Sea-land real-time data transmission and intelligent monitoring method for offshore fracture acidizing operation

Abstract

The invention provides a sea-land real-time data transmission and intelligent monitoring method for offshore fracture acidizing operation, and the method comprises the steps: collecting a multi-source heterogeneous signal, carrying out the unified access through a multi-protocol interface, converting the multi-source heterogeneous signal into a standardized numerical value, and generating a standardized data set after timestamp synchronization and physical unit unification; on the basis of the standardized data set, the acid liquor forward position, the near-well corrosion rate and the wellhead pressure drop trend are calculated in real time through a state calculation model fusing sea condition parameters, risk scoring is conducted in combination with a sea condition corrosion coupling mechanism, and a state parameter set and a risk level are output; generating a link strategy parameter set according to the state parameter set and the risk level; and carrying out compression, redundancy coding, channel selection and packet sending time sequence control on data according to the link strategy parameter set, sending the data to a shore end by a sending end through a cross-medium link, and completing decoding, verification and restoration at the shore end.

Description

Technical Field

[0001] This invention belongs to the field of data transmission, and particularly relates to a method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operations. Background Technology

[0002] Offshore fracturing and acidizing is a high-risk, high-tech production enhancement measure implemented in complex marine environments. Its core task is to inject acid into the wellbore and near-wellbore formation to improve permeability and increase production capacity. During the operation, real-time monitoring and control of various key parameters are required, including wellhead pressure, pump flow rate, acid concentration, fluid temperature, and pipeline corrosion status. This data comes from various sensors and instruments on the wellhead platform, subsea manifold, seabed trees, and the work vessel, with output signals encompassing analog current signals, digital bus signals, fiber optic signals, and acoustic signals. Traditional wired data transmission methods have significant limitations in this type of operation. Long-distance cable laying is difficult, cross-platform cabling is risky, cabling space is limited, and maintenance costs are high. Especially under harsh sea conditions, severe salt spray corrosion, and dispersed equipment, cables are easily damaged and have long repair cycles, affecting operational continuity and safety. While some operational units have attempted to replace some wired connections with wireless links such as LoRa, Wi-Fi, or satellite, they still face transmission instability issues caused by multipath fading, wave obstruction, and equipment vibration under fracturing and acidizing conditions, leading to delays or loss of high-risk data. Furthermore, data changes during acidizing operations are sudden and drastic, and existing wireless solutions cannot prioritize the real-time delivery of critical data when links are disrupted. Some sensors are installed far from power supply points and consume high power, making it difficult for existing solutions to balance power supply and transmission stability. Data monitoring and transmission control strategies are independent, lacking a unified coordination mechanism, resulting in a large amount of low-value data consuming bandwidth while truly critical data arrives late. In summary, existing technologies struggle to simultaneously meet the diverse needs of offshore fracturing and acidizing operations, including multi-source heterogeneous data acquisition, status calculation and risk identification, sea state impact correction, dynamic link strategy optimization, and stable cross-media transmission. Summary of the Invention

[0003] The purpose of this invention is to design a real-time data transmission and intelligent monitoring method for offshore fracturing and acidizing operations, which can solve the problems of unstable data links, delay or loss of key information, limited power consumption, and separation of monitoring and transmission strategies in the complex marine environment of existing technologies.

[0004] To achieve the above objectives, the first aspect of the present invention provides a method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operations, the method comprising:

[0005] Multi-source heterogeneous signals from wellhead platforms, subsea manifolds, work vessels, and sea state sensors are collected, including wellhead pressure, pump discharge, acid concentration, fluid temperature, pipeline corrosion rate, wave period, and wave amplitude. These signals are uniformly accessed and converted into standardized values ​​through multi-protocol interfaces, and after being synchronized with timestamps and unified with physical units, a standardized dataset is generated.

[0006] Based on the standardized dataset, the acid front location, near-well corrosion rate and wellhead pressure drop trend are calculated in real time by a state calculation model that integrates sea state parameters. Risk scoring is performed by combining the sea state corrosion coupling mechanism, and the state parameter set and risk level are output.

[0007] Based on the state parameter set and risk level, a link strategy parameter set is generated, including the redundancy coding ratio, compression level, priority category, transmission channel type and packet sending time window for each state quantity. The priority is determined by the risk level, data update frequency, data volume and acid front deviation.

[0008] Based on the link strategy parameter set, the data is compressed, redundantly encoded, channel selected, and packet transmission timing controlled. The sending end sends the data to the shore end through the cross-media link, where decoding, verification, and restoration are completed.

[0009] Furthermore, multi-source heterogeneous signals include analog current signals, digital bus signals, fiber optic signals, and acoustic signals.

[0010] Furthermore, the pipeline corrosion rate is collected by a distributed optical fiber corrosion monitoring system, which measures and calculates the changes in light intensity at the optical fiber sensing nodes.

[0011] Furthermore, the state calculation model includes a time-series feature extraction module, a physical mechanism calculation module, and an environmental impact fusion module. The time-series feature extraction module is composed of a multi-layer one-dimensional convolutional neural network, used to extract dynamic change features from time-series data of wellhead pressure, pump discharge, and fluid temperature. The physical mechanism calculation module is based on acidization reaction kinetics and wellbore flow equations, and introduces a wave disturbance correction term represented by the ratio of wave amplitude to period, used to calculate the acid front position, near-wellbore corrosion rate, and wellhead pressure drop trend.

[0012] Furthermore, the environmental impact fusion module combines sea state parameters with physical state quantities through a fully connected neural network and introduces a sea state-corrosion coupling term to perform nonlinear correction on the basic risk score, thereby outputting a comprehensive set of state parameters and risk level for the fused environmental impact.

[0013] Furthermore, during the generation of the link strategy parameter set, the priority category is determined by a comprehensive score of the basic priority weight for risk level conversion, data update frequency, data volume, and acid front deviation.

[0014] Furthermore, during the generation of the link strategy parameter set, the redundancy coding ratio is dynamically adjusted by the basic redundancy ratio, the weight of sea state influence, the state sensitivity coefficient, and the sea state-corrosion coupling coefficient.

[0015] Furthermore, the packet transmission time window is adjusted forward or delayed based on wave cycle and link quality prediction. Specifically, this includes: generating a prediction curve of link quality changes over time based on a prediction model established by real-time monitoring of wave cycle parameters and historical link quality data from sea state sensors; the scheduler advances the packet transmission time window of high-priority data by a compensation amount estimated by wave cycle and link delay to align with the predicted peak period of link quality; at the same time, the packet transmission time of low-priority data is delayed or arranged for batch transmission to avoid the low period of link quality.

[0016] Furthermore, cross-medium links include acoustic-satellite relay, direct satellite connection, and LoRa-microwave path.

[0017] Furthermore, when the cross-media link is an acoustic-satellite relay, the sea relay maintains a short buffer according to the arrival order, identifies the integrity, and then uploads to prevent decoding failures caused by out-of-order delivery.

[0018] The beneficial technical effects of the present invention are at least as follows:

[0019] To address the aforementioned issues, this invention provides a method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operations. It unifies and standardizes multi-source heterogeneous operational parameters and necessary sea state parameters at the acquisition end, constructing a standardized dataset with consistent time and dimensions. A state calculation model, incorporating sea state disturbance corrections, is used to calculate key state variables and risk levels in real time, such as near-wellbore corrosion rate, acid front location, and wellhead pressure drop trend. Based on this, a set of link strategy parameters that can be directly executed is generated using risk levels, dynamic state characteristics, and sea state parameters. This enables dynamic optimization of redundancy ratio, compression level, priority category, channel selection, and packet transmission time. During cross-media link execution, the strategy is losslessly mapped to specific encoding, modulation, scheduling, and channel switching operations, ensuring that high-risk data is delivered stably and prioritized when the link is disrupted or bandwidth is limited. By collaboratively designing the four stages of acquisition, calculation, decision-making, and execution within a unified framework, this invention significantly improves the real-time performance and reliability of data in offshore fracturing and acidizing operations without increasing cabling and maintenance burdens, while reducing energy consumption and operation and maintenance costs, and possessing the ability to operate stably for extended periods under harsh sea conditions. Attached Figure Description

[0020] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0021] Figure 1 This is a flowchart of the real-time data transmission and intelligent monitoring method for offshore fracturing and acidizing operations according to the present invention. Detailed Implementation

[0022] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0023] In one or more embodiments, such as Figure 1 As shown, a method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operations is disclosed, the method comprising the following:

[0024] S1: Collects multi-source heterogeneous signals from wellhead platforms, subsea manifolds, work vessels, and sea state sensors, including wellhead pressure, pump discharge, acid concentration, fluid temperature, pipeline corrosion rate, wave period, and wave amplitude; it is uniformly accessed through multi-protocol interfaces and converted into standardized values, and after time stamp synchronization and physical unit unification, a standardized dataset is generated.

[0025] Specifically, the goal of this step is to simultaneously collect and standardize core operational parameters and necessary sea state parameters at the offshore fracturing and acidizing operation site, forming a unified standardized dataset. Because there are many types of sensors on site, their distribution locations vary, and their signal formats differ greatly, this process not only requires hardware interface compatibility, but also requires uniformity in signal acquisition time, physical units, and data formats to ensure that the input data for subsequent state calculations and risk identification is accurate and consistent.

[0026] This step directly acquires raw signal data from various sensors and detection devices deployed on-site. These include: wellhead pressure sensors, pump displacement meters, acid concentration meters, fluid temperature sensors, distributed fiber optic corrosion monitoring systems, and sea state sensors (with built-in triaxial accelerometers and gyroscopes) installed on platforms or buoys. These sensors are connected to the acquisition module via wired or short-range wired interfaces, and the raw signal format may be analog current, digital serial bus data, or optical intensity signals transmitted via fiber optics.

[0027] In this embodiment, the hardware design of the acquisition module includes an analog input port, an RS485 digital interface, an optical fiber receiver unit, and a multi-protocol parsing chip to simultaneously receive signals from different types of sensors. Taking a wellhead pressure sensor as an example, it typically outputs a 4-20mA current signal, corresponding to a pressure range of 0-100MPa. The analog input terminal of the acquisition module converts the current into a voltage signal through a high-precision current sampling resistor, then digitizes it through a 24-bit analog-to-digital converter (ADC), and converts it into a pressure value according to the sensor calibration curve. The pump displacement meter outputs Modbus RTU protocol data frames through an RS485 interface. The serial port parsing chip of the acquisition module identifies the start character, address code, and data field, extracting the instantaneous displacement (m³ / min) and cumulative displacement of the pump. The acid concentration meter's working principle may be based on changes in conductivity or optical absorption; the output digital signal is also read through an RS485 interface and parsed into a mass percentage concentration value. The 4-20mA signal from the fluid temperature sensor undergoes the same current sampling and ADC conversion to be converted into a temperature value. The distributed fiber optic corrosion monitoring system deploys fiber optic sensing nodes on the inner wall of the pipeline. It measures changes in light intensity using optical time-domain reflectometry (OTDR), and the output intensity difference (normalized value) is read through the fiber optic interface. The acquisition module combines these changes with time intervals to calculate the corrosion rate index.

[0028] ;

[0029] in, This is an indicator of corrosion rate; The change in light intensity per unit time is calculated from the continuous measurement results of the fiber optic sensor. The data acquisition time interval is provided by a unified clock provided by the acquisition module.

[0030] In terms of sea state parameter acquisition, sea state sensors on buoys or platforms extract the dominant wave frequency using fast Fourier transform based on time-series data of triaxial acceleration and angular velocity. Calculate the wave period based on the dominant frequency:

[0031] ;

[0032] in, Indicates the wave cycle; The peak frequency is obtained through frequency domain analysis of the acceleration signal. The estimation accuracy of the peak frequency is determined by the number of sampling points and the sampling duration.

[0033] All acquired data undergoes unified timestamp synchronization, with the time reference derived from the GNSS timing module to ensure data from different signal sources are aligned on the same timeline. Physical units are uniformly converted at the software layer of the acquisition module; for example, pressure is converted to MPa, flow rate to m³ / min, temperature to Celsius, concentration to mass percentage, corrosion rate to light intensity change rate, and sea state to period and amplitude. Finally, the acquisition module packages all standardized parameters into a structured data package. This includes the timestamp, source identifier, physical quantity name, and standardized value for each data item.

[0034] S2: Based on the standardized dataset, the acid front location, near-well corrosion rate and wellhead pressure drop trend are calculated in real time by a state calculation model that integrates sea state parameters. Risk scoring is performed in combination with the sea state corrosion coupling mechanism, and the state parameter set and risk level are output.

[0035] Specifically, this step calculates the operational status and identifies the risk level under the actual working conditions of offshore fracturing and acidizing operations. Unlike conventional risk identification, this step not only analyzes operational data within the wellbore but also incorporates sea state factors to correct for the status calculation. This makes the risk assessment more closely reflect the operational stability requirements of the actual marine environment, thereby providing accurate and dynamic input for the generation of subsequent link strategies.

[0036] This includes indicators such as wellhead pressure, pump displacement, acid concentration, fluid temperature, and corrosion rate. Wave cycle and wave amplitude These data are first analyzed using a state computation model. This model consists of three parts:

[0037] Temporal feature extraction module: It consists of a three-layer one-dimensional convolutional network (convolutional kernel size 3, channel number 16, 32, 64 respectively), used to extract the dynamic change trend of pressure, displacement, temperature, etc.

[0038] Physical mechanism calculation module: Based on acidification reaction kinetics and wellbore flow equations, it calculates acid front propulsion, acid utilization rate, and corrosion evolution rate.

[0039] Environmental Impact Fusion Module: Combines sea state parameters with physical state quantities through a fully connected network to correct risk scores.

[0040] In the position of acid front In the calculation, the overall pump displacement of the physical module Acid effective utilization rate Cross-sectional area of ​​well shaft and reaction rate coefficient To reflect the impact of sea state on acid propulsion efficiency, a wave disturbance correction term is added to the denominator of the formula. This value, expressed as the ratio of wave amplitude to period, reflects the fluctuations in pumping efficiency caused by the undulations in the platform and pipeline.

[0041] ;

[0042] in, , The wave impact coefficient (calibrated by historical operation statistics). This is the coefficient representing the effect of acidification on fluid flow resistance. Thus, when the wave amplitude increases or the period shortens, The increased size corresponds to a reduced forward advance distance, which is more in line with the situation where pumping efficiency is disturbed during on-site operations.

[0043] Risk scoring is calculated based on each key state variable to generate an initial score. Then, environmental adjustments are made. Unlike conventional linear weighting, a "sea state-corrosion coupling term" is introduced here. This is used to amplify the impact of corrosion rates on risks under severe sea conditions.

[0044] ;

[0045] in, The weighting coefficient for the influence of sea state. The sea state-corrosion coupling coefficient (determined by statistical analysis of multiple field operations). For reference corrosion rate (measured under stable sea conditions). The environmentally corrected risk score reflects the overall risk level under the combined effects of current sea conditions and corrosion. The innovation of this formula lies in the fact that when sea conditions are severe and corrosion rates are high, the risk score increases non-linearly, allowing subsequent link strategies to prioritize allocating more resources to transmit critical data in this phase.

[0046] Risk level Depend on With preset threshold set The comparison yields the state parameter sets (low, medium, and high levels). This includes key parameters such as near-wellbore corrosion rate, acid front location, and wellhead pressure drop trend.

[0047] S3: Generate a link strategy parameter set based on the state parameter set and risk level, including the redundancy coding ratio, compression level, priority category, transmission channel type and packet sending time window for each state quantity, wherein the priority is jointly determined by the risk level, data update frequency, data volume and acid front deviation.

[0048] Specifically, this step will set the state parameter set. and risk level Used to generate link policy parameter sets In offshore fracturing and acidizing operations, communication links traverse multiple media paths, including the seabed, surface, and satellite. Limited by bandwidth, power consumption, and environmental disturbances, ensuring reliable transmission of high-risk data with limited resources is one of the core objectives of this patent. This step incorporates all state parameters (near-wellbore corrosion rate, acid front location, wellhead pressure drop trend) and risk levels calculated in step 2 during strategy generation. It also dynamically determines the redundancy coding ratio, compression rate, priority queue, transmission channel selection, and packet sending timing based on update frequency, data volume characteristics, and sea conditions.

[0049] First, the system will classify the risk level. Convert to basic priority weight (For example, low = 1, medium = 2, high = 3). Then, for Extract the update frequency of each state variable. (Number of data packet updates per unit time, directly output by the status calculation module) and data volume (The number of bytes of data before compression is counted by the acquisition module). To reflect the importance of high-frequency, large-volume status variables in link scheduling, a comprehensive priority score is defined. :

[0050] ;

[0051] in, , These are the weighting coefficients for update frequency and data volume, respectively, calibrated by bandwidth utilization testing. , For reference update frequency and reference data volume; Forward deviation penalty coefficient; The position of the acid front calculated in step 2. The expected position of the acid front (construction plan value) is given. This formula innovatively incorporates a "front deviation penalty term". When the position of the acid front deviates too much from the expected position, even if the risk level is not high, the priority will be increased due to the possibility of abnormal acid distribution, so that the status variable can be transmitted to the shore for manual assessment in a timely manner.

[0052] Next, according to The sorting results are assigned priority queue categories (high, medium, low), and the redundancy coding ratio for each state variable is generated. To significantly improve redundancy under adverse sea conditions and abnormal states, this step introduces a "sea state-state nonlinear coupling term" into the redundancy coding calculation:

[0053] ;

[0054] in, The basic redundancy ratio corresponding to the link type (preset for satellite, underwater acoustic, and LoRa respectively). The sea state influence weighting coefficient is obtained by fitting historical link packet loss rate with sea state data; This is the state sensitivity coefficient, which ensures that high-priority state variables can obtain additional redundancy under any sea state. The maximum value among the comprehensive priority scores of all state variables within the current strategy generation period is used to normalize the priority scores of each state variable, so that the redundancy ratio calculation between different state variables maintains a consistent dimensional scale. This is the sea state-corrosion coupling coefficient, which is used when the wave amplitude is large and the corrosion rate is high. Deviation from reference value When there are many cases, the redundancy ratio will be significantly increased to prevent the loss of critical corrosion monitoring data when the link fades.

[0055] The final step in strategy generation is channel selection and packet sending timing planning. Based on the current... (Wave period) and link prediction quality curve, advance the packet transmission time window of high-priority data to the peak channel quality period by a compensation factor. (Estimated by wave period and link delay), low-priority data is sent with delay or in batches to reduce packet loss and optimize bandwidth utilization. For channel selection, the system prioritizes channels with higher redundancy and better stability (such as direct satellite connection) for transmitting high-risk state quantities, while medium- and low-risk quantities can be transmitted via acoustic-satellite or LoRa-microwave paths to balance power consumption and bandwidth. The output of this step is the link strategy parameter set. The content includes the redundancy coding ratio, compression ratio, priority category, transmission channel type, and packet sending time window for each state variable.

[0056] S4: Based on the link strategy parameter set, the data is compressed, redundantly encoded, channel selected, and packet transmission timing controlled. The sending end sends the data to the shore end through the cross-media link, and the shore end completes decoding, verification, and restoration.

[0057] Specifically, this step uses the link policy parameter set output in step 3. The system takes input as input and applies each parameter to the actual execution of the cross-media link, completing the transmission, reception, verification, and restoration of data from the sea end to the shore end. Includes: the redundancy coding ratio for each state variable. Compression level Priority Category Channel type (e.g., acoustic to satellite, direct satellite connection, LoRa to microwave), packet transmission time window With lead time , as well as preset retransmission budgets and modulation level numbers, etc.

[0058] Scheduler according to Load the frames to be sent into three queues: real-time queue (high), delayed queue (medium), and batch queue (low); then press... Lossless compression is performed on the frames of the corresponding queue (higher levels result in higher compression ratios, with priority given to enabling this in latency and batch queues). Then, according to... Complete channel assembly: If For acoustic-to-satellite connections, the preset rate and error correction settings of the acoustic modem are activated on the seabed / wellhead side to transmit data to the surface relay, and then the satellite terminal uploads the data according to the specified link parameters. For direct satellite connection or LoRa-to-microwave connection, the preset settings of the corresponding terminals are activated respectively. To avoid sending packets during periods of low sea state, the scheduler aligns the execution time of frames in the real-time queue. The actual packet transmission time is determined by the following formula:

[0059] ;

[0060] in, This refers to the actual sending time; The nominal transmission time allocated by the scheduler for this frame (from (Internal time slot planning); The lead time given in step 3 during calculation (stored in...) (Based on combined sea state and link predictions). This timing adjustment does not require further estimation of environmental parameters in this step; the execution end only needs to follow the steps outlined. Once the distributed values ​​are aligned, that's all.

[0061] The process then proceeds to the redundancy and fragmentation stage. The sending end segments the compressed frame into segments of fixed length. A number of equal-length symbols, according to Total number of redundant coding output symbols generated Interleaving is performed to distribute burst errors; the interleaving depth and symbol length for different channels use pre-set settings verified by the equipment and are not modified during operation. The number of redundant symbols is given by the following formula:

[0062] ;

[0063] in, The number of input symbols obtained by segmenting the frame (divided by the frame length and the symbol length fixed by the device); For step 3 in The redundancy ratio is given in the code. The execution end uses this to call the application-layer fountain-class encoding or block code encoding module to generate the code. A transmittable symbol, and according to priority order and The physical queue is dequeued together; for real-time queues, if the acknowledgment count returned by the link side is lower than the retransmission budget threshold, then... The retransmission budget is used to repeatedly send key symbols. If the budget is exceeded, the symbols are transferred to the delay queue to avoid occupying too many time slots.

[0064] On the receiving side, deinterleaving and decoding are completed at the shore-end gateway of the corresponding channel: when the cumulative number of received symbols reaches the decoding threshold, the original frame is immediately restored and the hash and sequence number continuity are verified; if it comes from a two-hop path from acoustic to satellite, the sea relay maintains a short buffer according to the arrival order, marks the integrity, and then goes uplink to prevent decoding failure caused by out-of-order delivery. Successfully restored frames are decompressed and restored to a set of status parameters and risk labels, and written to the shore-end real-time database and monitoring interface; at the same time, arrival confirmation and error statistics are recorded in the execution record of this session.

[0065] This invention also provides a real-time on-shore data transmission and intelligent monitoring device for offshore fracturing and acidizing operations, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps described in the above embodiments of the real-time on-shore data transmission and intelligent monitoring method for offshore fracturing and acidizing operations. Figure 1 The steps S1 to S4 described above; or, when the processor executes the computer program, it implements the functions of each module in the above system embodiments.

[0066] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the real-time data transmission and intelligent monitoring equipment for offshore fracturing and acidizing operations.

[0067] The real-time data transmission and intelligent monitoring equipment for offshore fracturing and acidizing operations can be a desktop computer, laptop, handheld computer, or cloud server, etc. This equipment may include, but is not limited to, processors and memory. Those skilled in the art will understand that the equipment may also include input / output devices, network access devices, buses, etc.

[0068] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the real-time data transmission and intelligent monitoring equipment for offshore fracturing and acidizing operations, connecting various parts of the equipment via various interfaces and lines.

[0069] The memory can be used to store the computer programs and / or modules. The processor, by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory, realizes various functions of the real-time data transmission and intelligent monitoring equipment for offshore fracturing and acidizing operations. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the operation of the air conditioning controller, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart memory card (SMC), secure digital card (SD), flash memory card, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0070] The module integrating real-time data transmission and intelligent monitoring equipment for offshore fracturing and acidizing operations, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in 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 (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0071] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0072] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operations, characterized in that, The method comprises: Collecting multi-source heterogeneous signals from wellhead platforms, subsea manifolds, work vessels and sea state sensors, including wellhead pressure, pump displacement, acid concentration, fluid temperature, pipeline corrosion rate, wave period and wave amplitude; accessing and converting to standardized numerical values through a multi-protocol interface, generating a standardized data set after time stamp synchronization and physical unit unification; Based on the standardized data set, the acid liquid front position, near-well corrosion rate and wellhead pressure drop trend are calculated in real time by fusing the state calculation model of sea state parameters, and the risk score is combined with the sea state corrosion coupling mechanism to output the state parameter set and risk level; According to the state parameter set and risk level, a link strategy parameter set is generated, including the redundancy coding ratio, compression level, priority category, transmission channel type and packet sending time window of each state quantity, wherein the priority is determined by the risk level, data update frequency, data volume and acid liquid front deviation. According to the link strategy parameter set, data compression, redundancy coding, channel selection and packet sending time sequence control are performed, and the sending end sends data to the shore end through a cross-medium link, and decoding, verification and restoration are completed at the shore end.

2. The method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operation according to claim 1, characterized in that, The multi-source heterogeneous signals include analog current signals, digital bus signals, optical fiber signals and acoustic signals.

3. The method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operation according to claim 1, characterized in that, The pipeline corrosion rate is collected by a distributed optical fiber corrosion monitoring system, and is obtained by measuring the light intensity change of the optical fiber sensing node.

4. The method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operation according to claim 1, characterized in that, The state calculation model includes a time series feature extraction module, a physical mechanism calculation module and an environmental influence fusion module; the time series feature extraction module is composed of a multi-layer one-dimensional convolutional neural network, which is used to extract dynamic change features from the time series data of wellhead pressure, pump displacement and fluid temperature; the physical mechanism calculation module is based on acidification reaction kinetics and wellbore flow equation, and introduces a wave disturbance correction term represented by the ratio of wave amplitude and period, which is used to calculate the acid liquid front position, near-well corrosion rate and wellhead pressure drop trend.

5. The method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operation according to claim 4, characterized in that, The environmental influence fusion module combines sea state parameters with physical state quantities through a fully connected neural network, and introduces a sea state-corrosion coupling term, which is used to nonlinearly correct the basic risk score, thereby outputting the integrated state parameter set and risk level considering environmental influence.

6. The method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operation according to claim 1, characterized in that, In the link strategy parameter set generation process, the priority category is determined by the basic priority weight converted from the risk level and the comprehensive score of data update frequency, data volume and acid liquid front deviation.

7. The method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operation according to claim 1, characterized in that, In the link strategy parameter set generation process, the redundancy coding ratio is dynamically adjusted by the basic redundancy ratio, sea state influence weight, state sensitivity coefficient and sea state-corrosion coupling coefficient. In the link strategy parameter set generation process, the priority category is determined by the basic priority weight converted from the risk level and the comprehensive score of data update frequency, data volume and acid liquid front deviation. In the link strategy parameter set generation process, the redundancy coding ratio is dynamically adjusted by the basic redundancy ratio, sea state influence weight, state sensitivity coefficient and sea state-corrosion coupling coefficient.

8. The method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operations according to claim 1, characterized in that, The packet sending time window is adjusted in advance or delayed according to wave period and link quality prediction; specifically, a prediction curve of link quality changing with time is generated based on a wave period parameter monitored by a sea state sensor in real time and a prediction model established based on historical link quality data; the scheduler adjusts the packet sending time window of high-priority data in advance by a compensation amount estimated from the wave period and link delay according to the curve, so as to align with the predicted peak period of link quality; meanwhile, the packet sending time of low-priority data is delayed or arranged for batch sending, so as to avoid the trough period of link quality.

9. The method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operations according to claim 1, characterized in that, The cross-medium link comprises acoustic-satellite relay, satellite direct connection and LoRa-microwave path.

10. The method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operation according to claim 9, characterized in that, When the cross-medium link is acoustic-satellite relay, the sea surface relay maintains a short cache according to the arrival sequence, and then transmits upward after identifying integrity, so as to prevent decoding failure caused by out-of-order.

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