A method of monitoring corrosion in an oil pipeline

Abstract

The application discloses a kind of oil pipeline corrosion monitoring methods, S1, different frequency ultrasonic detection signals are emitted to oil pipeline by ultrasonic detection array, and the ultrasonic echo signal of corresponding area is synchronously collected, to obtain internal state information of oil pipeline.S2, the ultrasonic echo signal obtained by collection is carried out feature extraction, obtains spectrum fluctuation rate, transient pulse density, high-frequency scattering energy, multipath echo distribution and echo attenuation feature parameter etc.S3, based on the echo feature of extraction, bubble, wax and solid particle etc. Impurity state in oil medium is identified;The application can identify bubble impurities, wax deposition impurities and solid particle impurities in oil medium by the joint analysis of echo spectrum fluctuation rate, transient pulse density, high-frequency scattering energy and multipath echo distribution in ultrasonic echo signal, so as to realize the distinguishing processing of different types of impurity interference, improve the corrosion identification accuracy under complex oil conveying condition.

Description

Technical Field

[0001] This invention relates to the field of pipeline inspection, and more specifically, to a method for monitoring corrosion in oil pipelines. Background Technology

[0002] Oil pipelines, as crucial infrastructure in the oil transportation process, operate under high pressure, high flow rates, and complex media environments, making them susceptible to electrochemical corrosion, erosion corrosion, and localized media erosion. This can lead to problems such as pipe wall thinning, pitting corrosion, cracks, and even perforation and leakage. Failure to detect internal corrosion defects in oil pipelines in a timely manner can not only affect transportation efficiency but also potentially cause crude oil leaks, environmental pollution, and safety accidents. Therefore, long-term, stable, and high-precision corrosion monitoring of oil pipelines is essential.

[0003] In existing technologies, ultrasonic testing is widely used in the field of oil pipeline corrosion monitoring due to its non-destructive nature, high detection sensitivity, and suitability for online detection. Current ultrasonic corrosion monitoring methods typically involve emitting ultrasonic signals into the oil pipeline and determining the presence of corrosion defects based on echo propagation time, echo energy, and changes in wall thickness.

[0004] However, in actual oil transportation operations, the transported medium typically contains complex interfering factors such as air bubbles, wax deposits, solid particles, and oil-water mixed transport impurities. These impurities cause changes in the ultrasonic wave propagation path, propagation energy, and echo spectrum structure during flow, resulting in numerous random abnormal signals in the ultrasonic echo. Particularly under conditions of high flow velocity, high wax content, or large fluctuations in gas content, impurity interference is further amplified, easily leading to phenomena such as spectral drift, multipath reflection, enhanced local high-frequency scattering, and transient abnormal pulses in the ultrasonic echo.

[0005] Most existing ultrasonic corrosion monitoring methods employ fixed threshold analysis, single-echo judgment, or conventional filtering to process echo signals. These methods primarily focus on reducing random noise or improving the echo signal-to-noise ratio, but they struggle to effectively distinguish between abnormal echoes caused by flowing impurities and those corresponding to actual corrosion defects. When bubbles, wax, or particulate matter in the oil medium produce localized abnormal scattering, existing methods easily misjudge this interference as corrosion defects, leading to a higher false alarm rate. Conversely, when the corrosion area is covered by wax deposits or subjected to complex flow field interference, the true corrosion signal can be masked, resulting in missed detections.

[0006] Furthermore, existing technologies typically lack long-term analysis of the background characteristics of stable impurities under different oil transportation conditions, and also lack the ability to dynamically distinguish abnormal echoes based on spatial migration and temporal continuity characteristics. For random abnormal echoes generated by flowing impurities, existing methods often cannot effectively identify their positional drift characteristics with the flow state, thus making it difficult to accurately distinguish between stationary corrosion anomalies and flowing impurity anomalies. Summary of the Invention

[0007] The purpose of this invention is to provide a method for monitoring corrosion of oil pipelines to solve the problems mentioned in the background art.

[0008] Technical solution: A method for monitoring corrosion in oil pipelines, comprising the following steps: S1. Transmit at least two different frequencies of ultrasonic detection signals to the oil pipeline through an ultrasonic detection array installed on the outer wall of the oil pipeline, and receive the corresponding ultrasonic echo signals. S2. Perform feature extraction on the ultrasonic echo signal to obtain echo spectral fluctuation rate, transient pulse density, high-frequency scattering energy, multipath echo distribution, echo time-of-flight offset, and echo energy attenuation rate. S3. Identify the state of impurities in the oil transportation medium based on echo spectrum volatility, transient pulse density, high-frequency scattering energy and multipath echo distribution, and determine the corresponding impurity type and impurity interference level. S4. Establish a stable impurity background model based on the non-corrosive state echo data collected under historical operating conditions. The stable impurity background model includes the correspondence between stable impurity background echoes and non-corrosive echoes under different flow rates, different gas contents, different wax contents and different pressure conditions. S5. Match the corresponding stable impurity background echo according to the current oil transportation operating parameters, and perform differential processing on the current ultrasonic echo signal and the corresponding stable impurity background echo to eliminate stable impurity background interference. S6. Perform multi-dimensional purification processing on the echo signal after differential processing. The multi-dimensional purification processing includes time domain purification, frequency domain purification, spatial correlation purification and time continuity purification. S7. Calculate the echo confidence level based on the purified echo signal, and dynamically adjust the ultrasonic detection parameters according to the echo confidence level; S8. When the echo confidence is lower than a preset threshold, control the ultrasonic detection array to perform closed-loop re-detection of the target area, and adaptively adjust at least one of the following during the re-detection process: transmission frequency, frequency ratio, transmission power, scanning speed, sampling density, and scanning area range. S9. Analyze the spatial migration characteristics of abnormal echoes within a continuous sampling period, and determine that abnormal echoes with continuously drifting locations are interference from flowing impurities. S10. Perform time continuity verification on abnormal echoes that have not undergone continuous spatial drift. Only when the abnormal echoes continue to exist within a preset time window and the echo flight time offset or echo energy change trend continues to increase, it is determined that the oil pipeline is corroded.

[0009] Preferably, in step S1, the ultrasonic detection array includes multiple ultrasonic probes arranged at intervals along the axial and circumferential directions of the oil pipeline, and the ultrasonic probes acquire ultrasonic echo signals of the corresponding areas through synchronous sampling.

[0010] Preferably, in step S1, the at least two different frequency ultrasonic detection signals include: Low-frequency ultrasonic signals with a frequency of 1MHz to 3MHz, and high-frequency ultrasonic signals with a frequency of 8MHz to 15MHz; The low-frequency ultrasonic signal is used to acquire stable wall thickness echoes, and the high-frequency ultrasonic signal is used to acquire localized corrosion detail echoes.

[0011] Preferably, in step S3: When high-frequency random spikes and rapid spectral drift are present in the echo signal, it is determined to be bubble impurity; When there is continuous attenuation enhancement and high-frequency energy decline in the echo signal, it is determined to be a waxy deposit impurity. When there is an increase in discrete reflection points and an enhancement of multipath echoes in the echo signal, it is determined to be solid particulate impurities.

[0012] Preferably, in step S5, the differential processing includes: Calculate the energy difference, phase difference, and spectral offset between the current ultrasonic echo signal and the corresponding stable impurity background echo, and extract newly added abnormal echoes based on the energy difference, phase difference, and spectral offset.

[0013] Preferably, the spatial correlation purification in step S6 includes: Spatial consistency comparison is performed on the echo signals acquired by adjacent ultrasound probes, and random abnormal echoes that exist only in a single ultrasound probe are filtered out.

[0014] Preferably, in step S7, the echo confidence is calculated based on at least three of the following parameters: Signal-to-noise ratio, echo stability, spectral integrity, temporal continuity, spatial consistency, consistency between adjacent probes, and the recurrence rate of abnormal echoes.

[0015] Preferably, in step S8, after the closed-loop re-detection is completed, the stable impurity background echo corresponding to the current operating condition is updated and corrected based on the re-detection result.

[0016] Preferably, in step S10, the time continuity verification includes: The study analyzed the trends of echo flight time offset and echo energy variation in multiple consecutive sampling periods, and identified the abnormal echoes with continuously increasing trends as corrosion evolution signals.

[0017] Compared with the prior art, the advantages of this invention are: (1) By jointly analyzing the echo spectrum fluctuation rate, transient pulse density, high-frequency scattering energy and multipath echo distribution in the ultrasonic echo signal, this invention can identify bubble impurities, waxy deposit impurities and solid particle impurities in the oil transportation medium and determine the corresponding impurity interference level, thereby realizing the differentiation and processing of different types of impurity interference, avoiding the problem of misjudging flowing impurities as corrosion defects in traditional ultrasonic testing, and improving the accuracy of corrosion identification under complex oil transportation conditions.

[0018] (2) This invention establishes a stable impurity background model and matches the corresponding stable impurity background echo with the current oil transportation operating parameters. Furthermore, it performs differential processing on the current ultrasonic echo signal and the stable impurity background echo, thereby eliminating the fixed background interference formed by long-term stable impurities. Compared to traditional fixed threshold detection methods, this invention can more accurately extract abnormal echoes that newly appear compared to the background, improve the identification ability of newly added corrosion defects, and reduce the false alarm rate under complex background conditions.

[0019] (3) By combining the analysis of energy difference, phase difference and spectral offset, this invention can identify new abnormal echoes from multiple dimensions such as energy characteristics, propagation path characteristics and spectral structure characteristics, thereby avoiding the problem that single amplitude analysis is easily affected by local fluctuations in complex oil transportation environment and improving the stability of corrosion anomaly extraction.

[0020] (4) By setting up time-domain purification, frequency-domain purification, spatial correlation purification, and temporal continuity purification, this invention can gradually filter out random abnormal signals generated by local turbulence, bubble bursting, particle collision, and flow disturbance. Among them, spatial correlation purification can utilize the spatial continuity characteristics of real corrosion anomalies to filter out random abnormal echoes that exist only at a single detection location; temporal continuity purification can filter out short-term random fluctuation signals, thereby further improving the stability and reliability of corrosion anomaly echoes.

[0021] (5) By analyzing the spatial migration characteristics of abnormal echoes between different sampling periods, this invention can identify abnormal flow impurities that drift with the flow state and retain abnormal echoes that persist in a fixed position as suspected corrosion anomalies, thereby effectively distinguishing between flow impurity interference and real corrosion anomalies and improving the detection reliability in complex flow environments. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0023] Example An oil pipeline corrosion monitoring system, comprising: An ultrasonic detection array is used to transmit at least two different frequencies of ultrasonic detection signals to an oil pipeline and receive the corresponding ultrasonic echo signals. The feature extraction module is used to extract echo spectral volatility, transient pulse density, high-frequency scattering energy, multipath echo distribution, echo time-of-flight offset, and echo energy attenuation rate. The impurity identification module is used to identify the state of impurities in the oil transportation medium and determine the type and level of impurity interference. The background modeling module is used to establish a stable impurity background model and match the stable impurity background echo corresponding to the current operating condition. The differential noise reduction module is used to perform differential processing between the current ultrasonic echo signal and the stable impurity background echo; The multi-dimensional purification module is used to perform time-domain purification, frequency-domain purification, spatial correlation purification, and temporal continuity purification on the differentially processed echo signal. The credibility assessment module is used to calculate the credibility of the echo. The closed-loop control module is used to control the ultrasonic testing array to perform closed-loop re-detection and dynamically adjust the ultrasonic testing parameters when the echo reliability is lower than a preset threshold. The migration analysis module is used to analyze the spatial migration characteristics of anomalous echoes within a continuous sampling period; The corrosion assessment module is used to determine whether corrosion exists in oil pipelines based on the time continuity verification results.

[0024] The corrosion monitoring method for oil pipelines includes the following steps: S1. An ultrasonic detection array installed on the outer wall of the oil pipeline transmits at least two different frequencies of ultrasonic detection signals to the oil pipeline and receives the corresponding ultrasonic echo signals. In step S1, the ultrasonic detection array includes multiple ultrasonic probes arranged at intervals along the axial and circumferential directions of the oil pipeline. Each ultrasonic probe acquires the ultrasonic echo signal of the corresponding area through synchronous sampling.

[0025] In step S1, the at least two different frequency ultrasonic detection signals include: Low-frequency ultrasonic signals with a frequency of 1MHz to 3MHz, and high-frequency ultrasonic signals with a frequency of 8MHz to 15MHz; The low-frequency ultrasonic signal is used to acquire stable wall thickness echoes, and the high-frequency ultrasonic signal is used to acquire localized corrosion detail echoes.

[0026] S2. Perform feature extraction on the ultrasonic echo signal to obtain echo spectral fluctuation rate, transient pulse density, high-frequency scattering energy, multipath echo distribution, echo time-of-flight offset, and echo energy attenuation rate. S3. Identify the state of impurities in the oil transportation medium based on echo spectrum volatility, transient pulse density, high-frequency scattering energy, and multipath echo distribution, and determine the corresponding impurity type and impurity interference level; In step S3: When high-frequency random spikes and rapid spectral drift are present in the echo signal, it is determined to be bubble impurity; When there is continuous attenuation enhancement and high-frequency energy decline in the echo signal, it is determined to be a waxy deposit impurity. When there is an increase in discrete reflection points and an enhancement of multipath echoes in the echo signal, it is determined to be solid particulate impurities.

[0027] In step S3, the ultrasonic echo signal obtained in step S2 is used to identify the impurity status in order to determine whether there are bubble impurities, waxy deposit impurities or solid particle impurities in the current oil transportation medium, and to determine the corresponding impurity interference level.

[0028] Specifically, the original ultrasonic echo signal is first preprocessed, including bandpass filtering, gain normalization, and time window truncation. Bandpass filtering preserves the effective echo signal within the ultrasonic operating frequency band and filters out low-frequency mechanical vibration noise and high-frequency electromagnetic interference. Gain normalization eliminates the influence of overall echo energy fluctuations under different operating conditions on the detection results. Time window truncation extracts the effective echo corresponding to the target pipe wall region to reduce interference from reflected echoes from non-target regions.

[0029] After preprocessing, the ultrasonic echo signal is subjected to spectral analysis to obtain the corresponding spectral distribution characteristics, and the spectral volatility within a continuous sampling period is calculated. The spectral volatility is used to characterize the degree of change in spectral energy distribution over time, specifically including the dominant frequency offset, high-frequency energy change, and spectral width change.

[0030] When air bubbles are present in the oil medium, the random motion of the bubbles in the fluid causes random scattering and transient resonance in the ultrasonic waves propagating to the bubble region, resulting in high-frequency random spikes and rapid spectral drift in the echo signal. High-frequency random spikes manifest as sudden increases in local frequency band energy, with the spike position changing randomly within different sampling periods. Rapid spectral drift manifests as a continuous shift in the main frequency center position within consecutive sampling periods. When the number of high-frequency energy spikes exceeds a preset threshold, the main frequency drift rate exceeds a preset drift threshold, and the high-frequency energy distribution exhibits discrete changes, the current impurity type is determined to be air bubble impurity.

[0031] Furthermore, the transient pulse characteristics in the echo signal are analyzed. The transient pulse is a localized abnormal waveform with a short duration and abrupt amplitude changes. The system counts the number of abnormal pulses per unit time and calculates the pulse duration, pulse repetition rate, and pulse amplitude dispersion. When the number of bubbles increases, the number of transient pulses increases significantly, and the pulse positions are randomly distributed with low repeatability; when solid particles are present, the transient pulse energy is higher, the duration is longer, and it repeats in local areas.

[0032] Subsequently, the high-frequency scattering energy in the echo signal is extracted. This high-frequency scattering energy characterizes the scattering intensity formed when ultrasound encounters irregular structures. When wax deposits are present on the inner wall of the oil pipeline, the wax layer significantly attenuates the high-frequency ultrasonic signal. As the wax layer thickness increases, the high-frequency echo energy continuously decreases, while the low-frequency ultrasonic signal maintains a relatively stable propagation state. Therefore, when a continuous decrease in high-frequency energy, a continuous increase in the high-frequency attenuation rate, and low-frequency signal stability are detected within a preset range, the current impurity type is determined to be a waxy deposit impurity.

[0033] Simultaneously, the multipath propagation characteristics of the echo signal are analyzed. When ultrasonic waves encounter solid particles or irregular deposition structures during propagation, multiple reflections and multipath propagation phenomena occur, resulting in multiple delayed echoes. The system performs statistical analysis on the number of delayed echoes, the proportion of multipath echo energy, and the spatial reflection point dispersion. When an increase in the number of delayed echoes, an increase in the proportion of multipath echo energy, and a spatial reflection point dispersion exceeding a preset threshold are detected, the current impurity type is determined to be a solid particulate impurity.

[0034] After identifying the impurity type, the system further determines the impurity interference level. The impurity interference level is calculated comprehensively based on high-frequency attenuation, echo volatility, multipath echo ratio, transient pulse density, and spectral stability. When the high-frequency attenuation, multipath echo ratio, and transient pulse density continuously increase, the impurity interference level is correspondingly increased; when the echo reliability drops below a preset range, it is determined that the current impurity interference has significantly affected the corrosion identification results.

[0035] Through the above methods, the system can distinguish between bubble impurities, waxy deposit impurities, and solid particle impurities in the oil transportation medium, and analyze the dynamic change characteristics and interference intensity of the corresponding impurities. This provides basic data for subsequent stable impurity background modeling, background difference denoising, and corrosion determination, thereby reducing the impact of oil transportation medium impurities on ultrasonic corrosion monitoring results.

[0036] S4. Establish a stable impurity background model based on the non-corrosive state echo data collected under historical operating conditions. The stable impurity background model includes the correspondence between stable impurity background echoes and non-corrosive echoes under different flow rates, different gas contents, different wax contents and different pressure conditions. S5. Match the corresponding stable impurity background echo according to the current oil transportation operating parameters, and perform differential processing on the current ultrasonic echo signal and the corresponding stable impurity background echo to eliminate stable impurity background interference; In step S5, the differential processing includes: Calculate the energy difference, phase difference, and spectral offset between the current ultrasonic echo signal and the corresponding stable impurity background echo, and extract newly added abnormal echoes based on the energy difference, phase difference, and spectral offset.

[0037] In step S5, the corresponding stable impurity background echo is matched according to the current oil transportation operating parameters, and the current ultrasonic echo signal and the corresponding stable impurity background echo are differentially processed to eliminate stable impurity background interference, thereby improving the accuracy of extracting new abnormal echoes in the subsequent corrosion identification process.

[0038] Specifically, during the long-term operation of the oil pipeline, the system continuously collects ultrasonic echo data under non-corrosive conditions and establishes a stable impurity background model based on corresponding operating parameters. These operating parameters include at least one of flow velocity, pressure, temperature, gas content, wax content, and medium density. Because the distribution of bubbles, wax, and particulate matter in the oil medium varies under different operating conditions, the corresponding ultrasonic background echo characteristics will also change.

[0039] The system categorizes and stores historically acquired background echoes according to operating parameters and establishes a correspondence between stable impurity background echoes under different operating conditions and non-corrosive conditions. Specifically, stable impurity background echoes are used to characterize the long-term fixed interference echo features formed by stable impurities in the oil transportation medium under non-corrosive conditions.

[0040] During the current detection process, the system acquires the current oil transportation operating parameters in real time and matches the corresponding stable impurity background echo from the stable impurity background model based on the current operating parameters. Specifically, during the matching process, the system calculates the parameter deviation between the current operating parameters and the historical operating parameters, and selects the background echo with the smallest parameter deviation as the stable impurity background echo corresponding to the current operating condition.

[0041] After completing the background echo matching, the system performs differential processing on the current ultrasonic echo signal and the corresponding stable impurity background echo. This differential processing does not involve a simple subtraction of the original echo amplitudes, but rather performs difference analysis from three dimensions: energy characteristics, phase characteristics, and spectral characteristics.

[0042] Specifically, the energy difference between the current ultrasonic echo signal and the stable impurity background echo is first calculated. This energy difference characterizes the overall energy variation between the current echo and the background echo. The calculation method involves integrating the echo energy within the corresponding time window and calculating the difference between the two. When new corrosion occurs in a localized area of ​​the oil pipeline, the corrosion area causes changes in the ultrasonic wave reflection, scattering, and absorption characteristics, resulting in a localized abnormal change in the current echo energy compared to the background echo.

[0043] Subsequently, the system calculates the phase difference between the current ultrasonic echo signal and the stable impurity background echo. Since the stable impurity background is usually in a fixed position, its corresponding echo phase change is relatively stable. However, newly added corrosion areas will change the ultrasonic propagation path and propagation time, causing the current echo phase to shift. Therefore, by analyzing the phase difference, it is possible to further identify new abnormal echoes caused by corrosion and reduce the fixed phase interference caused by stable background impurities.

[0044] Furthermore, the system performs spectral analysis on the current ultrasonic echo signal and the stable impurity background echo, and calculates the spectral offset between the two. This spectral offset characterizes the degree of change in the current spectral distribution relative to the background spectral distribution, including dominant frequency offset, high-frequency energy offset, and spectral width variation. When corrosion occurs on the inner wall of the oil pipeline, the corroded area leads to enhanced local high-frequency scattering, thereby causing a change in the spectral structure, while the spectral structure corresponding to the stable impurity background usually remains relatively stable.

[0045] After obtaining the energy difference, phase difference, and spectral offset, the system further performs joint analysis on these parameters and extracts newly added anomalous echoes. Specifically, when a certain region simultaneously meets the following conditions: The energy difference exceeds the preset energy threshold; The phase difference exceeds the preset phase offset threshold; The spectral offset exceeds the preset spectral change threshold; This indicates that there are newly added abnormal echoes in the area.

[0046] Furthermore, the system performs spatial and temporal continuity analysis on newly added abnormal echoes. When an abnormal echo appears only in a single sampling and its spatial location changes randomly, it is determined to be transient interference from flowing impurities; when an abnormal echo remains stable in multiple consecutive sampling periods and its spatial location is fixed, it is retained as a suspected corrosion echo for subsequent corrosion analysis steps.

[0047] By using the above methods, the system can separate stable impurity background from newly added abnormal echoes, thereby avoiding long-term stable impurities in the oil transportation medium from causing continuous interference to corrosion monitoring results and improving the accuracy of identifying new corrosion defects under complex operating conditions.

[0048] S6. Perform multi-dimensional purification processing on the echo signal after differential processing. The multi-dimensional purification processing includes time-domain purification, frequency-domain purification, spatial correlation purification, and temporal continuity purification. The spatial correlation purification in step S6 includes: Spatial consistency comparison is performed on echo signals corresponding to adjacent detection positions within the same sampling period. The recurrence characteristics of abnormal echoes within a short time window are analyzed, and random abnormal echoes existing only in a single ultrasound probe are filtered out.

[0049] In step S6, the echo signal after differential processing is subjected to multi-dimensional purification processing to further reduce the impact of oil medium impurities, transient flow noise and local random anomalies on corrosion identification results, and improve the stability and reliability of newly added abnormal echoes.

[0050] The multidimensional purification process includes time-domain purification, frequency-domain purification, spatial correlation purification, and temporal continuity purification.

[0051] Temporal purification is primarily used to remove short-duration, discontinuous random abnormal signals. Due to phenomena such as localized turbulence, instantaneous bubble collapse, and particle collisions during oil flow, short-duration abrupt pulses are prone to appear in the echo signal. By analyzing the echo amplitude change rate, pulse duration, and pulse repetition characteristics, abnormal pulses with durations below a preset time threshold and that appear discontinuously are identified as transient interference signals and suppressed, thereby reducing the impact of transient noise on subsequent corrosion analysis.

[0052] Frequency domain cleaning is primarily used to remove unstable spectral components caused by impurities. Specifically, the echo signal after differential processing undergoes spectral decomposition, and the spectral energy distribution is analyzed. When the energy fluctuations in certain frequency bands exhibit random variations, and the spectral structure does not meet preset stability conditions, the corresponding frequency bands are identified as impurity interference bands, and their weight in subsequent corrosion analysis is reduced. Spectral components that remain stable over multiple consecutive sampling periods are retained as effective corrosion characteristic frequency bands.

[0053] Spatial correlation cleaning is mainly used to distinguish between local random impurity interference and real corrosion echoes. Since real corrosion areas usually have spatial continuity, echo signals collected from multiple adjacent locations will show relatively stable and consistent changes; while abnormal echoes generated by flowing impurities, local bubbles, or random particles usually only appear at a single detection location for a short time and lack spatial continuity.

[0054] Specifically, spatial consistency comparison is performed on echo signals corresponding to adjacent detection positions to analyze the amplitude, phase, and spectral variations of abnormal echoes at different detection positions. When an abnormal echo exists only at a single detection position and no corresponding abnormal feature is detected at adjacent positions, the abnormal echo is determined to be random impurity interference and is filtered out.

[0055] Furthermore, when similar abnormal features are detected at multiple adjacent detection locations, and the abnormal echoes are continuous in spatial distribution, the corresponding abnormal echoes are retained and used as suspected corrosion features in subsequent analysis.

[0056] Time-continuous filtering is primarily used to distinguish stable corrosion signals from random flow noise. Because flowing impurities in oil transport media are typically random and short-lived, their abnormal echoes change rapidly with the flow of the medium; while the abnormal echoes corresponding to corrosion areas usually remain stable over multiple consecutive sampling periods.

[0057] Specifically, a temporal continuity analysis is performed on anomalous echoes from the same area across multiple consecutive sampling periods, and the recurrence rate, duration, and trend of these anomalous echoes are statistically analyzed. When an anomalous echo appears only in a few sampling periods, or when the location of the anomalous echo shifts significantly over time, it is classified as random flow interference. When an anomalous echo remains stable across multiple consecutive sampling periods and its anomalous characteristics show a continuous trend, the corresponding anomalous echo is retained.

[0058] Through the above-mentioned time-domain purification, frequency-domain purification, spatial correlation purification, and temporal continuity purification processes, random abnormal signals caused by bubbles, wax deposits, particulate impurities, and flow disturbances can be gradually filtered out, thereby improving the stability and identification accuracy of newly added corrosion abnormal echoes and reducing the false alarm rate under complex oil transportation conditions.

[0059] S7. Calculate the echo reliability based on the purified echo signal, and dynamically adjust the ultrasonic detection parameters according to the echo reliability; in step S7, the echo reliability is calculated based on at least three of the following parameters: Signal-to-noise ratio, echo stability, spectral integrity, temporal continuity, spatial consistency, consistency between adjacent probes, and the recurrence rate of abnormal echoes.

[0060] S8. When the echo confidence is lower than a preset threshold, control the ultrasonic detection array to perform closed-loop re-inspection of the target area, and adaptively adjust at least one of the following during the re-inspection process: transmission frequency, frequency ratio, transmission power, scanning speed, sampling density, and scanning area range; In step S8, after the closed-loop re-inspection is completed, only when the re-inspection result does not detect corrosion features, the stable impurity background echo corresponding to the current working condition is updated and corrected.

[0061] S9. Analyze the spatial migration characteristics of abnormal echoes between different sampling periods, and determine that abnormal echoes with continuous position drift are interference from flowing impurities. S10. Perform time continuity verification on abnormal echoes that have not undergone continuous spatial drift. Only when the abnormal echo persists within a preset time window and the echo flight time offset or echo energy variation trend continuously increases is corrosion of the oil pipeline determined. In step S10, the time continuity verification includes: analyzing the echo flight time offset and echo energy variation trends of the abnormal echoes over multiple consecutive sampling periods, and identifying abnormal echoes with continuously increasing trends as corrosion evolution signals.

[0062] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and modifications can be made to the present invention without departing from the spirit and scope thereof, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring corrosion in oil pipelines, characterized in that, Includes the following steps: S1. Transmit at least two different frequencies of ultrasonic detection signals to the oil pipeline through an ultrasonic detection array installed on the outer wall of the oil pipeline, and receive the corresponding ultrasonic echo signals. S2. Perform feature extraction on the ultrasonic echo signal to obtain echo spectral fluctuation rate, transient pulse density, high-frequency scattering energy, multipath echo distribution, echo time-of-flight offset, and echo energy attenuation rate. S3. Identify the state of impurities in the oil transportation medium based on echo spectrum volatility, transient pulse density, high-frequency scattering energy and multipath echo distribution, and determine the corresponding impurity type and impurity interference level. S4. Establish a stable impurity background model based on the non-corrosive state echo data collected under historical operating conditions. The stable impurity background model includes the correspondence between stable impurity background echoes and non-corrosive echoes under different flow rates, different gas contents, different wax contents and different pressure conditions. S5. Match the corresponding stable impurity background echo according to the current oil transportation operating parameters, and perform differential processing on the current ultrasonic echo signal and the corresponding stable impurity background echo to eliminate stable impurity background interference. S6. Perform multi-dimensional purification on the differentially processed echo signal. The multi-dimensional purification includes time-domain purification, frequency-domain purification, spatial correlation purification, and temporal continuity purification. Spatial correlation purification includes comparing the spatial consistency of echo signals acquired by adjacent ultrasound probes and filtering out random abnormal echoes that exist only in a single ultrasound probe. Temporal continuity purification performs temporal continuity analysis on abnormal echoes in the same area over multiple consecutive sampling periods, and statistically analyzes the recurrence rate, duration, and trend of abnormal echoes. When an abnormal echo appears only in a few sampling periods, or when the abnormal location drifts significantly over time, it is determined to be random flow interference. When an abnormal echo remains stable over multiple consecutive sampling periods and the trend of abnormal characteristics is continuous, the corresponding abnormal echo is retained. S7. Calculate the echo confidence level based on the purified echo signal, and dynamically adjust the ultrasonic detection parameters according to the echo confidence level; S8. When the echo confidence is lower than a preset threshold, control the ultrasonic detection array to perform closed-loop re-detection of the target area, and adaptively adjust at least one of the following during the re-detection process: transmission frequency, frequency ratio, transmission power, scanning speed, sampling density, and scanning area range. S9. Analyze the spatial migration characteristics of abnormal echoes within a continuous sampling period, and determine that abnormal echoes with continuously drifting locations are interference from flowing impurities. S10. Perform time continuity verification on abnormal echoes that have not undergone continuous spatial drift. Only when the abnormal echoes continue to exist within a preset time window and the echo flight time offset or echo energy change trend continues to increase, it is determined that the oil pipeline is corroded.

2. The method for monitoring corrosion of oil pipelines according to claim 1, characterized in that, In step S1, the ultrasonic detection array includes multiple ultrasonic probes arranged at intervals along the axial and circumferential directions of the oil pipeline. Each ultrasonic probe acquires the ultrasonic echo signal of the corresponding area through synchronous sampling.

3. The method for monitoring corrosion of oil pipelines according to claim 1, characterized in that, In step S1, the at least two different frequency ultrasonic detection signals include: Low-frequency ultrasonic signals with a frequency of 1MHz to 3MHz, and high-frequency ultrasonic signals with a frequency of 8MHz to 15MHz; The low-frequency ultrasonic signal is used to acquire stable wall thickness echoes, and the high-frequency ultrasonic signal is used to acquire localized corrosion detail echoes.

4. The method for monitoring corrosion of oil pipelines according to claim 1, characterized in that, In step S3: When high-frequency random spikes and rapid spectral drift are present in the echo signal, it is determined to be bubble impurity; When there is continuous attenuation enhancement and high-frequency energy decline in the echo signal, it is determined to be a waxy deposit impurity. When there is an increase in discrete reflection points and an enhancement of multipath echoes in the echo signal, it is determined to be solid particulate impurities.

5. The method for monitoring corrosion of oil pipelines according to claim 1, characterized in that, In step S5, the differential processing includes: Calculate the energy difference, phase difference, and spectral offset between the current ultrasonic echo signal and the corresponding stable impurity background echo, and extract newly added abnormal echoes based on the energy difference, phase difference, and spectral offset.

6. The method for monitoring corrosion of oil pipelines according to claim 1, characterized in that, In step S7, the echo confidence level is calculated based on at least three of the following parameters: Signal-to-noise ratio, echo stability, spectral integrity, temporal continuity, spatial consistency, consistency between adjacent probes, and the recurrence rate of abnormal echoes.

7. The method for monitoring corrosion of oil pipelines according to claim 1, characterized in that, In step S8, after the closed-loop re-inspection is completed, the stable impurity background echo corresponding to the current operating condition is updated and corrected based on the re-inspection results.

8. The method for monitoring corrosion of oil pipelines according to claim 1, characterized in that, In step S10, the time continuity verification includes: The study analyzed the trends of echo flight time offset and echo energy variation in multiple consecutive sampling periods, and identified the abnormal echoes with continuously increasing trends as corrosion evolution signals.

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