Method and system for dynamically analyzing the working state of a geothermal well flowmeter

Abstract

The present application belongs to the technical field of geothermal resource monitoring, and discloses a method and system for dynamically analyzing the working state of a geothermal well flowmeter, the method comprising: synchronously collecting pressure and temperature data of a geothermal water pipeline and dynamic flow data of a target flowmeter at a frequency of no less than 2 seconds / time; inputting the data into a preset analysis model, performing dynamic correlation analysis by calculating the pressure fluctuation standard deviation sigma_P, the flow fluctuation standard deviation sigma_Q and the correlation coefficient R_PQ of the two, and dividing the results into three levels of "reliable", "suspect" and "unreliable"; generating a diagnosis report according to the research and judgment results and triggering a graded alarm. The system comprises a sensor group, a data acquisition and processing terminal integrated in an IP66 protection box, a man-machine interface and an alarm device. The present application realizes online real-time diagnosis and accurate early warning of the working state of the flowmeter under the interference of gas-liquid two-phase flow, effectively improving the accuracy and compliance supervision level of geothermal water exploitation and recharge measurement.

Description

Technical Field

[0001] This invention relates to the field of geothermal resource monitoring technology, and in particular to a method and system for dynamically analyzing the working status of a geothermal well flow meter. Background Technology

[0002] Geothermal water, as an important clean energy and mineral resource, is subject to strict national regulation regarding its extraction and reinjection volumes. Currently, flow meters are the core equipment for monitoring extraction and reinjection volumes. However, when geothermal water is extracted from deep, high-temperature, and high-pressure strata to surface pipelines, the sudden pressure drop causes a large amount of dissolved gases (mainly CO2 and CH4) to be released, forming a heterogeneous and unstable gas-liquid two-phase mixture. When this gas-liquid two-phase flow passes through flow meters (such as electromagnetic flow meters and turbine flow meters), the presence of air bubbles interferes with the flow field and affects the sensing signal, resulting in a significant positive deviation in the flow measurement value, i.e., an inflated reading.

[0003] In subsequent processes, geothermal water undergoes sand removal, venting, heat exchange, and long-distance pipeline transportation. Its gas content, temperature, pressure, and physical properties continuously change, potentially leading to the precipitation of dissolved minerals and fluid volume shrinkage. This results in the volume measured at the reinjection end often being far smaller than the measurement value at the extraction end, leading to the calculation of an extremely low or even non-compliant "reinjection rate." This issue not only poses a challenge to the operational compliance of geothermal companies but may also lead to misjudgments of the actual amount of resources extracted.

[0004] Traditional solutions primarily focus on periodically calibrating flow meters offline, installing venting devices, or optimizing pipeline processes. However, these methods have significant limitations: offline calibration cannot reflect the true performance of flow meters under dynamically changing two-phase flow conditions; the effectiveness of venting devices is difficult to quantify and evaluate; and process optimization lacks real-time data feedback guidance. Therefore, there is an urgent need for a technical solution that can evaluate the operating status of flow meters online and in real-time under complex conditions, proactively identify sources of metering interference, and provide decision support to achieve accurate metering and compliant management. Summary of the Invention

[0005] To overcome the technical deficiencies of existing technologies, this invention provides a method for dynamically analyzing the working status of geothermal well flow meters, applicable to geothermal water extraction and reinjection metering systems where gas-liquid two-phase flow interference exists. The method includes the following steps: S1: High-frequency data acquisition step: Through the data acquisition unit, at a acquisition frequency of not less than 2 seconds / time, synchronously and in real time acquire raw flow monitoring data from the target flow meter installed on the geothermal water delivery pipeline, and pipeline pressure data from at least one pressure sensor installed on the same pipeline or pipelines with fluid communication relationship; the raw flow monitoring data includes at least instantaneous flow value and / or cumulative flow value. S2: Dynamic Correlation Analysis and State Reliability Assessment Steps: Input the pipeline pressure data sequence and the original flow monitoring data sequence synchronously collected in step S1 into the preset flowmeter operating condition analysis model to perform dynamic correlation analysis; the analysis includes: calculating the correlation degree and deviation threshold between pressure fluctuation characteristic parameters and flow fluctuation characteristic parameters within a continuous time window, identifying the interference of unstable gas-liquid two-phase flow conditions caused by gas precipitation, accumulation, or slippage in the pipeline on the flowmeter measurement accuracy; based on the correlation analysis and deviation threshold comparison, output a quantitative assessment result of the reliability of the target flowmeter's data output in the current time period, the assessment result including at least three levels: "reliable", "suspected", and "unreliable"; S3: Diagnostic Information Generation and Output Steps: Based on the quantitative judgment results obtained in step S2, automatically generate a diagnostic report containing at least one of the following: current traffic data status evaluation, potential cause analysis, equipment maintenance suggestions, and process optimization suggestions; and display it in real time in a visual manner through a human-machine interface, and trigger a predefined alarm signal when the judgment result is "suspicious" or "unreliable".

[0006] Preferably, in step S1, the data acquisition unit also synchronously acquires status parameter data from the target flow meter. The status parameter data includes, but is not limited to, one or more of the flow meter's signal sampling value, empty pipe alarm status, and excitation alarm status. In step S2, the flow meter operating condition analysis model incorporates the status parameter data as an auxiliary judgment factor into the dynamic correlation analysis to distinguish between anomalies caused by the flow meter's own faults and anomalies caused by changes in fluid operating conditions.

[0007] Preferably, step S2, "calculating the correlation and deviation threshold between pressure fluctuation characteristic parameters and flow fluctuation characteristic parameters within a continuous time window," specifically includes: S2.1: Preprocess the synchronized pipeline pressure data sequence {P(t)} and instantaneous flow data sequence {Q(t)}, including outlier removal and filtering smoothing; S2.2: Calculate the standard deviation σ_P of the pressure data sequence and the standard deviation σ_Q of the flow data sequence within the time window, respectively; S2.3: Calculate the correlation coefficient R_{PQ} between pressure data and flow data within the time window; S2.4: Set pressure-flow joint stability criterion: When σ_P is lower than the first pressure fluctuation threshold and σ_Q is lower than the first flow fluctuation threshold, and R_{PQ} is higher than the positive correlation threshold, the operating condition is determined to be stable and the flow data is "reliable". S2.5: Set bubble interference identification criteria: When σ_P is higher than the second pressure fluctuation threshold, and σ_Q is abnormally increased and R_{PQ} shows a negative or weak correlation (below the set threshold), it is determined that there is significant bubble interference and the flow data is "unreliable". S2.6: Set auxiliary criteria for instrument abnormality: When σ_P is in the low fluctuation range, while σ_Q is abnormally high or continuously zero or other unreasonable values, and combined with the abnormal state parameters in claim 2, it is determined that the flow meter itself may be faulty and the data is "unreliable".

[0008] Preferably, the "triggering a predefined alarm signal" in step S3 is a tiered alarm mechanism: when the judgment result is "suspicious", a first-level warning is triggered, the specific status indicator light on the human-machine interface turns yellow and stays on or flashes, and pushes prompt text in the display area;

[0009] When the assessment result is "unreliable" and the cause is bubble interference, a level two alarm is triggered. The status indicator light turns red, and the sound alarm is activated to emit intermittent sounds. An alarm window pops up on the display interface and gives the primary suggestion of "checking the exhaust device" or "reducing the mining rate".

[0010] When the assessment result is "unreliable" and the cause is suspected flow meter failure, a level three alarm is triggered. The status indicator light turns red and flashes rapidly, the sound alarm emits a continuous and rapid sound, and in addition to the alarm window, the display interface automatically generates and displays an equipment maintenance work order template.

[0011] Preferably, the method further includes: S4: Data tracing and report generation step: continuously storing the raw data collected in step S1, the intermediate analysis data and final judgment results in step S2, and all diagnostic information generated in step S3; automatically generating a comprehensive report including reliability analysis of mining / reinjection volume, equipment operation health assessment, and retrospective analysis of the effect of process recommendations according to a preset cycle or trigger event, supporting presentation in the form of historical curve comparison and data table export.

[0012] In addition, as an extension of the embodiment, this method also synchronously collects pressure and temperature data of the geothermal water pipeline and dynamic data of the target flow meter at a frequency of 2 seconds / time; based on the rise and fall trends of pressure and temperature data as background values ​​over a long period (5 minutes), the cumulative flow growth curve of the flow meter is analyzed to determine the stability of the flow meter's working state and the reliability of the flow meter device (

Note

Note

[0013] A system for dynamically analyzing the working status of a geothermal well flow meter, the system comprising: a sensor group including at least one pressure sensor for measuring the fluid pressure in a pipeline and at least one target flow meter as the monitoring object; and a data acquisition and processing terminal, the signal input terminal of which is electrically or communicatively connected to the sensor group; wherein the data acquisition and processing terminal includes: a data acquisition module for synchronously acquiring the output signals of the pressure sensor and the target flow meter at a set high frequency;

[0014] The core processing module is connected to the data acquisition module and has the flow meter operating condition analysis model embedded in it. It is used to perform the dynamic correlation analysis and state reliability assessment steps.

[0015] The output control module is connected to the core processing module and is configured to control the display content of the human-machine interface and control the action of the alarm device; the power supply module supplies power to the various modules inside the terminal.

[0016] Preferably, the data acquisition and processing terminal is integrated and encapsulated in a housing; the housing is made of engineering plastic with an IP66 or higher protection rating; the front of the housing is provided with an openable cover, the cover is made of transparent material or has a transparent observation window, and at least a portion of the display screen and status indicator lights of the human-machine interface are positioned facing the transparent portion, so that it can still be observed from the outside when the cover is closed.

[0017] Preferably, the human-computer interaction interface includes:

[0018] The display screen is a touch screen, which is fixedly installed on the internal panel of the enclosure and is used to display real-time data, historical curves, diagnostic reports, and system settings interface.

[0019] The status indicator light is an LED light group set independently of the display screen, and includes at least three-color indicator lights of green, yellow and red corresponding to the three states of "reliable", "suspicious" and "unreliable" respectively; the alarm device includes an audible alarm and / or a visual alarm integrated inside or outside the enclosure.

[0020] Preferably, the system further includes a remote monitoring platform; the data acquisition and processing terminal is wirelessly or wiredly connected to the remote monitoring platform through its communication module; the data acquisition and processing terminal uploads the analysis results, alarm information and key data to the remote monitoring platform; the remote monitoring platform provides functions such as centralized monitoring of multiple wells, alarm information management, report aggregation and compliance data interface.

[0021] The beneficial effects of this invention are:

[0022] 1. The method of this invention acquires pipeline pressure and flow data synchronously at high frequency, performs dynamic correlation analysis using a dedicated model, intelligently identifies metering anomalies caused by gas-liquid two-phase flow interference or instrument malfunctions, and quantifies, classifies, and warns of the reliability of the data, thereby guiding on-site operation and maintenance and process optimization, fundamentally improving the accuracy of geothermal water metering and the credibility of regulatory data.

[0023] 2. This invention transforms the flow meter's "condition monitoring" from the traditional periodic, offline mode to a continuous, online mode through high-frequency acquisition and dynamic analysis, enabling it to instantly capture and respond to metering distortion caused by rapid changes in fluid conditions.

[0024] 3. This invention innovatively correlates pressure fluctuation characteristics with flow fluctuation characteristics and combines them with the flow meter's own state parameters to effectively distinguish whether the metering anomaly is caused by external fluid (bubble interference) or internal instrument failure, which greatly improves the accuracy of fault location and maintenance efficiency.

[0025] 4. This invention integrates the intelligent analysis core and industrial-grade hardware into a protective enclosure, featuring a high level of protection, a transparent observation window, and a durable outdoor design. It is particularly suitable for the harsh environment of geothermal well sites, characterized by high temperature, humidity, and dust, and is easy to install, observe, and maintain. The system can be connected to a remote monitoring platform via a communication module to achieve centralized monitoring, data aggregation, and intelligent management of multiple geothermal wells, providing technical support for regional geothermal resource management. Attached Figure Description

[0026] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0027] Figure 1 This is a schematic diagram of the steps of the present invention;

[0028] Figure 2 This is a flowchart illustrating step 2 of the present invention;

[0029] Figure 3 This is a system block diagram of the present invention.

[0030] Explanation of reference numerals in the attached diagram: 10, Sensor group; 11, Pressure sensor; 12, Target flow meter; 20, Data acquisition and processing terminal; 21, Data acquisition module; 22, Core processing module; 23, Output control module; 24, Power supply module; 25, Communication module; 30, Human-machine interface; 31, Display screen; 32, Status indicator light; 40, Alarm device; 41, Audible alarm; 42, Visual alarm; 50, Cabinet; 60, Remote monitoring platform. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the various embodiments of this invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this invention to facilitate a better understanding of this application. However, the technical solutions claimed in the claims of this application can be implemented even without these technical details and with various variations and modifications based on the following embodiments.

[0032] like Figure 3 As shown, this embodiment provides a method and system for dynamically analyzing the working status of a geothermal well flow meter. The system of the present invention mainly includes a sensor group 10, a data acquisition and processing terminal 20, a human-machine interface 30, an alarm device 40, and an optional remote monitoring platform 60.

[0033] The sensor group 10 is installed on the geothermal water delivery pipeline and includes at least one pressure sensor 11 and a target flow meter 12 as the monitored object. The pressure sensor 11 is preferably installed in the upstream straight pipe section of the target flow meter 12, at a distance of approximately 5-10 times the pipe diameter from the flow meter inlet, to more sensitively detect changes in fluid pressure. The target flow meter 12 can be an electromagnetic flow meter, a turbine flow meter, a vortex flow meter, etc. Preferably, the target flow meter 12 itself has the function of outputting status parameters (such as internal temperature, signal strength, battery voltage, and fault codes).

[0034] The data acquisition and processing terminal 20 is the core of the system, connected to the sensor group 10 via a signal cable or industrial bus. The terminal 20 integrates a data acquisition module 21, a core processing module 22, an output control module 23, a power supply module 24, and a communication module 25. It is encapsulated in an IP66-rated ABS engineering plastic enclosure 50, ensuring stable operation in humid and dusty outdoor environments.

[0035] The human-machine interface 30 and the alarm device 40 are the output parts of the terminal 20. The human-machine interface 30 includes an embedded touch screen 31 and a set of independent status indicator lights 32, such as... Figure 3 As shown. The alarm device 40 includes a built-in piezoelectric ceramic buzzer as an audible alarm 41 and a high-brightness red LED as a visual alarm 42.

[0036] The remote monitoring platform 60 can be deployed in the cloud or in an enterprise monitoring center. It connects to the communication module 25 of the terminal 20 via 4G / 5G, fiber optic, or other networks to achieve data aggregation and centralized management.

[0037] like Figure 1 and Figure 2 As shown, this method is executed cyclically in the core processing module 22 of the data acquisition and processing terminal 20. The specific steps are as follows:

[0038] S1: High-frequency data acquisition synchronization steps: The data acquisition module 21 drives multi-channel synchronous sampling with a precise timing clock. Taking 2 seconds / sample as an example (meeting the requirement of "not less than 2 seconds / sample"), the data acquisition module 21 simultaneously reads the current / voltage signal output by the pressure sensor 11 (corresponding to the pressure value P) and the pulse frequency or digital communication message output by the target flow meter 12 (parsed to obtain the instantaneous flow value Q). At the same time, if the flow meter supports it, it also reads its diagnostic information, such as internal temperature, signal quality, error codes, etc., as status parameters S. All data are stamped with the same millisecond-level timestamp, forming a synchronization data packet {P(t), Q(t), S(t)}, which is sent to the buffer of the core processing module 22.

[0039] S2: Dynamic correlation analysis and state reliability assessment steps, which is the core of this invention. The core processing module 22 has a pre-built "flowmeter operating condition analysis model." This model uses a sliding time window (e.g., window length N=60, corresponding to 15 data points) as a unit to perform real-time analysis of the synchronization sequence in the buffer. The analysis process is as follows: Figure 3 As shown, the details are as follows:

[0040] Data preprocessing (S2.1): Median filtering is performed on the pressure sequence {P} and flow rate sequence {Q} within the window to remove transient outliers caused by electromagnetic interference, etc.; then, a first-order low-pass digital filter is performed to smooth high-frequency noise and retain low-frequency components that reflect changes in the macroscopic state of the fluid.

[0041] Characteristic parameter calculation: Calculation of standard deviation of fluctuation (S2.2): Calculate the standard deviation σ_P of the preprocessed pressure series and the standard deviation σ_Q of the instantaneous flow series. The standard deviation quantifies the dispersion of the data within the time window, i.e., the degree of fluctuation.

[0042] Calculate the correlation coefficient (S2.3): Calculate the Pearson correlation coefficient R_PQ between the pressure and flow series within this window. R_PQ reflects the degree of linear correlation between the changes of the two variables, and its value ranges from -1 to 1. In steady single-phase flow, pipeline pressure and flow usually change together, and R_PQ is close to 1; when bubble interference exists, this correlation is disrupted. Intelligent judgment based on multiple criteria: The model compares the calculated (σ_P, σ_Q, R_PQ) with preset thresholds and, combined with the state parameter S, performs logical judgment: σ_P represents the standard deviation σ_P of the pressure data series within the time window, representing the degree of fluctuation of the pipeline pressure measurement value within a continuous time window (e.g., 30 seconds). Standard deviation is a statistical indicator that measures the dispersion (i.e., the magnitude of fluctuation) of a set of data; calculation method:

[0043] The raw pressure data sequence {P(t)} within the time window is preprocessed (e.g., filtered, outlier removal). The average value μ_P of all pressure data within this window is calculated using the standard deviation formula.

[0044] Low σ_P (e.g., < 0.05 MPa): indicates very stable pipeline pressure, smooth fluid flow, and good operating conditions. This is typically characteristic of stable flow of a single-phase liquid (water).

[0045] High σ_P (e.g., > 0.1 MPa): indicates drastic and frequent fluctuations in pipeline pressure. In geothermal systems, this is usually a strong signal of the presence of gas-liquid two-phase flow. The compression, expansion, accumulation, and collapse of gas (bubbles) within the pipeline directly lead to rapid changes in local pressure.

[0046] σ_Q represents the standard deviation of the flow data sequence, indicating the degree of fluctuation in the instantaneous flow value measured by the flow meter within the same time window. Calculation method: Similar to σ_P, the standard deviation is calculated after preprocessing the synchronized instantaneous flow data sequence {Q(t)}.

[0047] Engineering significance: Low σ_Q: indicates stable flow meter readings and reliable output. High σ_Q: indicates large fluctuations and instability in flow meter readings. This may be caused by two reasons:

[0048] Bubble interference: When a gas-water mixture passes through a flow meter, bubbles can interfere with the measurement principle (such as the distribution of the electromagnetic field and the speed of the turbine), leading to falsely high readings and drastic fluctuations. Instrument malfunction: Such as signal transmission problems, sensor damage, etc.

[0049] R_PQ represents the correlation coefficient R_{PQ} between pressure data and flow data within a time window (usually referring to the Pearson correlation coefficient). The calculation method is as follows: based on the preprocessed {P(t)} and {Q(t)} sequences, their Pearson correlation coefficient is calculated.

[0050] Engineering significance: R_PQ is a key criterion for distinguishing between "normal flow" and "abnormal interference".

[0051] In a stable, liquid-filled pipe, pressure and flow rate are governed by fluid dynamics and typically exhibit a positive correlation (high R_PQ). However, when a large number of air bubbles are present, their behavior (compression and expansion) dominates local pressure changes. This change loses its direct and regular correlation with the overall volumetric flow rate of the fluid (which the flow meter attempts to measure), leading to the disruption of the correlation (R_PQ decreases, approaches 0, or even becomes negative).

[0052] Apply the stability criterion (S2.4): If σ_P < Th_P_steady (e.g., 0.03 MPa) and σ_Q < Th_Q_steady (e.g., 1.5% of rated flow) and R_PQ > Th_R_pos (e.g., 0.75), then the current operating condition is considered stable, the fluid is close to single-phase flow, the flow meter is working normally, and the output judgment result is "reliable". Apply the bubble interference criterion (S2.5): If σ_P > Th_P_bubble (e.g., 0.08 MPa, this threshold is higher than Th_P_steady) and σ_Q increases significantly (e.g., > 4% of rated flow), while R_PQ < Th_R_weak (e.g., 0.2, showing a weak or negative correlation), then it strongly indicates that there is an unstable two-phase flow condition in the pipeline. The compression, expansion, and collapse of air bubbles cause drastic pressure fluctuations (high σ_P), which interfere with the flow meter, causing its readings to fluctuate wildly (high σ_Q), and the pressure and flow rate changes lose their normal correlation (low R_PQ). In this case, the output judgment result is "unreliable," and the cause is recorded as "bubble interference."

[0053] Execute the auxiliary criterion for instrument anomaly (S2.6): If σ_P is very low (indicating stable operating conditions), but σ_Q shows an abnormally high value, or is continuously zero, or exceeds the range, or exhibits other obviously unreasonable conditions, and the status parameter S shows abnormalities (such as "signal loss", "empty pipe alarm", "undervoltage power supply", etc.), then it is highly suspected that the flowmeter itself is malfunctioning. At this time, the output judgment result is "unreliable", and the reason is recorded as "suspected instrument malfunction".

[0054] If the situation does not meet any of the above explicit criteria, but individual parameters are in a critical state, it is judged as "suspicious" and requires attention.

[0055] S3: Diagnostic Information Generation and Output Steps: Output control module 23 drives peripheral devices based on the judgment results of S2.

[0056] Interface display: Real-time data is dynamically updated on the touch screen (31) and the current status is marked with "Reliable (green) / Suspicious (yellow) / Unreliable - Bubble interference (red) / Unreliable - Instrument failure (red)".

[0057] Tiered alarm:

[0058] Level 1 Warning (Suspicious): The yellow light in control status indicator 32 flashes, and a yellow banner appears at the top of the screen: "Note: Slight abnormality in traffic data, observation recommended."

[0059] Level 2 Alarm (Bubble Interference): Control status indicator 32 remains constantly red, the audible alarm 41 is activated and emits an intermittent "beep-beep-" sound (e.g., 0.5 seconds of sound followed by 1 second of silence), a red alarm window pops up on the screen displaying: "Alarm! Strong gas-liquid two-phase flow interference detected, flow metering value reliability is low. Primary recommendations: 1. Immediately check the exhaust valve; 2. Consider reducing the pump speed."

[0060] Level 3 Alarm (Instrument Failure): Control status indicator 32 flashes red rapidly (e.g., twice per second), and the audible alarm 41 emits a continuous, rapid "beep beep" sound. An advanced alarm window pops up on the screen and automatically generates a structured "Field Maintenance Work Order," pre-filled with information such as the fault time, flow meter ID, and suspected fault type (parsed from status parameter S), facilitating recording and subsequent handling by maintenance personnel.

[0061] S4: Data Traceability and Report Generation Steps (Enhanced Functionality) All data and analysis results are stored in the terminal's internal memory. The system can automatically generate various reports:

[0062] Reliability Analysis Report: This report calculates the total duration and cumulative flow of "reliable" status within a specified time period (e.g., one day) as "valid metering data," and lists details of all "unreliable" events. Equipment Health Report: This report calculates the frequency of "suspicious" and "instrument malfunction" alarms from the flow meter, assesses its stability, and predicts maintenance intervals.

[0063] Example Report on Process Optimization Effect: This report compares the incidence of "bubble interference" alarm events before and after issuing the "check exhaust" suggestion to quantitatively evaluate the actual effect of the process adjustment. The report can be displayed on the local screen 31 as a historical curve overlay, or it can be uploaded to the remote platform 60 via the communication module 25 for summary analysis. Example 3: Hardware Implementation Details of the System.

[0064] The hardware design of the data acquisition and processing terminal 20 fully considers the harsh environment of the geothermal well site:

[0065] Enclosure 50: Constructed from impact-resistant, corrosion-resistant, and UV-resistant reinforced ABS engineering plastic, using integrated injection molding to ensure IP66 protection rating, completely preventing dust intrusion and withstanding powerful water spray. Transparent Lid: The entire lid is made of high-transparency, high-strength polycarbonate (PC). A tight, waterproof closure is achieved through embedded silicone sealing strips and multiple stainless steel snap locks. This design allows maintenance personnel to clearly observe the main readings on the display screen 31 and the colors of the status indicator lights 32 inside the enclosure without opening the lid, greatly facilitating daily inspections.

[0066] Internal Layout: The enclosure features a mounting panel with a touchscreen display 31 (e.g., a 7-inch TFT capacitive screen) embedded on the front. Status indicator lights 32 (using high-brightness LEDs) are independently mounted above the screen. The core motherboard (containing the core processing module 22, etc.) is fixed to the back of the panel. The bottom area houses the power module 24 (supporting wide voltage DC 12-36V input), terminal blocks, and a communication module 25. An audible alarm 41 (piezoelectric ceramic buzzer) is mounted on the inner side of the enclosure.

[0067] In addition, as an extension of the embodiment, this method also synchronously collects pressure and temperature data of the geothermal water pipeline and dynamic data of the target flow meter at a frequency of 2 seconds / time; a temperature sensor is added to the system for temperature data acquisition; based on the rise and fall trends of pressure and temperature data as background values ​​over a long period (5 minutes), the cumulative flow growth curve of the flow meter is analyzed to determine the stability of the flow meter's working state and the reliability of the flow meter device (

Note

Note

[0068] In a networked application example of the system, in large-scale geothermal field applications, terminals 20 at multiple wellheads can access a unified remote monitoring platform 60 through their communication modules 25. Data reporting: Each terminal uploads its assessment status, key data, and alarm information to the platform periodically (e.g., every 5 minutes) or in real-time (when an alarm occurs). Centralized monitoring: The remote monitoring platform 60 provides a web or client interface to centrally display the real-time status of all wells (distinguished by green, yellow, and red icons) in the form of maps, lists, and dashboards, achieving a comprehensive overview. Intelligent management: The platform has functions such as alarm information management (receiving, distributing, confirming, and archiving), in-depth analysis of historical data, and automatic aggregation of multi-well reports. Compliance interface: The platform can automatically generate extraction / reinjection reports based on "reliable" data according to the format requirements of regulatory departments such as natural resources and water resources, or report data from "unreliable" periods after compliance labeling, providing authoritative data support for enterprises.

[0069] Those skilled in the art will understand that the above embodiments are specific examples of implementing the present invention, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of the present invention.

Claims

1. A method for dynamically analyzing the working status of a geothermal well flow meter, applied to a geothermal water extraction and reinjection metering system with gas-liquid two-phase flow interference, characterized in that: The method includes the following steps: S1: High-frequency data acquisition step: Through the data acquisition unit, at a acquisition frequency of not less than 2 seconds / time, synchronously and in real time acquire raw flow monitoring data from the target flow meter installed on the geothermal water delivery pipeline, and pipeline pressure data from at least one pressure sensor installed on the same pipeline or pipelines with fluid communication relationship; the raw flow monitoring data includes at least instantaneous flow value and / or cumulative flow value. S2: Dynamic Correlation Analysis and State Reliability Assessment Steps: Input the pipeline pressure data sequence and the original flow monitoring data sequence synchronously collected in step S1 into the preset flowmeter operating condition analysis model to perform dynamic correlation analysis; the analysis includes: calculating the correlation degree and deviation threshold between pressure fluctuation characteristic parameters and flow fluctuation characteristic parameters within a continuous time window, identifying the interference of unstable gas-liquid two-phase flow conditions caused by gas precipitation, accumulation, or slippage in the pipeline on the flowmeter measurement accuracy; based on the correlation analysis and deviation threshold comparison, output the quantitative assessment result of the data output reliability of the target flowmeter in the current time period, the assessment result including at least three levels: "reliable", "suspicious", and "unreliable"; S3: Diagnostic Information Generation and Output Steps: Based on the quantitative judgment results obtained in step S2, automatically generate a diagnostic report containing at least one of the following: current traffic data status evaluation, potential cause analysis, equipment maintenance suggestions, and process optimization suggestions; and display it in real time in a visual manner through a human-machine interface, and trigger a predefined alarm signal when the judgment result is "suspicious" or "unreliable".

2. The method for dynamically analyzing the working status of a geothermal well flow meter according to claim 1, characterized in that: In step S1, the data acquisition unit also synchronously acquires status parameter data from the target flow meter. The status parameter data includes, but is not limited to, one or more of the following: internal temperature of the flow meter, signal strength, battery voltage, and error code. In step S2, the flow meter operating condition analysis model incorporates the status parameter data as an auxiliary judgment factor into the dynamic correlation analysis to distinguish between anomalies caused by the flow meter's own malfunction and anomalies caused by changes in fluid operating conditions.

3. The method for dynamically analyzing the working status of a geothermal well flow meter according to claim 2, characterized in that: Step S2, "calculating the correlation and deviation threshold between pressure fluctuation characteristic parameters and flow fluctuation characteristic parameters within a continuous time window," specifically includes: S2.1: Preprocess the synchronized pipeline pressure data sequence {P(t)} and instantaneous flow data sequence {Q(t)}, including threshold removal and filtering smoothing; S2.2: Calculate the standard deviation σ_P of the pressure data sequence and the standard deviation σ_Q of the flow data sequence within the time window, respectively; S2.3: Calculate the correlation coefficient R_{PQ} between pressure data and flow data within the time window; S2.4: Set pressure-flow joint stability criterion: When σ_P is lower than the first pressure fluctuation threshold and σ_Q is lower than the first flow fluctuation threshold, and R_{PQ} is higher than the positive correlation threshold, the operating condition is determined to be stable and the flow data is "reliable"; S2.5: Set bubble interference identification criteria: When σ_P is higher than the second pressure fluctuation threshold, and σ_Q is abnormally increased and R_{PQ} shows a negative or weak correlation (below the set threshold), it is determined that there is significant bubble interference and the flow data is "unreliable". S2.6: Set auxiliary criteria for instrument abnormality: When σ_P is in the low fluctuation range, while σ_Q is abnormally high or continuously zero or other unreasonable values, and combined with the abnormal state parameters in claim 2, it is determined that the flow meter itself may be faulty and the data is "unreliable".

4. The method for dynamically analyzing the working status of a geothermal well flow meter according to claim 3, characterized in that: The "triggering a predefined alarm signal" mentioned in step S3 is specifically a tiered alarm mechanism: when the judgment result is "suspicious", a first-level warning is triggered, the specific status indicator light on the human-machine interface turns yellow and stays on or flashes, and pushes prompt text in the display area; When the assessment result is "unreliable" and the cause is bubble interference, a level two alarm is triggered, the status indicator light turns red, the sound alarm is activated and emits an intermittent sound, and an alarm window pops up on the display interface and gives the primary suggestions such as "check the exhaust device" or "check the signal cable". When the assessment result is "unreliable" and the cause is suspected flow meter failure, a level three alarm is triggered. The status indicator light turns red and flashes rapidly, the sound alarm emits a continuous and rapid sound, and in addition to the alarm window, the display interface automatically generates and displays an equipment maintenance work order template.

5. The method for dynamically analyzing the working status of a geothermal well flow meter according to claim 4, characterized in that: The method also includes: S4: Data tracing and report generation steps: continuously storing the raw data collected in step S1, the intermediate analysis data and final judgment results in step S2, and all diagnostic information generated in step S3; automatically generating a comprehensive report including reliability analysis of mining / reinjection volume, equipment operation health assessment, and retrospective analysis of the effects of process recommendations according to a preset cycle or trigger event, supporting presentation in the form of historical curve comparison and data table export.

6. A system implementing the method for dynamically analyzing the working status of a geothermal well flow meter according to any one of claims 1-4, characterized in that: The system includes: a sensor group (10), including at least one pressure sensor (11) for measuring the pressure of fluid in the pipeline and at least one target flow meter (12) as the monitoring object; and a data acquisition and processing terminal (20), whose signal input terminal is electrically or communicatively connected to the sensor group (10); The data acquisition and processing terminal (20) includes: a data acquisition module (21) for synchronously acquiring the output signals of the pressure sensor (11) and the target flow meter (12) at a set high frequency; The core processing module (22) is connected to the data acquisition module (21) and has the flow meter operating condition analysis model embedded in it, which is used to perform the dynamic correlation analysis and state reliability assessment steps; The output control module (23) is connected to the core processing module (22) and is configured to control the display content of the human-machine interface (30) and control the action of the alarm device (40); The power module (24) supplies power to the various modules inside the terminal.

7. The system for dynamically analyzing the working status of a geothermal well flow meter according to claim 6, characterized in that: The data acquisition and processing terminal (20) is integrated and encapsulated in a box (50); the box (50) is made of engineering plastic with an IP66 or higher protection level; the front of the box (50) is provided with an openable box cover, the box cover is made of transparent material or has a transparent observation window, and at least a part of the display screen (31) and status indicator light (32) of the human-machine interface (30) are set facing the transparent part, so that it can still be observed from the outside when the box cover is closed.

8. The system for dynamically analyzing the working status of a geothermal well flow meter according to claim 6, characterized in that: The human-computer interaction interface (30) includes: The display screen (31) is a touch screen, which is fixedly installed on the internal panel of the housing (50) and is used to display real-time data, historical curves, diagnostic reports and system settings interface; The status indicator (32) is an LED light group set independently of the display screen (31), and includes at least three-color indicator lights of green, yellow and red corresponding to the three states of "reliable", "suspicious" and "unreliable" respectively; the alarm device (40) includes an audible alarm (41) and / or a visual alarm (42) integrated inside or outside the enclosure (50).

9. A system for dynamically analyzing the working status of a geothermal well flow meter according to claim 6, characterized in that: The system also includes a remote monitoring platform (60); the data acquisition and processing terminal (20) is wirelessly or wiredly connected to the remote monitoring platform (60) through its communication module (25); the data acquisition and processing terminal (20) uploads the judgment results, alarm information and key data to the remote monitoring platform (60); the remote monitoring platform (60) provides functions such as centralized monitoring of multiple wells, alarm information management, report summary and compliance data interface.

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