Multi-effect desanding and metering integrated method for coal bed gas wellhead

Abstract

The invention relates to the technical field of coal bed gas treatment, in particular to a multi-effect desanding and metering integrated method for a coal bed gas wellhead. According to the technical scheme, the multi-effect desanding and metering integrated method for the coal-bed gas wellhead comprises the following steps that S1, multi-stage desanding treatment is conducted on sand-containing gas flow from the coal-bed gas wellhead, and pretreated gas flow is obtained; in the desanding treatment process, first process parameters related to the desanding efficiency are obtained in real time; s2, a flow metering device insensitive to solid particles is used for conducting flow measurement on the preprocessed airflow, original flow data are obtained, and second process parameters related to the flow field state are synchronously obtained; and S3, monitoring the solid particulate matter concentration in the pretreated airflow in real time to obtain particulate matter concentration data. Deep cooperation of multi-effect sand removal and high-precision metering is achieved, well condition changes are actively adapted, and composite interference of solid residues and flow field disturbance on metering is eliminated on line.

Description

Technical Field

[0001] This invention relates to the field of coalbed methane treatment technology, and in particular to an integrated method for multi-effect sand removal and metering at coalbed methane wellheads. Background Technology

[0002] Produced gas from coalbed methane wellheads typically carries a large amount of solid particles such as coal dust and rock cuttings, requiring efficient desanding to ensure the safe and stable operation of subsequent pipeline transportation and metering equipment. Currently, wellhead systems typically employ a traditional process of cascading desanding devices and metering instruments. Desanding largely relies on equipment such as gravity settlers and cyclone separators, while metering commonly uses instruments such as turbine flow meters and orifice flow meters. These two functional units are usually designed independently and installed in sections, connected only by simple pipelines. In actual operation, the separation efficiency of the desanding equipment is significantly affected by changes in inlet gas load and particle size. Unremoved fine particles can scour and wear down critical components of the metering instruments (such as turbine blades) at high speed, leading to a rapid deterioration in metering accuracy. Simultaneously, the pressure loss and flow field disturbances generated by the desanding equipment itself reduce the flow field stability required by the upstream and downstream straight pipe sections for metering instruments, further introducing measurement errors. Furthermore, existing technologies lack real-time integrated adjustment and closed-loop control of desanding efficiency, particle concentration, and metering accuracy. They cannot dynamically adjust operating parameters according to changes in operating conditions, and lack online diagnostic and calibration capabilities for performance degradation and measurement deviations. Therefore, existing technical solutions have limitations when dealing with the complex and variable sand production conditions of coalbed methane wells, such as poor metering reliability, frequent maintenance, and low overall system efficiency.

[0003] In existing series-connected processes, incomplete sand removal leads to solid particles causing wear and interference with downstream metering instruments. Simultaneously, flow field disturbances generated by the sand removal equipment affect metering stability, resulting in mutual constraints between the gas-solid separation and metering processes, making it difficult to simultaneously guarantee high sand removal efficiency and high metering accuracy. Current technologies lack intelligent adjustment capabilities based on real-time operating conditions, failing to dynamically optimize the operating parameters of the sand removal unit when sand concentration and particle characteristics change. This leads to the system operating under suboptimal conditions for extended periods, resulting in low overall energy efficiency. Existing systems lack online calibration and verification functions for their key parameters (such as particle concentration monitoring values ​​and flow meter accuracy), and cannot provide early diagnosis and warning of equipment performance degradation. Maintenance relies on manual labor and periodic shutdowns for overhaul, impacting production continuity. Summary of the Invention

[0004] This invention proposes an integrated method for multi-effect sand removal and metering at coalbed methane wellheads, which solves the problems of mutual interference between sand removal and metering processes in existing technologies, and the lack of self-adaptation and self-calibration capabilities in the system, resulting in low metering accuracy and poor reliability at coalbed methane wellheads.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for integrating multi-effect sand removal and metering at the coalbed methane wellhead includes the following steps: S1: Perform multi-stage desanding treatment on the sand-laden gas flow from the coalbed methane wellhead to obtain pretreated gas flow; during the desanding process, acquire the first process parameters related to the desanding efficiency in real time; S2: Measure the flow rate of the pre-treated airflow using a flow metering device that is insensitive to solid particles to obtain raw flow data, and simultaneously acquire second process parameters related to the flow field state; S3: Monitor the concentration of solid particulate matter in the pre-treated airflow in real time to obtain particulate matter concentration data; S4: Using the particulate matter concentration data as the main feedback signal, and in conjunction with the first process parameters, dynamically adjust the operating intensity of at least one sand removal unit through the first control model to keep the particulate matter concentration data stable within a preset target range. S5: Based on the particulate matter concentration data, the original flow rate data, and the second process parameters, the original flow rate data is dynamically corrected through the second compensation model to eliminate the combined effects of solid residue, flow field fluctuations, and the drift of the measuring device itself on the measurement accuracy, and output an accurate flow rate value. S6: System fault self-diagnosis; S7: Online self-calibration of separation efficiency.

[0006] Furthermore, in step S2, the flow metering device that is insensitive to solid particles is an ultrasonic flow meter; In step S3, the particulate matter concentration data is acquired using an online particulate matter concentration monitoring sensor; The second process parameters include at least one of the intensity attenuation of the received signal when the ultrasonic flowmeter is working and the signal-to-noise ratio, and the dynamic pressure fluctuation value of the pre-processed airflow between the inlet and outlet of the measurement section.

[0007] Furthermore, in step S5, the process of establishing and executing the second compensation model includes: S51: Obtain the relationship between the reference ultrasonic propagation time difference and the reference flow rate under different flow rates, temperatures, and pressures under pure airflow calibration conditions, as well as the reference signal quality parameters; S52: In actual operation, the current ultrasonic propagation time difference, particulate matter concentration data, real-time signal quality parameters and dynamic pressure fluctuation values ​​are collected simultaneously. S53: Compare the current signal quality parameters with the reference signal quality parameters under the same operating conditions, analyze the signal attenuation characteristics through wavelet transform, and combine the particulate matter concentration data to quantify the measurement deviation ΔQ1 caused by solid adhesion and scattering. S54: Analyze the spectral characteristics of the dynamic pressure fluctuation value and quantify the measurement deviation ΔQ2 caused by the turbulent flow field; S55: Calculate the accurate flow value Q_corrected according to the formula Q_corrected = Q_raw - f(ΔQ1, ΔQ2), where Q_raw is the original flow data and f is the correction function based on the second compensation model.

[0008] Furthermore, in step S1, the first process parameters include the pressure difference data between at least two desanding units in the multi-stage desanding process; In step S4, the logic of the first control model is as follows: When the particulate matter concentration data exceeds the upper limit of the target range and the differential pressure data exceeds the first preset threshold, it is determined that the current sand removal unit is blocked, and the automatic backwashing program of the sand removal unit is triggered first. When the particulate matter concentration data exceeds the upper limit of the target range, but the differential pressure data is lower than the second preset threshold, it is determined that the separation efficiency of the current sand removal unit is insufficient, and the operating intensity parameters of the sand removal unit are dynamically adjusted.

[0009] Furthermore, the multi-stage sand removal process includes at least a primary inertial separation and a secondary cyclone separation in series; The specific method for dynamically adjusting the operating intensity parameters of the sand removal unit is as follows: for the secondary cyclone separator, the inlet airflow tangential velocity is dynamically controlled by adjusting the opening of the electric regulating valve on the inlet pipe; The adjustment amount ΔV is positively correlated with the magnitude ΔC of the particulate matter concentration data exceeding the target range and the rate of change dP / dt of the differential pressure data, and is output in real time based on a pre-established fuzzy control rule table.

[0010] Furthermore, step S6 includes: S61: Real-time monitoring of the operating status signals of the flow metering device and the particulate matter concentration monitoring sensor; S62: Based on historical operational big data, a dynamic correlation model is established using a neural network model to create a model of the relationship between the particulate matter concentration data, the precise flow rate value, the first process parameter, and the second process parameter. S63: Input the current real-time parameter set into the dynamic correlation model and calculate the residual between the predicted value and the actual measured value; S64: When the residual of a specific parameter combination continuously exceeds a set threshold, it is determined that the equipment unit related to the parameter combination has experienced performance degradation or failure, and a fault warning message containing the specific suspected unit is output.

[0011] Furthermore, in step S1, an active controllable flow field rectifier is provided between the last-stage sand removal unit and the flow metering device. In step S5, the second compensation model also outputs rectifier control commands for the current flow field state; Based on the turbulent characteristics of the flow field in the second process parameters, the guide vane angle or damping of the active controllable flow field rectifier is dynamically adjusted to actively suppress pulsations at a specific frequency and provide optimal flow field conditions for the flow metering device.

[0012] Furthermore, a particulate matter concentration monitoring method based on the principle of resonant acoustics is adopted, specifically as follows: A pair of sound wave transmitting and receiving transducers are installed on the side wall of the airflow channel to emit sound waves of a specific frequency that pass through the airflow; By analyzing the frequency attenuation spectrum of the received sound waves and identifying the characteristic frequency attenuation valleys caused by the resonant absorption of solid particles, the concentration and characteristic particle size distribution of the solid particles can be calculated.

[0013] Furthermore, step S7 includes: Downstream of the final sand removal unit, which has the highest precision requirement in the multi-stage sand removal process, a high-precision sampling bypass is set up. This bypass is equipped with a precision filter and a micro flow meter. The self-calibration procedure is initiated periodically or in a controlled manner, and a portion of the airflow is directed into the bypass to completely capture residual solids through the precision filter, with the filtered air flow rate measured by the microflow meter. By comparing the particulate matter concentration monitoring data of the main road and the bypass road, as well as the captured solid mass, the readings of the particulate matter concentration monitoring sensor of the main road are calibrated online, and the reference value of the target range described in step S4 is updated.

[0014] Furthermore, the online calibration of the main road particulate matter concentration monitoring sensor readings described in step S7, and the updating of the reference value of the target range described in step S4, specifically includes the following steps: S71: During the execution of the self-calibration procedure, the total mass M of solids captured by the precision filter and the total volume V_bypass of the filtered airflow measured by the microflow meter are recorded simultaneously. S72: Calculate the precise particulate matter concentration C_bypass of the bypass sampling gas flow according to the formula C_bypass = M / V_bypass; S73: Obtain the concentration data sequence C_main output by the particulate matter concentration monitoring sensor on the main road within the same time period; S74: By fitting with the least squares method, establish the correction coefficient K and offset B between the average value of C_bypass and C_main, i.e., C_bypass_cal = K * AVG(C_main) + B, and perform real-time correction on the subsequent readings of the particulate matter concentration monitoring sensor. S75: Using the calculated precise concentration C_bypass as the benchmark for separation efficiency under the current operating conditions, the theoretical optimal efficiency that the multi-stage sand removal treatment should achieve is deduced in reverse, and the parameters of the first control model in step S4 and the target range of the particulate matter concentration data are dynamically optimized and updated accordingly.

[0015] The positive effects of this invention are as follows: By constructing a closed-loop intelligent control system of "perception-decision-compensation-diagnosis-calibration," deep synergy between multi-effect sand removal and high-precision metering is achieved. It can proactively adapt to changes in well conditions, eliminate the combined interference of solid residues and flow field disturbances on metering online, and significantly improve the long-term reliability, accuracy, and autonomy of coalbed methane wellhead flow data and system operation through dynamic optimization of the separation process and real-time self-diagnosis and self-calibration of the system status. This effectively solves the problem of mutual constraints between separation and metering in traditional segmented methods. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0017] Example A method for integrating multi-effect sand removal and metering at the coalbed methane wellhead includes the following steps: S1: Perform multi-stage desanding treatment on the sand-laden gas flow from the coalbed methane wellhead to obtain a pretreated gas flow; during the desanding process, acquire the first process parameter related to the desanding efficiency in real time; S2: Measure the flow rate of the pretreated gas flow using a flow metering device that is insensitive to solid particles to obtain raw flow data, and simultaneously acquire the second process parameter related to the flow field state; S3: Monitor the concentration of solid particles in the pretreated gas flow in real time to obtain particle concentration data; S4: Use the particle concentration data as the main feedback signal, combined with the first process parameter, dynamically adjust the operating intensity of at least one desanding unit through a first control model to stabilize the particle concentration data within a preset target range; S5: Based on the particle concentration data, the raw flow data, and the second process parameter, dynamically correct the raw flow data through a second compensation model to eliminate the combined effects of solid residue, flow field fluctuations, and the drift of the measuring device itself on the measurement accuracy, and output an accurate flow value; S6: System fault self-diagnosis; S7: Online self-calibration of separation efficiency.

[0018] In actual operation, the sand-laden gas flow from the coalbed methane wellhead first enters a multi-stage desander unit arranged in series, for example, passing sequentially through a primary separation chamber based on gravity settling and a high-efficiency cyclone separator. During the desandering process, the pressure difference is acquired in real time by pressure sensors installed before and after each stage unit as the first process parameter, which directly reflects the degree of blockage and separation load. The treated gas flow then flows through a flow metering device, where a solid-interference-resistant ultrasonic flow meter is selected. Its transducer is installed at a specific angle on the outside of the pipe wall. The flow velocity is calculated by measuring the time difference of ultrasonic wave propagation in the forward and reverse directions to obtain the raw flow data. Simultaneously, the processor inside the flow meter synchronously analyzes the amplitude and signal-to-noise ratio of the received ultrasonic signal, supplemented by the readings of the high-frequency pressure sensors at both ends of the measurement section, together forming the second process parameter reflecting the purity and stability of the flow field. Near the upstream end of the flow meter, an online particulate matter concentration sensor based on the principle of laser forward scattering is installed. The laser beam emitted by the sensor passes through the gas flow, is scattered by solid particles, and is received by a photodetector. The light intensity signal is processed and converted into real-time particulate matter concentration data. The system's core controller operates the first control model, a PID control loop that uses particulate matter concentration data as the process variable (PV) and the target concentration as the setpoint (SP). Its output signal adjusts the opening of the electrically operated regulating valve at the inlet of the secondary cyclone separator, dynamically controlling the separation intensity by changing the inlet airflow tangential velocity to stabilize the concentration within a preset range. Simultaneously, the second compensation model—a microprocessor module with an embedded deviation propagation algorithm—operates continuously. It receives particulate matter concentration, ultrasonic signal quality parameters (such as attenuation), and pressure pulsation spectrum as input. Using a pre-calibrated error mapping matrix, it calculates in real-time the acoustic delay deviation ΔT1 caused by solid adhesion and the velocity distribution distortion deviation ΔT2 caused by turbulence. These time deviations are converted into flow correction values, which are then subtracted from the original flow data to output a precise flow value. The system also includes a fault self-diagnosis thread that continuously monitors the correlation of signals from various sensors; and an online self-calibration program that can be started periodically. This program uses a switching valve to introduce a portion of the airflow into a parallel calibration bypass equipped with an absolute filter and a reference flow meter to obtain an absolute concentration reference for calibrating the main circuit sensors and evaluating the system's separation efficiency.

[0019] This method achieves deep integration of sand removal and metering processes at the information perception and decision control levels by constructing a complete closed loop that includes real-time monitoring, dynamic control, intelligent compensation, self-diagnosis, and self-calibration. This enables the system to proactively adapt to changes in well conditions and intelligently maintain optimal working conditions, fundamentally solving the problem of mutual interference between separation and metering in traditional technologies. It significantly improves the long-term reliability and accuracy of coalbed methane wellhead flow data and greatly reduces reliance on external intervention and maintenance.

[0020] In step S2, the flow metering device that is insensitive to solid particles is an ultrasonic flow meter; In step S3, the particulate matter concentration data is acquired using an online particulate matter concentration monitoring sensor; The second process parameters include at least one of the intensity attenuation of the received signal when the ultrasonic flowmeter is working and the signal-to-noise ratio, and the dynamic pressure fluctuation value of the pre-processed airflow between the inlet and outlet of the measurement section.

[0021] In practice, the ultrasonic flow meter is installed as follows: a pair of piezoelectric ultrasonic transducers are installed on both sides of the measuring pipe section using the Z-method (diagonal path), which alternately transmit and receive high-frequency ultrasonic pulses. The flow meter's processor accurately measures the downstream propagation time t1 and the upstream propagation time t2 of the pulse in the airflow, and calculates the flow velocity using the formula v = (L / (2cosθ)) * ((1 / t1) -(1 / t2)), where L is the sound path length and θ is the angle between the sound path and the pipe axis. At the receiving end, the processor simultaneously calculates the peak voltage of the received pulse to obtain the intensity attenuation, and calculates the power ratio of the signal to the background noise to obtain the signal-to-noise ratio. The dynamic pressure fluctuation value is obtained by installing high-frequency response piezoelectric dynamic pressure sensors at 1D (D is the pipe diameter) upstream and 0.5D downstream of the measuring section. After the signals from the two sensors are processed by a differential amplifier, their fluctuation variance is used as a quantitative indicator of flow field stability. The online particulate matter concentration monitoring sensor uses laser scattering. Its gas chamber is connected to the main pipeline by a bypass. A miniature sampling pump draws sample gas through the measurement chamber at a constant flow rate to ensure the representativeness of the measurement.

[0022] By using the quality parameters (intensity, signal-to-noise ratio) of ultrasonic signals and the dynamic pressure fluctuations of the flow field as key monitoring objects, the early impacts of solid residues and flow field turbulence on the metering process can be captured directly and sensitively from the perspective of measurement principle. This provides a precise and multi-dimensional data foundation for subsequent high-precision intelligent compensation, thereby effectively extending the effective service life and metering reliability of the flow meter under harsh working conditions.

[0023] In step S5, the process of establishing and executing the second compensation model includes: S51: Obtain the relationship between the reference ultrasonic propagation time difference and the reference flow rate under different flow rates, temperatures, and pressures under pure airflow calibration conditions, as well as the reference signal quality parameters; S52: In actual operation, the current ultrasonic propagation time difference, particulate matter concentration data, real-time signal quality parameters and dynamic pressure fluctuation values ​​are collected simultaneously. S53: Compare the current signal quality parameters with the reference signal quality parameters under the same operating conditions, analyze the signal attenuation characteristics through wavelet transform, and combine the particulate matter concentration data to quantify the measurement deviation ΔQ1 caused by solid adhesion and scattering. S54: Analyze the spectral characteristics of the dynamic pressure fluctuation value and quantify the measurement deviation ΔQ2 caused by the turbulent flow field; S55: Calculate the accurate flow value Q_corrected according to the formula Q_corrected = Q_raw - f(ΔQ1, ΔQ2), where Q_raw is the original flow data and f is the correction function based on the second compensation model.

[0024] The establishment of the second compensation model begins with the calibration of the ultrasonic flow meter in a laboratory using clean air. The standard propagation time difference Δt_std and its corresponding standard flow rate Q_std are recorded under different flow velocities, temperatures, and pressures. Simultaneously, the standard amplitude A_std and standard signal-to-noise ratio SNR_std of the received signal are also recorded, forming a benchmark database. In practical applications, the model execution flow is as follows: First, the actual time difference Δt_raw, particulate concentration C, real-time signal amplitude A, and signal-to-noise ratio SNR under the current operating conditions (temperature, pressure, flow velocity) are collected. Then, Δt_std, A_std, and SNR_std under the current operating conditions are obtained by interpolation from the benchmark database. Next, the signal attenuation characteristics are analyzed through wavelet transform: Discrete wavelet decomposition is performed on the real-time ultrasonic signal to extract the wavelet coefficient energy E_dirty of a specific frequency band (usually corresponding to the dominant frequency of particle scattering), which is compared with the energy E_clean of the corresponding frequency band under clean conditions. The relative attenuation rate α = (E_clean - E_dirty) / E_clean is calculated. The deviation ΔQ1 is calculated using the function ΔQ1 = k1*C^m + k2*α^n, where k1, k2, m, and n are coefficients calibrated experimentally. Simultaneously, the spectrum of dynamic pressure fluctuations is analyzed: a Fast Fourier Transform is performed on the dynamic pressure signal to identify the dominant frequency pulsations exceeding the threshold energy, and their proportion β in the total energy is calculated. The deviation ΔQ2 is calculated using the function ΔQ2 = g(β, average flow velocity), where g is an empirical function fitted based on computational fluid dynamics simulation data. Finally, the correction function f employs a linear weighted combination: Q_corrected = Q_raw - (w1ΔQ1 + w2ΔQ2), where the weighting coefficients w1 and w2 are adaptively adjusted according to the deviation of the current signal's A and SNR from the standard values.

[0025] This compensation method independently quantifies the measurement deviations introduced by the two main sources of interference, solid adhesion and turbulent flow, and makes precise corrections based on calibration data and signal analysis techniques. It can eliminate composite errors at their source, making the flow measurement results more accurately reflect the actual airflow value, and greatly improving the measurement accuracy and anti-interference capability under complex and variable well conditions.

[0026] In step S1, the first process parameters include the pressure difference data between at least two desanding units in the multi-stage desanding process; In step S4, the logic of the first control model is as follows: When the particulate matter concentration data exceeds the upper limit of the target range and the differential pressure data exceeds the first preset threshold, it is determined that the current sand removal unit is blocked, and the automatic backwashing program of the sand removal unit is triggered first. When the particulate matter concentration data exceeds the upper limit of the target range, but the differential pressure data is lower than the second preset threshold, it is determined that the separation efficiency of the current sand removal unit is insufficient, and the operating intensity parameters of the sand removal unit are dynamically adjusted.

[0027] The first process parameters are obtained by measuring the pressure difference ΔP1 between the outlet of the primary inertial separator and the inlet of the secondary cyclone separator, and the pressure difference ΔP2 between the inlet and outlet of the secondary cyclone separator. The first control model is a program combining decision-making and PID control. Its decision-making logic is as follows: continuously compare the concentration data C with the upper limit C_max. When C>C_max, further determine the value of ΔP2. If ΔP2 exceeds the first preset threshold (this threshold is set to 1.5-2 times the clean pressure difference), it is determined that there is blockage inside the cyclone separator, and the model immediately sends a pulse command to open the backwash solenoid valve connected to the bottom of the cyclone separator cone, using high-pressure gas or liquid for instantaneous backwashing. If C>C_max but ΔP2 is lower than the second preset threshold (e.g., only 1.1-1.2 times the clean pressure difference), it is determined that the separation efficiency is insufficient, rather than blockage. At this point, the model switches to PID control mode, using (C_max - C) as the input deviation e(t), and outputs an adjustment signal. This signal controls the electric regulating valve at the inlet of the cyclone separator through a servo amplifier, changing the valve opening and thus dynamically adjusting the inlet airflow speed until the concentration C falls back to the target range.

[0028] This control logic makes a joint judgment based on the phenomenon of "excessive concentration" and the characteristics of "abnormal pressure difference". It can intelligently distinguish between two different fault modes: "equipment blockage" and "insufficient efficiency" and trigger distinct optimization strategies (backwashing or adjusting operation intensity). This realizes a leap from passive response to active optimization and from single control to intelligent decision-making, significantly improving the stability and automation level of the sand removal system.

[0029] The multi-stage sand removal process includes at least a primary inertial separation and a secondary cyclone separation in series. The specific method for dynamically adjusting the operating intensity parameters of the sand removal unit is as follows: for the secondary cyclone separator, the inlet airflow tangential velocity is dynamically controlled by adjusting the opening of the electric regulating valve on the inlet pipe; The adjustment amount ΔV is positively correlated with the magnitude ΔC of the particulate matter concentration data exceeding the target range and the rate of change dP / dt of the differential pressure data, and is output in real time based on a pre-established fuzzy control rule table.

[0030] In the implementation involving primary inertial separation and secondary cyclone separation in series, the specific actuator for dynamic adjustment is an electrically operated regulating valve installed on the tangential inlet pipe of the cyclone separator. Changes in valve opening are directly and linearly converted into changes in inlet flow velocity. The adjustment amount ΔV (percentage change in valve opening) is determined by a fuzzy control rule table. This fuzzy controller uses particulate matter concentration deviation ΔC (defined as (C - C_set) / C_set, where C_set is the set concentration) and differential pressure change rate d(ΔP2) / dt as input variables. The linguistic variables for ΔC are set as {negative large, negative small, zero, positive small, positive large}, and the linguistic variables for dP / dt are set as {negative, zero, positive}. The linguistic variables for the output ΔV are set as {close large, close small, unchanged, open small, open large}. Example rule representation: IF ΔC is positive (large) AND dP / dt is zero, THEN ΔV is open (large); IF ΔC is positive (small) AND dP / dt is positive, THEN ΔV is open (small). The system fuzzifies the precise input values ​​in real time, queries this rule table, and performs defuzzification (e.g., using the center of gravity method) to output precise valve opening adjustment commands.

[0031] By fuzzifying the two key parameters—concentration deviation and pressure difference change trends—and making intelligent decisions based on empirical rules, precise and adaptive adjustment of the inlet flow velocity of the cyclone separator is achieved. This method avoids the oscillation and overshoot problems of traditional PID control in nonlinear, large-time-lag systems, making the adjustment of separation intensity smoother, faster, and more in line with actual operating conditions, thus optimizing the balance between separation effect and energy consumption.

[0032] Step S6 includes: S61: Real-time monitoring of the operating status signals of the flow metering device and the particulate matter concentration monitoring sensor; S62: Based on historical operational big data, a dynamic correlation model is established using a neural network model to create a model of the relationship between the particulate matter concentration data, the precise flow rate value, the first process parameter, and the second process parameter. S63: Input the current real-time parameter set into the dynamic correlation model and calculate the residual between the predicted value and the actual measured value; S64: When the residual of a specific parameter combination continuously exceeds a set threshold, it is determined that the equipment unit related to the parameter combination has experienced performance degradation or failure, and a fault warning message containing the specific suspected unit is output.

[0033] The system's self-diagnostic function is executed by a separate diagnostic module. This module first monitors the digital status signals of each device in real time via the communication bus (e.g., the "low signal strength" alarm of the ultrasonic flow meter and the "light source failure" indicator of the concentration sensor). Simultaneously, based on historical operational big data, it uses a neural network model to establish a dynamic correlation model: during normal system operation, it continuously collects time series data of parameters including particulate matter concentration C, precise flow rate Q, differential pressures ΔP1 and ΔP2 at various levels, and ultrasonic signal-to-noise ratio (SNR). A Long Short-Term Memory (LSTM) network is used to train a multivariate time series prediction model, which can predict the values ​​of key parameters such as C and Q at the next moment based on a sequence of past time windows. During online operation, the diagnostic module inputs the current and a short historical period's actual parameters into the trained LSTM model to obtain a set of predicted values. Then, it calculates the residual between each parameter's predicted value and the actual measured value. Under normal conditions, the residual should follow a Gaussian distribution with a mean of zero and a small variance. The diagnostic module continuously monitors the residual sequence. When the residuals of a specific parameter combination continuously exceed a set threshold—for example, if the residual of Q is consistently positive while the residual of SNR is consistently negative, and the residual of ΔP2 shows no significant change—the diagnostic module will match it against a fault knowledge base (which stores residual feature vectors for various fault modes). The matching result may indicate "slight adhesion on the ultrasonic transducer surface, causing signal attenuation but not complete failure," and a fault warning message containing this specific suspected unit and possible causes will be generated and displayed through the human-machine interface.

[0034] By using neural network models to learn the "health fingerprint" of a system during normal operation and diagnosing anomalies by monitoring parameter residuals in real time, potential faults such as sensor drift and equipment performance degradation can be detected in advance. This enables a shift from "post-fault maintenance" to "predictive maintenance," improving the availability and operational safety of the entire system and reducing the risk of unexpected downtime.

[0035] In step S1, an active controllable flow field rectifier is installed between the last-stage sand removal unit and the flow metering device. In step S5, the second compensation model also outputs rectifier control commands for the current flow field state; Based on the turbulent characteristics of the flow field in the second process parameters, the guide vane angle or damping of the active controllable flow field rectifier is dynamically adjusted to actively suppress pulsations at a specific frequency and provide optimal flow field conditions for the flow metering device.

[0036] The active controllable flow field rectifier is installed between the last-stage desanding device and the ultrasonic flowmeter measurement section. Its interior contains multiple guide vanes that can rotate around their own axes, evenly arranged circumferentially. The rotation angle of the guide vanes is uniformly driven by a stepper motor via a linkage mechanism. When analyzing the turbulent characteristics of the flow field, the second compensation model performs spectral analysis on the acquired dynamic pressure fluctuation signal to identify the dominant pulsating frequency f_dominant and its amplitude A_dominant, where the energy is most concentrated. Then, based on a pre-stored "guide vane angle-attenuation frequency response" curve obtained through flow field simulation and experiments, the model finds the optimal guide vane angle θ_opt that produces the maximum attenuation of frequency f_dominant. Subsequently, the model outputs a rectifier control command containing the target angle θ_opt. The stepper motor receives the command and drives the guide vane to rotate to θ_opt. This angle causes a change in the equivalent hydraulic diameter and streamline curvature of the internal flow channel of the rectifier, thereby exciting a secondary disturbance in the flow field that is opposite in phase to the original main pulsation frequency and matches the amplitude. The two interfere with each other and cancel each other out, achieving the purpose of actively suppressing the pulsation of a specific frequency, thus forming a more uniform and stable velocity profile at the flow meter inlet.

[0037] By identifying the main pulsation frequency of the flow field online and dynamically adjusting the guide vane angle of the rectifier to produce a targeted suppression effect, the flow field flowing towards the flow meter can be actively "purified," creating near-ideal fluid conditions for flow measurement. This active flow field shaping technology reduces flow field disturbance errors introduced by the separation process itself or pipeline layout from the source, improving the basic accuracy and repeatability of measurement.

[0038] In step S3, a particulate matter concentration monitoring method based on the principle of resonant acoustics is adopted, specifically as follows: A pair of sound wave transmitting and receiving transducers are installed on the side wall of the airflow channel to emit sound waves of a specific frequency that pass through the airflow; By analyzing the frequency attenuation spectrum of the received sound waves and identifying the characteristic frequency attenuation valleys caused by the resonant absorption of solid particles, the concentration and characteristic particle size distribution of the solid particles can be calculated.

[0039] A particulate matter concentration monitoring method based on resonant acoustic principles is employed. Specifically, a pair of dedicated piezoelectric ceramic transducers for transmitting and receiving sound waves are installed on both sides of the airflow duct. Driven by a control circuit, the transmitting transducer emits a sweeping continuous sound wave with a frequency linearly varying range of 20kHz-200kHz. The sound wave passes through the dust-laden airflow and is captured by the receiving transducer. The received signal is amplified, filtered, and then sent to a digital signal processor for frequency attenuation spectrum analysis. Because solid particles undergo forced vibration in the sound field, when their natural frequency matches the sound wave frequency, they absorb a large amount of sound energy due to resonance. Particles of different sizes have different natural frequencies. The processor compares the spectrum of the received signal with the reference spectrum of the transmitted signal to identify characteristic frequency attenuation valleys. The center frequency of each valley corresponds to a characteristic particle size, and the depth of the valley (i.e., the attenuation) is proportional to the concentration of particles of that size. By using a pre-calibrated standard attenuation spectrum library containing various monodisperse particles, and employing an inversion algorithm (such as the Tikhonov regularized iterative algorithm), the concentration and characteristic particle size distribution of solid particles in the airflow can be calculated from the measured complex attenuation spectrum.

[0040] The monitoring method based on the principle of resonant acoustics can simultaneously obtain information on particulate matter concentration and particle size distribution non-contactly and online by analyzing the characteristic attenuation valleys of the sound wave spectrum. This not only provides more accurate input for concentration control, but also helps to deeply analyze the performance status and wear of sand removal equipment, realizing an improvement from single concentration monitoring to multi-dimensional particle characteristic analysis.

[0041] Step S7 includes: Downstream of the final sand removal unit, which has the highest precision requirement in the multi-stage sand removal process, a high-precision sampling bypass is set up. This bypass is equipped with a precision filter and a micro flow meter. The self-calibration procedure is initiated periodically or in a controlled manner, and a portion of the airflow is directed into the bypass to completely capture residual solids through the precision filter, with the filtered air flow rate measured by the microflow meter. By comparing the particulate matter concentration monitoring data of the main road and the bypass road, as well as the captured solid mass, the readings of the particulate matter concentration monitoring sensor of the main road are calibrated online, and the reference value of the target range described in step S4 is updated.

[0042] The online self-calibration function relies on an independent bypass system. A sampling branch pipe is welded to the main pipeline downstream of the final-stage desanding unit, forming a high-precision sampling bypass. This bypass is sequentially equipped with a switchable on / off valve, a high-efficiency absolute filter capable of completely capturing ultrafine particles (e.g., efficiency >99.97% for 0.3μm particles), and a high-precision thermal micro-flowmeter. When the self-calibration program starts, the controller opens the bypass on / off valve, allowing a portion of the airflow (e.g., approximately 1% of the total flow) to pass through the bypass. All solid particles in the airflow are captured by the absolute filter. After the program runs for a preset time T, the valve is closed. The total mass M of the captured solids is obtained by weighing the filter before and after time T using a high-precision electronic balance. The micro-flowmeter integrates the filtered airflow during time T to obtain the total volume V_bypass. The absolute mass concentration of the bypass airflow, C_bypass_abs = M / V_bypass, can then be calculated. Simultaneously, the average reading C_main_avg of the main pipeline online concentration sensor during time T is recorded. By comparing C_bypass_abs and C_main_avg, the readings of the main path sensors can be calibrated online (e.g., by calculating correction factors). Furthermore, by combining the estimation of the inlet particulate load (obtainable through historical data or upstream monitoring), the actual total separation efficiency of the system can be calculated and compared with the design efficiency. The controller uses this information to determine whether the current sand removal system performance meets the standards and dynamically adjusts the PID parameters (e.g., proportional gain) or target range setpoint of the first control model in step S4 accordingly, so that the system maintains or recovers to its optimal operating state.

[0043] By setting up a high-precision sampling bypass based on the principle of absolute filtering, a traceable physical benchmark is provided for the online concentration sensor, enabling online, in-situ calibration of the main monitoring system. This method eliminates drift errors that may occur during long-term sensor operation, ensuring the long-term accuracy of the concentration feedback signal, thus enabling the entire closed-loop control system to always make decisions and optimize based on real and reliable data.

[0044] The online calibration of the main road particulate matter concentration monitoring sensor readings described in step S7, and the updating of the reference value for the target range described in step S4, specifically includes the following steps: S71: During the execution of the self-calibration procedure, the total mass M of solids captured by the precision filter and the total volume V_bypass of the filtered airflow measured by the microflow meter are recorded simultaneously. S72: Calculate the precise particulate matter concentration C_bypass of the bypass sampling gas flow according to the formula C_bypass = M / V_bypass; S73: Obtain the concentration data sequence C_main output by the particulate matter concentration monitoring sensor on the main road within the same time period; S74: By fitting with the least squares method, establish the correction coefficient K and offset B between the average value of C_bypass and C_main, i.e., C_bypass_cal = K * AVG(C_main) + B, and perform real-time correction on the subsequent readings of the particulate matter concentration monitoring sensor. S75: Using the calculated precise concentration C_bypass as the benchmark for separation efficiency under the current operating conditions, the theoretical optimal efficiency that the multi-stage sand removal treatment should achieve is deduced in reverse, and the parameters of the first control model in step S4 and the target range of the particulate matter concentration data are dynamically optimized and updated accordingly.

[0045] During the self-calibration procedure, the following calculation steps are performed: First, the program records the initial mass M_init of the filter element removed from the absolute filter after cleaning and drying, and the mass M_final of the dust-laden filter element after calibration. The total mass of captured solids is M = M_final - M_init. Simultaneously, the cumulative volume value V_bypass within time T is read from the integrator of the microflowmeter. Next, the absolute mass concentration of particulate matter in the bypass airflow is calculated according to the formula C_bypass = M / V_bypass. Then, the concentration reading sequence output by the mainline online sensor within the same time period T is retrieved from the data history database, and its arithmetic mean AVG(C_main) is calculated. Then, a correction relationship is established through least squares fitting: In multiple (e.g., no less than 5) self-calibrations under different operating conditions, multiple sets of (C_bypass_i, AVG(C_main)_i) data pairs are collected. Through least squares linear regression, the linear equation C_bypass = K * AVG(C_main) + B is fitted, obtaining the correction coefficient K and the offset B. Subsequently, the real-time readings C_main_raw of the main road sensor will be corrected in real time according to C_main_corrected = K * C_main_raw + B. Finally, reverse deduction is performed: taking the obtained C_bypass as the "true value" of the sand removal system outlet concentration under the current operating conditions, and combining it with the expected inlet concentration C_in_design of the system design (or estimated based on upstream information), the theoretical optimal total efficiency η_optimal = 1 - (C_bypass / C_in_design) under the current operating conditions is calculated. This η_optimal is compared with the efficiency target value η_current currently used in the first control model, and the parameters used to calculate the target concentration in the first control model and the parameters of the PID controller are dynamically optimized and updated according to a certain adaptive law (such as gradient descent method), so that the system's control target always tracks the theoretical optimal performance point under changing operating conditions.

[0046] The absolute concentration values ​​obtained from bypass calibration are not only used for sensor correction, but also used to reverse-engineer and dynamically update the target parameters of the system control. This allows the "target" of the control system to adaptively optimize according to actual operating conditions and equipment status. This ensures that the control system always pursues and maintains the theoretically optimal performance point under current conditions, realizing the self-learning and continuous optimization of system performance.

[0047] The above-described embodiments are detailed and specific, illustrating preferred embodiments of the present invention. They are only used to illustrate the technical ideas and features of the present invention, with the aim of enabling those skilled in the art to understand the content of the present invention and implement it accordingly. However, they are not limited to the present invention, and the patent scope of the present invention cannot be limited by this embodiment alone. That is, any equivalent changes or modifications made to the spirit disclosed in the present invention, without departing from the structure of the present invention, such as local improvements within the system and modifications or transformations between subsystems, are still within the patent scope of the present invention.

Claims

1. A method for integrating multi-effect sand removal and metering at the coalbed methane wellhead, characterized in that, Includes the following steps: S1: Perform multi-stage desanding treatment on the sand-laden gas flow from the coalbed methane wellhead to obtain pretreated gas flow; during the desanding process, acquire the first process parameters related to the desanding efficiency in real time; S2: Measure the flow rate of the pre-treated airflow using a flow metering device that is insensitive to solid particles to obtain raw flow data, and simultaneously acquire second process parameters related to the flow field state; S3: Monitor the concentration of solid particulate matter in the pre-treated airflow in real time to obtain particulate matter concentration data; S4: Using the particulate matter concentration data as the main feedback signal, and in conjunction with the first process parameters, dynamically adjust the operating intensity of at least one sand removal unit through the first control model to stabilize the particulate matter concentration data within a preset target range. S5: Based on the particulate matter concentration data, the original flow rate data, and the second process parameters, the original flow rate data is dynamically corrected through the second compensation model to eliminate the combined effects of solid residue, flow field fluctuations, and the drift of the measuring device itself on the measurement accuracy, and output an accurate flow rate value. S6: System fault self-diagnosis; S7: Online self-calibration of separation efficiency.

2. The integrated method for multi-effect sand removal and metering at the coalbed methane wellhead according to claim 1, characterized in that, In step S2, the flow metering device that is insensitive to solid particles is an ultrasonic flow meter; In step S3, the particulate matter concentration data is acquired using an online particulate matter concentration monitoring sensor; The second process parameters include at least one of the intensity attenuation of the received signal when the ultrasonic flowmeter is working and the signal-to-noise ratio, and the dynamic pressure fluctuation value of the pre-processed airflow between the inlet and outlet of the measurement section.

3. The integrated method for multi-effect sand removal and metering at the coalbed methane wellhead according to claim 2, characterized in that, In step S5, the process of establishing and executing the second compensation model includes: S51: Obtain the relationship between the reference ultrasonic propagation time difference and the reference flow rate under different flow rates, temperatures, and pressures under pure airflow calibration conditions, as well as the reference signal quality parameters; S52: In actual operation, the current ultrasonic propagation time difference, particulate matter concentration data, real-time signal quality parameters and dynamic pressure fluctuation values ​​are collected simultaneously. S53: Compare the current signal quality parameters with the reference signal quality parameters under the same operating conditions, analyze the signal attenuation characteristics through wavelet transform, and combine the particulate matter concentration data to quantify the measurement deviation ΔQ1 caused by solid adhesion and scattering. S54: Analyze the spectral characteristics of the dynamic pressure fluctuation value and quantify the measurement deviation ΔQ2 caused by the turbulent flow field; S55: Calculate the accurate flow value Q_corrected according to the formula Q_corrected = Q_raw - f(ΔQ1, ΔQ2), where Q_raw is the original flow data and f is the correction function based on the second compensation model.

4. The integrated method for multi-effect sand removal and metering at the coalbed methane wellhead according to claim 1, characterized in that, In step S1, the first process parameters include the pressure difference data between at least two desanding units in the multi-stage desanding process; In step S4, the logic of the first control model is as follows: When the particulate matter concentration data exceeds the upper limit of the target range and the differential pressure data exceeds the first preset threshold, it is determined that the current sand removal unit is blocked, and the automatic backwashing program of the sand removal unit is triggered first. When the particulate matter concentration data exceeds the upper limit of the target range, but the differential pressure data is lower than the second preset threshold, it is determined that the separation efficiency of the current sand removal unit is insufficient, and the operating intensity parameters of the sand removal unit are dynamically adjusted.

5. The integrated method for multi-effect sand removal and metering at the coalbed methane wellhead according to claim 4, characterized in that, The multi-stage sand removal process includes at least a primary inertial separation and a secondary cyclone separation in series. The specific method for dynamically adjusting the operating intensity parameters of the sand removal unit is as follows: for the secondary cyclone separator, the inlet airflow tangential velocity is dynamically controlled by adjusting the opening of the electric regulating valve on the inlet pipe; The adjustment amount ΔV is positively correlated with the magnitude ΔC of the particulate matter concentration data exceeding the target range and the rate of change dP / dt of the differential pressure data, and is output in real time based on a pre-established fuzzy control rule table.

6. The integrated method for multi-effect sand removal and metering at the coalbed methane wellhead according to claim 1, characterized in that, Step S6 includes: S61: Real-time monitoring of the operating status signals of the flow metering device and the particulate matter concentration monitoring sensor; S62: Based on historical operational big data, a dynamic correlation model is established using a neural network model to create a model of the relationship between the particulate matter concentration data, the precise flow rate value, the first process parameter, and the second process parameter. S63: Input the current real-time parameter set into the dynamic correlation model and calculate the residual between the predicted value and the actual measured value; S64: When the residual of a specific parameter combination continuously exceeds a set threshold, it is determined that the equipment unit related to the parameter combination has experienced performance degradation or failure, and a fault warning message containing the specific suspected unit is output.

7. The integrated method for multi-effect sand removal and metering at the coalbed methane wellhead according to claim 1, characterized in that, In step S1, an active controllable flow field rectifier is installed between the last-stage sand removal unit and the flow metering device. In step S5, the second compensation model also outputs rectifier control commands for the current flow field state; Based on the turbulence characteristics of the flow field in the second process parameters, the guide vane angle or damping of the active controllable flow field rectifier is dynamically adjusted to actively suppress pulsations at a specific frequency and provide optimal flow field conditions for the flow metering device.

8. The integrated method for multi-effect sand removal and metering at the coalbed methane wellhead according to claim 1, characterized in that, In step S3, a particulate matter concentration monitoring method based on the principle of resonant acoustics is adopted, specifically as follows: A pair of sound wave transmitting and receiving transducers are installed on the side wall of the airflow channel to emit sound waves of a specific frequency that pass through the airflow; By analyzing the frequency attenuation spectrum of the received sound waves and identifying the characteristic frequency attenuation valleys caused by the resonant absorption of solid particles, the concentration and characteristic particle size distribution of the solid particles can be calculated.

9. A method for integrating multi-effect sand removal and metering at the coalbed methane wellhead according to any one of claims 1-8, characterized in that, Step S7 includes: Downstream of the final sand removal unit, which has the highest precision requirement in the multi-stage sand removal process, a high-precision sampling bypass is set up. This bypass is equipped with a precision filter and a micro flow meter. The self-calibration procedure is initiated periodically or in a controlled manner, and a portion of the airflow is directed into the bypass to completely capture residual solids through the precision filter, with the filtered air flow rate measured by the microflow meter. By comparing the particulate matter concentration monitoring data of the main road and the bypass road, as well as the captured solid mass, the readings of the particulate matter concentration monitoring sensor of the main road are calibrated online, and the reference value of the target range described in step S4 is updated.

10. The integrated method for multi-effect sand removal and metering at the coalbed methane wellhead according to claim 9, characterized in that, The online calibration of the main road particulate matter concentration monitoring sensor readings described in step S7, and the updating of the reference value for the target range described in step S4, specifically includes the following steps: S71: During the execution of the self-calibration procedure, the total mass M of solids captured by the precision filter and the total volume V_bypass of the filtered airflow measured by the microflow meter are recorded simultaneously. S72: Calculate the precise particulate matter concentration C_bypass of the bypass sampling gas flow according to the formula C_bypass = M / V_bypass; S73: Obtain the concentration data sequence C_main output by the particulate matter concentration monitoring sensor on the main road within the same time period; S74: By fitting with the least squares method, establish the correction coefficient K and offset B between the average value of C_bypass and C_main, i.e., C_bypass_cal = K * AVG(C_main) + B, and perform real-time correction on the subsequent readings of the particulate matter concentration monitoring sensor. S75: Using the calculated precise concentration C_bypass as the benchmark for separation efficiency under the current operating conditions, the theoretical optimal efficiency that the multi-stage sand removal treatment should achieve is deduced in reverse, and the parameters of the first control model in step S4 and the target range of the particulate matter concentration data are dynamically optimized and updated accordingly.