A method for detecting corrosion defects of buried oil and gas steel pipelines

Abstract

The present invention provides a method for detecting corrosion defects of buried oil and gas steel pipelines, and relates to the technical field of pipeline detection. A method for detecting corrosion defects of buried oil and gas steel pipelines includes pre-detection preparation, magnetic field loading and data collection, data analysis and processing, and result evaluation and report generation. It can timely detect corrosion defects, prevent fires, explosions and other safety accidents caused by oil and gas leakage, and protect the lives, property and environment of residents; detect potential dangerous defects such as stress corrosion cracks in advance, prevent crack expansion from causing sudden breakage of pipelines, and ensure the integrity and stability of the pipeline operation structure; accurately locate and evaluate corrosion defects, facilitate timely repair and maintenance, such as replacing or repairing severely corroded local pipe sections, preventing corrosion from further deteriorating, and extending the overall service life of the pipeline; combine regular detection with scientific maintenance to keep the pipeline in good operating condition, reduce pipe wall thinning, maintain the pipeline's load-bearing and transportation capacity, and give full play to its economic value.

Description

Technical Field

[0001] The present application relates to the technical field of pipeline detection, and in particular to a method for detecting corrosion defects of buried oil and gas steel pipelines. Background Art

[0002] Buried steel pipelines are widely used in the transportation of energy such as oil and natural gas, as well as urban water supply and heating. However, due to long-term burial underground, pipelines are very susceptible to corrosion due to the influence of various factors such as soil pH, humidity, and stray current. Corrosion not only causes the thickness of the pipeline wall to decrease, reducing the pressure-bearing capacity of the pipeline, but may also cause pipeline leakage, resulting in serious consequences such as environmental pollution, energy waste, and safety accidents. Traditional detection methods such as ultrasonic detection and magnetic leakage detection have certain limitations when applied to buried steel pipelines. For example, ultrasonic detection requires a good coupling medium, which is inconvenient for buried pipeline detection; magnetic leakage detection is easily affected by external factors such as pipeline surface conditions and geomagnetism.

[0003] Therefore, there is an urgent need for an efficient, accurate and suitable detection method for corrosion defects in buried steel pipelines. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a method for detecting corrosion defects of buried oil and gas steel pipelines, comprising the following steps:

[0005] S1. Preparation before testing

[0006] Use high-precision pipeline positioning equipment to accurately locate the position and direction of buried steel pipelines, draw detailed pipeline route maps, and ensure that the detection path is clear.

[0007] Clear away covering materials above and around the pipeline within a range of at least 5 square meters, including stones, weeds, small metal objects, etc., to prevent them from interfering with the magnetic field distribution and to ensure detection.

[0008] The testing instruments are strictly calibrated to control the sensitivity error of the magnetic stress sensor within ±3%, the accuracy error of the data acquisition system within ±0.5%, and the coordinate error of the positioning system within ±0.1 meter to ensure the accuracy of the test data.

[0009] S2. Magnetic field loading and data acquisition

[0010] A high-performance magnetic field generating device is arranged 15-30 cm above the pipeline and along the direction of the pipeline to generate an alternating magnetic field with a frequency of 40-60 Hz and an intensity of 0.4-0.6 Tesla, which puts the pipeline in a stable magnetic field environment and stimulates changes in the magnetic properties of the pipeline.

[0011] A high-precision non-contact magnetic stress sensor is used 10-20 cm above the pipeline, moving uniformly along the axial direction of the pipeline at a speed of 3-6 cm per second to collect the magnetic stress response data generated by the pipeline under the action of the magnetic field. At the same time, the sensor position information is accurately recorded with the help of a high-precision positioning system to ensure that the magnetic stress data accurately corresponds to the pipeline position.

[0012] S3. Data analysis and processing flow

[0013] The collected magnetic stress data is transmitted to a professional data processing center, and advanced filtering algorithms are used to remove environmental noise and electromagnetic interference signals to restore the real data.

[0014] The feature extraction algorithm is used to extract the key characteristic parameters in the magnetic stress data, including the shape parameters of the hysteresis loop, the rate of change of magnetic permeability, etc. These parameters are closely related to pipeline corrosion defects.

[0015] Build an intelligent corrosion defect recognition model based on machine learning or deep learning, input the extracted feature parameters into the model for deep training and precise recognition, judge whether there are corrosion defects in the pipeline, and accurately determine the location, size and severity of the corrosion defects.

[0016] S4. Result evaluation and report generation steps

[0017] According to the data analysis results, a comprehensive and integrated assessment of pipeline corrosion defects is conducted based on a scientific assessment system, and the pipeline safety level is divided into levels one to four, providing a basis for subsequent decision-making.

[0018] Generate detailed and standardized inspection reports, covering basic pipeline information, inspection process data, corrosion defect assessment results, targeted maintenance suggestions, and reasonable follow-up inspection cycle recommendations, providing all-round guidance for pipeline maintenance management.

[0019] According to a method for detecting corrosion defects of a buried oil and gas steel pipeline according to an embodiment of the present application, the beneficial effects are:

[0020] 1. Safety assurance is significantly improved, corrosion defects are discovered in time, pipeline perforation and rupture caused by corrosion are effectively prevented, fires, explosions and other safety accidents caused by oil and gas leakage are eliminated, and the safety of residents' lives, property and the environment is protected; potential dangerous defects such as stress corrosion cracks are detected in advance to prevent crack expansion from causing sudden rupture of the pipeline, ensuring the integrity and stability of the pipeline operation structure;

[0021] 2. Extend the service life of the pipeline, accurately locate and evaluate corrosion defects, and facilitate timely repair and maintenance, such as replacing or repairing the locally corroded pipe section to prevent further deterioration of corrosion and extend the overall service life of the pipeline; combine regular inspection with scientific maintenance to keep the pipeline in good operating condition, reduce pipe wall thinning, maintain the pipeline's load-bearing and transportation capacity, and give full play to its economic value;

[0022] 3. Reduce economic losses. Early detection and repair of corrosion defects can significantly reduce maintenance costs compared to large-scale repairs or replacement of leaking and damaged pipelines. Avoid oil and gas leakage losses and economic compensation caused by pipeline failures, and reduce indirect economic losses caused by suspension of transportation for maintenance.

[0023] 4. Optimize maintenance strategies. The detection data provides a scientific basis for pipeline maintenance, helping maintenance personnel to formulate reasonable maintenance plans, achieve preventive and targeted maintenance, improve maintenance efficiency, and reduce costs; reasonably allocate maintenance resources, focus on high-risk corrosion sections, avoid excessive or insufficient maintenance, and make maintenance work more scientific and efficient;

[0024] 5. Effective environmental protection, preventing leakage of media from polluting soil and groundwater, and protecting ecological balance; reducing damage to the environment caused by corrosion maintenance and excavation, reducing construction noise, dust and other impacts, and meeting the requirements of sustainable development;

[0025] 6. Ensure stable production operations, ensure normal operation of pipelines, ensure stable supply of energy such as oil and gas, maintain normal operation of industrial production and residents' lives, and avoid adverse effects caused by energy interruptions; enhance the safety of pipeline transportation systems, enhance corporate and social confidence, and promote the healthy development of related industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the implementation methods of the present application, the drawings required for use in the implementation methods will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 is a block diagram of a method for detecting corrosion defects of buried oil and gas steel pipelines according to an embodiment of the present application;

[0028] Figure 2 is a schematic diagram of the leakage magnetic field distribution formed when there are macroscopic defects or peripheral structural defects in the pipeline according to the embodiment of the present application;

[0029] Figure 3 It is a schematic diagram of the magnetic memory effect when there is a defect in the pipeline according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the implementation methods of this application clearer, the technical solutions in the implementation methods of this application will be clearly and completely described below in conjunction with the drawings in the implementation methods of this application. Obviously, the described implementation methods are part of the implementation methods of this application, not all of the implementation methods. Based on the implementation methods in this application, all other implementation methods obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] A method for detecting corrosion defects of buried oil and gas steel pipelines of the present invention is described in detail below through specific implementation methods.

[0032] Embodiment 1

[0033] 1. Preparation before testing

[0034] A high-precision underground pipeline detector was used to determine the precise direction and location of a section of buried steel natural gas pipeline, and a detailed route map was drawn. Stones, weeds, and discarded metal objects within 8 square meters above and around the pipeline were then cleared. The magnetic stress detection instrument was calibrated to ensure that the sensitivity error of the magnetic stress sensor was within ±2%, the accuracy error of the data acquisition system was within ±0.3%, and the coordinate error of the positioning system was within ±0.08 meters.

[0035] 2. Magnetic field loading and data acquisition

[0036] The magnetic field generator was installed 20 cm from the top of the pipeline, and the parameters were adjusted to generate an alternating magnetic field with a frequency of 50 Hz and an intensity of 0.5 Tesla. The high-precision non-contact magnetic stress sensor was started and moved at a constant speed of 4 cm per second 15 cm above the pipeline to collect magnetic stress response data, and the sensor position information was synchronously recorded through a high-precision positioning system.

[0037] 3. Data analysis and processing

[0038] The large amount of magnetic stress data collected is transmitted to the data processing center, and the wavelet transform and adaptive filtering algorithm are used to filter the data. The principal component analysis method is used to extract the characteristic parameters, which are input into the pre-trained neural network model for corrosion defect recognition.

[0039] 4. Result evaluation and report generation

[0040] After analysis, it was found that the pipeline had multiple corrosion defects, one of which was located at 120 meters along the pipeline axis, with a corrosion depth of about 3.5 mm, and the pipeline safety level was rated as level 3. A detailed inspection report was generated, including basic information of the pipeline (such as material X70 steel, specification of 600 mm diameter, laying time 20XX), inspection process data (such as magnetic field strength, sensor movement speed, acquisition time, etc.), corrosion defect assessment results (such as defect location, size, severity, safety level), maintenance suggestions (such as replacement of locally severely corroded pipe sections) and subsequent inspection cycle recommendations (such as re-inspection after 5 months), providing a strong basis for pipeline maintenance and management.

[0041] Embodiment 2

[0042] 1. Preparation before testing

[0043] A high-precision pipeline positioning system combined with the global positioning system (GPS) was used to determine the location and direction of a buried steel crude oil pipeline and draw an accurate pipeline route map. Debris and small metal objects within 10 square meters above and around the pipeline were cleared to ensure that there was no interference in the detection area. The magnetic stress detection instrument was fully calibrated to control the sensitivity error of the magnetic stress sensor within ±1.5%, the accuracy of the data acquisition system reached ±0.2%, and the coordinate accuracy of the positioning system was ±0.05 meters.

[0044] 2. Magnetic field loading and data acquisition

[0045] A high-performance magnetic field generator is placed 25 cm above the pipeline to generate an alternating magnetic field with a frequency of 55 Hz and an intensity of 0.55 Tesla. The non-contact magnetic stress sensor is located 18 cm above the pipeline and moves at a constant speed of 5 cm per second to collect data. At the same time, the sensor position is accurately recorded through a high-precision positioning system to ensure accurate correspondence between data and position.

[0046] 3. Data analysis and processing

[0047] The collected data is transmitted to the data processing center, the median filter algorithm is used to remove noise interference, the independent component analysis method is used to extract characteristic parameters, and they are input into the support vector machine model for corrosion defect judgment and analysis.

[0048] 4. Result evaluation and report generation

[0049] The test results showed that the pipeline had corrosion defects, one of which was located 180 meters along the pipeline axis, with a corrosion depth of about 4.2 mm, and the safety level was determined to be level 2. A test report covering comprehensive information is generated, including basic pipeline parameters (such as material, specification, service life, etc.), detailed test data (such as magnetic field parameters, sensor movement parameters, etc.), corrosion defect conditions (such as location, size, severity), targeted maintenance suggestions (such as grinding and welding of corroded parts) and subsequent test cycle suggestions (such as re-inspection after 3 months) to guide pipeline maintenance work.

[0050] Embodiment 3

[0051] 1. Preparation before testing

[0052] The global positioning system (GPS) combined with underground pipeline detection radar determines the exact direction and location of the buried steel water supply pipeline, clears the covering above the pipeline and within 6 square meters around it, and ensures that there are no large metal obstacles affecting the magnetic field. Carefully calibrate the magnetic stress detection instrument to ensure that the sensitivity error of the magnetic stress sensor does not exceed ±2%, the accuracy of the data acquisition system is around ±0.3%, and the coordinate error of the positioning system is within ±0.06 meters.

[0053] 2. Magnetic field loading and data acquisition

[0054] The magnetic field generator was placed 28 cm from the top of the pipeline to generate an alternating magnetic field with a frequency of 45 Hz and an intensity of 0.48 Tesla. The magnetic stress sensor moved at a speed of 5.5 cm per second 16 cm above the pipeline to collect magnetic stress response data and simultaneously record position information.

[0055] 3. Data analysis and processing

[0056] After the collected data is transmitted to the data processing center, an adaptive filtering algorithm is used to eliminate noise, and a linear discriminant analysis method is used to extract feature parameters, which are then input into a deep learning model (such as a convolutional neural network) for corrosion defect identification.

[0057] 4. Result evaluation and report generation

[0058] The pipeline was evaluated to have corrosion defects, one of which was located at 90 meters along the pipeline axis, with a corrosion depth of about 2.8 mm, and the pipeline safety level was rated as Level 4. A detailed inspection report was generated, covering various information about the pipeline, all data during the inspection process, details of the corrosion defects, reasonable maintenance suggestions (such as partial replacement of the corroded pipe section) and subsequent inspection cycle recommendations (such as re-inspection after 8 months), providing strong support for pipeline maintenance management.

[0059] like Figure 2As shown in the figure, when there are macroscopic defects or microstructural defects in the pipeline, it will cause abnormal local magnetic field distribution and form a leakage magnetic field (magnetic field intensity Hp). The normal component Hp(y) of the leakage magnetic field changes its sign and passes through zero, and the tangential component Hp(z) reaches its maximum value. Therefore, the defect location can be judged based on the magnetic field distribution around the pipeline. When the normal component Hp(y) of the abnormal magnetic field passes through zero and changes its sign or the tangential component Hp(z) reaches its maximum value, the abnormal magnetic field is the corrosion defect site.

[0060] like Figure 3 As shown in the figure, the pipeline has magnetism under the action of the geomagnetic field, and is subjected to periodically changing loads during operation. As the load increases, under the action of magnetostrictive effect and magnetoelastic effect, leakage magnetic field will be generated at the defective part of the buried pipeline, thereby changing the direction of the original magnetic field. After the load is removed, the leakage magnetic field will not disappear. In this process, the pipeline retains the previous magnetic field strength and continues to increase. This phenomenon is called the magnetic memory effect. Under the joint action of the geomagnetic field and the working load, the magnetic memory effect at the pipeline defect will be more obvious. Therefore, by obtaining the leakage magnetic field information at the defect, the location of the defect during pipeline operation can be determined. If there is no defect on the pipeline surface, its magnetic field will be evenly distributed.

[0061] The above embodiments are only used to illustrate the specific embodiments of the present invention and are not limited thereto. For those skilled in the art, various similar deformations and changes can be made according to the concept of the present invention, and these deformations and changes should be regarded as the protection scope of the present invention.

Claims

1. A method for detecting corrosion defects of buried oil and gas steel pipelines, characterized in that: The following steps are involved: S1. Preparation before testing: Use pipeline positioning devices to determine the exact location and direction of buried steel pipelines, and draw pipeline route maps; clean the coverings above and around the pipelines to ensure that there are no large metal obstacles in the testing area that interfere with the magnetic field distribution; calibrate the testing instruments, including sensitivity calibration of magnetic stress sensors, accuracy calibration of data acquisition systems, and coordinate calibration of positioning systems; S2. Magnetic field loading and data acquisition: A magnetic field generating device is arranged above the pipeline along the direction of the pipeline to place the pipeline in a magnetic field environment; a non-contact magnetic stress sensor is used to move at a uniform speed above the pipeline along the axial direction of the pipeline to collect the magnetic stress response data generated by the pipeline under the action of the magnetic field, and the position information of the sensor is recorded through the positioning system to achieve a one-to-one correspondence between the magnetic stress data and the pipeline position; S3. Data analysis and processing: The collected magnetic stress data is transmitted to the data processing center for filtering to remove environmental noise and electromagnetic interference signals. The filtering process adopts one or more combinations of wavelet transform and adaptive filtering algorithm; the key characteristic parameters in the magnetic stress data are extracted by feature extraction algorithm, and the key characteristic parameters include the shape parameters of the hysteresis loop and the rate of change of magnetic permeability. The feature extraction algorithm adopts one or more combinations of principal component analysis and independent component analysis; a corrosion defect recognition model based on machine learning or deep learning is established, and the extracted characteristic parameters are input into the model for training and recognition to determine whether there are corrosion defects in the pipeline, and determine the location, size and severity of the corrosion defects. The corrosion defect recognition model is one or more combinations of neural network model, support vector machine model and convolutional neural network model; S4. Result evaluation and report generation: Based on the data analysis results, a comprehensive evaluation is conducted on the corrosion defect status of the pipeline, and the safety level of the pipeline is divided into four levels: one to four. A detailed inspection report is generated, including basic pipeline information, inspection process data, corrosion defect assessment results, maintenance suggestions, and subsequent inspection cycle recommendations. The maintenance suggestions include one or more combinations of replacement, repair, grinding and welding of locally corroded pipe sections with severe corrosion, and local replacement of corroded pipe sections, depending on the specific circumstances of the corrosion defects.

2. A method for detecting corrosion defects of buried oil and gas steel pipelines according to claim 1, characterized in that: In the pre-detection preparation step, the pipeline positioning device is one or more combinations of an underground pipeline detector, a high-precision pipeline positioning system, or a global positioning system combined with an underground pipeline detection radar.

3. A method for detecting corrosion defects of buried oil and gas steel pipelines according to claim 1, characterized in that: In the magnetic field loading and data acquisition steps, the alternating magnetic field generated by the magnetic field generating device has a frequency of 45-55 Hz and an intensity of 0.45-0.55 Tesla.

4. A method for detecting corrosion defects of buried oil and gas steel pipelines according to claim 1, characterized in that: During the magnetic field loading and data acquisition steps, the non-contact magnetic stress sensor was moved 12-16 cm above the pipe.

5. A method for detecting corrosion defects of buried oil and gas steel pipelines according to claim 1, characterized in that: In the data analysis and processing step, the filtering process may also use a median filtering algorithm.

6. A method for detecting corrosion defects of buried oil and gas steel pipelines according to claim 1, characterized in that: In the data analysis and processing step, the feature extraction algorithm may also use a linear discriminant analysis method.

7. A method for detecting corrosion defects of buried oil and gas steel pipelines according to claim 1, characterized in that: In the result evaluation and report generation step, the pipeline safety level is divided based on one or more factors including the location, size, severity of the corrosion defects, the service life of the pipeline, and the type of transported medium.

8. A method for detecting corrosion defects of buried oil and gas steel pipelines according to claim 1, characterized in that: In the result evaluation and report generation step, the subsequent inspection cycle recommendation is determined based on one or more factors of the pipeline safety level, the corrosion defect development trend, and the environmental factors of the pipeline.

9. A method for detecting corrosion defects of buried oil and gas steel pipelines according to claim 1, characterized in that: In the pre-test preparation step, when cleaning the covering above and around the pipeline, the pipeline surface needs to be preliminarily cleaned to remove surface dirt and impurities to improve the detection accuracy.

10. A method for detecting corrosion defects of buried oil and gas steel pipelines according to claim 1, characterized in that: In the magnetic field loading and data acquisition steps, the magnetic field generating device and the non-contact magnetic stress sensor can ensure that their moving trajectories are consistent with the direction of the pipeline through a real-time monitoring system during movement, and the deviation is controlled within ±0.05 meters.

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