Method for detecting hydrate blockage of natural gas conveying pipeline

Abstract

The invention relates to a method for detecting hydrate blockage of a natural gas pipeline. The method comprises the following steps: S1, acquiring an original pressure wave signal of a natural gas pipeline to be detected; s2, performing signal preprocessing according to the original pressure wave signal to obtain a to-be-detected signal; s3, de-noising the signal to be detected by using a wavelet function to obtain a de-noised signal; s4, performing feature point identification on the denoised signal, and aggregating the extracted feature points to obtain a candidate feature point set; and S5, judging whether the natural gas conveying pipeline is blocked or not according to whether the candidate feature point set is abnormal or not.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas transportation, and in particular to a method for detecting hydrate blockage in a natural gas transmission pipeline. Background Art

[0002] As a clean energy, natural gas occupies an increasingly important position in the energy structure. In the existing technology, natural gas can be transported over long distances using gas pipelines.

[0003] Natural gas pipelines often face the problem of hydrate blockage during long-distance transportation. Hydrates are solid cage-like structures formed by the reaction of water in natural gas with hydrocarbon gases under certain temperature and pressure conditions. These structures will accumulate on the inner wall of the pipeline, resulting in a reduction in the pipeline flow cross-sectional area or even complete blockage, which will cause a decrease in transportation efficiency and an increase in transportation costs. In severe cases, it may also cause safety accidents. Hydrate blockage will increase the flow resistance inside the pipeline and reduce the transportation efficiency of natural gas. The removal of hydrates and the repair of pipelines require a lot of manpower and material resources, which increases maintenance costs.

[0004] Hydrate blockage may cause pressure fluctuations in the transmission system, affecting the stability and reliability of the system. Excessive or low pressure caused by pipeline blockage may cause safety accidents such as rupture and leakage. Pressure wave detection of hydrate blockage in natural gas pipelines is an efficient, real-time and non-invasive solution. This method can quickly respond to pressure changes in the pipeline, has high sensitivity and accuracy, can capture the initial signals of blockage in time and accurately locate the blockage. Pressure wave detection technology is suitable for various complex terrains and environmental conditions, and has good multi-scenario applicability. Through long-term monitoring and analysis, this technology can also achieve predictive maintenance, early warning of potential blockage risks, avoid serious safety accidents caused by blockage, and ensure the operation efficiency of the pipeline and the stability of energy supply.

[0005] However, the pressure wave signal in the pipeline is often interfered by various noise sources, including environmental noise, equipment vibration, fluid turbulence, etc. These noises will mask the subtle pressure changes caused by blockage and reduce the accuracy of detection. Therefore, how to extract useful signals from the pressure wave signal and perform more accurate pipeline blockage detection has become a problem that needs to be solved. Summary of the invention

[0006] The present invention provides a method for detecting hydrate blockage in a natural gas transmission pipeline, which can solve the above-mentioned technical problems.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present application provides a method for detecting hydrate blockage in a natural gas transmission pipeline, the method comprising:

[0009] S1, obtaining the original pressure wave signal of the natural gas pipeline to be inspected;

[0010] S2, performing signal preprocessing according to the original pressure wave signal to obtain a signal to be detected;

[0011] S3, denoising the signal to be detected using a wavelet function to obtain a denoised signal;

[0012] S4, identifying feature points of the denoised signal, and aggregating the extracted feature points to obtain a set of candidate feature points;

[0013] S5, judging whether the natural gas transmission pipeline is blocked according to whether the candidate feature point set is abnormal.

[0014] In one implementation, the S2 includes:

[0015] The DC component of the original pressure wave signal is calculated according to the original pressure wave signal, and the DC component is removed from the original pressure wave signal to obtain the signal to be detected.

[0016] In one implementation, the DC component is calculated based on an average value of the original pressure wave signal after sampling.

[0017] In one implementation, the S3 includes:

[0018] Select a wavelet basis to perform wavelet decomposition on the signal to be detected;

[0019] The median absolute value is used to estimate the noise standard deviation in the signal to be detected, and the threshold is calculated based on the noise standard deviation;

[0020] Apply soft threshold processing to calculate the wavelet coefficients after wavelet denoising;

[0021] The signal is reconstructed according to the wavelet coefficients after wavelet denoising to obtain the denoised signal.

[0022] In one implementation, after obtaining the denoised signal, the method further smoothes the denoised signal.

[0023] In one implementation, the smoothing is moving average smoothing.

[0024] In one implementation, the identification of the feature points is to identify the peak or trough extreme points of the denoised signal.

[0025] In one implementation, the aggregation includes:

[0026] Set a distance threshold, and for feature points whose distance does not exceed the distance threshold, calculate their mean as the representative point.

[0027] In one implementation, the method further includes: visually displaying the original pressure wave signal and identifying characteristic points.

[0028] The technical solution of the present invention can separate useful information from the noisy pressure wave signal by using advanced signal processing technologies such as wavelet transform, remove or reduce the influence of noise, and improve the signal-to-noise ratio of the signal. In the denoised pressure wave signal, specific feature points, such as the maximum amplitude point of the pressure wave, can indicate abnormal conditions in the pipeline, such as the formation and accumulation of hydrates. By accurately identifying these feature points, the location and degree of blockage can be quantitatively analyzed, thereby achieving early detection and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Schematic diagram of the original pressure wave signal and the denoised signal;

[0030] Figure 2 Schematic diagram for marking signal feature points. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0032] In view of the defects and problems of the prior art, the present application provides a method for detecting hydrate blockage in a natural gas transmission pipeline, the method comprising:

[0033] S1, obtaining the original pressure wave signal of the natural gas pipeline to be inspected;

[0034] S2, performing signal preprocessing according to the original pressure wave signal to obtain a signal to be detected;

[0035] S3, denoising the signal to be detected using a wavelet function to obtain a denoised signal;

[0036] S4, identifying feature points of the denoised signal, and aggregating the extracted feature points to obtain a set of candidate feature points;

[0037] S5, judging whether the natural gas transmission pipeline is blocked according to whether the candidate feature point set is abnormal.

[0038] The above method flow is described in more detail in the following embodiments in conjunction with the accompanying drawings of the present invention.

[0039] The method provided in this application includes the following specific steps:

[0040] First, a pressure wave signal is obtained from a sensor or a data recorder installed on a natural gas transmission pipeline.

[0041] Further calculation of the average value: Analyze the collected pressure wave signal and calculate its average value (DC component). This can be achieved by summing all sampling points and dividing by the total number of sampling points.

[0042] Then, remove the DC component: subtract its mean value from the original signal, so that the processed signal will have a distribution centered at zero. This process can be expressed by the following formula:

[0043]

[0044] Among them, s(t) is the original signal, s'(t) is the signal after removing the DC component, is the average value of the signal.

[0045] Verification and analysis: After removing the DC component, the signal should be verified and further analyzed. You can plot the original signal and the signal after removing the DC component to visually compare the difference between the two or use a chart to display the waveform of the signal to check for abnormal fluctuations or distortion. Ensure that the DC component has been effectively removed and the key features of the signal are not affected.

[0046] Select appropriate wavelet to denoise the signal, effectively remove high-frequency noise in the signal, and retain the main features of the signal. Multi-step processing, combining wavelet transform and moving average filtering, realizes multi-level denoising of the signal. Adaptive threshold, dynamically calculates the threshold, and adapts to signals with different noise levels. Soft threshold processing, while reducing noise, retains the high-frequency information of the signal.

[0047] Wavelet decomposition: To make the algorithm more accurate, select a wavelet basis (such as db4, sym8) to perform wavelet decomposition on the signal x(t), set the number of decomposition levels, and obtain wavelet coefficients of different scales.

[0048] Use the absolute value of the median to estimate the noise standard deviation σ and calculate the threshold:

[0049]

[0050] Where k is a proportionality constant and N is the length of the signal.

[0051] Soft thresholding: Apply soft thresholding to the wavelet coefficients, the formula is:

[0052]

[0053] Among them, c ′ is the wavelet coefficient, and θ is the threshold. The threshold θ is dynamically calculated based on the length of the signal and the noise estimate, ensuring that the algorithm adapts to different signal conditions.

[0054] Reconstruct the signal: Use the wavelet coefficients c after wavelet denoising ′ Reconstruct the signal.

[0055] Apply moving average filtering to the denoised signal y(t) to smooth the signal:

[0056]

[0057] Where M is the window size of the moving average.

[0058] Through this combined wavelet transform and moving average filtering method, the algorithm can effectively remove noise while maintaining the main features and structure of the signal. The multi-scale analysis capability of the wavelet transform enables it to capture the local characteristics of the signal, while the soft threshold processing provides an effective means of balancing denoising and signal fidelity, reducing the amplitude of the wavelet coefficients instead of completely removing them, which helps to retain the high-frequency components of the signal while suppressing noise. The moving average filter further smoothes the high-frequency noise that may remain after wavelet denoising, achieving signal smoothing.

[0059] The feature points in the signal are identified through a series of systematic processing steps, covering data preprocessing, feature point detection, feature point aggregation and visualization. The specific workflow and corresponding formulas are described as follows:

[0060] After denoising using the wavelet function, the signal is denoised again using the Gaussian smoothing method to detect feature points. In this step, the smoothed signal is used to detect local extreme points, namely peaks and troughs. The amplitude threshold is used to identify peaks and troughs, which is defined as:

[0061] Peak: If S [i] >S [i-1] And S [i] >S [i+1] And S [i] >T p , then i is a peak, where T p is the predetermined peak threshold.

[0062] Trough: Accordingly, if S [i] [i-1] And S [i] <S​[i+1] And S [i] <T v , then i is a trough, where T v is the trough threshold.

[0063] After detection, the code records the indices of all identified peaks and troughs to form a set of candidate feature points.

[0064] In order to eliminate the feature point error caused by signal fluctuations, the code aggregates the adjacent feature points. For the peak and trough data, an allowable distance threshold d is set, and for the feature points whose distance does not exceed d, their mean is calculated as the representative point. The specific calculation formula is:

[0065]

[0066] Among them, P avg is the peak or trough position after aggregation, P [j] is the set of feature point positions within the distance threshold d, and n is the number of feature points that meet the conditions.

[0067] Finally, the identified peaks and troughs are displayed through the visualization module to generate a chart containing the original signal and its characteristic points, making the location of the characteristic points of the signal more intuitive. By marking the peaks (red dots) and troughs (blue dots) in the chart, users can intuitively analyze the dynamic characteristics of the signal and its fluctuations.

[0068] Whether the natural gas pipeline is blocked is determined based on whether the candidate feature point set is abnormal.

[0069] Specifically, by comparing the changes in the amplitude of the pressure wave, blockage will cause the propagation of the pressure wave in the pipeline to be blocked, so that the amplitude of the pressure wave reaching the sensor is reduced. This is partly because the blockage absorbs part of the energy, or the pressure wave is reflected at the blockage point. It is also possible to observe whether the pressure waveform is distorted. Blockage may cause changes in the pressure waveform, such as additional peaks and troughs in the waveform. For example, when incomplete blockage occurs and a layer of hydrate is formed on the pipe wall, the pipe diameter is reduced and the pressure wave is blocked, the pressure wave energy is weakened, and the pressure wave amplitude is reduced.

[0070] In the several embodiments provided by the present invention, it should be understood that the disclosed method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0071] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (Processor) to perform some steps of the above-mentioned method of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0072] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting hydrate blockage in a natural gas pipeline, characterized in that: The method comprises: S1, obtaining the original pressure wave signal of the natural gas pipeline to be inspected; S2, performing signal preprocessing according to the original pressure wave signal to obtain a signal to be detected; S3, denoising the signal to be detected using a wavelet function to obtain a denoised signal; S4, identifying feature points of the denoised signal, and aggregating the extracted feature points to obtain a set of candidate feature points; S5, judging whether the natural gas transmission pipeline is blocked according to whether the candidate feature point set is abnormal.

2. The method for detecting hydrate blockage in a natural gas pipeline according to claim 1, characterized in that: The S2 includes: The DC component of the original pressure wave signal is calculated according to the original pressure wave signal, and the DC component is removed from the original pressure wave signal to obtain the signal to be detected.

3. The method for detecting hydrate blockage in a natural gas pipeline according to claim 2, characterized in that: The DC component is calculated based on the average value of the original pressure wave signal after sampling.

4. The method for detecting hydrate blockage in a natural gas pipeline according to claim 1, characterized in that: The S3 includes: Select a wavelet basis to perform wavelet decomposition on the signal to be detected; The median absolute value is used to estimate the noise standard deviation in the signal to be detected, and the threshold is calculated based on the noise standard deviation; Apply soft threshold processing to calculate the wavelet coefficients after wavelet denoising; The signal is reconstructed according to the wavelet coefficients after wavelet denoising to obtain the denoised signal.

5. The method for detecting hydrate blockage in a natural gas pipeline according to claim 4, characterized in that: The method, after obtaining the denoised signal, further smoothes the denoised signal.

6. The method for detecting hydrate blockage in a natural gas pipeline according to claim 5, characterized in that: The smoothing is moving average smoothing.

7. The method for detecting hydrate blockage in a natural gas pipeline according to claim 1, characterized in that: The identification of the feature points is to identify the peak or trough extreme points of the denoised signal.

8. The method for detecting hydrate blockage in a natural gas transmission pipeline according to claim 7, characterized in that: The polymerization comprises: Set a distance threshold, and for feature points whose distance does not exceed the distance threshold, calculate their mean as the representative point.

9. The method for detecting hydrate blockage in a natural gas pipeline according to claim 1, characterized in that: The method further includes: visually displaying the original pressure wave signal and identifying characteristic points.

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