A pipeline multiphase flow metering and monitoring method and system

Abstract

The present invention discloses a method and system for pipeline multiphase flow metering and monitoring, belonging to the technical field of fluid measurement. It includes obtaining the actual flow velocity value and bimodal sensing signals of the fluid in the pipeline; obtaining historical operating condition data to dynamically adjust the sampling frequency of the bimodal sensing signals, generating an optimized sensing sampling signal, and performing time-frequency domain joint noise reduction processing to generate edge-end preprocessed data; using a time series prediction model to perform multi-step prediction on the edge-end preprocessed data to generate a dynamic compensation coefficient; calculating the multiphase flow rate value according to the dynamic compensation coefficient and the actual flow velocity value, and generating an abnormal event warning signal according to the multiphase flow rate value. The present invention adopts the fusion of resistive and ultrasonic bimodal signals, time-frequency domain joint noise reduction, and cloud-edge collaborative dynamic compensation technology, which can realize high-precision real-time monitoring of multiphase flow rate, and based on the flow rate fluctuation, intelligently trigger abnormal warnings and adjustment operations, comprehensively improving the safety of pipeline operation and measurement reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of fluid measurement, and in particular to a pipeline multiphase flow metering and monitoring method and system. Background Art

[0002] Pipeline multiphase flow metering technology is mainly used for real-time flow monitoring of oil, gas, and water mixed fluids, and involves the measurement of core technical parameters such as conductivity difference, Doppler frequency shift, and phase fraction ratio.

[0003] In the prior art, a multi-sensor combination scheme with a fixed sampling frequency is mostly adopted, and the multiphase flow rate is calculated by the weighted average method or the static calibration model. For example, a resistive sensor and an ultrasonic probe are installed in parallel, and after the signals are collected at a preset frequency, the flow velocity and phase fraction ratio are linearly fitted based on the historical data of a single working condition, so as to realize the separation metering of oil, gas, and water. Such a scheme often corrects the measurement error by means of off-line calibration or interpolation compensation.

[0004] However, the data synchronization between sensors in the above technical scheme is poor, resulting in the accumulation of matching errors between the phase fraction ratio and the flow velocity; the fixed sampling frequency cannot adapt to the dynamic characteristics of multiphase flow, and key signals are easily lost in the scenarios of high gas content or rapid phase change; the static calibration model is difficult to correct the real-time deviation caused by pressure fluctuation and temperature change, and lacks the ability to robustly process complex noise interference. These defects directly affect the accuracy and real-time performance of multiphase flow rate monitoring, and limit the application reliability in industrial scenarios. Summary of the Invention

[0005] To solve the above problems, the present invention provides a pipeline multiphase flow metering and monitoring method and system, which adopts resistive and ultrasonic dual-mode signal fusion, time-frequency domain joint noise reduction, and cloud-edge collaborative dynamic compensation technology, can realize high-precision real-time monitoring of multiphase flow rate, and intelligently trigger abnormal early warning and adjustment operations based on flow rate fluctuations, comprehensively improving the safety and measurement reliability of pipeline operation.

[0006] The above object can be achieved by the following solutions:

[0007] A pipeline multiphase flow metering and monitoring method includes obtaining the actual flow velocity value of the fluid in the pipeline and bimodal sensing signals, where the bimodal sensing signals include resistive phase fraction signals and ultrasonic frequency shift signals; obtaining historical operating condition data, and dynamically adjusting the sampling frequency of the bimodal sensing signals based on the historical operating condition data to generate an optimized sensing sampling signal; performing time-frequency domain joint noise reduction processing on the optimized sensing sampling signal to generate edge-end preprocessed data; using a time series prediction model preset in the cloud to perform multi-step prediction on the edge-end preprocessed data to generate a dynamic compensation coefficient; calculating the multiphase flow rate value according to the dynamic compensation coefficient and the actual flow velocity value; outputting the multiphase flow rate value, and generating an abnormal event warning signal according to the multiphase flow rate value.

[0008] Optionally, the generating the optimized sensing sampling signal includes: obtaining the real-time pressure data, temperature data and historical medium ratio data of the pipeline to obtain a basic data set; generating flow velocity prediction models for different phases based on the basic data set; calculating the correlation weights of the resistive phase fraction signal and the ultrasonic frequency shift signal according to the flow velocity prediction models; adjusting the sampling frequencies of each mode in the bimodal sensing signal based on the correlation weights to generate an optimized sensing sampling signal.

[0009] Optionally, the performing time-frequency domain joint noise reduction processing on the optimized sensing sampling signal to generate edge-end preprocessed data includes: performing wavelet transform on the ultrasonic frequency shift signal in the sensing sampling signal to filter out frequency domain noise to generate a preliminary noise reduction signal; combining the pulse characteristics of the resistive phase fraction signal to perform waveform matching on the preliminary noise reduction signal to remove the remaining noise to generate edge-end preprocessed data.

[0010] Optionally, the generating the edge-end preprocessed data further includes: extracting the mutation waveform in the resistive phase fraction signal as a reference template; calculating the local correlation coefficient between the preliminary noise reduction signal and the reference template; when the local correlation coefficient is lower than a preset correlation threshold, correcting the edge-end preprocessed data through backpressure simulation testing.

[0011] Optionally, the using a time series prediction model preset in the cloud to perform multi-step prediction on the edge-end preprocessed data to generate a dynamic compensation coefficient includes: dividing the edge-end preprocessed data into a high-confidence data set and a low-confidence data set according to a preset time stamp through a preset rule; using a reinforcement learning method to optimize the compensation parameters of the low-confidence data set to generate a compensation parameter increment; inputting the compensation parameter increment into a long short-term memory network for training to generate a dynamic compensation coefficient.

[0012] Optionally, the division into a high-confidence data set and a low-confidence data set by a preset rule includes: calculating a phase fraction ratio error value between the resistive phase fraction signal and the ultrasonic frequency shift signal; determining whether the phase fraction ratio error value is less than a preset noise threshold and the noise energy is lower than a preset energy threshold; if so, marking the edge preprocessed data as a high-confidence data set; if not, marking the edge preprocessed data as a low-confidence data set.

[0013] Optionally, the generation of the dynamic compensation coefficient includes: constructing a multi-objective loss function based on the high-confidence data set and the compensation parameter increment; using the multi-objective loss function as the loss function of the long short-term memory network, and optimizing the multi-objective loss function by the gradient descent method to obtain the dynamic compensation coefficient.

[0014] Optionally, the calculation of the multiphase flow rate value according to the dynamic compensation coefficient and the actual flow rate value includes: extracting the phase fraction ratio and the frequency shift value from the edge preprocessed data; performing density weighted integration on the phase fraction ratio by using the dynamic compensation coefficient to generate a phase fraction density value; performing flow rate correction calculation on the actual flow rate value by using the dynamic compensation coefficient and the frequency shift value to generate a corrected flow rate value; calculating the multiphase flow rate value based on the phase fraction density value and the corrected flow rate value.

[0015] Optionally, the generation of the abnormal event warning signal according to the multiphase flow rate value includes: when the fluctuation amplitude of the multiphase flow rate value exceeds the safety threshold, triggering a pipeline backpressure regulation operation; synchronously uploading a warning log including the abnormal event type and the pipe section location information to the cloud platform.

[0016] Based on the same inventive concept, the present invention also provides a pipeline multiphase flow metering and monitoring system, the system includes: a data acquisition module, configured to acquire the actual flow rate value of the fluid in the pipeline and a bimodal sensing signal, the bimodal sensing signal includes a resistive phase fraction signal and an ultrasonic frequency shift signal; a signal optimization module, configured to dynamically adjust the sampling frequency of the bimodal sensing signal based on historical working condition data to generate an optimized sensing sampling signal; a data processing module, configured to perform time-frequency domain joint noise reduction processing on the optimized sensing sampling signal to generate edge preprocessed data; a compensation calculation module, configured to perform multi-step prediction on the edge preprocessed data by using a preset time series prediction model in the cloud to generate a dynamic compensation coefficient; a flow rate calculation module, configured to calculate a multiphase flow rate value according to the dynamic compensation coefficient and the actual flow rate value; a flow rate output and warning module, configured to output the multiphase flow rate value and generate an abnormal event warning signal according to the multiphase flow rate value.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] 1. Through complementary measurement of bimodal sensing signals and optimization of dynamic sampling, the present invention significantly improves the detection accuracy of the phase fraction ratio and flow velocity of multiphase flow under complex flow patterns; the collaborative use of resistive phase fraction signals and ultrasonic frequency shift signals can not only effectively distinguish fluid phases with different electrical conductivity characteristics, but also capture changes in flow velocity based on the Doppler effect, avoiding misjudgment problems caused by sudden changes in fluid physical properties in traditional single-modal sensors;

[0019] 2. By adopting a joint time-frequency domain noise reduction and cloud-edge collaborative computing model, the noise interference is greatly reduced and the data processing efficiency is improved; through a noise reduction technology combining wavelet transform and waveform matching, while retaining the real fluid phase change signal, mechanical vibration and environmental interference noise are filtered out, and combined with a dynamic compensation mechanism of edge-end preprocessing and cloud-side time series prediction, the anti-interference and real-time performance of multiphase flow rate calculation are improved simultaneously;

[0020] 3. Through the closed-loop design of the dynamic compensation coefficient and the abnormal warning mechanism, the adaptability and safety of the system to working condition changes are enhanced; the multi-step prediction model based on reinforcement learning and long short-term memory network can correct measurement errors in real time, dynamically adjust flow rate calculation parameters, and quickly trigger backpressure regulation and cloud warning in combination with flow rate fluctuation monitoring, effectively preventing pipeline blockage or leakage accidents and reducing operation and maintenance risks.

[0021] Other features and advantages of the present invention will be described in the following specification, and part of them will become obvious from the specification, or be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures pointed out in the specification, claims and drawings. Description of the Drawings

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 It is a schematic flow chart of a pipeline multiphase flow metering and monitoring method according to an embodiment of the present invention.

[0024] Figure 2 It is a change curve graph of the resistive phase change signal according to an embodiment of the present invention.

[0025] Figure 3 It is a schematic structural diagram of a pipeline multiphase flow metering and monitoring method according to an embodiment of the present invention. Detailed Embodiments

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] Referring to Figure 1 , an embodiment of the present invention proposes a method for pipeline multiphase flow measurement and monitoring. By adopting the technologies of resistance and ultrasonic dual-modal signal fusion, time-frequency domain joint noise reduction, and cloud-edge collaborative dynamic compensation, it can achieve high-precision real-time monitoring of multiphase flow rate, and based on the flow rate fluctuation, intelligently trigger abnormal early warnings and adjustment operations, comprehensively improving the safety of pipeline operation and measurement reliability.

[0028] The method of this embodiment specifically includes:

[0029] Obtain the actual flow velocity value of the fluid in the pipeline and the dual-modal sensing signals, where the dual-modal sensing signals include resistance-based phase fraction signals and ultrasonic frequency shift signals:

[0030] Specifically, install a circular electrode array composed of 16 groups of platinum electrodes on the outer surface of the pipeline in a circular shape. By applying 5 kHz alternating current to measure the conductivity difference of the fluid, generate resistance-based phase fraction signals to reflect the phase fraction ratios of oil, gas, and water; at the same time, arrange 8 groups of distributed ultrasonic probes at intervals of 1 meter along the axial direction of the pipeline. Each group of probes emits 1 MHz ultrasonic waves and receives the reflected signals, and calculate the flow velocity difference through Doppler frequency shift to generate ultrasonic frequency shift signals.

[0031] Among them, the dual-modal sensing signals are composite signals that simultaneously measure electrical and acoustic characteristics; the resistance-based phase fraction signals are potential distribution electrical signals based on the conductivity differences of different-phase fluids; the ultrasonic frequency shift signals are acoustic signals caused by the movement of discrete-phase particles in the fluid, which change the ultrasonic frequency.

[0032] Obtain historical operating condition data, and dynamically adjust the sampling frequency of the dual-modal sensing signals based on the historical operating condition data to generate optimized sensing sampling signals;

[0033] Perform time-frequency domain joint noise reduction processing on the optimized sensing sampling signals to generate edge-end preprocessed data;

[0034] Use the time series prediction model preset in the cloud to perform multi-step prediction on the edge-end preprocessed data to generate dynamic compensation coefficients;

[0035] Calculate the multiphase flow rate value according to the dynamic compensation coefficients and the actual flow velocity value;

[0036] Output the multiphase flow rate value, and generate an early warning signal for abnormal events based on the multiphase flow rate value.

[0037] This method solves the misjudgment problem of traditional single sensors under complex flow patterns through the complementary fusion of dual-mode signals, dynamically adjusts the sampling frequency to improve resource utilization efficiency; joint time-frequency domain noise reduction suppresses environmental interference while retaining real phase boundary signals; the cloud-edge collaborative model combines long-term memory and real-time optimization, significantly improving the anti-interference ability and working condition adaptability of flow rate calculation; the closed-loop early warning mechanism enables rapid response to abnormal events such as pipeline blockage and phase separation, reducing operation and maintenance costs and safety risks.

[0038] This implementation method constructs a dual-mode sensing system by fusing resistive phase separation signals and ultrasonic frequency shift signals to synchronously sense the distribution and motion characteristics of different-phase media in the pipeline. The system dynamically optimizes the sampling frequency of the sensing signal in combination with historical working condition data to ensure the sampling efficiency and quality of the signal under different working conditions. Subsequently, noise interference in the signal is removed through a joint time-frequency domain noise reduction method (such as wavelet transform combined with waveform matching) to improve data accuracy. The edge-processed data is sent to a cloud-based time series prediction model such as Long Short-Term Memory (LSTM) for signal trend prediction in the next few steps, thereby generating a dynamic coefficient for error compensation. Finally, the measured actual flow velocity value is adjusted based on this dynamic compensation coefficient to achieve accurate calculation of the multiphase flow rate, and whether there are abnormal events is judged based on the flow rate change, thereby triggering the corresponding early warning mechanism. This implementation method realizes more accurate capture of multiphase fluid characteristics by introducing dual-mode signal acquisition and dynamic sampling frequency control technology; combines time-frequency joint noise reduction and edge-cloud collaborative calculation models to improve the real-time performance and accuracy of data processing; the introduction of the dynamic compensation coefficient effectively corrects the sensing error, enhancing the measurement accuracy and robustness; the integration of the abnormal early warning mechanism greatly improves the safety response ability and intelligent level of the system, and is applicable to complex and changeable industrial field environments.

[0039] Optionally, the generating of the optimized sensing sampling signal includes:

[0040] Obtain the real-time pressure data, temperature data and historical medium ratio data of the pipeline to obtain a basic data set;

[0041] Specifically, pressure data and temperature data are collected in real time from the pressure sensors and temperature sensors installed on the pipeline wall, and historical medium ratio data of oil, gas, and water in the same pipe section within the past 30 days is extracted from the historical database, such as a ratio record of 60% oil phase, 25% gas phase, and 15% water phase. The pressure sensor is a piezoelectric sensor with an accuracy class of 0.1% FS; the temperature sensor uses a PT100 platinum resistance with a resolution of 0.1 °C. After aligning the three types of data in time series, a basic data set is formed and stored as an array sequence in JSON format. Each timestamp corresponds to a three-dimensional vector containing pressure, temperature, and medium ratio.

[0042] Generate flow rate prediction models for different phases based on the basic data set;

[0043] Specifically, the basic data set is input into a Gaussian process regression model for training, with the historical flow rate as the dependent variable and pressure, temperature, and medium ratio as independent variables. Gaussian process regression is a non-parametric Bayesian model based on kernel functions. By optimizing covariance function parameters such as the radial basis function kernel, the non-linear relationship of the flow rates of each phase is learned. For example, when the gas phase is dominant, the flow rate is positively correlated with the square of the pressure; when the water phase is dominant, the flow rate has a negative exponential relationship with the medium viscosity. The model output is independent prediction equations for different phases of oil, gas, and water, and its mathematical expression is:

[0044] ;

[0045] In the formula, is the predicted value of the flow rate of the phase; is the phase flow coefficient calculated by the Gaussian kernel function of the phase, is the real-time pressure, is the real-time temperature, is the viscosity attenuation coefficient, is the phase fluid viscosity, is the noise term.

[0046] Calculate the correlation weight of the resistive phase fraction signal and the ultrasonic frequency shift signal according to the flow rate prediction model;

[0047] Specifically, the similarity between the two signals is analyzed through the covariance matrix. Calculate the cross-covariance between the phase fraction in the resistive phase fraction signal and the flow velocity of the ultrasonic frequency shift signal, and then obtain the correlation weight of the two signals through a weight assignment algorithm such as the entropy weight method. The phase fraction in the resistive phase fraction signal is such as the oil phase conductivity of 0.1 S / m and the gas phase of 0.01 S / m, and the flow velocity of the ultrasonic frequency shift signal is such as 3 m / s for the liquid phase and 2.5 m / s for the gas-liquid mixed flow. For example, when the gas phase ratio is higher than 70%, due to the decreased sensitivity of ultrasonic waves to the bubble group, the weight of the resistive signal is increased to 0.8, and the weight of the ultrasonic signal is decreased to 0.2. The weight formula is:

[0048] ;

[0049] In the formula, is the weight coefficient of the resistive phase fraction signal, is the weight coefficient of the ultrasonic frequency shift signal, is the variance of the resistive phase fraction signal, is the variance of the ultrasonic frequency shift signal.

[0050] Based on the correlation weight, adjust the sampling frequency of each mode in the dual-mode sensing signal to generate an optimized sensing sampling signal.

[0051] Specifically, the resistive phase fraction signal and the ultrasonic frequency shift signal adopt frequencies according to the weight assignment. For the sampling frequency of the resistive phase fraction signal, there is:

[0052] ;

[0053] In the formula, is the set total sampling frequency. For the sampling frequency of the ultrasonic frequency shift signal, there is:

[0054] ;

[0055] Specifically, set the total sampling frequency to 1.5 kHz. When the weight coefficient of the resistive phase fraction signal is 0.6, the sampling frequency of the resistive phase fraction signal is 900 Hz at this time, and the sampling frequency of the ultrasonic frequency shift signal is 600 Hz.

[0056] Exemplarily, taking the three-phase flow containing 65% crude oil, 25% formation water, and 10% natural gas as an example, the pressure sensor reads 3.5 MPa and the temperature is 50 °C. The average oil phase in the historical ratio is 68% to construct a basic data set. The flow rate prediction model predicts the oil phase flow rate of 2.8 m / s, the gas phase of 3.2 m / s, and the water phase of 2.5 m / s. The variance of the resistive signal is calculated to be 0.12, and the variance of the ultrasonic signal is 0.35. According to the formula, the weight coefficient of the resistive phase fraction signal is 0.74, and the weight coefficient of the ultrasonic frequency shift signal is 0.26. Therefore, the sampling rate of the resistive electrode array is increased to 2 kHz, and the ultrasonic probe is reduced to 800 Hz; wavelet transform filters out the 10 kHz high-frequency noise caused by pump vibration, and waveform matching eliminates droplet impact interference, and the output signal-to-noise ratio is increased by 22 dB. By dynamically adjusting the dual-mode sampling frequency, the resolution of the resistive signal is preferentially guaranteed in the oil-gas dominant scenario, avoiding invalid sampling of the ultrasonic signal due to bubble interference; time-frequency noise reduction combined with physical feature templates effectively filters out mechanical vibration noise unrelated to phase fraction changes; dynamic weight allocation makes subsequent calculations more dependent on high-confidence signal types, improving the flow accuracy.

[0057] Optionally, the joint time-frequency domain noise reduction processing of the optimized sensing sampling signal to generate edge-end preprocessed data includes:

[0058] Performing wavelet transform on the ultrasonic frequency shift signal in the sensing sampling signal to filter out frequency domain noise and generate a preliminary noise reduction signal;

[0059] Specifically, first perform discrete wavelet transform on the ultrasonic frequency shift signal. Discrete wavelet transform is a time-frequency analysis method that decomposes the signal through the mother wavelet function at different scales and time positions, separating high-frequency noise from low-frequency useful signals. Specifically, the Daubechies 4 wavelet basis is used, and the decomposition level is 5 layers. Hard thresholds are set for the high-frequency components of the first to third layers to filter out noise components, and a preliminary noise reduction signal is generated after reconstructing the signal. The frequency domain noise is the high-frequency random fluctuation caused by pipeline mechanical vibration or electromagnetic interference in the ultrasonic signal, such as high-frequency harmonic interference above 100 kHz.

[0060] Combining the pulse characteristics of the resistive phase fraction signal, performing waveform matching on the preliminary noise reduction signal to eliminate the remaining noise and generate edge-end preprocessed data.

[0061] Specifically, the pulse feature of the resistive phase discrimination signal is a waveform of sudden change in conductivity caused by the change of the fluid phase interface. For example, the sudden drop in conductivity at the oil-gas / water interface is manifested as a rectangular pulse with a decreasing amplitude. Such a segment of the waveform of sudden change is extracted from the resistive signal as a feature template, and the length of the intercepted time window is 50 ms. In the corresponding time window of the preliminarily noise-reduced signal, its local similarity with the feature template is calculated through a sliding window. The waveform matching is a similarity measurement method based on the dynamic time warping algorithm, which allows time offset during waveform alignment and minimizes the Euclidean distance between the two window signals. If the similarity is lower than the threshold of 0.7, it is determined as a remaining noise segment and set to zero for elimination.

[0062] Optionally, the generation of edge-side preprocessed data further includes:

[0063] extracting the waveform of sudden change in the resistive phase discrimination signal as a reference template;

[0064] Specifically, the reference template is a normalized waveform intercepted from the step change region of the resistive phase discrimination signal. The waveform of sudden change is defined as the interval where the phase discrimination ratio changes by more than 15% within 0.5 seconds. For example, the conductivity drop segment corresponding to the oil phase dropping from 60% to 40%. After traversing the signal using the sliding window method and screening out the segments of sudden change that meet the conditions, the peak points of multiple samples are aligned and the average waveform is calculated to generate a reference template with a length of 200 sampling points.

[0065] calculating the local correlation coefficient between the preliminarily noise-reduced signal and the reference template;

[0066] Specifically, based on the reference template, the normalized cross-correlation calculation is performed at each window position of the preliminarily noise-reduced signal, and the formula is:

[0067] ;

[0068] In the formula, is the local correlation coefficient, is the value of the th sampling point in the current window of the preliminarily noise-reduced signal, is the average value of all sampling points in the current window of the preliminarily noise-reduced signal, is the value of the th sampling point in the position corresponding to the current window of the preliminarily noise-reduced signal of the reference signal, is the average value of all sampling points in the position corresponding to the current window of the preliminarily noise-reduced signal of the reference signal, is the number of sampling points in the window. If is lower than a preset threshold such as 0.75, it is determined that there is noise in this window that needs to be corrected.

[0069] When the local correlation coefficient is lower than a preset correlation threshold, the edge-end preprocessed data is corrected through a backpressure simulation test.

[0070] Specifically, the backpressure simulation test is a method for verifying the stability of fluid phase separation by generating a reverse pressure wave by adjusting the valve at the end of the pipeline. During the correction stage, the valve opening is reduced by 20% for 10 seconds, and the resistive and ultrasonic signals under the current pressure fluctuation are collected simultaneously. If the correlation coefficient during the correction stage is higher than the correlation threshold, the multiphase flow characteristic parameters at this time are used to overwrite the original abnormal window data; if it is still lower than the correlation threshold, the data during this period is considered unreliable and marked as a segment to be excluded.

[0071] Exemplarily, taking the oil-water two-phase flow in an oilfield pipeline as an example, as Figure 2 shown, there are 3 sudden drops in conductivity at the phase interface changes in the measured resistive phase fraction signal, with the amplitude dropping from 2.1 S / m to 0.8 S / m. There is flow velocity fluctuation noise in the ultrasonic frequency shift signal, and high-frequency interference still remains after preliminary noise reduction. The first sudden drop segment (timestamp 10:05:23 - 10:05:25) of the resistive signal is selected to generate a reference template. The local correlation coefficient calculated for the same time period window of the preliminarily noise-reduced signal is 0.82, which is higher than the correlation threshold of 0.75, and the data is retained; while in the non-phase change interference segment from 10:06:15 to 10:06:17, the local correlation coefficient is calculated to be 0.48, triggering a backpressure test. After the valve is closed during the correction stage, the local correlation coefficient of the noise segment is increased to 0.91, overwriting the original data; the corrected flow velocity of the abnormal segment is adjusted from 1.8 m / s to 2.3 m / s. Using the mutation waveform of the resistive signal as a physical feature template can effectively distinguish real phase changes from random noise, avoiding misjudging droplet impacts as phase interface changes; the backpressure test combined with the dynamic compensation mechanism repairs the measurement deviation caused by bubble accumulation in the fluid stability verification, ensuring the reliability of the preprocessed data.

[0072] Optionally, the multi-step prediction of the edge-end preprocessed data using a preset time series prediction model in the cloud to generate a dynamic compensation coefficient includes:

[0073] Dividing the edge-end preprocessed data into a high-confidence data set and a low-confidence data set according to a preset timestamp through a preset rule;

[0074] Specifically, arrange the edge - end pre - processed data in the order of collection time, and the time stamp is in UTC format including year, month, day, hour, minute, and second. The preset rules perform the following operations: For the data at each time stamp, calculate the phase - fraction ratio error value between the resistive phase - fraction signal and the ultrasonic frequency - shift signal, that is, the absolute value of the difference between the oil, gas, and water ratio inverted by the resistive signal and the phase - fraction ratio calculated by the ultrasonic frequency - shift signal through the Doppler formula. At the same time, calculate the noise energy within this time period, which is the energy integral of the residual high - frequency components in the signal after wavelet denoising. Determine whether the phase - fraction ratio error value is less than a preset noise threshold such as 0.05, and whether the noise energy is lower than a preset energy threshold such as 10 dBm. If the above conditions are met, mark the corresponding data as a high - confidence dataset; otherwise, mark it as a low - confidence dataset.

[0075] Among them, the time stamp is the absolute time identifier at the time of data collection, accurate to milliseconds; the noise threshold is the maximum allowable deviation of the phase - fraction ratio; the energy threshold is the upper limit of the energy for judging the signal purity.

[0076] Use the reinforcement learning method to optimize the compensation parameters of the low - confidence dataset and generate a compensation parameter increment;

[0077] Specifically, adopt the Deep Deterministic Policy Gradient algorithm as the reinforcement learning method. Take the noise energy, phase - fraction error, and flow velocity fluctuation in the low - confidence dataset as the state space, the compensation parameter adjustment direction as the action space, and set the objective function to minimize the root - mean - square value of the prediction error in subsequent training. The agent generates the compensation parameter increment through exploration and exploitation policy iteration, and each increment includes an amplitude adjustment coefficient and a phase - lag correction amount. For example, in the oil - gas mixture flow scenario, the agent generates an increment with an amplitude coefficient of 0.8 and a phase - lag correction amount of 5° according to the fluctuation law dominated by bubble noise. Among them, the reinforcement learning method is a machine - learning paradigm for learning the optimal policy by interacting with the environment; the compensation parameter increment is the adjustment amount value of the compensation parameter per unit time.

[0078] Input the compensation parameter increment into a long - short - term memory network for training to generate a dynamic compensation coefficient.

[0079] Specifically, the long - short - term memory network structure includes an input layer, two hidden layers, and an output layer. The input layer receives the compensation parameter increment in the form of a time series, the number of hidden - layer units is 128, and the tanh activation function is used. During training, use the high - confidence dataset as the reference truth value, the low - confidence dataset combined with the compensation parameter increment as the input, and the loss function is the mean - square error between the predicted flow rate and the actual flow velocity. For the dynamic compensation coefficient at time , there is:

[0080] ;

[0081] In the formula, is the non - linear mapping function of the long - short - term memory network, is from time to the sequence of compensation parameter increments at time, is the state memory of the hidden layer of the long - short - term memory network at time. Among them, the long - short - term memory network is a time - series prediction neural network with a gating mechanism; the dynamic compensation coefficient is a time - varying parameter used to correct the deviation of flow velocity and phase - fraction ratio.

[0082] Optionally, the division into a high - confidence data set and a low - confidence data set by a preset rule includes:

[0083] Calculate the phase - fraction ratio error value between the resistive phase - fraction signal and the ultrasonic frequency - shift signal;

[0084] Specifically, extract the phase - fraction ratio values of oil, gas, and water from the resistive phase - fraction signal, for example, oil phase 65%, gas phase 20%, water phase 15%; at the same time, inversely calculate the phase - fraction ratio through the Doppler flow - velocity formula of the ultrasonic frequency - shift signal, for example, oil phase 60%, gas phase 25%, water phase 15%. For the phase - fraction ratio error value , there is:

[0085] ;

[0086] In the formula, is the number of phase states. For example, oil, gas, and water are 3 phases, is the phase - fraction ratio of the phase of the resistive phase - fraction signal, is the phase - fraction ratio of the phase of the ultrasonic frequency - shift signal. Among them, the resistive phase - fraction signal is a phase - fraction measurement signal based on the difference in fluid conductivity; the ultrasonic frequency - shift signal is a phase - fraction signal calculated by inverting the flow velocity through the Doppler effect; the phase - fraction ratio error value is the absolute difference in the measurement results of the same phase - state ratio by the two - mode signals.

[0087] Judge whether the phase - fraction ratio error value is less than a preset noise threshold and the noise energy is lower than a preset energy threshold;

[0088] Specifically, the noise threshold is the upper limit of the allowable phase - fraction error, set according to experimental calibration or historical data statistics, for example, 0.05, corresponding to a 5% deviation of the phase - fraction ratio; the noise energy is calculated by integrating the energy of the high - frequency components of the signal decomposed by wavelet transform. For the noise energy , there is:

[0089] ,

[0090] In the formula, is the total number of layers of the high - frequency components after wavelet decomposition, is the high-frequency component of the m-th layer after wavelet decomposition, is the integral over the time interval. The preset energy threshold is 10 dBm. If both ≤ noise threshold and ≤ energy threshold are satisfied, then the next step is executed. Among them, the noise threshold is the signal consistency standard set by experience; the energy threshold is the allowable upper limit of the high-frequency interference intensity.

[0091] If so, mark the edge-side preprocessed data as a high-confidence data set;

[0092] If not, mark the edge-side preprocessed data as a low-confidence data set.

[0093] Specifically, for the edge-side preprocessed data within a certain time window, if , is less than the noise threshold of 0.05 and , is less than the energy threshold of 10 dBm, then it is marked as a high-confidence data set and stored in the main partition of the database for model training; if , is greater than the noise threshold of 0.05 or , is greater than the energy threshold of 10 dBm, then it is marked as a low-confidence data set and stored in the auxiliary partition for reinforcement learning optimization.

[0094] Optionally, the generation of the dynamic compensation coefficient includes:

[0095] Construct a multi-objective loss function based on the high-confidence data set and the compensation parameter increment;

[0096] Specifically, the high-confidence data set provides groups of sample data (such as phase fraction ratio, flow rate, density), and the compensation parameter increment is the compensation parameter adjustment amounts output by reinforcement learning. When constructing the loss function, the main objective is set as the mean square error between the predicted flow rate and the actual flow velocity, and the secondary objective is the stability constraint of the compensation parameter. The multi-objective loss function formula is:

[0097] ;

[0098] In the formula, is the total loss value, and are the weight coefficients, is the measured flow rate of the th group of high-confidence samples, is the predicted flow rate, is the th compensation parameter increment. Among them, the multi-objective loss function is a composite evaluation function that simultaneously optimizes the main task accuracy and parameter stability; the compensation parameter increment is the dynamic adjustment coefficient generated by reinforcement learning.

[0099] Take the multi-objective loss function as the loss function of the long short-term memory network, and optimize the multi-objective loss function by the gradient descent method to obtain the dynamic compensation coefficient.

[0100] Specifically, the long short-term memory network adopts the Adam optimizer, and the learning rate is set to 0.001. Each time during training, the input data is the time-serialized high-confidence dataset and the compensation parameter increment, and the network output is the predicted value of the dynamic compensation coefficient. During the gradient descent process, calculate the total loss value of the partial derivatives of the weight parameters of each layer, and update the network weights by backpropagation. After the final model converges, the average deviation between the predicted flow rate after compensation and the measured flow rate is less than 1%.

[0101] Among them, the long short-term memory network is a recurrent neural network with temporal memory ability; the gradient descent method is an optimization algorithm that minimizes the loss function by iteratively adjusting parameters.

[0102] Optionally, the calculation of the multiphase flow rate value according to the dynamic compensation coefficient and the actual flow velocity value includes:

[0103] Extract the phase fraction ratio and the frequency shift value from the edge-side preprocessed data;

[0104] Specifically, perform time window truncation on the edge-side preprocessed data, and the length of each window is 1 second. Parse the phase fraction ratios of oil, gas, and water from the resistive phase fraction signal. For example, the oil phase fraction ratio is 65%, the gas phase fraction ratio is 25%, and the water phase fraction ratio is 10%; calculate the flow velocity displacement from the ultrasonic frequency shift signal based on the Doppler frequency shift formula and convert it to an equivalent frequency shift value. For the equivalent frequency shift value , there is:

[0105] ;

[0106] In the formula, is the flow velocity displacement, is the ultrasonic reference frequency of 1 MHz. Among them, the phase fraction ratio is the volume ratio of each phase fluid in the total flow rate; the frequency shift value is the normalized difference between the ultrasonic emission and reception frequencies caused by the fluid motion.

[0107] Use the dynamic compensation coefficient to perform density weighted integration on the phase fraction ratio to generate the phase fraction density value;

[0108] Specifically, according to the dynamic compensation coefficient correct the phase fraction ratios of each phase, and combine the standard densities of each phase to calculate the density weighted integration. For the phase fraction density value , there is:

[0109] ;

[0110] In the formula, is the standard density of the phase, such as 850 kg / m³ for crude oil, 0.8 kg / m³ for natural gas, and 1000 kg / m³ for water. Among them, the density weighted integral is a composite physical quantity combining the phase fraction ratio and the substance density; the dynamic compensation coefficient is a time-varying parameter for correcting the measurement error of multiphase flow.

[0111] Perform flow velocity correction calculation on the actual flow velocity value by using the dynamic compensation coefficient and the frequency shift value to generate a corrected flow velocity value;

[0112] Specifically, the actual flow velocity value can be measured by a turbine flowmeter, and the frequency shift value is the equivalent frequency shift value reflecting the velocity difference of fluid micro-elements. For the corrected flow velocity value , there is:

[0113] ;

[0114] In the formula, is the actual flow velocity value, is the conversion coefficient, calibrated by the probe spacing and the sound velocity, such as . Among them, the flow velocity correction calculation is a method for compensating the actual flow velocity measurement deviation through the frequency shift signal; the conversion coefficient is a fixed correlation factor between the geometric parameters of the ultrasonic measurement system and the sound velocity.

[0115] Calculate the multiphase flow rate value based on the phase fraction density value and the corrected flow velocity value.

[0116] Specifically, the multiphase flow rate value is the total mass flow rate flowing through the pipeline cross-section per unit time. For the multiphase flow rate value , there is:

[0117] ;

[0118] In the formula, is the pipeline cross-sectional area.

[0119] Exemplarily, in a subsea oil pipeline, the edge-end preprocessed data shows that the oil phase is 68%, the gas phase is 10%, and the water phase is 22%. The dynamic compensation coefficient , and the weight needs to be reduced due to bubble interference. The actual flow velocity , and the frequency shift value is the equivalent frequency shift value , corresponding to the flow fluctuation. The density weighted integral gives , and the corrected flow velocity is calculated. The final flow rate, that is, the multiphase flow rate value . Compared with when there is no compensation , , the compensated flow rate is closer to the measured value of 41.2 kg / s obtained from offline sampling, and the error is reduced from 11.6% to 1.2%. By adaptively weakening the dual interference of bubbles on density weighting and flow velocity through the dynamic compensation coefficient, it avoids overestimating the contribution of the light gas phase, making the calculated value of the mixed flow rate more consistent with the actual transportation mass, especially suitable for oil-gas-water multiphase flow scenarios with fluctuating gas holdup.

[0120] Optionally, generating an abnormal event warning signal based on the multiphase flow rate value includes:

[0121] When the fluctuation amplitude of the multiphase flow rate value exceeds the safety threshold, trigger the pipeline backpressure regulation operation;

[0122] Specifically, calculate the fluctuation amplitude of the multiphase flow rate value in real time. Set the sliding time window to 3 seconds, and calculate the percentage difference between the maximum and minimum flow rates within the window. The safety threshold is a preset absolute value (such as 15%). When the instantaneous fluctuation amplitude exceeds this threshold, trigger the backpressure regulation operation. The backpressure regulation controls the opening of the regulating valve at the end of the pipeline, reducing the valve opening by 40% within 30 seconds to increase the front-end pressure by 0.5 - 1.2 MPa to suppress the sudden change in flow velocity. For example, when it is detected that the flow rate drops suddenly from 50 kg / s to 32 kg / s (fluctuation amplitude 36%), the valve opening is adjusted from 80% to 48% to increase the pipeline resistance to restore flow stability. Among them, the fluctuation amplitude of the multiphase flow rate value is the relative percentage of the flow rate change per unit time; the pipeline backpressure regulation operation is an emergency measure to balance the flow rate by controlling the valve opening to adjust the pipeline end resistance.

[0123] Synchronously upload the warning log containing the abnormal event type and pipe section location information to the cloud platform.

[0124] Specifically, the abnormal event type is matched with a preset database according to the flow rate fluctuation pattern, such as "slug flow mutation", "phase separation blockage", etc. The pipe section location information is determined through the mapping of pipeline pressure gradient analysis and GPS coordinates. The warning log is uploaded in JSON format, including the timestamp, fluctuation amplitude, regulating valve action parameters, and location coordinates, such as the 15th stake section of the oil pipeline at longitude 118.76°E and latitude 32.04°N. The data is encrypted and transmitted to the cloud storage through the MQTT protocol, triggering the audible and visual alarm and work order dispatching of the operation and maintenance terminal. Among them, the abnormal event type is a predefined classification of fluid dynamic anomalies; the pipe section location information is an associated identifier of the physical location and the pipeline topology structure.

[0125] Through the closed-loop linkage of flow dynamic monitoring and pressure regulation, this method actively intervenes in the fluid state at the initial stage of abnormal fluctuations and synchronously constructs an accurate fault tracing information chain. The technical effect is reflected in reducing the risk of equipment damage caused by phase separation or sudden flow changes in multiphase flow transportation through a mechanical control and data collaboration mechanism, while improving the timeliness of abnormal event handling and the location positioning accuracy, and ensuring the safety and economy of pipeline system operation and maintenance.

[0126] Based on the same inventive concept, as Figure 3 shown, the present invention also provides a [system], and the system includes:

[0127] A data acquisition module, configured to acquire the actual flow velocity value of the fluid in the pipeline and a dual-modal sensing signal, where the dual-modal sensing signal includes a resistive phase separation signal and an ultrasonic frequency shift signal;

[0128] A signal optimization module, configured to dynamically adjust the sampling frequency of the dual-modal sensing signal based on historical operating condition data to generate an optimized sensing sampling signal;

[0129] A data processing module, configured to perform time-frequency domain joint noise reduction processing on the optimized sensing sampling signal to generate edge-end preprocessed data;

[0130] A compensation calculation module, configured to perform multi-step prediction on the edge-end preprocessed data by using a time series prediction model preset in the cloud to generate a dynamic compensation coefficient;

[0131] A flow calculation module, configured to calculate the multiphase flow rate value according to the dynamic compensation coefficient and the actual flow velocity value;

[0132] A flow output and warning module, configured to output the multiphase flow rate value and generate an abnormal event warning signal according to the multiphase flow rate value.

[0133] It should be noted that the electrical connections between the above-mentioned various units do not necessarily represent direct connections of the lines. Indirect connection methods, as long as they can achieve the purpose of the present invention, are applicable to the embodiments of the present invention. The above are only exemplary embodiments of the present invention and cannot be used to limit the scope of the present invention.

[0134] That is, all equivalent changes and modifications made according to the teachings of the present invention still fall within the scope covered by the present invention. Those skilled in the art will easily think of other implementation schemes of the present invention after considering the specification and the disclosure of the practical truth. This application aims to cover any variations, uses, or adaptive changes of the present invention, and these variations, uses, or adaptive changes follow the general principles of the present invention and include common general knowledge or conventional technical means in the technical field not recorded in the present invention.

Claims

1. A pipeline multiphase flow metering and monitoring method, characterized in that, The method includes: Obtaining the actual flow velocity value of the fluid in the pipeline and the dual-modal sensing signal, where the dual-modal sensing signal includes a resistive phase fraction signal and an ultrasonic frequency shift signal; Obtaining historical operating condition data and dynamically adjusting the sampling frequency of the dual-modal sensing signal based on the historical operating condition data to generate an optimized sensing sampling signal; wherein, generating the optimized sensing sampling signal includes: obtaining the real-time pressure data, temperature data and historical medium ratio data of the pipeline to obtain a basic data set; generating flow velocity prediction models for different phases based on the basic data set; calculating the correlation weight values of the resistive phase fraction signal and the ultrasonic frequency shift signal according to the flow velocity prediction models; adjusting the sampling frequencies of each modality in the dual-modal sensing signal based on the correlation weight values to generate an optimized sensing sampling signal; Performing time-frequency domain joint noise reduction processing on the optimized sensing sampling signal to generate edge-end preprocessed data; wherein, generating the edge-end preprocessed data includes: performing wavelet transform on the ultrasonic frequency shift signal in the sensing sampling signal to filter out frequency domain noise and generate a preliminary noise reduction signal; combining the pulse characteristics of the resistive phase fraction signal to perform waveform matching on the preliminary noise reduction signal to remove the remaining noise and generate the edge-end preprocessed data; Using a time series prediction model preset in the cloud to perform multi-step prediction on the edge-end preprocessed data to generate a dynamic compensation coefficient; wherein, generating the dynamic compensation coefficient includes: dividing the edge-end preprocessed data into a high-confidence data set and a low-confidence data set according to a preset time stamp through a preset rule; optimizing the compensation parameters of the low-confidence data set by using a reinforcement learning method to generate a compensation parameter increment; constructing a multi-objective loss function based on the high-confidence data set and the compensation parameter increment; using the multi-objective loss function as the loss function of a long short-term memory network and optimizing the multi-objective loss function by using the gradient descent method to obtain the dynamic compensation coefficient; Calculating the multiphase flow rate value according to the dynamic compensation coefficient and the actual flow velocity value; wherein, calculating the multiphase flow rate value includes: extracting the phase fraction ratio and the frequency shift value from the edge-end preprocessed data; performing density weighted integration on the phase fraction ratio by using the dynamic compensation coefficient to generate a phase fraction density value; performing flow velocity correction calculation on the actual flow velocity value by using the dynamic compensation coefficient and the frequency shift value to generate a corrected flow velocity value; calculating the multiphase flow rate value based on the phase fraction density value and the corrected flow velocity value; Outputting the multiphase flow rate value and generating an abnormal event warning signal according to the multiphase flow rate value; Wherein, the dividing into the high-confidence data set and the low-confidence data set through the preset rule includes: calculating the phase fraction ratio error value between the resistive phase fraction signal and the ultrasonic frequency shift signal; determining whether the phase fraction ratio error value is less than a preset noise threshold and the noise energy is lower than a preset energy threshold; if so, marking the edge-end preprocessed data as the high-confidence data set; if not, marking the edge-end preprocessed data as the low-confidence data set.

2. The method for pipeline multiphase flow metering and monitoring according to claim 1, wherein The generating the edge-end preprocessed data further includes: Extracting the mutation waveform in the resistive phase fraction signal as a reference template; Calculate the local correlation coefficient between the preliminary noise-reduced signal and the reference template; When the local correlation coefficient is lower than a preset correlation threshold, correct the edge-side preprocessed data through a backpressure simulation test.

3. A pipeline multiphase flow metering and monitoring method according to claim 1, characterized in that, The generating an abnormal event warning signal according to the multiphase flow rate value includes: When the fluctuation amplitude of the multiphase flow rate value exceeds a safety threshold, trigger a pipeline backpressure regulation operation; Synchronously upload a warning log containing the abnormal event type and pipe section location information to the cloud platform.

4. A pipeline multiphase flow metering and monitoring system, applied to the pipeline multiphase flow metering and monitoring method according to any one of claims 1-3, characterized in that, The system includes: A data acquisition module for acquiring the actual flow velocity value of the fluid in the pipeline and a dual-mode sensing signal, where the dual-mode sensing signal includes a resistive phase fraction signal and an ultrasonic frequency shift signal; A signal optimization module for dynamically adjusting the sampling frequency of the dual-mode sensing signal based on historical operating condition data to generate an optimized sensing sampling signal; A data processing module for performing joint time-frequency domain noise reduction processing on the optimized sensing sampling signal to generate edge-side preprocessed data; A compensation calculation module for performing multi-step prediction on the edge-side preprocessed data by using a time series prediction model preset in the cloud to generate a dynamic compensation coefficient; A flow rate calculation module for calculating a multiphase flow rate value according to the dynamic compensation coefficient and the actual flow velocity value; A flow rate output and warning module for outputting the multiphase flow rate value and generating an abnormal event warning signal according to the multiphase flow rate value.

Images