Oil and gas storage and transportation pipeline monitoring method and system

Abstract

The invention provides an oil and gas storage and transportation pipeline monitoring method and system, and relates to the field of pipeline monitoring, and the method comprises the following steps: constructing a finite element model of a pipeline, setting a number for each section of pipeline, and obtaining monitoring parameters of each section of pipeline and fluid in the pipeline; and analyzing the monitoring parameters of each section of pipeline and the fluid in the pipeline to obtain the condition in the pipeline under the current condition. According to the oil and gas storage and transportation pipeline monitoring method and system, through the full-chain intelligent design of sensing, diagnosis, positioning, early warning and disposal, the intrinsic safety level of the oil and gas pipeline is remarkably improved, and a reproducible technical normal form is provided for digital transformation of the oil and gas storage and transportation industry.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of pipeline monitoring, in particular to an oil and gas storage and transportation pipeline monitoring method and system. BACKGROUND

[0002] The oil and gas industry is facing risks such as pipeline leakage and equipment failure. Traditional monitoring methods such as point sensors and manual inspection have limitations such as limited coverage, weak anti-interference ability, short service life, high maintenance cost, high safety risk, and are difficult to meet the needs of long-distance and continuous monitoring. Therefore, more advanced monitoring methods are needed to ensure the safe operation of oil and gas storage and transportation pipelines.

[0003] With the development of sensing technology and signal processing technology, new monitoring technologies such as distributed optical fiber sensing technology have gradually been applied to oil and gas pipeline monitoring due to their long-distance coverage and anti-electromagnetic interference, providing a possibility to solve the shortcomings of traditional monitoring methods. At the same time, the continuous optimization of related algorithms also improves the accuracy and efficiency of data processing, making more complex and accurate pipeline monitoring methods possible.

[0004] Some oil and gas pipeline monitoring solutions based on distributed optical fiber sensing technology may only focus on vibration monitoring or temperature monitoring, without considering comprehensive monitoring of multiple parameters such as temperature, acoustic emission signals, and negative pressure waves. Or in terms of leakage positioning and leakage level assessment, similar accurate algorithms and decision rules are not used. SUMMARY

[0005] The present application provides an oil and gas storage and transportation pipeline monitoring method and system, which realizes the whole cycle fine management of oil and gas storage and transportation pipeline from normal operation to fault disposal through early warning-accurate identification-classified response-precise positioning closed loop logic, significantly improves the accuracy, timeliness and adaptability of monitoring, and provides efficient technical support for pipeline safe operation.

[0006] To achieve the above purpose, the present application provides an oil and gas storage and transportation pipeline monitoring method, comprising the following steps:

[0007] Constructing a finite element model of the pipeline, setting a number for each section of the pipeline, and obtaining monitoring parameters of each section of the pipeline and the fluid in the pipeline;

[0008] The monitoring parameters of each pipeline and the fluid in the pipeline are analyzed to obtain the current situation in the pipeline, and an index is set for each pipeline, including the distance from the beginning to the end of the pipeline to one end of the pipeline and the related parameters of the pipeline. The physical pipeline is completely mapped to the virtual digital space. This provides an accurate data basis for all subsequent analysis, realizes the global and traceable management of the pipeline health status, and greatly improves the scientific nature and efficiency of management. When an anomaly occurs, the specific pipe section can be quickly located, and all historical parameters can be retrieved to support decision-making.

[0009] Preferably, the monitoring parameters of the fluid in the pipeline are analyzed, including:

[0010] According to the material of the pipeline and the properties of the medium, a temperature gradient model of the pipeline over time is established by Fourier transform;

[0011] The medium temperature, pipeline temperature and time are combined to form a first temperature feature vector, which is input into the temperature gradient model to obtain the temperature difference at the corresponding time:

[0012] When the temperature difference at the corresponding time exceeds the first threshold range, the flow rate, pressure and purity of the medium in the pipeline have problems;

[0013] When the temperature difference at the corresponding time is lower than the first threshold range, the flow of the medium in the pipeline is insufficient, and the upstream and production of the medium need to be checked.

[0014] Real-time and non-invasive indirect diagnosis of the fluid state in the pipeline is realized. The temperature gradient model established by Fourier transform ingeniously uses the easily measured parameter of temperature to infer the key indicators such as flow rate, pressure and purity, which are difficult to directly and comprehensively monitor in real time. Its effect lies in the early detection of abnormal trends, such as when the flowmeter fails or its accuracy decreases, the temperature difference anomaly can provide redundant judgment, prompting the operator to check the upstream production or pumping equipment, preventing process problems or potential operation risks caused by insufficient flow.

[0015] Preferably, the pipeline condition is analyzed, including the following steps:

[0016] The real-time acoustic emission signal amplitude is labeled with time;

[0017] The real-time acoustic emission signal amplitude is converted to decibel value and compared with the preset decibel threshold, and the signal segments exceeding the decibel threshold are subjected to time-frequency analysis:

[0018] The signal segments exceeding the decibel threshold are marked, and the frequency domain features of the marked signal segments, such as the frequency of the main frequency, are extracted. The time domain features of the marked signal segments, such as the energy dispersion degree, are extracted.

[0019] By comparing the dominant frequency with the standard frequency threshold, and by comparing the energy dispersion with its matching spectrum, we obtain:

[0020] If the decibel value is greater than the threshold A, the main frequency is greater than 50 Hz, and the energy dispersion is greater than β, then leakage is considered.

[0021] If the decibel value is greater than the threshold A, the main frequency is less than or equal to 50 Hz, and the energy concentration area matches the pump frequency, then the equipment is considered to be vibrating.

[0022] This method achieves highly sensitive and interference-resistant identification of leakage events. By combining multi-feature fusion analysis of time domain (decibels), frequency domain (dominant frequency), and signal morphology (energy dispersion), it can effectively distinguish leakage signals from equipment operating vibration noise. Its core effect is a significant reduction in false alarm rate. Traditional single-threshold methods are prone to false alarms due to pump and valve start-up and shutdown, while this method, through spectrum matching, can identify the vibration of these conventional equipment, triggering an alarm only when the signal characteristics simultaneously match a leakage pattern, thereby improving the reliability and trustworthiness of the monitoring system.

[0023] Preferably, the analysis of the level of pipeline leakage includes the following steps:

[0024] Time-domain analysis was performed on the fiber optic vibration data to obtain the RMS amplitude;

[0025] Frequency domain analysis was performed on the fiber optic vibration data to obtain the proportion of high-frequency energy.

[0026] The initial baseline is set based on the RMS amplitude: the RMS mean value for 72 consecutive hours under leak-free conditions is μ0 ± 3σ0, where μ0 is the initial baseline and σ0 is the standard deviation of the RMS amplitude.

[0027] The baseline is dynamically updated hourly using an exponentially weighted moving average: μ n =α·x n +(1-α)·μ n-1 ;

[0028] In the formula, α ranges from 0.01 to 0.1, and x... n Let μ be the RMS value at the current time. n-1 This is the baseline value from the previous moment;

[0029] RMS exceeding the limit judgment rule:

[0030] Level 1 Alert: v rms >μ0+3σ0 and duration>5s;

[0031] Level 2 Alarm: v rms >μ0+5σ0 or growth rate >50% / min;

[0032] Level 3 Emergency: High-frequency energy accounts for more than 30%.

[0033] Adaptive baseline adjustment: The baseline is dynamically updated through exponentially weighted moving average (EWMA), enabling the system to adapt to slow environmental changes (such as changes in soil stress and minor pipeline deformation caused by seasonal temperature changes), avoiding decreased sensitivity or false alarms caused by a fixed baseline, and maintaining the accuracy of long-term monitoring.

[0034] Multi-dimensional hierarchical alarm system: Combining the duration of amplitude exceeding limits, the rate of change in amplitude, and the proportion of high-frequency energy, a precise classification is achieved from "preliminary anomaly" (Level 1) to "serious leakage" (Level 2) and then to "suspected rupture" (Level 3). This enables the dispatch center to take drastically different emergency response measures based on the alarm level, optimizing the allocation of emergency resources and avoiding overreaction.

[0035] Rupture identification capability: The high-frequency energy ratio (above 30%) is a key indicator for Level 3 emergency alarms and is very suitable for identifying the high-frequency stress waves generated when a natural gas pipeline ruptures. It provides key decision-making basis for the highest level of emergency response, such as quickly shutting off valves and initiating area evacuation.

[0036] Preferably, if a pipeline leaks, the location of the leak is determined based on the time difference of arrival of the negative pressure wave, including the following steps:

[0037] Continuously acquire pressure signals from both ends and store the raw waveform data;

[0038] A dynamic threshold is set based on historical data statistics, and a record is triggered when the rate of pressure drop at a certain end exceeds the threshold.

[0039] The negative pressure wave data was resampled to a uniform frequency using the generalized cross-correlation method, and dynamic time scaling correction was used. The search range was limited to the theoretical propagation time to obtain the measured value of Δt.

[0040] The location of the leak point is derived from Bernoulli's equation. After considering corrections for the effects of medium compressibility and pipe wall elasticity, the coordinates of the leak point are:

[0041]

[0042]

[0043] In the formula, L is the pipe length, and Δt is the t value. eff denoted by , a' represents the effective time difference affected by terrain, D represents the pipe inner diameter, c represents the sound velocity under ideal conditions, δ represents the pipe wall thickness, E represents the elastic modulus, v represents Poisson's ratio, K represents the bulk modulus, λ(h) represents the additional location compensation term caused by terrain elevation difference, x' represents the original leakage location, and g represents the gravitational acceleration.

[0044] High-precision time difference measurement: The GCC-PHAT and DTW algorithms are adopted to effectively overcome the time difference measurement error caused by sensor performance differences, signal attenuation and noise. In particular, reliable time delay information can still be extracted when the signal is weak, which improves the measurement accuracy of Δt.

[0045] Model correction: The positioning formula not only considers the ideal sound speed, but also introduces correction terms for the compressibility of the medium and the elasticity of the pipe wall, so that the sound speed value c is more in line with the actual working conditions, avoiding positioning errors caused by the discrepancy between the theoretical sound speed and the actual sound speed.

[0046] Terrain compensation: The system innovatively introduces an additional position compensation term (Δx) caused by the terrain elevation difference (Δh), which solves the industry problem of inaccurate positioning in pipelines with large elevation differences using traditional positioning formulas, and significantly improves the positioning accuracy of pipeline sections with complex terrain such as mountainous areas and valleys.

[0047] Final result: The location error of the leak point is reduced from hundreds of meters in traditional methods to tens of meters or even less, which greatly reduces the scope and cost of excavation to find the leak point, shortens the repair time, and reduces secondary safety and environmental risks.

[0048] Based on the index of each pipeline, the specific pipeline is identified, and relevant parameters facilitate faster pipeline replacement. This achieves a seamless and efficient connection from "location" to "response." Its effect lies in ultimately grounding all the aforementioned analysis results (whether there is a leak, the leak level, and the leak location) in specific physical objects and action plans. The index immediately retrieves all information about the leaking pipe section (such as pipe diameter, wall thickness, material, manufacturer, maintenance records, etc.), providing precise data support for emergency repair planning and spare parts preparation. This makes maintenance and replacement decisions faster and more accurate, minimizing downtime and improving the overall pipeline network's operational recovery efficiency.

[0049] The present invention also provides an oil and gas storage and transportation pipeline monitoring system, comprising:

[0050] Finite element modeling and data acquisition module: Constructs finite element models of pipelines, assigns a unique number to each pipeline segment, and collects monitoring parameters of each pipeline segment and the internal fluid.

[0051] Temperature field dynamic analysis module: Based on the pipe material and medium properties, a temperature gradient model that changes over time is established using Fourier transform to determine the condition of the medium inside the pipe;

[0052] Acoustic emission signal leakage detection module: determines whether the pipeline is leaking based on the real-time acoustic emission signal amplitude;

[0053] Leakage Level Quantitative Analysis Module: Used to analyze pipeline leakage levels;

[0054] Leakage point precise location module: Determines the location of the leak point based on the pressure signals collected at both ends of the pipeline.

[0055] Therefore, the technical effects of the oil and gas storage and transportation pipeline monitoring method and system described above are as follows:

[0056] Full-dimensional status monitoring enables early warning and prediction of potential risks: By analyzing pipeline and fluid parameters through a temperature gradient model, and combining pipeline material, medium properties, and Fourier transform to establish a time-related temperature difference model, abnormal medium flow rate, pressure, purity, and insufficient flow can be identified at an early stage.

[0057] High-precision leak identification significantly reduces false alarm and missed alarm rates: To distinguish between leaks and equipment vibration, the solution uses three-dimensional characteristics of acoustic emission signals: decibel value, dominant frequency, and energy dispersion.

[0058] Dynamic hierarchical alarms enable precise response to leakage levels: Based on the RMS amplitude and high-frequency energy ratio of fiber optic vibration data, combined with dynamic baseline updates (exponentially weighted moving average) and multi-level alarm rules, a refined classification of leakage levels is achieved.

[0059] High-precision leak point location improves fault handling efficiency: The leak point location process achieves high-precision location by combining the arrival time difference of negative pressure waves with multi-factor correction. Attached Figure Description

[0060] Figure 1 This is a flowchart of a method for monitoring oil and gas storage and transportation pipelines according to the present invention. Detailed Implementation

[0061] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0062] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0063] Example 1

[0064] like Figure 1 As shown, a method for monitoring oil and gas storage and transportation pipelines includes the following steps:

[0065] Construct a finite element model of the pipeline, assign a number to each pipeline segment, and obtain monitoring parameters for each pipeline segment and the fluid inside the pipeline.

[0066] The monitoring parameters of each pipeline segment and the fluid inside the pipeline are analyzed to obtain the current situation inside the pipeline. An index is set for each pipeline segment, including the distance from the beginning and end of the pipeline to one end, as well as the relevant parameters of the pipeline.

[0067] The monitoring parameters include: pipe material (such as steel grade, elastic modulus E, Poisson's ratio ν), geometric parameters (inner diameter D, wall thickness δ, length L), fluid properties (density ρ, bulk modulus K), and real-time sensor data (pressure P, temperature T, flow velocity v).

[0068] Index parameters: Pipe start / end position coordinates (distance S from one end of the pipe) start S end ), elevation difference Δh.

[0069] The monitoring parameters of the fluid inside the pipeline are analyzed, including:

[0070] Based on the material of the pipe and the properties of the medium, a temperature gradient model of the pipe changing over time is established using Fourier transform.

[0071] The temperature gradient is established using the Fourier heat conduction model, and the governing equation is: (α is the thermal diffusivity).

[0072] The medium temperature, pipe temperature, and time are combined to form the first temperature feature vector [T]. 流体 T 管壁 , t], are input into the temperature gradient model to obtain the temperature difference ΔT at the corresponding time;

[0073] When the temperature difference at a corresponding time exceeds the first threshold range (set based on historical data, e.g., ΔT), max At 5℃, there are problems with the flow rate, pressure, and purity of the medium in the pipeline;

[0074] When the temperature difference at a given time point is below the first threshold range, the flow rate of the medium in the pipeline is insufficient, requiring an inspection of the upstream medium production status. Specifically:

[0075] ΔT>ΔT max It will alert you to abnormal flow rate, pressure, or purity.

[0076] ΔT<ΔT min When the flow rate is insufficient (ΔT), an error message will be displayed. min (The lower limit threshold).

[0077] The analysis of the pipeline conditions includes the following steps:

[0078] Set time tags for the amplitude of real-time acoustic emission signals;

[0079] The amplitude of the real-time acoustic emission signal is converted to a decibel value and compared with a preset decibel threshold. Time-frequency analysis is performed on signal segments that exceed the decibel threshold.

[0080] Signal segments exceeding the decibel threshold are marked, and the frequency domain features and dominant frequency of the marked signal segments are extracted; the time domain features and energy dispersion of the marked signal segments are also extracted.

[0081] By comparing the dominant frequency with the standard frequency threshold, and by comparing the energy dispersion with its matching spectrum, we obtain:

[0082] If the decibel value is greater than the threshold A, the main frequency is greater than 50 Hz, and the energy dispersion is greater than β, then leakage is considered.

[0083] If the decibel value is greater than the threshold A, the main frequency is less than or equal to 50 Hz, and the energy concentration area matches the pump frequency, then the equipment is considered to be vibrating.

[0084] Specifically:

[0085] Time-frequency analysis is performed using Short-Time Fourier Transform (STFT) or Wavelet Transform to extract the dominant frequency f in the frequency domain. dominant And the time-domain energy dispersion η.

[0086] parameter:

[0087] Decibel threshold A (e.g., 60dB); main frequency threshold 50Hz; energy dispersion threshold β (needs to be matched and calibrated according to the pump frequency spectrum); pump frequency feature library (pre-collected equipment vibration spectrum).

[0088] Judgment criteria: Leakage conditions: decibel value > A, main frequency > 50 Hz, energy dispersion > β;

[0089] Equipment vibration conditions: decibel value > A, main frequency ≤ 50Hz, energy concentration area matched with pump frequency.

[0090] Analyzing the severity of pipeline leaks includes the following steps:

[0091] Time-domain analysis was performed on the fiber optic vibration data to obtain the RMS amplitude;

[0092] Frequency domain analysis was performed on the fiber optic vibration data to obtain the proportion of high-frequency energy.

[0093] The initial baseline is set based on the RMS amplitude: the RMS mean value for 72 consecutive hours under leak-free conditions is μ0 ± 3σ0, where μ0 is the initial baseline and σ0 is the standard deviation of the RMS amplitude.

[0094] The baseline is dynamically updated hourly using an exponentially weighted moving average: μ n =α·x n +(1-α)·μ n-1 (α=0.05);

[0095] In the formula, α ranges from 0.01 to 0.1, and x... nLet μ be the RMS value at the current time. n-1 This is the baseline value from the previous moment;

[0096] RMS exceeding the limit judgment rule:

[0097] Level 1 Alert: v rms >μ0+3σ0 and duration>5s;

[0098] Level 2 Alarm: v rms >μ0+5σ0 or growth rate >50% / min;

[0099] Level 3 Emergency: High-frequency energy accounts for more than 30%.

[0100] If a pipeline leaks, the location of the leak is determined based on the time difference of arrival of the negative pressure wave, including the following steps:

[0101] Continuously acquire pressure signals from both ends and store the raw waveform data;

[0102] A dynamic threshold is set based on historical data statistics, and a record is triggered when the rate of pressure drop at a certain end exceeds the threshold.

[0103] The negative pressure wave data was resampled to a uniform frequency using the generalized cross-correlation method (GCC-PHAT), and the dynamic time stretching correction (DTW algorithm) was used. The search range was limited to the theoretical propagation time (L / v±Δt) to obtain the measured value of Δt.

[0104] The location of the leak point is derived from Bernoulli's equation. After considering corrections for the effects of medium compressibility and pipe wall elasticity, the coordinates of the leak point are:

[0105]

[0106]

[0107] In the formula, L is the pipe length, and Δt is the t value. eff denoted by , a' represents the effective time difference affected by terrain, D represents the pipe inner diameter, c represents the sound velocity under ideal conditions, δ represents the pipe wall thickness, E represents the elastic modulus, v represents Poisson's ratio, K represents the bulk modulus, λ(h) represents the additional position compensation term caused by terrain elevation difference, x' represents the original leakage location, g represents the gravitational acceleration, and x represents the current leakage location.

[0108] Based on the index of each pipe, identify which pipe it is, and at the same time, use the relevant parameters of the pipe to facilitate faster pipe replacement.

[0109] An oil and gas storage and transportation pipeline monitoring system includes:

[0110] Finite element modeling and data acquisition module: Constructs finite element models of pipelines, assigns a unique number to each pipeline segment, and collects monitoring parameters of each pipeline segment and the internal fluid.

[0111] Temperature field dynamic analysis module: Based on the pipe material and medium properties, a temperature gradient model that changes over time is established using Fourier transform to determine the condition of the medium inside the pipe;

[0112] Acoustic emission signal leakage detection module: determines whether the pipeline is leaking based on the real-time acoustic emission signal amplitude;

[0113] Leakage Level Quantitative Analysis Module: Used to analyze pipeline leakage levels;

[0114] Leakage point precise location module: Determines the location of the leak point based on the pressure signals collected at both ends of the pipeline.

[0115] Therefore, the present invention adopts the above-mentioned oil and gas storage and transportation pipeline monitoring method and system, which realizes the full-cycle refined management of oil and gas storage and transportation pipelines from normal operation to fault handling through the closed-loop logic of early warning, accurate identification, hierarchical response and precise positioning. It significantly improves the accuracy, timeliness and adaptability of monitoring and provides efficient technical support for the safe operation of pipelines.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for monitoring oil and gas storage and transportation pipelines, characterized in that, Includes the following steps: Construct a finite element model of the pipeline, assign a number to each pipeline segment, and obtain monitoring parameters for each pipeline segment and the fluid inside the pipeline. The monitoring parameters of each pipeline segment and the fluid inside the pipeline are analyzed to obtain the current situation inside the pipeline. An index is set for each pipeline segment, including the distance from the beginning and end of the pipeline to one end, as well as the relevant parameters of the pipeline.

2. The method for monitoring oil and gas storage and transportation pipelines according to claim 1, characterized in that, The monitoring parameters of the fluid inside the pipeline are analyzed, including: Based on the material of the pipe and the properties of the medium, a temperature gradient model of the pipe changing over time is established using Fourier transform. The medium temperature, pipe temperature, and time are combined to form a first temperature feature vector, which is then input into the temperature gradient model to obtain the temperature difference at the corresponding time: When the temperature difference at a given time exceeds the first threshold range, there are problems with the flow rate, pressure, and purity of the medium in the pipeline. When the temperature difference at a given time is below the first threshold range, the flow rate of the medium in the pipeline is insufficient, and it is necessary to check the upstream and the production status of the medium.

3. The method for monitoring oil and gas storage and transportation pipelines according to claim 2, characterized in that, The analysis of the pipeline conditions includes the following steps: Set time tags for the amplitude of real-time acoustic emission signals; The amplitude of the real-time acoustic emission signal is converted to a decibel value and compared with a preset decibel threshold. Time-frequency analysis is performed on signal segments that exceed the decibel threshold. Signal segments exceeding the decibel threshold are marked, and the frequency domain features and dominant frequency of the marked signal segments are extracted; the time domain features and energy dispersion of the marked signal segments are also extracted. By comparing the dominant frequency with the standard frequency threshold, and by comparing the energy dispersion with its matching spectrum, we obtain: If the decibel value is greater than the threshold A, the main frequency is greater than 50 Hz, and the energy dispersion is greater than β, then leakage is considered. If the decibel value is greater than the threshold A, the main frequency is less than or equal to 50 Hz, and the energy concentration area matches the pump frequency, then the equipment is considered to be vibrating.

4. The method for monitoring oil and gas storage and transportation pipelines according to claim 3, characterized in that, Analyzing the severity of pipeline leaks includes the following steps: Time-domain analysis was performed on the fiber optic vibration data to obtain the RMS amplitude; Frequency domain analysis was performed on the fiber optic vibration data to obtain the proportion of high-frequency energy. The initial baseline is set based on the RMS amplitude: the RMS mean value for 72 consecutive hours under leak-free conditions is μ0 ± 3σ0, where μ0 is the initial baseline and σ0 is the standard deviation of the RMS amplitude. The baseline is dynamically updated hourly using an exponentially weighted moving average: μ n =α·x n +(1-α)·μ n-1 ; In the formula, α ranges from 0.01 to 0.1, and x... n Let μ be the RMS value at the current time. n-1 This is the baseline value from the previous moment; RMS exceeding the limit judgment rule: Level 1 Alert: v rms >μ0+3σ0 and duration>5s; Level 2 Alarm: v rms >μ0+5σ0 or growth rate >50% / min; Level 3 Emergency: High-frequency energy accounts for more than 30%.

5. The method for monitoring oil and gas storage and transportation pipelines according to claim 4, characterized in that, If a pipeline leaks, the location of the leak is determined based on the time difference of arrival of the negative pressure wave, including the following steps: Continuously acquire pressure signals from both ends and store the raw waveform data; A dynamic threshold is set based on historical data statistics, and a record is triggered when the rate of pressure drop at a certain end exceeds the threshold. The negative pressure wave data was resampled to a uniform frequency using the generalized cross-correlation method, and dynamic time scaling correction was used. The search range was limited to the theoretical propagation time to obtain the measured value of Δt. The location of the leak point is derived from Bernoulli's equation. After considering corrections for the effects of medium compressibility and pipe wall elasticity, the coordinates of the leak point are: In the formula, L is the pipe length, and Δt is the t value. eff denoted by , a' represents the effective time difference affected by terrain, D represents the pipe inner diameter, c represents the sound velocity under ideal conditions, δ represents the pipe wall thickness, E represents the elastic modulus, v represents Poisson's ratio, K represents the bulk modulus, λ(h) represents the additional location compensation term caused by terrain elevation difference, x' represents the original leakage location, and g represents the gravitational acceleration. Based on the index of each pipe, identify which pipe it is, and at the same time, use the relevant parameters of the pipe to facilitate faster pipe replacement.

6. A monitoring system for oil and gas storage and transportation pipelines, characterized in that, include: Finite element modeling and data acquisition module: Constructs finite element models of pipelines, assigns a unique number to each pipeline segment, and collects monitoring parameters of each pipeline segment and the internal fluid. Temperature field dynamic analysis module: Based on the pipe material and medium properties, a temperature gradient model that changes over time is established using Fourier transform to determine the condition of the medium inside the pipe; Acoustic emission signal leakage detection module: determines whether the pipeline is leaking based on the real-time acoustic emission signal amplitude; Leakage Level Quantitative Analysis Module: Used to analyze pipeline leakage levels; Leakage point precise location module: Determines the location of the leak point based on the pressure signals collected at both ends of the pipeline.

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