vector, which serves as the input to the oilfield downhole data transmission parameter optimization model. During model training, temporal feature extraction and a self-attention mechanism are introduced to capture the dynamic correlation between channel changes and transmission parameters. The error between the predicted transmission parameters and the actual optimal transmission parameters is calculated. The model parameters are updated through backpropagation, and the training process is repeated until the error converges, resulting in the trained oilfield downhole data transmission parameter optimization model. The threshold used to determine error convergence can be preset, specifically with an error value ≤ 0.03. When the model training error reaches this threshold, the model training is considered complete, ensuring that the model can accurately adapt to the dynamic changes of the downhole channel.

[0045] The oilfield downhole data transmission method provided in this application achieves accurate matching between channel change characteristics and transmission parameter characteristics, improves the sensitivity of the oilfield downhole data transmission parameter optimization model to dynamic changes in the downhole channel and the pertinence of feature learning, and makes the generated multiple oilfield downhole data transmission parameter information more in line with the complex and ever-changing transmission scenarios in the well, thereby improving the accuracy, efficiency and stability of oilfield downhole data transmission to meet the downhole data transmission needs of deep and complex structure wells.

[0046] Figure 4 The flowchart illustrating the implementation of the oilfield downhole data transmission method provided in Embodiment 4 of this application is shown. The difference between this method and Embodiment 3 is that step S403 specifically includes: Step S401: Perform multiplication calculation based on the multiple current oilfield downhole channel change feature reference vector merging information and the preset oilfield downhole channel change feature query vector to obtain multiple oilfield downhole channel change feature query information.

[0047] In this embodiment, the preset oilfield downhole channel change feature query vector can be manually preset, specifically using a learnable parameter vector in a self-attention mechanism. The vector can be a 128-dimensional vector with elements ranging from 0 to 1. The oilfield downhole data transmission parameter optimization model can employ a Transformer+LSTM hybrid deep learning model, implemented by training a downhole channel state-transmission parameter dataset, combined with a CNN pre-trained model (mapping transmission parameter features) and an attention mechanism. Multiple current oilfield downhole channel change feature baseline vectors can be merged and multiplied element-wise with the preset oilfield downhole channel change feature query vector. During the operation, the core feature dimensions of each current oilfield downhole channel change feature baseline vector are retained, and redundant interference terms are removed. The results are then standardized to generate multiple oilfield downhole channel change feature query information. Each query information corresponds to a set of fused features of channel change features and query vectors, used for subsequent correlation calculations.

[0048] Step S402: Perform multiplication calculation based on the multiple current oilfield downhole channel change feature reference vector merging information and the preset oilfield downhole channel change feature key vector to obtain multiple oilfield downhole channel change feature key information.

[0049] In this embodiment, the preset oilfield downhole channel change feature key vector can be manually preset. Specifically, it can be a learnable parameter vector from the self-attention mechanism, specifically a 128-dimensional vector with elements ranging from 0 to 1, consistent with the dimension of the preset oilfield downhole channel change feature query vector, ensuring compatibility for subsequent matrix operations. Multiple current oilfield downhole channel change feature benchmark vectors can be merged and multiplied element-wise with the preset oilfield downhole channel change feature key vector. During the operation, the correlation between core features such as channel attenuation and channel interference intensity is preserved. The results are then denoised to remove irrelevant features, generating multiple oilfield downhole channel change feature key information. This information, combined with the oilfield downhole channel change feature query information, is used to quantify the correlation of different channel change features.

[0050] Step S403: Perform multiplication calculation based on the multiple current oilfield downhole channel change feature reference vector merging information and the preset oilfield downhole channel change feature value vector to obtain multiple oilfield downhole channel change feature value information.

[0051] In this embodiment, the preset oilfield downhole channel change feature value vector can be manually preset, specifically using a learnable parameter vector from a self-attention mechanism. Specifically, it can be a 128-dimensional vector with elements ranging from 0 to 1, used to store the channel change feature information to be fused. Multiple current oilfield downhole channel change feature benchmark vectors can be merged and then multiplied element-wise with the preset oilfield downhole channel change feature value vector. During this process, the representational ability of core features is enhanced, and the results are then used for feature filtering, retaining key features related to transmission parameter optimization. This generates multiple oilfield downhole channel change feature value information, which is subsequently used for weighted fusion using attention weights, providing core input for training the oilfield downhole data transmission parameter optimization model.

[0052] Step S404: Multiply the multiple oilfield downhole channel change feature query information and the multiple oilfield downhole channel change feature key information to obtain multiple oilfield downhole channel change feature query key information.

[0053] In this embodiment, multiple oilfield downhole channel change feature query information and multiple oilfield downhole channel change feature key information can be subjected to matrix dot product operation. During the operation, it is ensured that each oilfield downhole channel change feature query information is accurately matched with the corresponding oilfield downhole channel change feature key information, and then the degree of correlation between the two is calculated to obtain the original correlation matrix. Then, the original correlation matrix is ​​subjected to dimensionality reduction processing to avoid the gradient vanishing problem caused by excessive feature dimension, thereby generating multiple oilfield downhole channel change feature query key information. This information is used to quantify the correlation strength between different channel change feature dimensions and provide a basis for subsequent weight calculation.

[0054] Step S405: Multiply the query key information of the multiple oilfield downhole channel change features and the feature value information of the multiple oilfield downhole channels to obtain the correlation value information of the multiple oilfield downhole channel change features.

[0055] In this embodiment, multiple query key information of oilfield downhole channel change features and multiple feature value information of oilfield downhole channel change can be subjected to weighted matrix multiplication. During the operation, the correlation strength represented by the query key information of oilfield downhole channel change features is used as the weight to initially weight the feature value information of oilfield downhole channel change, thereby strengthening the influence of high correlation features and weakening the interference of low correlation features. Then, the operation result is normalized preprocessed to ensure that the feature values ​​are within a reasonable range, thereby generating multiple correlation value information of oilfield downhole channel change features. This information integrates the correlation relationship and core feature values ​​of channel change features, providing a basis for subsequent weight normalization.

[0056] Step S406: Normalize the multiple downhole channel change feature correlation values ​​of the oilfields to obtain multiple current downhole channel change feature vector weight information.

[0057] In this embodiment, the normalization process can employ the softmax function, which can be preset and its specific values ​​can be configured with default parameters to ensure accurate and reliable normalization results. Softmax operations can be performed on each set of data related to the correlation values ​​of multiple oilfield downhole channel change features. During the operation, the weight differences of different channel change feature dimensions are preserved, resulting in an attention weight matrix. The weight matrix is ​​then validated, removing abnormal weight values ​​(such as weight values ​​greater than 0.8 or less than 0.05), and the validated weights are re-normalized. This generates multiple current oilfield downhole channel change feature vector weight information. This weight information is used to measure the importance of different channel change feature dimensions; for example, the weight for channel attenuation is 0.4, the weight for channel interference intensity is 0.3, and the weight for channel transmission rate fluctuation is 0.3, providing accurate weighting basis for subsequent training of the oilfield downhole data transmission parameter optimization model.

[0058] The oilfield downhole data transmission method provided in this application accurately quantifies the correlation degree of different channel change characteristics. The weight information of multiple current oilfield downhole channel change feature vectors generated is more targeted, improving the recognition accuracy of the oilfield downhole data transmission parameter optimization model for channel change characteristics. This makes the generated multiple oilfield downhole data transmission parameter information more closely match the dynamic changes of downhole channels, thereby improving the accuracy, efficiency and stability of oilfield downhole data transmission to meet the downhole data transmission needs of deep and complex structure wells.

[0059] Figure 5 The flowchart illustrating the implementation of the oilfield downhole data transmission method provided in Embodiment 5 of this application is shown. The difference between this method and Embodiment 2 described above is that step S204 specifically includes: Step S501: Based on the characteristic information of the multiple oilfield downhole data transmission parameters and the current oilfield downhole data to be transmitted, perform splicing processing to obtain multiple oilfield downhole transmission parameters and data splicing information.

[0060] In this embodiment, the current downhole data to be transmitted in the oilfield can refer to the raw and preprocessed data collected by various downhole monitoring devices, including operating condition data, environmental data, and auxiliary monitoring data. The specific data volume can be 100KB-10MB / time, which can be obtained by summarizing through the downhole data acquisition terminal. First, the dimension of the feature information of multiple downhole data transmission parameters in the oilfield can be unified to ensure that the dimension of each downhole data transmission parameter feature information is 128-dimensional, consistent with the feature dimension of the current downhole data to be transmitted. Then, the feature information of each downhole data transmission parameter is concatenated with the feature information of the current downhole data to be transmitted row by row. During the concatenation process, core features such as the spectral peak value and bandwidth occupancy of the downhole data transmission parameter feature information, as well as key information such as the priority and data type of the current downhole data to be transmitted, are retained. Then, the concatenated vector is subjected to feature fusion processing to remove duplicate features and redundant interference, thereby generating multiple oilfield downhole transmission parameter and data concatenation information. Each concatenation information corresponds to a set of fused vectors of transmission parameter features and data features to be transmitted, providing complete feature support for subsequent model input.

[0061] Step S502: Based on the multiple oilfield downhole transmission parameters and data splicing information, as well as the trained oilfield downhole data transmission parameter optimization model, generate multiple oilfield downhole data transmission parameter information, so as to perform oilfield downhole data transmission processing through the multiple oilfield downhole data transmission parameter information.

[0062] In this embodiment, the post-trained oilfield downhole data transmission parameter optimization model can employ a Transformer+LSTM hybrid deep learning model. It has been trained using current oilfield downhole channel variation characteristics and multiple oilfield downhole data transmission parameter characteristics, with the error converging to a preset threshold (specifically, an error value ≤ 0.05), demonstrating precise parameter optimization capabilities. Multiple oilfield downhole transmission parameters and data splicing information can be input into the post-trained oilfield downhole data transmission parameter optimization model. The model uses a self-attention mechanism to mine the correlation between transmission parameter features and the features of the data to be transmitted. Combined with the dynamic characteristics of the downhole channel, it optimizes and adjusts the transmission parameters corresponding to each spliced ​​information. This generates suitable transmission parameter combinations for different priorities and types of data to be transmitted. For example, for high-priority operating condition data, it generates a parameter combination of "transmission rate 5Mbps, modulation method 16QAM, relay transmission power 30dBm, and communication frequency band 5GHz," while for medium-priority environmental data, it generates a parameter combination of "transmission rate..." The parameter combination of "2Mbps, 8PSK modulation, 20dBm relay transmission power, and 2.4GHz communication band" is used to generate a parameter combination of "100kbps transmission rate, QPSK modulation, 10dBm relay transmission power, and Sub-6GHz communication band" for low-priority auxiliary monitoring data. Then, the generated multiple oilfield downhole data transmission parameter information is verified to ensure that the parameter values ​​are within the preset range (transmission rate 100kbps-10Mbps, relay transmission power 10-50dBm, etc.), thereby generating the final multiple oilfield downhole data transmission parameter information for oilfield downhole data transmission processing.

[0063] The oilfield downhole data transmission method provided in this application integrates transmission parameter features with data features to be transmitted, enabling the trained oilfield downhole data transmission parameter optimization model to more accurately capture the correlation between the two. This ensures that the generated multiple oilfield downhole data transmission parameter information matches the actual needs of the data to be transmitted, improving the targeting and reliability of oilfield downhole data transmission. It effectively solves the problem of insufficient adaptability between transmission parameters and data requirements in the prior art, and adapts to diverse data transmission scenarios in deep and complex well structures.

[0064] Figure 6 The flowchart illustrating the implementation of the oilfield downhole data transmission method provided in Embodiment Six of this application is shown. The difference between this method and Embodiment One described above is that, after step S104, the method further includes: Step S601: Based on the preset mapping relationship between oilfield downhole data transmission parameters and relay transmission power, multiple oilfield downhole relay transmission power control information is generated according to the multiple oilfield downhole data transmission parameter information, so as to perform oilfield downhole relay transmission power control processing through the multiple oilfield downhole relay transmission power control information.

[0065] In this embodiment, the preset mapping relationship between oilfield downhole data transmission parameters and relay transmission power can be pre-defined. Specifically, it can be constructed using a CNN-based mapping model. This mapping model can employ a CNN+LSTM hybrid deep learning model, which can be achieved by training an oilfield downhole data transmission parameter—relay transmission power dataset, combined with feature extraction and regression analysis, to accurately correlate the adaptation relationship between transmission parameters and relay transmission power. The specific values ​​of the preset mapping relationship can cover multiple sets of corresponding rules. The core corresponding relationships include: a transmission rate of 100kbps-1Mbps corresponds to a relay transmission power of 10-20dBm, a transmission rate of 1Mbps-5Mbps corresponds to a relay transmission power of 20-35dBm, and a transmission rate of 5Mbps-10Mbps corresponds to a relay transmission power of 35-50dBm. Simultaneously, it is dynamically adjusted in conjunction with the channel attenuation level; for every 10dB increase in channel attenuation, the relay transmission power increases by 5dB. First, feature analysis can be performed on multiple downhole data transmission parameters from oilfields to extract core parameters such as transmission rate, channel multiplexing type, and communication frequency band. Then, based on a preset mapping relationship, the relay transmission power value corresponding to each set of transmission parameters can be matched. The matched power value is then verified to ensure that it is within the preset range of 10-50dBm. Subsequently, multiple downhole relay transmission power control information for oilfields is generated. Each control information is presented in the form of an electrical signal command, including the power setting value and control duration (the specific value is continuous control, verified every 5 minutes). The control information is transmitted to the downhole relay equipment, controlling the relay equipment to transmit signals at the specified power, thereby achieving coordinated adaptation between relay transmission power and transmission parameters and improving the stability of downhole data transmission.

[0066] Step S602: Based on the preset mapping relationship between oilfield downhole data transmission parameters and frequency band configuration parameters, multiple oilfield downhole frequency band configuration parameters are generated according to the multiple oilfield downhole data transmission parameter information, so as to perform oilfield downhole frequency band configuration processing through the multiple oilfield downhole frequency band configuration parameter information.

[0067] In this embodiment, the pre-defined mapping relationship between the oilfield downhole data transmission parameters and the frequency band configuration parameters can be pre-defined by humans. Specifically, it can be constructed using a Transformer-based mapping model. This mapping model can be a Transformer+CNN hybrid deep learning model, which can be achieved by training the oilfield downhole data transmission parameter-frequency band configuration parameter dataset and combining time-series feature extraction and feature matching to ensure accurate adaptation between frequency band configuration and transmission parameters. The preset mapping relationships can cover three core correspondence rules: high-priority data (data volume ≥ 60%) corresponds to the 5GHz communication band, with a transmission rate of 5Mbps-10Mbps and a modulation method of 16QAM; medium-priority data (data volume 30%-60%) corresponds to the 2.4GHz communication band, with a transmission rate of 1Mbps-5Mbps and a modulation method of 8PSK; and low-priority data (data volume < 30%) corresponds to the Sub-6GHz communication band, with a transmission rate of 100kbps-1Mbps and a modulation method of QPSK. At the same time, the mapping relationships are dynamically adjusted in conjunction with the channel interference intensity. When the channel interference intensity is > 50%, the mapping relationship switches to the Sub-6GHz band to improve anti-interference capability. First, multiple downhole data transmission parameters from oilfields can be categorized. Based on parameters such as data priority, transmission rate, and modulation method, corresponding frequency bands in a preset mapping relationship can be matched. Then, the configuration parameters corresponding to the frequency band can be extracted, including the center frequency (5GHz corresponds to 5.1-5.8GHz, 2.4GHz corresponds to 2.4-2.48GHz, and Sub-6GHz corresponds to 3.3-4.2GHz) and the bandwidth (specifically 20MHz). Then, the configuration parameters are verified for compatibility to ensure they are adapted to the downhole link's operating status. This generates multiple downhole frequency band configuration parameter information for oilfields. This information is then transmitted to the downhole communication equipment, which switches to the specified frequency band and loads the corresponding configuration. This achieves coordinated optimization of frequency band configuration and transmission parameters, reducing the impact of channel interference on transmission quality.

[0068] The oilfield downhole data transmission method provided in this application realizes dynamic control of relay transmission power and frequency band configuration, enabling the hardware parameters and transmission parameters of downhole data transmission to be coordinated and adapted, thereby improving the anti-interference capability and transmission stability of oilfield downhole data transmission. It effectively solves the problems of low transmission efficiency and data loss caused by mismatch between hardware parameters and transmission parameters in the prior art, so as to adapt to the downhole transmission scenario of deep and complex well structures.

[0069] Figure 7 The flowchart illustrating the implementation of the oilfield downhole data transmission method provided in Embodiment Seven of this application is shown. The difference between this method and Embodiment Six is ​​that, after step S602, the method further includes: Step S701: Obtain the downhole link operation information and the downhole data transmission quality information of the oilfield after regulation.

[0070] In this embodiment, the downhole link operation information after regulation can include the downhole wireless link signal-to-noise ratio information, the downhole wired communication link loss information, and the downhole relay node working status information after regulation. The specific value range is the same as before regulation. Specifically, the downhole wireless link signal-to-noise ratio information after regulation is -10dB to 20dB, the downhole wired communication link loss information after regulation is 0-30dB, and the downhole relay node working status information after regulation is normal (1) and abnormal (0). It can be collected in real time by the downhole link monitoring unit when the relay transmission power regulation and frequency band configuration regulation are continuously in progress. The specific collection interval is 1 minute / time to ensure the real-time performance of the data. The quality information of downhole data transmission in the oilfield after regulation can include the bit error rate, data transmission delay, and data transmission throughput after regulation. The specific values ​​are: bit error rate 0-1%, data transmission delay 0-500ms, and data transmission throughput 100kbps-10Mbps. This information can be collected by the downhole data transmission monitoring module and verified using a data verification algorithm (such as CRC cyclic redundancy check algorithm, which can be preset by the user with default verification parameters). The core transmission quality indicators are then extracted to generate the quality information of downhole data transmission in the oilfield after regulation, which is used to evaluate the actual effect of the regulation measures.

[0071] Step S702: Send the adjusted oilfield downhole link operation information and the adjusted oilfield downhole data transmission quality information to the ground terminal.

[0072] In this embodiment, the ground terminal can be an interactive terminal of the oilfield downhole monitoring center, such as a monitoring host or a touch screen, for staff to monitor the downhole data transmission status in real time. The acquired downhole link operation information and downhole data transmission quality information after regulation can be formatted, converting various parameters into visual values ​​and charts (such as line graphs and bar charts) to clearly present the parameter changes before and after regulation. Then, the formatted information is sent to the ground terminal via a downhole wireless communication module (using the Sub-6GHz band to ensure transmission stability). Simultaneously, the transmitted data is encrypted (the encryption algorithm can be preset, specifically using the AES encryption algorithm with a 128-bit key length) to prevent leakage or tampering during data transmission. In this embodiment, preferably, after sending the adjusted oilfield downhole link operation information and the adjusted oilfield downhole data transmission quality information to the ground terminal, it is also possible to determine whether the current oilfield downhole data transmission status has changed compared to before the adjustment, based on the adjusted oilfield downhole link operation information and the adjusted oilfield downhole data transmission quality information, and to determine whether the current transmission status meets the preset standards based on the adjusted data (the preset standards can be manually preset, specifically: data transmission error rate ≤ 0.5%, data transmission delay ≤ 300ms, downhole wireless link signal-to-noise ratio ≥ 0dB), and then regenerate multiple oilfield downhole data transmission parameter information, and adjust the relay transmission power and frequency band configuration parameters based on the regenerated multiple oilfield downhole data transmission parameter information. For example, if the data transmission error rate is 1% before adjustment and remains at 0.8% after adjustment, failing to meet the preset standard, it indicates that the adjustment effect of the current transmission parameters, relay transmission power, and frequency band configuration is insufficient. It is necessary to re-optimize multiple downhole data transmission parameters, such as reducing the transmission rate and adjusting the modulation method to the more interference-resistant QPSK. Then, based on the regenerated transmission parameters, new downhole relay transmission power adjustment information and downhole frequency band configuration parameters can be generated for secondary adjustment. For example, if the data transmission delay is 400ms before adjustment and decreases to 200ms after adjustment, meeting the preset standard, it indicates a good adjustment effect. The current transmission parameters, relay transmission power, and frequency band configuration can be maintained, or the parameters can be further optimized to achieve a better transmission quality. For example, if the downhole wireless link signal-to-noise ratio is -5dB before adjustment and increases to 5dB after adjustment, significantly improving transmission quality, the transmission rate can be appropriately increased, and suitable transmission parameters and corresponding adjustment information can be regenerated to achieve a balance between transmission efficiency and stability.

[0073] The oilfield downhole data transmission method provided in this application not only allows surface personnel to monitor changes in downhole transmission status in real time, but also provides precise basis for continuously and dynamically adjusting transmission parameters, relay transmission power, and frequency band configuration. This improves the accuracy, stability, and efficiency of oilfield downhole data transmission, meets the downhole data transmission needs of deep and complex wells, and solves the problems of not being able to monitor the effect in real time and being difficult to dynamically optimize after adjustment in the prior art.

[0074] Corresponding to the method in the above embodiments, Figure 8 A structural block diagram of an oilfield downhole data transmission system provided in an embodiment of this application is shown. For ease of explanation, only the parts related to the embodiment of this application are shown. Figure 8 The example oilfield downhole data transmission system can be the execution entity of the oilfield downhole data transmission method provided in the aforementioned embodiment one.

[0075] Reference Figure 8 The oilfield downhole data transmission system includes: The information acquisition module 810 is used to acquire oilfield downhole depth information, current oilfield wellbore condition monitoring information, current oilfield downhole environment information, current oilfield downhole link operation information, current oilfield downhole data to be transmitted, historical oilfield wellbore condition monitoring information, historical oilfield downhole environment information, historical oilfield downhole link operation information, and oilfield downhole data transmission parameter optimization model. The oilfield downhole change information generation module 820 is used to obtain oilfield downhole condition change information, oilfield downhole environment change information, and oilfield downhole link operation change information based on the oilfield downhole depth information, current oilfield wellbore condition monitoring information, current oilfield downhole environment information, current oilfield downhole link operation information, historical oilfield wellbore condition monitoring information, historical oilfield downhole environment information, and historical oilfield downhole link operation information. The current oilfield downhole channel change information generation module 830 is used to obtain the current oilfield downhole channel change information based on the oilfield downhole working condition change information, oilfield downhole environment change information, and oilfield downhole link operation change information. The oilfield downhole data transmission parameter information generation module 840 is used to generate multiple oilfield downhole data transmission parameter information based on the current oilfield downhole channel change information, the current oilfield downhole data to be transmitted, the oilfield downhole data transmission parameter optimization model, and the preset oilfield downhole data transmission parameter set, so as to perform oilfield downhole data transmission processing through the multiple oilfield downhole data transmission parameter information.

[0076] The process by which each module in the oilfield downhole data transmission system provided in this application implements its respective function can be found in the foregoing. Figure 1 The description of Embodiment 1 shown will not be repeated here.

[0077] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0078] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0079] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0080] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0081] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0082] The oilfield downhole data transmission method provided in this application can be applied to terminal devices such as mobile phones, tablets, wearable devices, vehicle-mounted devices, augmented reality / virtual reality devices, laptops, super mobile personal computers, netbooks, and personal digital assistants. This application does not impose any restrictions on the specific type of terminal device.

[0083] For example, the terminal device may be a station in a WLAN, a cellular phone, a cordless phone, a session initiation protocol phone, a wireless local loop station, a personal digital processing device, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, an in-vehicle device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a set-top box, a user premises equipment, and / or other devices for communication over a wireless system, as well as next-generation communication systems, such as mobile terminals in 5G networks or mobile terminals in future evolved public terrestrial mobile networks, etc.

[0084] Figure 9 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. For example... Figure 9 As shown, the terminal device 9 of this embodiment includes: at least one processor 90 ( Figure 9 (Only one is shown in the image) A memory 91 stores a computer program 92 that can run on the processor 90. When the processor 90 executes the computer program 92, it implements the steps in the various oilfield downhole data transmission method embodiments described above, for example... Figure 1 Steps S101 to S104 are shown. Alternatively, when the processor 90 executes the computer program 92, it implements the functions of each module / unit in the above system embodiments, for example... Figure 8 The functions of modules 810 to 840 are shown.

[0085] The terminal device 9 can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor 90 and a memory 91. Those skilled in the art will understand that... Figure 9 This is merely an example of terminal device 9 and does not constitute a limitation on terminal device 9. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal device may also include input transmission devices, network access devices, buses, etc.

[0086] The processor 90 may be a central processing unit, or it may be other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0087] In some embodiments, the memory 91 may be an internal storage unit of the terminal device 9, such as a hard disk or memory of the terminal device 9. The memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc., equipped on the terminal device 9. Furthermore, the memory 91 may include both internal and external storage units of the terminal device 9. The memory 91 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 91 can also be used to temporarily store data that has been sent or will be sent.

[0088] 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.

[0089] This application also provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor. When the processor executes the computer program, it causes the terminal device to implement the steps in any of the above method embodiments.

[0090] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0091] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0092] If the integrated module / 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 medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0093] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0094] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0095] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0096] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for transmitting data from downhole oilfield wells, characterized in that, include: Acquire oilfield downhole depth information, current oilfield wellbore condition monitoring information, current oilfield downhole environment information, current oilfield downhole link operation information, current oilfield downhole data to be transmitted, historical oilfield wellbore condition monitoring information, historical oilfield downhole environment information, historical oilfield downhole link operation information, and oilfield downhole data transmission parameter optimization model; Based on the oilfield well depth information, current oilfield wellbore condition monitoring information, current oilfield well environment information, current oilfield well link operation information, historical oilfield wellbore condition monitoring information, historical oilfield well environment information, and historical oilfield well link operation information, we obtain oilfield well condition change information, oilfield well environment change information, and oilfield well link operation change information. Based on the information on changes in oilfield downhole operating conditions, changes in oilfield downhole environment, and changes in oilfield downhole link operation, the current oilfield downhole channel change information is obtained. Based on the current oilfield downhole channel change information, the current oilfield downhole data to be transmitted, the oilfield downhole data transmission parameter optimization model, and the preset oilfield downhole data transmission parameter set, multiple oilfield downhole data transmission parameter information is generated to perform oilfield downhole data transmission processing through the multiple oilfield downhole data transmission parameter information.

2. The oilfield downhole data transmission method as described in claim 1, characterized in that, The step of generating multiple oilfield downhole data transmission parameter information based on the current oilfield downhole channel change information, the current oilfield downhole data to be transmitted, the oilfield downhole data transmission parameter optimization model, and a preset oilfield downhole data transmission parameter set, and then performing oilfield downhole data transmission processing using the multiple oilfield downhole data transmission parameter information, specifically includes: Based on the preset oilfield downhole data transmission parameter feature extraction rules, multiple oilfield downhole data transmission parameter feature information are generated according to the preset oilfield downhole data transmission parameter set. The current downhole channel change information of the oilfield is subjected to feature encoding processing to generate current downhole channel change feature information of the oilfield; Based on the current oilfield downhole channel change characteristics and multiple oilfield downhole data transmission parameter characteristics, the oilfield downhole data transmission parameter optimization model is trained to generate a trained oilfield downhole data transmission parameter optimization model. Based on the characteristic information of the multiple oilfield downhole data transmission parameters, the current oilfield downhole data to be transmitted, and the optimized model of oilfield downhole data transmission parameters after training, multiple oilfield downhole data transmission parameter information is generated to process oilfield downhole data transmission.

3. The oilfield downhole data transmission method as described in claim 2, characterized in that, The step of training the oilfield downhole data transmission parameter optimization model based on the current oilfield downhole channel change characteristic information and multiple oilfield downhole data transmission parameter characteristic information to generate a trained oilfield downhole data transmission parameter optimization model specifically includes: Based on the current downhole channel change feature information of the oilfield, a reference vector is extracted to obtain multiple current downhole channel change feature reference vector information of the oilfield. The multiple current oilfield downhole channel change feature reference vector information and the preset current oilfield downhole channel change feature reference vector map are merged to obtain multiple current oilfield downhole channel change feature reference vector merged information. Based on the multiple current oilfield downhole channel change feature benchmark vector merging information, the preset oilfield downhole channel change feature query vector, the preset oilfield downhole channel change feature key vector, and the preset oilfield downhole channel change feature value vector, the weight information of multiple current oilfield downhole channel change feature vectors is obtained. Based on the weight information of the multiple current oilfield downhole channel change feature vectors and the multiple current oilfield downhole channel change feature reference vectors, multiple current oilfield downhole channel change feature vectors are generated. Based on the multiple current oilfield downhole channel change feature vectors and multiple oilfield downhole data transmission parameter feature information, the oilfield downhole data transmission parameter optimization model is trained to generate a trained oilfield downhole data transmission parameter optimization model.

4. The oilfield downhole data transmission method as described in claim 3, characterized in that, The step of obtaining the weight information of multiple current oilfield downhole channel change feature vectors based on the merged information of multiple current oilfield downhole channel change feature benchmark vectors, a preset oilfield downhole channel change feature query vector, a preset oilfield downhole channel change feature key vector, and a preset oilfield downhole channel change feature value vector specifically includes: Based on the combined information of multiple current oilfield downhole channel change feature benchmark vectors and the preset oilfield downhole channel change feature query vector, a multiplication calculation is performed to obtain multiple oilfield downhole channel change feature query information. Based on the merging information of multiple current oilfield downhole channel change feature reference vectors and the preset oilfield downhole channel change feature key vectors, a multiplication calculation is performed to obtain multiple oilfield downhole channel change feature key information. Based on the combined information of multiple current oilfield downhole channel change feature vectors and the preset oilfield downhole channel change feature value vectors, a multiplication calculation is performed to obtain multiple oilfield downhole channel change feature value information. The query key information for multiple oilfield downhole channel change features is obtained by multiplying the query key information for multiple oilfield downhole channel change features with the query key information for multiple oilfield downhole channel change features. Based on the multiplication of the query key information of the multiple oilfield downhole channel change characteristics and the multiple downhole channel change characteristic value information of the multiple oilfields, the correlation value information of the multiple downhole channel change characteristics of the multiple oilfields is obtained. The normalization process is performed on the correlation values ​​of the multiple downhole channel change features of the oilfield to obtain the weight information of the multiple current downhole channel change feature vectors of the oilfield.

5. The oilfield downhole data transmission method as described in claim 2, characterized in that, The step of generating multiple oilfield downhole data transmission parameter information based on the multiple oilfield downhole data transmission parameter feature information, the current oilfield downhole data to be transmitted, and the trained oilfield downhole data transmission parameter optimization model, and then performing oilfield downhole data transmission processing using the multiple oilfield downhole data transmission parameter information, specifically includes: Based on the characteristic information of the multiple oilfield downhole data transmission parameters and the current downhole data to be transmitted in the oilfield, the splicing process is performed to obtain multiple oilfield downhole transmission parameters and data splicing information. Based on the multiple oilfield downhole transmission parameters and data splicing information, as well as the trained oilfield downhole transmission data transmission parameter optimization model, multiple oilfield downhole transmission data transmission parameter information is generated to perform oilfield downhole data transmission processing through the multiple oilfield downhole transmission data transmission parameter information.

6. The oilfield downhole data transmission method as described in claim 1, characterized in that, After the step of generating multiple oilfield downhole data transmission parameter information based on the current oilfield downhole channel change information, the current oilfield downhole data to be transmitted, the oilfield downhole data transmission parameter optimization model, and a preset oilfield downhole data transmission parameter set, and then performing oilfield downhole data transmission processing through the multiple oilfield downhole data transmission parameter information, the following is further included: Based on the preset mapping relationship between oilfield downhole data transmission parameters and relay transmission power, multiple oilfield downhole relay transmission power control information is generated according to the multiple oilfield downhole data transmission parameter information, so as to perform oilfield downhole relay transmission power control processing through the multiple oilfield downhole relay transmission power control information; Based on the preset mapping relationship between oilfield downhole data transmission parameters and frequency band configuration parameters, multiple oilfield downhole frequency band configuration parameters are generated according to the multiple oilfield downhole data transmission parameter information, so as to perform oilfield downhole frequency band configuration processing through the multiple oilfield downhole frequency band configuration parameter information.

7. The oilfield downhole data transmission method as described in claim 6, characterized in that, After the step of generating multiple oilfield downhole frequency band configuration parameter information based on the preset mapping relationship between oilfield downhole data transmission parameters and frequency band configuration parameters, and performing oilfield downhole frequency band configuration processing through the multiple oilfield downhole frequency band configuration parameter information, the method further includes: Acquire information on the operation of the oilfield downhole link after regulation and information on the quality of downhole data transmission after regulation; The adjusted oilfield downhole link operation information and the adjusted oilfield downhole data transmission quality information are sent to the ground terminal.

8. An oilfield downhole data transmission system, characterized in that, include: The information acquisition module is used to acquire oilfield downhole depth information, current oilfield wellbore condition monitoring information, current oilfield downhole environment information, current oilfield downhole link operation information, current oilfield downhole data to be transmitted, historical oilfield wellbore condition monitoring information, historical oilfield downhole environment information, historical oilfield downhole link operation information, and oilfield downhole data transmission parameter optimization model. The oilfield downhole change information generation module is used to obtain oilfield downhole condition change information, oilfield downhole environment change information, and oilfield downhole link operation change information based on the oilfield downhole depth information, current oilfield wellbore condition monitoring information, current oilfield downhole environment information, current oilfield downhole link operation information, historical oilfield wellbore condition monitoring information, historical oilfield downhole environment information, and historical oilfield downhole link operation information. The current oilfield downhole channel change information generation module is used to obtain the current oilfield downhole channel change information based on the oilfield downhole working condition change information, oilfield downhole environment change information, and oilfield downhole link operation change information. The oilfield downhole data transmission parameter information generation module is used to generate multiple oilfield downhole data transmission parameter information based on the current oilfield downhole channel change information, the current oilfield downhole data to be transmitted, the oilfield downhole data transmission parameter optimization model, and the preset oilfield downhole data transmission parameter set, so as to perform oilfield downhole data transmission processing through the multiple oilfield downhole data transmission parameter information.

9. A terminal device, characterized in that, The terminal device includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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