Real-time data transmission and intelligent monitoring method for offshore fracturing and acidizing operation

Abstract

The present application provides a sea-land real-time data transmission and intelligent monitoring method for offshore fracturing and acidizing operation, which comprises the following steps: collecting multi-source heterogeneous signals, uniformly accessing and converting them into standardized 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, real-time calculation of acid liquid front position, near-well corrosion rate and wellhead pressure drop trend is carried out through a state calculation model fused with sea state parameters, and risk scoring is carried out in combination with the sea state corrosion coupling mechanism, and a state parameter set and a risk level are output; according to the state parameter set and the risk level, a link strategy parameter set is generated; according to the link strategy parameter set, data compression, redundant coding, channel selection and packet timing control are carried out, and the sending end sends the data to the shore end through a cross-medium link, and decoding, checking and restoration are completed at the shore end.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of data transmission, and particularly relates to a method for real-time data transmission and intelligent monitoring between sea and land for offshore fracturing and acidizing operation. BACKGROUND

[0002] Offshore fracturing and acidizing operation is a high-risk and high-technology requirement stimulation measure implemented in complex marine environment, and its core task is to inject acid liquid into wellbore and near wellbore formation to improve permeability and increase productivity. During the operation, real-time monitoring and control of multiple key parameters such as wellhead pressure, pump displacement, acid concentration, fluid temperature, pipeline corrosion state, etc. are required. These data come from multiple types of sensors and detectors on the wellhead platform, subsea manifold, subsea tree and operation ship, and the output signal forms include analog current signal, digital bus signal, optical fiber signal, acoustic signal and other types. Traditional wired data transmission has significant limitations in such operations. Long-distance cable laying is difficult, cross-platform wiring is risky, wiring space is limited and maintenance cost is high. Especially in poor sea conditions, severe salt spray corrosion and scattered equipment distribution, the cable is easily damaged and the repair cycle is long, which affects the continuity and safety of the operation. Although some operation units have tried to use LoRa, Wi-Fi or satellite wireless links to replace some wired connections, they still face problems such as unstable transmission, high-risk data delay or loss caused by multi-path fading, sea wave blocking and equipment shaking in fracturing and acidizing conditions. In addition, the data changes during acidizing operation are sudden and severe, and existing wireless solutions cannot prioritize real-time delivery of critical data when the link is disturbed. Some sensors are installed far from the power supply point and have high power consumption, and existing solutions cannot balance power supply and transmission stability. Data monitoring and transmission control strategies are independent of each other, and there is a lack of unified coordination mechanism, resulting in a large amount of low-value data occupying bandwidth and the real critical data being delayed to arrive. In summary, existing technologies cannot meet the needs of multi-source heterogeneous data acquisition, state calculation and risk identification, sea condition influence correction, link strategy dynamic optimization and cross-medium transmission stable execution in offshore fracturing and acidizing operation. SUMMARY

[0003] The purpose of the present application is to design a method for real-time data transmission and intelligent monitoring between sea and land for offshore fracturing and acidizing operation, which can solve the problems of unstable data link, delayed or lost critical information, limited power consumption and separated monitoring and transmission strategies in existing technologies in complex marine environment.

[0004] To achieve the above purpose, in the first aspect of the present application, a method for real-time data transmission and intelligent monitoring between sea and land for offshore fracturing and acidizing operation is provided, which comprises:

[0005] The multi-source heterogeneous signals from the wellhead platform, subsea manifold, workship and sea state sensors are collected, including wellhead pressure, pump displacement, acid concentration, fluid temperature, pipeline corrosion rate, wave period and wave amplitude; the multi-protocol interface is uniformly accessed and converted into standardized numerical values, and after time stamp synchronization and physical unit unification, the standardized data set is generated;

[0006] 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 the sea state parameters, and the risk score is combined with the sea state corrosion coupling mechanism, and the state parameter set and risk level are output;

[0007] According to the state parameter set and risk level, the 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;

[0008] According to the link strategy parameter set, the data is compressed, redundantly encoded, channel selected and packet sending time controlled, and the sending end sends the data to the shore end through the cross-medium link, and the decoding, verification and restoration are completed at the shore end.

[0009] Further, the multi-source heterogeneous signals include analog current signals, digital bus signals, optical fiber signals and acoustic signals.

[0010] Further, 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 and calculating.

[0011] Further, 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.

[0012] Further, the environmental influence fusion module combines the sea state parameters with the 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, so as to output the comprehensive state parameter set and risk level considering the environmental influence.

[0013] Further, 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.

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

[0015] Further, the packet sending time window is adjusted in advance or delayed according to the wave period and link quality prediction; specifically, based on the wave period parameters monitored by the sea state sensor in real time and the prediction model established based on historical link quality data, a prediction curve of the change of link quality with time is generated; the scheduler adjusts the packet sending time window of high-priority data in advance by a compensation amount estimated by the wave period and link delay, so as to align with the predicted peak period of link quality; at the same time, 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.

[0016] Further, the cross-medium link includes acoustic-satellite relay, satellite direct connection and LoRa-microwave path.

[0017] Further, when the cross-medium link is acoustic-satellite relay, the sea surface relay maintains a short cache according to the arrival sequence, and identifies the integrity before uploading, to prevent decoding failure caused by out-of-order.

[0018] The beneficial technical effects of the present application are at least the following points:

[0019] In view of the above problems, the present application provides a real-time data transmission and intelligent monitoring method for offshore fracturing acidizing operation, which uniformly accesses and standardizes operation parameters and necessary sea state parameters of multiple source heterogeneous through a collection end, constructs a standardized data set after time and dimension consistency processing, and calculates the near-well corrosion rate, acid liquid front position, wellhead pressure drop trend and other key state quantities and risk levels in real time through a state calculation model fused with sea state disturbance correction. On this basis, the link strategy parameter set which can be directly issued and executed is generated by using the risk level, state dynamic characteristics and sea state parameters, the dynamic optimization of redundancy ratio, compression level, priority category, channel selection and packet sending time is realized, and the strategy is losslessly mapped to specific coding, modulation, scheduling and channel switching operations in the cross-medium link execution link, so that high-risk data is preferentially and stably delivered when the link is disturbed or the bandwidth is limited. Through the collaborative design of the four links of collection, calculation, decision and execution in a unified framework, the present application can significantly improve the real-time data and reliability of offshore fracturing acidizing operation without increasing the wiring and maintenance burden, while reducing energy consumption and operation and maintenance cost, and has the ability to operate stably for a long time in severe sea conditions. BRIEF DESCRIPTION OF DRAWINGS

[0020] The application is further illustrated by the accompanying drawings, but the embodiments in the drawings do not constitute any limitation to the application, and other embodiments can be obtained by those skilled in the art without creative labor on the basis of the following drawings.

[0021] Figure 1 The flow chart of the sea-land real-time data transmission and intelligent monitoring method for offshore fracturing acidizing operation of the application. DETAILED DESCRIPTION

[0022] Embodiments of the application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the drawings are exemplary and are only used to explain the application, and cannot be understood as a limitation to the application.

[0023] In one or more embodiments, as shown in Figure 1 The sea-land real-time data transmission and intelligent monitoring method for offshore fracturing acidizing operation is disclosed, which comprises the following steps:

[0024] S1: Collecting multi-source heterogeneous signals from wellhead platforms, subsea manifolds, operation vessels and sea state sensors, including wellhead pressure, pump displacement, acid concentration, fluid temperature, pipeline corrosion rate, wave period and wave amplitude; through multi-protocol interface unified access and conversion to standardized numerical values, after time stamp synchronization and physical unit unification, generating a standardized data set;

[0025] Specifically, the goal of this step is to synchronize the collection and standardization of core operation parameters and necessary sea state parameters on the offshore fracturing acidizing operation site, forming a unified standardized data set . Due to the large number of types of field sensors, different distribution locations, and large differences in signal formats, this process not only requires compatibility of hardware interfaces, but also needs to achieve uniformity in signal acquisition time, physical units and data formats, so as to ensure the accuracy and consistency of the input data for subsequent state calculation and risk identification.

[0026] This step directly obtains raw signal data from various sensors and detection devices deployed on site. Including: wellhead pressure sensors, pump displacement meters, acid concentration meters, fluid temperature sensors, distributed optical fiber corrosion monitoring systems, and sea state sensors (built-in three-axis accelerometer and gyroscope) installed on platforms or buoys. These sensors are connected to the acquisition module through wired or short-range wired interfaces, and the original signal form may be analog current, digital serial bus data or optical intensity signal transmitted by optical fiber.

[0027] The analog input port, RS485 digital interface, optical fiber receiving unit and multi-protocol analysis chip are configured in the hardware design of the acquisition module to simultaneously access different types of sensor signals. Taking the wellhead pressure sensor as an example, it usually outputs a 4-20mA current signal, and the corresponding pressure range is 0-100MPa. The analog input of the acquisition module converts the current into a voltage signal through a high-precision current sampling resistor, and 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 a ModbusRTU protocol data frame through the RS485 interface, and the serial port analysis chip of the acquisition module identifies the start symbol, address code and data field, and extracts the instantaneous displacement (m³ / min) and cumulative displacement of the pump. The working principle of the acid concentration meter may be based on conductivity change or optical absorption, and the output digital signal is also read through the RS485 interface and parsed into a mass percentage concentration value. The 4-20mA signal of the fluid temperature sensor is converted into a temperature value through the same current sampling and ADC conversion. The distributed optical fiber corrosion monitoring system arranges the optical fiber sensing nodes on the inner wall of the pipeline, measures the light intensity change through optical time domain reflectometry (OTDR), and reads the light intensity difference (unitized value) through the optical fiber interface. The acquisition module cooperates these change values with time intervals to calculate the corrosion rate index:

[0028] ;

[0029] wherein, is the corrosion rate index; is the light intensity change amount per unit time, calculated from the continuous measurement results of the optical fiber sensor; is the acquisition time interval, provided by the acquisition module unified clock.

[0030] In terms of sea state parameter acquisition, the sea state sensor on the buoy or platform extracts the main frequency of the wave by using fast Fourier transform on the time series data of three-axis acceleration and angular velocity

[0031] ;

[0032] wherein, represents the wave period; is the peak main frequency, obtained by frequency domain analysis of the acceleration signal, and the estimation accuracy of the main frequency is determined by the number of sampling points and the sampling time length.

[0033] All collected values are uniformly processed by timestamp synchronization, and the time reference comes from the GNSS time-providing module, ensuring that data from different signal sources are aligned on the same time axis. The physical units are uniformly converted at the software layer of the acquisition module, such as converting all pressures to MPa, converting flow rates to m³ / min, converting temperatures to the Celsius scale, converting concentrations to mass percentages, converting corrosion rates to light intensity change rates, and converting sea conditions to period and amplitude. Finally, the acquisition module packages all standardized parameters into a structured data packet , including the timestamp, source identification, physical quantity name, and standardized value of each data item.

[0034] S2: Based on the standardized data set, the position of the acid liquid front, the near-well corrosion rate, and the trend of the wellhead pressure drop are calculated in real time by a state calculation model that fuses sea condition parameters, and a risk score is calculated in combination with the sea condition corrosion coupling mechanism, and a state parameter set and a risk level are output.

[0035] Specifically, this step calculates the operation state and identifies the risk level under the actual working conditions of offshore fracturing acidizing operations. Unlike conventional risk identification, this step not only analyzes the operation data in the wellbore, but also introduces the sea condition factor to correct the state calculation, so that the risk determination is more close to the actual operation stability requirements in the marine environment, thereby providing accurate and dynamic input for the generation of subsequent link strategies.

[0036] The wellhead pressure, pump displacement, acid concentration, fluid temperature, corrosion rate index , wave period , and wave amplitude are included. These data are first analyzed by a state calculation model. The model consists of three parts:

[0037] Time sequence feature extraction module: composed of three one-dimensional convolutional networks (convolution kernel size 3, channel number 16, 32, 64 in turn), used to extract the dynamic change trend of pressure, displacement, temperature, etc.

[0038] Physical mechanism calculation module: based on acidizing reaction kinetics and wellbore flow equation, the acid liquid front advance, acid liquid utilization rate, and corrosion evolution rate are calculated.

[0039] Environmental influence fusion module: combines sea condition parameters with physical state quantities through a fully connected network to correct the risk score.

[0040] In the calculation of the position of the acid liquid front , the physical module integrates pump displacement , acid effective utilization rate , wellbore cross-sectional area , and reaction rate coefficient To reflect the influence of sea state on acid liquid propulsion efficiency, a wave disturbance correction term is added in the denominator of the formula , which is represented by the ratio of wave amplitude to period, reflecting the fluctuation of pumping efficiency caused by wave fluctuation of platform and pipeline:

[0041] ;

[0042] wherein, , is the wave influence coefficient (calibrated by historical operation statistics), is the influence coefficient of acidification reaction on fluid flow resistance. In this way, when the wave amplitude increases or the period shortens, increases, and the front propulsion distance decreases accordingly, which is more consistent with the situation that pumping efficiency is disturbed in field operation.

[0043] The risk score calculation generates an initial score based on each key state quantity , and then performs environmental correction. Unlike conventional linear weighting, a "sea state-corrosion coupling term" is introduced here to amplify the influence of corrosion rate on risk in severe sea state:

[0044] ;

[0045] wherein, is the sea state influence weight coefficient, is the sea state-corrosion coupling coefficient (determined by statistical analysis of multiple field operations), is the reference corrosion rate (measured in stable sea state), is the risk score after environmental correction, used to reflect the comprehensive risk level under the joint action of current sea state and corrosion state. The innovation of this formula is that when the sea state is severe and the corrosion rate is high, the risk score will rise nonlinearly, so that the subsequent link strategy allocates more resources to transmit the key data of this stage.

[0046] Risk level is obtained by comparing with a set of preset thresholds (low, medium, and high levels), and the state parameter set contains key quantities such as near-well corrosion rate, acid liquid front position, and wellhead pressure drop trend.

[0047] S3: According to the state parameter set and the risk level, a link strategy parameter set is generated, including the redundancy encoding 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;

[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] wherein, is the base redundancy ratio corresponding to the link type (preset for satellite, underwater acoustic, and LoRa respectively); is the sea state influence weight coefficient, obtained by fitting historical link packet loss rate and sea state data; is the state sensitivity coefficient, so that high-priority state quantities can obtain additional redundancy under any sea state; is the maximum value among the comprehensive priority scores of all state quantities in the current strategy generation period, used for normalizing the priority scores of each state quantity to keep the redundancy ratio calculation consistent in dimension scale among different state quantities; is the sea state-corrosion coupling coefficient, when the wave amplitude is large and the corrosion rate deviates from the reference value by a large margin, the redundancy ratio will be significantly increased to prevent key corrosion monitoring data from being lost when the link is fading.

[0055] The last step of strategy generation is channel selection and packet sending timing planning. According to the current (wave period) and the link prediction quality curve, the packet sending time window of high-priority data is advanced by a compensation factor (estimated from the wave period and link delay) to the peak period of channel quality, while low-priority data is delayed or sent in batches to reduce packet loss and optimize bandwidth utilization. In terms of channel selection, the system prefers to use channels with higher redundancy and better stability (such as satellite direct connection) to transmit high-risk state quantities, and medium and low-risk quantities can be transmitted through acoustic-satellite or LoRa-microwave paths to balance power consumption and bandwidth. The output of this step is the link strategy parameter set , which includes the redundancy encoding ratio, compression ratio, priority category, transmission channel type, and packet sending time window of each state quantity.

[0056] S4: According to the link strategy parameter set, compress, redundantly encode, select channels, and control the packet sending timing of the data, and the sending end sends the data to the shore end through the cross-medium link, and the decoding, verification, and restoration are completed at the shore end.

[0057] Specifically, in this step, the link strategy parameter set output by step 3 is taken as input, and each parameter in it is implemented one by one in the actual execution of the cross-medium link, completing the sending, receiving, verifying, and restoring of data from the sea end to the shore end. has already included: the redundancy encoding ratio , compression level , priority category , and channel type corresponding to each state quantity (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), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the sea-land real-time data transmission and intelligent monitoring equipment for offshore fracturing and acidizing operations, and connects various parts of the sea-land real-time data transmission and intelligent monitoring equipment for offshore fracturing and acidizing operations through various interfaces and lines.

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

[0070] If the sea-land real-time data transmission and intelligent monitoring equipment integrated module of the offshore fracturing and acidizing operation is implemented in the form of a software function 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 above-mentioned embodiment methods can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the processor executes the computer program, the steps of the above-mentioned various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form. The computer-readable medium can include any entity or device that can carry the computer program code, a recording medium, a U disk, a mobile hard disk, 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 of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned various method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0072] The above is the preferred embodiment of the present application. It should be noted that those of ordinary skill in the art can make several improvements and refinements without departing from the principles of the present application. These improvements and refinements are also considered within the scope of protection of the present application.

Claims

1. A method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operations, characterized in that, The method includes: 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. Based on the standardized dataset, a state calculation model integrating sea state parameters is used to calculate the acid front location, near-wellbore corrosion rate, and wellhead pressure drop trend in real time. Risk scoring is then performed by combining the sea state-corrosion coupling mechanism, outputting a set of state parameters and a risk level. 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, composed of a multi-layer one-dimensional convolutional neural network, is used to extract dynamic change features from time-series data of wellhead pressure, pump displacement, and fluid temperature. The physical mechanism calculation module, based on acidization reaction kinetics and wellbore flow equations, introduces a wave disturbance correction term represented by the ratio of wave amplitude to period to calculate the acid front location, near-wellbore corrosion rate, and wellhead pressure drop trend. 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 a risk level integrating environmental impact. The sea state parameters include wave period and wave amplitude. 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. 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.

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

3. The method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operations 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 and calculating it.

4. The method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operations according to claim 1, characterized in that, 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.

5. The method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operations according to claim 1, characterized in that, During the generation of the link strategy parameter set, the redundancy coding ratio is dynamically adjusted by the basic redundancy ratio, the sea state influence weight, the state sensitivity coefficient, and the sea state-corrosion coupling coefficient.

6. 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 transmission time window is adjusted forward or delayed based on wave cycle and link quality prediction. Specifically, this includes: establishing a prediction model based on wave cycle parameters monitored in real time by sea state sensors and historical link quality data to generate a prediction curve of link quality changes over time; 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.

7. 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 links include acoustic-satellite relay, direct satellite connection, and LoRa-microwave path.

8. The method for real-time data transmission and intelligent monitoring of offshore fracturing and acidizing operations according to claim 7, characterized in that, 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.

Images