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Energies
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Detection and Diagnosis in Oil and Gas Pipelines
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Detection and Diagnosis in Oil and Gas Pipelines

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "K: State-of-the-Art Energy Related Technologies".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 20322

Special Issue Editors

Prof. Dr. Songling Huang

E-Mail Website
Guest Editor
State Key Lab. of Power System, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Interests: nondestructive testing and evaluation, electromagnetic measurement, intelligent sensors, signal processing, pipeline fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaochun Song

E-Mail Website
Guest Editor
School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China
Interests: magnetic flux leakage testing; electromagnetic ultrasonic guided wave testing; structural health monitoring technology
Dr. Lisha Peng

E-Mail Website
Guest Editor
State Key Lab. of Power System, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Interests: magnetic flux leakage testing, electromagnetic ultrasonic guided wave testing, defect inversion imaging, signal processing, physical heuristic neural network

Special Issue Information

Dear Colleagues,

Oil and gas pipelines are the most economical, safe, and effective transportation facilities for oil and gas resources, called “energy blood vessel”. At present, the total mileage of oil and gas pipelines in the world has exceeded 2.5 million kilometers, including terrestrial and submarine transportation. With the extensive laying and long-term operation of oil and gas pipelines, due to foundation change, pipe corrosion, construction quality, human-made damage, and other factors, deformation and/or damage to pipelines are inevitable. Further, the normal transmission of the pipeline may be affected, potentially leading to oil and gas leakage, environmental pollution, economic loss, and other serious problems. Therefore, pipeline safety management, detection and monitoring, fault diagnosis, accident prevention, and other issues are of great concern around the world.

This Special Issue aims to present and disseminate the most recent concepts, methods, and technological and equipment advances related to the prediction, detection, monitoring, analysis, diagnosis, and prevention of oil and gas pipeline issues based on all types of detection technologies.

Topics of interest for publication include but are not limited to:

  • Basic theory of pipeline nondestructive testing;
  • Application technology of pipeline nondestructive testing;
  • Sensors and detection equipment;
  • Signal processing and inversion evaluation;
  • Pipeline defect diagnosis;
  • Pipeline failure analysis;
  • Pipeline integrity management;
  • Pipeline online monitoring;
  • Automation, intelligence, and visualization of pipeline diagnosis;
  • Safety and reliability of pipeline inspection;
  • Other pipeline inspection and diagnosis methods, technologies, and applications.

Prof. Dr. Songling Huang
Prof. Dr. Xiaochun Song
Dr. Lisha Peng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • oil and gas pipeline
  • nondestructive testing
  • online monitoring
  • sensing technology
  • signal processing
  • fault diagnosis
  • failure analysis
  • defect evaluation
  • pipeline integrity management

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Published Papers (6 papers)

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Research

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13 pages, 3573 KiB  
Article
Machine-Learning-Based Classification for Pipeline Corrosion with Monte Carlo Probabilistic Analysis
by Mohd Fadly Hisham Ismail, Zazilah May, Vijanth Sagayan Asirvadam and Nazrul Anuar Nayan
Energies 2023, 16(8), 3589; https://doi.org/10.3390/en16083589 - 21 Apr 2023
Cited by 9 | Viewed by 3515
Abstract
Pipeline corrosion is one of the leading causes of failures in the transmission of gas and hazardous liquids in the oil and gas industry. In-line inspection is a non-destructive inspection for detecting corrosion defects in pipelines. Defects are measured in terms of their [...] Read more.
Pipeline corrosion is one of the leading causes of failures in the transmission of gas and hazardous liquids in the oil and gas industry. In-line inspection is a non-destructive inspection for detecting corrosion defects in pipelines. Defects are measured in terms of their width, length and depth. Consecutive in-line inspection data are used to determine the pipeline’s corrosion growth rate and its remnant life, which set the operational and maintenance activities of the pipeline. The traditional approach of manually processing in-line inspection data has various weaknesses, including being time consuming due to huge data volume and complexity, prone to error, subject to biased judgement by experts and challenging for matching of in-line inspection datasets. This paper aimed to contribute to the adoption of machine learning approaches in classifying pipeline defects as per Pipeline Operator Forum requirements and matching in-line inspection data for determining the corrosion growth rate and remnant life of pipelines. Machine learning techniques, namely, decision tree, random forest, support vector machines and logistic regression, were applied in the classification of pipeline defects using Phyton programming. The performance of each technique in terms of the accuracy of results was compared. The results showed that the decision tree classifier model was the most accurate (99.9%) compared with the other classifiers. Full article
(This article belongs to the Special Issue Detection and Diagnosis in Oil and Gas Pipelines)
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12 pages, 5577 KiB  
Article
A Pipe Ultrasonic Guided Wave Signal Generation Network Suitable for Data Enhancement in Deep Learning: US-WGAN
by Lisha Peng, Shisong Li, Hongyu Sun and Songling Huang
Energies 2022, 15(18), 6695; https://doi.org/10.3390/en15186695 - 13 Sep 2022
Cited by 7 | Viewed by 2225
Abstract
A network ultrasonic Wasserstein generative adversarial network (US-WGAN), which can generate ultrasonic guided wave signals, is proposed herein to solve the problem of insufficient datasets for pipe ultrasonic nondestructive testing based on deep neural networks. This network was trained with pre-enhanced and US-WGAN-enhanced [...] Read more.
A network ultrasonic Wasserstein generative adversarial network (US-WGAN), which can generate ultrasonic guided wave signals, is proposed herein to solve the problem of insufficient datasets for pipe ultrasonic nondestructive testing based on deep neural networks. This network was trained with pre-enhanced and US-WGAN-enhanced datasets with 3000 epochs; the ultrasound signals generated by the US-WGAN were proved to be of high quality (peak signal-to-noise ratio scores in the range of 30–50 dB) and belong to the same population distribution as the original dataset. To verify the effectiveness of the US-WGAN, a fully connected neural network with seven layers was established, and the performances of the network after data enhancement using the US-WGAN and popular virtual defects were verified for the same network parameters and structures. The results show that adoption of the US-WGAN effectively suppresses the overfitting phenomenon while training the network and increases the dataset size, thereby improving the training and testing accuracies (>97%). Additionally, we noted that a simple, fully connected shallow neural network was sufficient for achieving high-accuracy defect classification using the US-WGAN data enhancement method. Full article
(This article belongs to the Special Issue Detection and Diagnosis in Oil and Gas Pipelines)
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14 pages, 2862 KiB  
Article
A Fast Signal-Processing Method for Electromagnetic Ultrasonic Thickness Measurement of Pipelines Based on UKF and SMO
by Huichao Zhu, Jun Tu, Chen Cai, Zhiyang Deng, Qiao Wu and Xiaochun Song
Energies 2022, 15(18), 6554; https://doi.org/10.3390/en15186554 - 8 Sep 2022
Cited by 7 | Viewed by 1884
Abstract
Electromagnetic ultrasonic testing technology has advantages in measuring the thickness of pipelines in service. However, the ultrasonic signal is susceptible to corrosions on the internal and external surfaces of the pipeline. Since the electromagnetic ultrasonic signal is nonlinear, and a dynamic model is [...] Read more.
Electromagnetic ultrasonic testing technology has advantages in measuring the thickness of pipelines in service. However, the ultrasonic signal is susceptible to corrosions on the internal and external surfaces of the pipeline. Since the electromagnetic ultrasonic signal is nonlinear, and a dynamic model is difficult to establish accurately, in this paper, a new unscented Kalman filter (UKF) method based on a sliding mode observer (SMO) is proposed. The experiments, conducted on five different testing samples, validate that the proposed method can effectively process the signals drowned in noise and accurately measure the wall thickness. Compared with FFT and UKF, the signal-to-noise ratio of the signals processed by SMO–UKF shows a maximum increase of 155% and 171%. Meanwhile, a random assignment method is proposed for the self-regulation of hyper parameters in the process of Kalman filtering. Experimental results show that the automatic adjustment of hyper parameters can be accomplished in finite cycle numbers and greatly shortens the overall filtering time. Full article
(This article belongs to the Special Issue Detection and Diagnosis in Oil and Gas Pipelines)
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19 pages, 6233 KiB  
Article
Simulation and Design of a Balanced-Field Electromagnetic Technique Sensor for Crack Detection in Long-Distance Oil and Gas Pipelines
by Lijian Yang, Jiayin Li, Wenxue Zheng and Bin Liu
Energies 2022, 15(14), 5274; https://doi.org/10.3390/en15145274 - 20 Jul 2022
Cited by 10 | Viewed by 2298
Abstract
Due to the extremely small size and arbitrary orientation of the cracks, a highly sensitive sensor based on the balanced-field electromagnetic technique was designed for in-line inspection of oil and gas pipeline cracks. A balanced-field electromagnetic technique sensor mutual inductance model was established [...] Read more.
Due to the extremely small size and arbitrary orientation of the cracks, a highly sensitive sensor based on the balanced-field electromagnetic technique was designed for in-line inspection of oil and gas pipeline cracks. A balanced-field electromagnetic technique sensor mutual inductance model was established and used to theoretically analyze the parameters affecting sensitivity. Finite element simulation was used to analyze the specific effects of the magnetically conductive medium, the number of coil turns, and the sensor lift-off height on the sensor output, respectively, and the sensor parameters of high sensitivity were determined. The detection effect of the sensor on the pipeline crack was tested by the single-sensor experiment and the pulling test. The results show that the designed balanced-field electromagnetic technique sensor is effective in detecting both circumferential and axial cracks of 0.5 to 6 mm in depth. As the crack depth increases, the sensitivity decreases and the detection voltage amplitude increases linearly. The sensitivity of the sensor is highest when detecting circumferential and axial cracks of 1 mm in depth at 1.76 and 0.87 mV/mm, respectively. In addition, the amplitude of the circumferential crack signal at the same depth is approximately twice that of the axial crack signal. Full article
(This article belongs to the Special Issue Detection and Diagnosis in Oil and Gas Pipelines)
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20 pages, 13077 KiB  
Article
Application of Alternating Current Stress Measurement Method in the Stress Detection of Long-Distance Oil Pipelines
by Jinyao Duan, Kai Song, Wenyu Xie, Guangming Jia and Chuang Shen
Energies 2022, 15(14), 4965; https://doi.org/10.3390/en15144965 - 6 Jul 2022
Cited by 4 | Viewed by 2057
Abstract
With the development of pipeline networks, many safety accidents were caused by pipeline stress concentration; it is of great significance to accurately monitor the pipeline stress state for maintaining pipeline safety. In this paper, based on alternating current stress measurement (ACSM) methods, a [...] Read more.
With the development of pipeline networks, many safety accidents were caused by pipeline stress concentration; it is of great significance to accurately monitor the pipeline stress state for maintaining pipeline safety. In this paper, based on alternating current stress measurement (ACSM) methods, a 3D simulation model of a pipeline electromagnetic field was established by ANSYS software. The distribution law of the pipeline magnetic field and eddy current field were analyzed, and the influence of size and structure parameters of the coil inside the probe were studied. The internal stress detection system of the pipeline was designed, and the static tensile stress measurement experiment was carried out. Simulation and test results showed that the excitation coil with a larger diameter-to-height ratio had a higher measurement sensitivity. The sensitivity of the probe decreased monotonically with the increase of the difference between inner diameter and outer diameter of the detection coil. It increased monotonically with the increase of the equivalent radius of the detection coil. The best measurement results were obtained when the detection coil was located at the center of the two legs of the U-magnetic core. The results showed that the system could identify the pipeline stress concentration area effectively after detection engineering. Full article
(This article belongs to the Special Issue Detection and Diagnosis in Oil and Gas Pipelines)
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Review

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27 pages, 6125 KiB  
Review
Deep Learning for Magnetic Flux Leakage Detection and Evaluation of Oil & Gas Pipelines: A Review
by Songling Huang, Lisha Peng, Hongyu Sun and Shisong Li
Energies 2023, 16(3), 1372; https://doi.org/10.3390/en16031372 - 29 Jan 2023
Cited by 28 | Viewed by 6458
Abstract
Magnetic flux leakage testing (MFL) is the most widely used nondestructive testing technology in the safety inspection of oil and gas pipelines. The analysis of MFL test data is essential for pipeline safety assessments. In recent years, deep-learning technologies have been applied gradually [...] Read more.
Magnetic flux leakage testing (MFL) is the most widely used nondestructive testing technology in the safety inspection of oil and gas pipelines. The analysis of MFL test data is essential for pipeline safety assessments. In recent years, deep-learning technologies have been applied gradually to the data analysis of pipeline MFL testing, and remarkable results have been achieved. To the best of our knowledge, this review is a pioneering effort on comprehensively summarizing deep learning for MFL detection and evaluation of oil and gas pipelines. The majority of the publications surveyed are from the last five years. In this work, the applications of deep learning for pipeline MFL inspection are reviewed in detail from three aspects: pipeline anomaly recognition, defect quantification, and MFL data augmentation. The traditional analysis method is compared with the deep-learning method. Moreover, several open research challenges and future directions are discussed. To better apply deep learning to MFL testing and data analysis of oil and gas pipelines, it is noted that suitable interpretable deep-learning models and data-augmentation methods are important directions for future research. Full article
(This article belongs to the Special Issue Detection and Diagnosis in Oil and Gas Pipelines)
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